RecA family recombinases are the core enzymes in the process of homologous recombination, and their normal operation ensures the stability of the genome and the healthy development of organisms. The UvsX protein from bacteriophage T4 is a member of the RecA family recombinases and plays a central role in T4 phage DNA repair and replication, which provides an important model for the biochemistry and genetics of DNA metabolism. UvsX shares a high degree of structural similarity and function with RecA, which is the most deeply studied member of the RecA family. However, the detailed molecular mechanism of UvsX has not been resolved. In this study, a comprehensive all-atom molecular dynamics simulation of the UvsX protein dimer complex was carried out in order to investigate the conformational and binding properties of UvsX in combination with ATP and DNA, and the simulation of RecA was synchronized with the property comparison learning for UvsX. This study confirmed the highly conserved molecular structure characteristics and catalytic centers of RecA and UvsX, and also discovered differences in regional conformation, volatility and the ability to bind DNA between the two proteins at different temperatures, which would be helpful for the subsequent understanding and application of related recombinases. E. coli is the prototype of this kind of protein and has received the most in-depth study; its functional characteristics and structure have been comprehensively studied . Th. Th22]. The communication between ATP and DNA was also explored by applying the principles of graph theory. We used the ATP-binding region or the DNA-binding region as a collection of residues at the starting and ending points to find communication pathways that could connect the ATP and DNA-binding domains . The Cα UvsX is similar to RecA in general conformation and reaction mechanism, but there are obvious differences between UvsX and RecA in motion correlation, conformation and dynamics in some parts. This difference may be closely related to UvsX’s unique features.Homologous recombination as an important process of stable biological genes. The functioning of its core protein of DNA recombinase is closely inter-related with the normal development of living organisms ,4,23. AmBoth proteins come from the same family and are highly identical in structure, so their conservation has been proved in many aspects of this study . From thE. coli RecA protein. The mechanism by which the UvsX protein facilitates DNA strand exchange is identical to that of the RecA protein overall, but differs from it in some details. In the analysis of protein Rg and SASA, the values of UvsX were significantly higher than those of RecA, indicating that the protein conformation was loose and exposed in the water environment, forming a more expansive monomer volume on the DNA. This may be related to the average helical pitch of UvsX being larger than that of RecA when the average number of subunits per revolution is nearly equal [The UvsX protein is the most characteristic DNA strand exchange protein after the ly equal . It alsoWe also demonstrate the communication route connecting the two binding sites in these states. The residues Ile195 and Thr196, corresponding to RecA and UvsX, respectively, were found to act as the link between ATP and DNA domains. Perhaps we can explain our results in terms of ATP linkage to DNA domains. During the formal function of the RecA protein, ATP c-phosphate is sensed by two lysine residues through the RecA-RecA interface, which are called lysine fingers and stimulate ATP hydrolysis . The ATPThe above results elucidate to a certain extent the mechanism of how RecA and UvsX proteins interact with ATP and dsDNA molecules simultaneously. This may be of great importance for understanding the robust occurrence of homologous recombination and the activity mode during the relevant metabolic period.http://www.chemspider.com (accessed on 26 June 2021)”. The structure of RecA was reached at PDB database “https://www.rcsb.org (accessed on 26 June 2021)” and its PDB ID is 3cmx [AutoDock 4.2.6 was used to dock ATP in RecA and UvsX . The str is 3cmx . Polar h is 3cmx . The act is 3cmx , with a https://www.ncbi.nlm.nih.gov (accessed on 26 June 2021)”. The known but partially missing UvsX protein structure (PDB ID: 3io5) and its homologous RecA protein structure (PDB ID: 3cmx) were used as templates [Homologous modeling is a computational method to generate the 3D structure of a target protein with reference to a similar protein structure. In the absence of a definite structure of the target protein, we can use computational methods to fill this gap, and use at least one known protein 3D model, namely as a template related to the structure of the target protein, to conduct homology modeling ,31. The emplates ,21. The All atom MD simulations were performed using GROMACS 2021.2 package . Amber14ijC of DCCMs were calculated by using the coordinates of the Cα atoms. The formula can be expressed as:ix/jx is the coordinate of the ith/jth atom of the systems.Principal component analysis (PCA) method, also known as quasi-harmonic analysis or fundamental dynamics method, aims to reduce the dimensions of complex systems. Most fluctuations of the system can be described by a few principal component feature vectors or principal components . ResidueBK is the Boltzmann constant, T is the temperature of simulation systems and P(X) is the probability distribution of the molecular system along the PCs.The Energy Landscape Analysis can be represented by the minimum that represents the metastable conformation states of the system and the potential barriers connecting those states. Therefore, an appropriate energy landscape can represent the correct amount of energy and position of metastable and potential barriers, which can be used to understand the stable folding and function of proteins. The formula is:Molecular mechanics/Generalized-Born Surface Area (MM/GBSA) is one of the most popular methods to estimate binding free energies. This method is accurate and efficient in dealing with proteins and protein nucleic acid systems ,48. MM/GbindG is the free binding energy, ΔH is the enthalpy of binding, −TΔS is the conformational entropy after ligand binding. ΔMME corresponds to the change in molecular mechanical energy in the gas phase. ΔbondE is known as internal energy, and ΔnonbondedE corresponds to the van der Waals and electrostatic contributions.In the above equations, ΔGBG and the LCPO method was employed to calculate the nonpolar solvation contribution energy ΔSAG. All residues less than 4 angstroms between the receptor and the ligand were exported as decomposition components of energy.Here we used gmx_MMPBSA program, which is based on AMBER’s mmpbsa.py and uses GROMACS files to perform MM/GBSA calculations . Prior ti and j are residue identifiers, iN and jN are normalized values of residues i and j from the statistically significant protein data set [The PSN method uses graph formalism to define the network of interacting residues in a given protein from the number of non-covalent interacting atoms.data set .We used the PyInteraph suite to reveal the paths of long-range structural communication between the two binding region of ATP and DNA . We appl


Understanding how genetic variation affects phenotypes represents a major challenge, particularly in the context of human disease. Although numerous disease-associated genes have been identified, the clinical significance of most human variants remains unknown. Despite unparalleled advances in genomics, functional assays often lack sufficient throughput, hindering efficient variant functionalization. There is a critical need for the development of more potent, high-throughput methods for characterizing human genetic variants. Here, we review how yeast helps tackle this challenge, both as a valuable model organism and as an experimental tool for investigating the molecular basis of phenotypic perturbation upon genetic variation. In systems biology, yeast has played a pivotal role as a highly scalable platform which has allowed us to gain extensive genetic and molecular knowledge, including the construction of comprehensive interactome maps at the proteome scale for various organisms. By leveraging interactome networks, one can view biology from a systems perspective, unravel the molecular mechanisms underlying genetic diseases, and identify therapeutic targets. The use of yeast to assess the molecular impacts of genetic variants, including those associated with viral interactions, cancer, and rare and complex diseases, has the potential to bridge the gap between genotype and phenotype, opening the door for precision medicine approaches and therapeutic development. The OMIM database describes over 6200 genetic disorders and ~5000 genes with disease-causing mutations ,2,3,4. HHuman inherited disorders are caused by mutations in genes resulting in disruption of normal cellular function. However, a daunting challenge to characterizing genotype-to-phenotype relationships is understanding the molecular functions these variants perturb. Indeed, well-established mechanistic connections that can spur the development of novel therapies are vanishingly rare. For the vast majority of disease-causing variants, we do not understand their impact on molecular and cellular functions.The functional interpretation of disease-causing human variants is indeed turning out to be more complicated than the classical one-gene/one-function/one-phenotype model: (i) different mutations in a gene can be associated with distinct disorders , (ii) a disorder can be caused by mutations in any one of several genes (locus heterogeneity), (iii) many gene products mediate different functions (pleiotropy), (iv) not all individuals affected by a given mutation are affected equally (variable expressivity), and (v) only a subset of individuals carrying a mutation are affected by the disease at all (incomplete penetrance) ,19.Currently, most variants identified by clinicians cannot be confidently classified and are reported as variants of uncertain significance (VUS). In fact, there are currently 840,000 coding VUSs reported in ClinVar, which is approximately five times the number of annotated pathogenic variants . This hiA system can be defined as an arrangement of essential parts or subsystems that are interconnected and interdependent, operating in consonance with a set of rules to form a unified whole exhibiting behavior or meaning that the individual constituents do not have and is arranged to achieve a specific objective. This definition can be dissected into five distinct characteristics. First, a system implies structure and order between its different components, a feature that is referred to as an organization. Second, a system is defined by its interactions, i.e., how each constituent functions with other parts. Third, a system’s components are interdependent, as one part is dependent on the output of another part or subsystem for proper functioning. Then, the different components are integrated and tied together to allow the system to eventually achieve a central objective. Numbers within mathematical operations are organized in a well-defined order (organization) and interconnected by mathematical symbols (interactions). The outcome of the first operation will define the outcome of the second, which will, in turn, affect the result of the next (interdependence), and the integration of all operations eventually leads to the final result . Another example of a system consists of the air traffic control system, which consists of a complex network of subsystems, such as radar systems, communication systems, and navigation systems, that are connected (organization) through a network of computer systems (interactions) that provide real-time information about the location and movement of aircraft (integration), and that work together to ensure the safe and efficient movement of aircraft through the airspace . The system also includes contingency plans for dealing with unexpected events, such as equipment failures, severe weather, and security threats (interdependence). Likewise, the financial system is a complex network of institutions, markets, and individuals that are interconnected (organization) through a network of transactions and financial instruments such as loans and bonds (interactions) that facilitate the flow of money and credit throughout the economy (integration), ensuring that the financial system remains stable and resilient and that it can continue to provide the necessary funding to support economic growth and development .Systems biology is an interdisciplinary approach to studying complex biological structures, including cells, tissues, and organisms. It aims to investigate biological systems in a holistic manner by taking into account the interactions between different components of the system, such as genes, proteins, metabolites, and signaling molecules. Moreover, it seeks to uncover the underlying mechanisms that govern the behavior of biological systems, identify the key regulatory pathways and feedback loops that control their function, and understand how these structures work and how they respond to perturbations. Systems biology relies on a wide range of experimental techniques, including but not restricted to genomics, transcriptomics, proteomics, metabolomics, and imaging. One of the primary assets, and challenges, of systems biology lies in the integration of these different types of data together to generate a thorough, global representation of biological systems. These models can ultimately be used to simulate the behavior of the system under varied conditions and to make predictions about how it will respond to perturbations. Systems biology has many applications in fundamental biology and medicine, including drug discovery, personalized medicine, and the development of new therapies for diseases. It hence undoubtedly represents a necessary approach that has the potential to transform our understanding of complex biological systems and to provide new insights into the fundamental principles that govern life.The one gene-one function model is a concept that proposes that each gene in an organism’s genome is responsible for producing a single functional product, such as a protein, and that this product has a specific and well-defined function in the cell or organism. However, this model is circumscribed and rapidly reaches limitations, as it is not always accurate, particularly in more complex organisms. It often oversimplifies the complexity of genetic interactions and the role of genes in the development and function of organisms. In reality, many genes can have multiple functions depending on the cellular context, the developmental stage of the organism, or the environmental conditions. Additionally, some genes may, for instance, produce non-coding RNAs or other regulatory molecules that can influence the expression or function of other genes. Another drawback of the one gene-one function model is that it does not account for genetic redundancy or compensation. In some cases, multiple genes may be involved in performing a specific function, and the loss of one gene may be compensated for by the function of another gene. This can make it difficult to identify the specific function of each individual gene. Furthermore, many genetic disorders are caused by mutations in genes that affect multiple functions rather than just one function. For example, mutations in the BRCA1 and BRCA2 genes, which are associated with an increased risk of breast or ovarian cancer, affect multiple cellular processes, including DNA repair, transcriptional regulation, and cell cycle control. Overall, while the one gene-one function model can be useful in understanding basic genetic mechanisms, it presents weaknesses in accurately describing the dynamic and systemic nature of how genes function in complex organisms.Edgetics is the study of the rewiring of interactions in a context that is alternative to the one used to establish a reference interactome map. While interactomics establishes reference interaction networks by mapping interactions, edgetics aims to annotate and understand the structure and function of edges composing these biological networks . The terIn conclusion, the study of protein–protein interactions provides important insights into the molecular mechanisms of diseases and can help identify potential targets for therapeutic intervention.All organisms share biological processes mediated by gene products conserved through evolution . While oIn view of these attributes, yeast represents an incredibly powerful and popular structure for studying basic biological processes and for investigating the molecular mechanisms underlying many human diseases. It indeed provides a powerful platform for investigating a wide range of biological questions and has provided numerous insights into the workings of the cell and the mechanisms underlying human diseases . In addiS. cerevisiae species) as a model organism paved the way for understanding basic biological processes such as gene expression, protein synthesis, and cell division. Oftentimes, studies in model organisms reveal the first clues to the identity of a genetic defect in human disease. For example, MSH2 and MLH1, two genes involved in DNA mismatch repair mechanisms and the maintenance of the genome, and deleterious mutations which can lead to Lynch syndrome and non-polyposis colorectal cancer, were identified in yeast prior to being sequenced in humans [On the one hand, yeast . Thanks to a much higher network density, the identification of densely connected modules that are enriched for proteins with related functions was facilitated. This reference map of the human binary interactome they constructed, in conjunction with other omics data, represents an unprecedented resource for understanding the organization and function of the human proteome, as well as for identifying new drug targets and biomarkers. More recently, Luck and colleagues published a third iteration of the effort to map the human interactome . In 2020In addition to systematic mapping of protein interactions in model organisms, systems biology has enabled extensive investigation of human diseases using the two-hybrid system in yeast. Using this approach, virus PPI networks and virus-host PPI networks have been generated. For example, Calderwood et al. used this system to study herpes viruses, revealing a reference interactome between the Epstein-Barr virus and human proteins  as well In summary, the two-hybrid system in yeast has been widely used for mapping biomolecular interactions. According to the IntAct database, which records biophysical interactions from the literature across various organisms, over two-thirds of reported interactions have been identified using this assay A. HoweveMultiple technologies are generally necessary to comprehensively map and characterize any biological system, whether it consists of DNA, RNA, proteins, etc. As illustrated by Choi et al., no single assay is indeed able to identify all biomolecular interactions within a given system . SpecifiPertaining to protein–protein interactions, two main classes of assays can be considered: co-complex association or binary and direct assays . Co-complex association methods seek the identification of proteins that are concomitantly recruited and come together in a cellular context without determining the direct points of contact between them. These methods are usually directly conducted on cells from the species that are studied . The most widely used co-complex association method, the affinity purification-mass spectrometry (AP-MS) strategy, consists in fusing bait proteins to peptide tags that are used to pull them down along with other biomolecules that physically associate with the baits. Identification of these molecules follows by mass spectrometry. Co-fractionation represents another extensively used PPI-mapping by association approach that has the benefit of not introducing exogenously expressed proteins. It instead relies on biochemical separation protocols of cell extracts generating multiple fractions that are then analyzed by mass spectrometry. Protein associations are then inferred by co-elution profiles. These two techniques, while efficient in picking out proteins that associate within cells, lack some granularity in the sense that they do not reveal which molecular species are in direct contact in a protein complex. E. coli repressor LexA, a well-characterized TF known to bind to the promoter of SOS-response regulatory genes involved in DNA repair mechanisms in the bacterium, is exploited [GAL1, GAL2, GAL7, or GAL10 [SPALn promoters, which contain the upstream repressing sequence of the yeast SPO13 promoter and n Gal4-binding sites [Other protocols are more suitable to address that question, which are referred to as binary and direct methods. These can be conducted in vitro (vN2H), in yeasto  or in cellulo . While most of these assays are conducted in mammalian cells, yeast represents a suitable environment for a couple of these techniques: yeast two-hybrid and nanoluc two-hybrid. First, the Y2H system is based on the reconstitution of a transcription factor (TF) summarized as DB-X:AD-Y, where DB is a sequence-specific DNA-binding domain, AD is a transcriptional activation domain, and X and Y are proteins, or protein variants, being tested for interaction ,104. Sevxploited . In thisxploited , which ior GAL10 . Promoteor GAL10 ,108,109.ng sites . GAL2 gene promoter to the NanoLuc reporter gene [Regardless of the origin of the DNA-binding and activation domains, a variety of options exist with respect to the choice of a reporter gene. The most widely used versions of the yeast two-hybrid system rely on the expression of auxotrophic markers usually involved in biosynthesis pathways of nucleotides or amino acids, thus calling for a growth readout on appropriate dropout media . While ater gene . The latter gene . DNA barIn addition to the yeast two-hybrid system, protein complementation assays have proven valuable in studying binary and direct protein–protein interactions. In contrast to Y2H, which relies on the activation of a full-length reporter gene, a complementation assay typically consists in splitting the reporter gene into two halves that are each expressed as recombinant proteins fused to the interrogated X and Y proteins. In the instance where X and Y interact, the two fragments of the reporter protein are brought in close proximity, which allows the reconstitution of the full-length  active species. While most of these systems, such as GPCA or YFP-PCA, are carried out in a mammalian cell context, the NanoLuc two-hybrid system (N2H) devised by Choi et al. can be conducted in vitro, in yeasto and in cellulo, using a unique set of expression vectors. The strength of the system is based on the engineering of a tripartite promoter allowing expression of the queried genes in these three distinct environments. Both Y2H and N2H technologies have the advantage of being versatile and can be executed in a variety of flavors. First, either method uses a set of two plasmids expressing recombinant proteins, and the combination of fragmented peptide-queried proteins can be swapped. Then, from an architectural perspective, the queried proteins and peptide fragments can be expressed as N-terminal or C-terminal fusions, likely resulting in diverse molecular conformations and hence decreasing the likelihood of potential spatial hindrance. Finally, different levels of gene expression can be achieved by using various origins of replication on the DNA vector backbones.Regardless of the primary screening method that is chosen for interactome mapping and edgetic functionalization, it is important to validate protein–protein interaction datasets obtained by high-throughput studies to ensure their quality using orthogonal methods, along with benchmark datasets. Orthogonal validation refers to the process of validating results obtained through a given method by using a different, independent approach that provides complementary information. In the context of protein–protein interaction studies, this means that results obtained using a particular high-throughput assay, such as yeast two-hybrid or co-immunoprecipitation, are validated using a different assay or method that provides independent confirmation of the authenticity of these interactions. With respect to the benchmark datasets, they typically consist of well-characterized interaction pairs that serve as a positive reference set (PRS) and a set of protein pairs selected at random that is used as a random reference set (RRS) . Any higThe concept of “edgetic perturbation” in protein–protein interaction networks were extensively discussed by Zhong et al., in 2009, in an article in which the authors highlight the potential of such an approach for understanding the genetic basis of human inherited disorders ,119,120.In 2013, Sahni and colleagues implemented the concept of “edgotype” as a way to bridge the gap between genotype and phenotype, which is critical for developing personalized medicine . In theiTwo years later, the same group investigated the molecular basis of a larger set of genetic disorders by analyzing the impact of a range of mutations on PPI networks and then identifying disease-associated genes and the network modules they participate in . They hyMany Mendelian diseases display tissue-specific phenotypes. Yet, disease genes are often uniformly expressed across tissue types . In 2020Protein–protein interaction networks represent only a specific set of networks that provide a foundational framework upon which additional layers of functional connections can be built to fine-tune the representation of biological reality. A full understanding of the internal organization of a cell requires the integration of other types of interactome networks, such as transcriptional profiling networks, phenotypic profiling networks, genetic interaction networks, gene regulatory networks, or metabolic networks. While cellular networks based on functional links differ significantly from protein–protein interaction maps in terms of the nature of their connections, they can complement each other and provide valuable insights into biological reality. This creates a reciprocal relationship between the two approaches, which eventually enables the construction of multidimensional maps that follow a series of basic organizing principles and that can be used to better understand genotype-to-phenotype relationships. Vidal et al. exhaustively reviewed why considering perturbations of biological networks is critical in interpreting genetic variation and can shed light on how it relates to phenotypic differences .The edgetic perturbation approach has proven useful in providing a valuable tool for understanding the molecular basis of human inherited disorders. The approach could be used to identify potential drug targets and to develop more personalized therapies based on the specific perturbations present in each patient’s disease. The development of therapies using knowledge of protein–protein interactions is an active area of research in the field of drug discovery. In recent years, there has been an increasing focus on targeting protein–protein interactions to develop new treatments for a wide range of diseases, including cancer, infectious diseases, and neurological disorders. One approach to developing therapies based on protein–protein interactions is to identify small molecules or peptides that can disrupt or modulate the interaction. These molecules can be designed to target specific binding interfaces or to stabilize or destabilize the interaction ,129,130.One example of a drug that has been developed based on the knowledge and study of protein–protein interactions is Venetoclax, which is used for the treatment of chronic lymphocytic leukemia (CLL) and some types of non-Hodgkin’s lymphoma. It is a BH3 mimetic that targets a protein–protein interaction between B-cell lymphoma 2 (BCL-2) and BCL-2-associated X protein (BAX), which are both members of the BCL-2 family of proteins that regulate apoptosis, or programmed cell death. BCL-2 is an anti-apoptotic protein that inhibits cell death, while BAX is a pro-apoptotic protein that promotes cell death. The interaction between BCL-2 and BAX is critical for regulating apoptosis in healthy cells, but it can also be exploited by cancer cells to promote their survival and proliferation. Venetoclax is a small molecule inhibitor that binds to the BH3-binding groove of BCL-2, preventing its interaction with BAX and other pro-apoptotic proteins. This leads to the activation of the apoptotic pathway and the death of cancer cells ,135. TheAnother example of a drug developed based on the knowledge and study of protein–protein interactions is Enbrel, which is used for the treatment of autoimmune diseases such as rheumatoid arthritis, psoriasis, and ankylosing spondylitis. Enbrel is a biologic drug that works by inhibiting the activity of the TNF-α protein, which is a key regulator of inflammation and immune response. In autoimmune diseases, the immune system mistakenly attacks healthy tissues, leading to chronic inflammation and tissue damage. TNF-α is a cytokine that plays a major role in this inflammatory response, and inhibiting its activity can help reduce inflammation and relieve symptoms. Enbrel is a fusion protein that consists of a human TNF receptor and the Fc region of a human antibody. The TNF receptor binds to TNF-α and prevents it from interacting with its cell surface receptors, thereby inhibiting its activity. The Fc region of the antibody allows the drug to have a longer half-life and enhances its ability to engage with the immune system . The druOne last example of a drug developed based on the knowledge and study of protein–protein interactions is Herceptin, which is used for the treatment of certain types of breast cancer. Herceptin targets a protein called human epidermal growth factor receptor 2 (HER2), which is overexpressed in about 20% of breast cancers. HER2 is a receptor tyrosine kinase that promotes cell growth and survival, and its overexpression leads to increased cell proliferation and tumor growth. Herceptin is a monoclonal antibody that binds to the extracellular domain of HER2 and prevents its dimerization, and, thus, activation, thereby inhibiting cell growth and inducing cell death. It also triggers the immune system to attack the cancer cells . The druIn summary, the development of Venetoclax, Enbrel, and Herceptin are great examples of how the study of protein–protein interactions can lead to the identification of new drug targets and the development of effective therapies for cancer and other diseases.In the last two decades, extensive sequencing efforts have led to the identification of a vast amount of genetic variation. However, the full potential of genomic data can only be unleashed in concert with high-throughput functionalization technologies. Accurately determining the clinical significance of genetic variants is a pressing need for developing prophylactic measures and therapeutics and currently represents a major bottleneck for delivering on the promise of personalized medicine. In this review, we have highlighted the key role of yeast in addressing this urgency. As a eukaryotic organism evidently much simpler than humans, it allows the implementation of experimental procedures that are utterly inaccessible in higher-order eukaryotes, with yet an undeniable repercussion on our understanding of fundamental human biology. The genetic tractability and rapid growth of yeast make it an ideal system for large-scale studies. It has notably played a central role in establishing reference protein–protein interaction maps and the investigation of the potential involvement of these biophysical interactions in human diseases. Leveraging yeast’s strengths as a model system and an experimental tool represents an inevitable step to shed light on the molecular basis of human disease, including cancer, neurodegenerative disorders, and infectious diseases, and bridging the gap between genotype and phenotype.Overall, yeast has been and will continue to be used extensively as an experimental structure for studying protein–protein interactions and functionalizing mutations involved in human disease. Indeed, while the ideal scenario would consist in relying on the interrogation of rapid, streamlined, and resource-effective computational tools to determine the impact of genetic variation on phenotype, such approaches still leave too much room for uncertainty in the context of precision medicine. The development of prediction algorithms requires extensive training on data generated through empirical, experimental testing, but, despite the seemingly large amount of existing data, computational variant effect predictors still lack accuracy. It appears clear that yeast, because of the significant advantages it offers, will play an essential role in the endeavor of building accurate, decisive models for personalized medicine for years to come.


Tissular hypoxia stimulates vascular morphogenesis. Vascular morphogenesis shapes the cell and, consecutively, tissue growth. The development of new blood vessels is intermediated substantially through the tyrosine kinase pathway. There are several types of receptors inferred to be located in the blood vessel structures. Vascular endothelial growth factor A (VEGF-A) is the leading protagonist of angiogenesis. VEGF-A’s interactions with its receptors VEGFR1, VEGFR2, and VEGFR3, together with disintegrin and metalloproteinase with thrombospondin motifs 1 (ADAMTS1), connective tissue growth factor (CTGF), and neuropilin-1 (NRP1), independently, are studied computationally. Peripheral artery disease (PAD), which results in tissue ischemia, is more prevalent in the senior population. Presently, medical curatives used to treat cases of PAD—antiplatelet and antithrombotic agents, statins, antihypertensive remedies with ACE (angiotensin-converting enzyme) impediments, angiotensin receptor blockers (ARB) or β- blockers, blood glucose control, and smoking cessation—are not effective. These curatives were largely established from the treatment of complaint cases of coronary disease. However, these medical curatives do not ameliorate lower limb perfusion in cases of PAD. Likewise, surgical or endovascular procedures may be ineffective in relieving symptoms. Eventually, after successful large vessel revascularization, the residual microvascular circulation may well limit the effectiveness of curatives in cases of PAD. It would thus feel rational to attempt to ameliorate perfusion in PAD by enhancing vascular rejuvenescence and function. Likewise, stimulating specific angiogenesis in these cases (PAD) can ameliorate the patient’s symptomatology. Also, the quality of life of PAD patients can be improved by developing new vasodilative and angiogenetic molecules that stimulate the tyrosine kinase pathway. In this respect, the VEGFA angiogenetic pathway was explored computationally. Docking methodologies, molecular dynamics, and computational molecular design methodologies were used. VEGFA’s interaction with its target was primarily studied. Common motifs in the vascular morphogenesis pathway are suggested using conformational energy and Riemann spaces. The results show that interaction with VEGFR2 and ADAMTS1 is pivotal in the angiogenetic process. Also, the informational content of two VEGFA complexes, VEGFR2 and ADAMTS1, is crucial in the angiogenesis process. The tissue environment plays a critical role in modeling the angiogenesis process. Integrins regulate these interactions ,2. VesseFurthermore, VEGF initiates EC proliferation, degradation of the extracellular matrix, and, more importantly, the chemotaxis process . HypoxiaRemodeling of the vessel wall is also implied in angiogenesis. ANG2 antagonizes ANG1 activity on TIE2 to destabilize vessels and aid angiogenic remodeling. Homophilic VE–cadherin interactions maintain EC–EC junctions. Delta-like 4 (DLL4)-mediated activation of Notch receptors represses angiogenic cell behavior and promotes vessel stability upon the proteolytic release of the Notch intracellular domain (NICD). Roundabout homolog 4 (ROBO4) mediates the inhibition of VEGFR signaling and thus acts like a process signaling pathway to maintain vessel integrity ,30.Furthermore, EPHB4–ephrin B2 signaling is involved in vascular development. This complex allows for VEGFR2 or VEGFR3 membrane internalization and amplifies angiogenetic signaling) ,32,33. CThe most studied and essential pathway is the VEGFR2 pathway. VEGFR2 activation induces DLL4 expression in TCs, which activates Notch on adjacent SCs and VEGF-mediated disruption of a repressive translocation ETS leukemia (TEL) and carboxy-terminal-binding protein (CtBP) complex . Also, rRegarding VEGFR3, Notch signaling in SCs downregulates its expression. Notch-induced Notch-regulated ankyrin repeat-containing protein (NRARP) expression enhances WNT signaling in SCs, maintaining EC–EC junctions, promoting proliferation, and augmenting DLL4 expression via β-catenin . Also, feedback inhibition of Notch signaling is performed by NRARP. All VEGF members bind to tyrosine kinase receptors on the cell surface, producing a dimerization, and are thought to become active through transphosphorylation . There are three sorts of VEGFR: VEGFR 1-3. VEGFR1 interacts mainly with VEGFA. VEGFR2 mediates the cellular responses to VEGF, and VEGFR1 modulates VEGFR2 signaling. VEGFR1 also acts as a dummy receptor for VEGF. VEGFR-3 regulates lymphangiogenesis in response to VEGF-C and VEGF-D. As stated above, the angiogenesis pathway supported by VEGFA can be complex. Multiple interactions are required to initiate and shape the vascular morphogenesis process. The VEGFR system supports the leading process dynamic. Interacting with all VEGFR members ) finally results in distinct effects on vascular morphogenesis. Some essential features offer each VEGFR type distinct properties . This computational research studies VEGFA’s interaction with its designated receptors and some related molecules that bind VEGFa. Computational methods were used to quantify and characterize the interaction between VEGFA and its targets. VEGFA’s interactions were studied in light of the information content and chemical spaces using molecular descriptors and Riemann surfaces.Peptide–peptide docking retrieved more than 100 stable conformations for each of the complexes. As a result, 96 protein–protein complexes were selected for each receptor. In In Regarding the volume of the VEGFA–VEGEFRB complexes’ conformational states, each complex volume is represented in ADMTS-1, CTGF, and NRP1, along with VEGEFR complex energies, are shown in The close-contact energy trendline equations are represented in In in is the number of atomic number (i) occurrences in the molecule. Also, ip = in/n, where n is the sum of the in. The value of a_ICM is the negative of the sum over all i of ip log ip. Atom total information content is computed to be a_ICM times n. Vertex adjacency information (number of magnitudes): 1 + log2 m, where m is the number of heavy–heavy bonds. Vertex adjacency information is computed using the formula: −(1 − f)log2(1 − f) − f log2 f, where f = (n2 − m)/n2, n is the number of heavy atoms and m is the number of heavy–heavy bonds(Atom information content (mean) is the entropy of element distribution in the molecule, where avy bonds [41,42,4The total information content and mean information content of each VEGFA complex represented in The entropy obtained for each complex is as follows: VEGFA–VEGEFR1; VEGFA–VEGEFR2 = 25.0591; VEGFA–VEGEFR3 = 42.638 ; VEGFA–ADAMTS1; VEGFA–CTGF; VEGFA–NRP1.Considering the interaction dimensionality computed with Riemann spaces using the close-contact energy, all VEGFA complexes have the same vertex adjacent information, meaning that they have the same dimensionality but are distinct in their information complexity judging by the information indices. Regarding protein structural information, some properties computed like the area of hydrophobic protein residues, the area of positive protein residues, the area of negative protein residues, the area of ionic residues, net charges, and protein mass (ligan–receptor complex mass) have no variation or slight variation through the complexes conformations. For example, the VEGFA–VEGFR1,2,3, ADAMTS1, CTGF, and NRP1 complex mass values are as follows: 116,9536; 116,9496; 116,956; 116,9476; 116,9557; and 116,9516 (kDa); furthermore, the other protein-related properties vary slightly in all other conformation populations. In The interaction between the two molecules  finally ends up in a complex mathematical system shown by the equations in Simple graphs are digraphs with undirected edges. For example, two vertices in an exceedingly simple graph are considered adjacent if they are the endpoints of the very edge. Entropy quantifies the quantity of missing information before the reception. By Shannon, entropy is expressed in terms of a discrete set of probabilities (pi): (X) = −rrelated ,48,49,50rrelated  Also, thAs stated in the introductory part, the thyroxin kinase pathway is involved in all members of VEGF molecules. Their mode of action is similar in all families of molecules—forming bonds by dimerization. VEGF receptors possess an extracellular domain and an intracellular domain with a tyrosin-kinase-specific site. Regarding each VEGFR function, the function of VEGFR-1 is not entirely defined, although it is thought to modulate VEGFR-2 signaling ,52,53,54VEGFR-3 mediates lymphangiogenesis in correlation with VEGF-C and VEGF-D ,52. AlsoAlso relevant to the antiangiogenic actions of ADAMTS-1 is that TSP1 can interact with CD36, a membrane glycoprotein receptor on endothelial cells ,63. ThisFurthermore, the aggrecanases ADAMTS-1, -4, -5, -8, -9, and -15 aggrecan is the foremost important proteoglycan responsible for flexibility . It contThe G1 domain interacts with mucopolysaccharides and links proteins to make large aggregates trapped within the cartilage collagen matrix. Aggrecan protects cartilage collagen from degradation . Regarding their active site, two major proteolytic cleavage sites are identified in this domain: one at Asn341–Phe342 and one at Glu373–Ala374. Regarding ADAMTS1’s absence, it was shown that its absence in Adamts1-null mice causes growth retardation, tissue malformation, and decreased fertility with changes within the histology of the uterus and ovaries ,69. AlsoLastly, PAD’s prevalence exceeds 10 million people. PAD is typically caused by atherosclerotic obstructions within the large arteries of the leg(s). Also, despite extensive research, medical therapies to increase perfusion to the distal limbs are of limited benefit. Although early angiogenesis and cell therapy studies show some isolated results, the employment of angiogenesis and arteriogenesis therapies remains limited . As show+). In order to analyze VEGFA’s interaction with its targets, protein–protein docking studies were performed. VEGFA was considered as a ligand and VEGFR1,2,3 and ADAMTS1, CTGF, and NRP1 were considered as receptors. Docking evaluation was performed by considering 96 ligand–receptor complexes’ conformations for all six systems studied. The docking studies were implemented by using the Schrodinger software package [In order to explore the angiogenetic process, specifically VEGFA’s interaction with its known targets, the protein data bank (PDB) structures of VEGFA(1BJ1)  and its  package . For CTG package . After tA conformational energy population was used to evaluate the interaction between VEGFA and its targets. By representing the conformational energy confrontations for all VEGFA complexes, a polynomial trendline was computed for each complex. In order to obtain 2D and 3D quantification, polynomial six-degree equations were used to generate Riemann surfaces. The 3D resultant Rieman surfaces were analyzed using their 2D diagrams. Finally, the Riemann surfaces and complexity maps were used to compare and assess the VEGFA protein interactions.In order to validate the interactions, fingerprint protein–ligand interaction was used. Using fingerprint classification, a protein–ligand interaction fingerprint (PLIF) is a methodology for exploring the interactions between two proteins . Also, a series of molecular descriptors were computed. Also, to characterize the molecular population studied, some protein properties were computed for each complex and conformation: the area of hydrophobic protein residues, the area of positive protein residues, the area of negative protein residues, the area of ionic residues, net charges, protein mass (complex mass), the radius of gyration, accessible surface area, hydrophobic surface area, hydrophilic surface area, protein volume (complex volume), and protein dipole moment. The properties were computed using an AMBER force field . The computations were performed using the online server VCCL . The radVEGF A interacts with multiple protein targets. The primary targets are its receptors VEGFR 1,2 and 3. Interactions with VEGFR are specific concerning each VEGFR type. For example, VEGFR1 interacts strongly with VEGFA, followed by VEFEGR types 2 and 3. Also, ADAMTS1 seems to have strong interaction with VEGFA. Considering the Riemann surfaces, VEGFR1 is the most complex interaction with VEGFA, followed by NRP1.Regarding the information content of the VEGFA–receptor complexes, VEGFR2 and ADAMTS1, in interaction with VEGFA, carry virtually the same information. Finally, molecular motifs implied in angiogenesis share some common structural and energetical futures. Overall, by studying VEGFA’s interaction with its primary targets, crucial insights into molecular interactions will be obtained. In addition, studying this tyrosine-kinase-related pathway will endow the molecular design of a tyrosine-kinase-specific stimulator- that will promote angiogenesis and arteriogenesis. Furthermore, drawbacks like vascular mimicry (VM) stimulation by the novel compound must be avoided. Presumably, these novel therapies will improve PAD treatment and patients’ quality of life.


Peptides mediate up to 40% of protein interactions, their high specificity and ability to bind in places where small molecules cannot make them potential drug candidates. However, predicting peptide–protein complexes remains more challenging than protein–protein or protein–small molecule interactions, in part due to the high flexibility peptides have. In this review, we look at the advances in docking, molecular simulations and machine learning to tackle problems related to peptides such as predicting structures, binding affinities or even kinetics. We specifically focus on explaining the number of docking programmes and force fields used in molecular simulations, so a prospective user can have an educated guess as to why choose one modelling tool or another to address their scientific questions. In disease, some of these pathways become aberrant; therefore, identifying ways of inhibiting them is of great therapeutic importance. Inhibiting PPI with small molecules is not always possible due to the large interface region, lack of binding cavities and specificity of the interaction. However, between 15 and 40% of PPI .et al., GalaxyPepDock  for each hit in the database, which is calculated combining the TM score of the receptor in the database with respect to the target receptor, and an interaction similarity score which is calculated based on the protein structure, peptide sequence and the interacting residue pairs  on the protein surface to guide sampling – but requires different peptide conformations as input  to limit sampling to the relative position of the protein/peptide without sampling the peptide conformations  rather than strictly docking tools. These methods need input structures of either coarse peptide–protein complexes (for refinement) or protein–protein complexes (for inhibitor design)   methods such as CABS-dock   either in the docking stage or in refinement stage generally have higher accuracy than the other methods  combined with Rosetta FlexPepDock refinement, which outperforms other methods  based on structural RMSD – but they have their own way to use clustering for selecting structure. Cluspro PeptiDock assigns highest score to the most populated cluster’s medoid, whereas CABS-dock selects the consensus medoid obtained from different clustering protocols as the best model . The availability of open sharing resources would make the preparation and dissemination of such a benchmark test an easy endeavour. Community efforts such as the OpenFF are already on their way for continuous optimisation of small molecule force fields , and carried out by a few expert groups. One of the challenges is the unexpected consequence of parameter changes as modifying one parameter might affect the accuracy of another parameter that was not adjusted due to the coupling between different terms. Even long MD simulations on a set of systems with times series, distributions, behaviour and stability analysis might not be enough to capture all possible issues. Some issues might arise in timescales beyond those studied during development or in systems not included in the benchmark test. While some groups might have made et al., et al., Here, we focus on recent efforts to improve the description of peptides and intrinsically disordered proteins (IDPs) while maintaining the stable properties for folded proteins , more exet al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., et al., Most effort is concentrated on directly refining the global backbone dihedral parameters including AMBER ff99SB* and ff03*  Refinement of docking results; 2) estimating binding affinities based on known bound complexes and 3) use of integrative modelling strategies to determine structures of the complexes.et al., Docking approaches described in the previous section favour speed at the expense of accuracy, while MD approaches are accurate, but inefficient at identifying where and how a peptide binds from conventional MD simulations. Thus, short MD simulations are often the last step of docking calculations to eliminate steric overlap, account for local conformational changes and identify structures based on physico-chemical principles rather than relying on a scoring function. While this application is standard, it does not leverage the full potential of MD such as calculating binding affinities. Thus, recent integration of docking and MD-based techniques such as the combination of ClusPro PeptiDock with GaMD goes beyond refining the structure to provide free energy profiles  have been widely applied to estimate kinetic quantities of biomolecular conformational dynamics from a set of short atomistic MD simulations  event  in the 13et al., The success of such approaches begs the question of why AF is performing well on complexes such as peptides. One observation points to the structural complementarity between peptides and proteins, where many peptides adopt well-defined secondary structures upon binding. Indeed, peptides that bind as coils are not predicted as well, although generally the binding site is still identified. The field of docking uses scoring functions to rank the different poses and compounds – a strategy that is very successful for small molecules, but which has not reached the same level of maturity for peptides (as described above in the docking section). AF’s pLDDT measure also lacks the possibility of ranking different peptides as different sets of peptides might be predicted with similar pLDDT scores despite very different binding affinities. Other ML approaches directly use structural ensembles and a measure of accuracy such as RMSD to assess the quality of the predicted structures . A current limitation in machine learning is the dependence on natural amino acids. MD on the other hand has transferable potentials and can be used as an end stage in peptide optimisation for studying the effect of controlling flexibility  or replacing some residues with non-natural amino acids such as peptoids. Comments to Author: “Modeling peptide-protein complexes: docking, simulations, and machine learning”This review explores three main approaches in modeling peptide-protein interactions; molecular docking, molecular dynamic simulations, and machine learning. Overall it is a comprehensive and well written review that provides prospective users with the advantages and limitations of each approach. This kind of reviews is indeed of a great importance to the filed of computational chemistry as it lowers the barrier of entry to newcomers and guide them through choosing the correct approach. I suggest considering the publication of this review after performing the following minor revisions:1. Molecular docking section: This section is well written and the authors provided a very good literature review of protein peptide-docking tools. My sole concern is that with the large amount of information provided here, the reader may find a difficulty in extracting information easily. Adding summary tables of each section can help the reader extract information efficiently. For example, the authors can provide a table summarizing the main features of each databases and sampling method [or at least the top performing], its pros, cons, and suggested application. Based on the scientific question, the prospective user can pick a database or sampling method that matches their system characteristics and refer to the the details in the main text.2. Molecular dynamics section:The authors provided a summary of the evolution and limitation of the current forcefields and explained the applications of different MD simulations methods in studying protein-peptide interactions. Although the authors explained the applications of several end-point free energy methods in protein-ligand/protein- peptide interactions such as MM-PBSA and MM-GBSA,they did not mention examples of using pathway free energy methods especially the highly rigorous “from statistical mechanics point of view” alchemical free energy approaches. Alchemical free energy calculations is a pathway free energy method that compute the binding free energy difference between the bound and unbound states of the peptide-protein complex by linking them through a suitable thermodynamic non physical “alchemical” path. Although the application of this approach in protein-peptide interaction is less common and more challenging than other end-point methods, it is worth shedding the light on the few successful examples of this approach as it will make this review more comprehensive [please refer to the following examples]:1- Kilburg, D., & Gallicchio, E. (2018). Assessment of a single decoupling alchemical approach for the calculation of the absolute binding free energies of protein-peptide complexes. Frontiers in molecular biosciences, 5, 22.2- Rashid, M. H., Heinzelmann, G., Huq, R., Tajhya, R. B., Chang, S. C., Chhabra, S.,& Kuyucak, S. (2013). A potent and selective peptide blocker of the Kv1. 3 channel: prediction from free-energy simulations and experimental confirmation.Â PloS one,Â 8(11), e78712.3- Panel, N., Villa, F., Fuentes, E. J., & Simonson, T. (2018). Accurate PDZ/peptide binding specificity with additive and polarizable free energy simulations.Â Biophysical journal,Â 114(5), 1091-1102.3. Machine Learning: No revision suggested. Comments to Author: Brief Summary:Firstly, I would like to thank the Editorial Board of Quarterly Reviews of Biophysics, Discovery, for the opportunity to review this article.The review focuses on current computational approaches to analyze peptide-protein interactions. Due to the highly flexible nature of peptides, the problem of determining the peptide-protein complex is nontrivial, and is crucial for subsequent analysis of interactions. As such, the review primarily discusses modeling peptide-protein complexes, starting with docking-based methods, continuing with the molecular-dynamics based refinements and integrative modeling platforms, and concluding with the recent breakthroughs in machine learning-based techniques. The authors also comment - in the 'MD Section' - on using molecular dynamics, in particular enhanced sampling methods, to study binding and unbinding kinetics. Ultimately, this review gives the reader a good foundation of the current tools available to both sample bound configurations of peptides with their receptors, and to generally investigate peptide-protein interactions.Major Comments:The review is extensive, demonstrating the rather large breadth of resources available for modeling peptides and peptide-protein complexes. Therefore, the reader can make informed decisions on which docking programs to use, whether to use an MD-based refinement strategy, and further which force fields to use for said refinement. Additionally, the authors do not shy away from discussing the limitations of the tools presented, and difficulties in the field of peptide studies generally. Due to these successes, my comments have largely been focused on discussing clarity of the review and presentation.This review discusses numerous tools for studying peptide-protein complexes, especially the available docking and scoring programs. As a recommendation, if possible, it may be beneficial to add tables to list the common programs, the subcategories they fall in (e.g. template-based docking tool) and where they find the greatest success - if unambiguous. This will make it easy for the reader to organize the information and to quickly identify which tools they'd like to study further. It may also allow the main text to discuss the tools in greater detail.The 'Summary' subsection of the 'Docking Section' was especially appreciated, as it tied together the salient points of docking and scoring. That being said, the bulk of the first paragraph (lines 484 to 491) would seem better placed in the beginning of the 'Scoring' section. The 'Summary' seems more appropriately initiated by line 495.Minor Comments / Grammar:Some of the paragraphs of this review could use transition sentences to switch topics in a coherent fashion.It is highly recommended that the paper be analyzed for grammatical errors and typos.All in all, this review would certainly benefit those who'd like to know more about the available tools to analyze peptide-protein complexes, thank you for your time. Comments to Author: Reviewer #1: "Modeling peptide-protein complexes: docking, simulations, and machine learning"This review explores three main approaches in modeling peptide-protein interactions; molecular docking, molecular dynamic simulations, and machine learning. Overall it is a comprehensive and well written review that provides prospective users with the advantages and limitations of each approach. This kind of reviews is indeed of a great importance to the filed of computational chemistry as it lowers the barrier of entry to newcomers and guide them through choosing the correct approach. I suggest considering the publication of this review after performing the following minor revisions:1.Molecular docking section: This section is well written and the authors provided a very good literature review of protein peptide-docking tools. My sole concern is that with the large amount of information provided here, the reader may find a difficulty in extracting information easily. Adding summary tables of each section can help the reader extract information efficiently. For example, the authors can provide a table summarizing the main features of each databases and sampling method [or at least the top performing], its pros, cons, and suggested application. Based on the scientific question, the prospective user can pick a database or sampling method that matches their system characteristics and refer to the the details in the main text.2.Molecular dynamics section:The authors provided a summary of the evolution and limitation of the current forcefields and explained the applications of different MD simulations methods in studying protein-peptide interactions. Although the authors explained the applications of several end-point free energy methods in protein-ligand/protein- peptide interactions such as MM-PBSA and MM-GBSA,they did not mention examples of using pathway free energy methods especially the highly rigorous “from statistical mechanics point of view” alchemical free energy approaches. Alchemical free energy calculations is a pathway free energy method that compute the binding free energy difference between the bound and unbound states of the peptide-protein complex by linking them through a suitable thermodynamic non physical “alchemical” path. Although the application of this approach in protein-peptide interaction is less common and more challenging than other end-point methods, it is worth shedding the light on the few successful examples of this approach as it will make this review more comprehensive [please refer to the following examples]:1- Kilburg, D., & Gallicchio, E. (2018). Assessment of a single decoupling alchemical approach for the calculation of the absolute binding free energies of protein-peptide complexes. Frontiers in molecular biosciences, 5, 22.2- Rashid, M. H., Heinzelmann, G., Huq, R., Tajhya, R. B., Chang, S. C., Chhabra, S.,& Kuyucak, S. (2013). A potent and selective peptide blocker of the Kv1. 3 channel: prediction from free-energy simulations and experimental confirmation.Â PloS one,Â 8(11), e78712.3- Panel, N., Villa, F., Fuentes, E. J., & Simonson, T. (2018). Accurate PDZ/peptide binding specificity with additive and polarizable free energy simulations.Â Biophysical journal,Â 114(5), 1091-1102.3. Machine Learning: No revision suggested.Reviewer #2: Brief Summary:Firstly, I would like to thank the Editorial Board of Quarterly Reviews of Biophysics, Discovery, for the opportunity to review this article.The review focuses on current computational approaches to analyze peptide-protein interactions. Due to the highly flexible nature of peptides, the problem of determining the peptide-protein complex is nontrivial, and is crucial for subsequent analysis of interactions. As such, the review primarily discusses modeling peptide-protein complexes, starting with docking-based methods, continuing with the molecular-dynamics based refinements and integrative modeling platforms, and concluding with the recent breakthroughs in machine learning-based techniques. The authors also comment - in the 'MD Section' - on using molecular dynamics, in particular enhanced sampling methods, to study binding and unbinding kinetics. Ultimately, this review gives the reader a good foundation of the current tools available to both sample bound configurations of peptides with their receptors, and to generally investigate peptide-protein interactions.Major Comments:The review is extensive, demonstrating the rather large breadth of resources available for modeling peptides and peptide-protein complexes. Therefore, the reader can make informed decisions on which docking programs to use, whether to use an MD-based refinement strategy, and further which force fields to use for said refinement. Additionally, the authors do not shy away from discussing the limitations of the tools presented, and difficulties in the field of peptide studies generally. Due to these successes, my comments have largely been focused on discussing clarity of the review and presentation.This review discusses numerous tools for studying peptide-protein complexes, especially the available docking and scoring programs. As a recommendation, if possible, it may be beneficial to add tables to list the common programs, the subcategories they fall in (e.g. template-based docking tool) and where they find the greatest success - if unambiguous. This will make it easy for the reader to organize the information and to quickly identify which tools they'd like to study further. It may also allow the main text to discuss the tools in greater detail.The 'Summary' subsection of the 'Docking Section' was especially appreciated, as it tied together the salient points of docking and scoring. That being said, the bulk of the first paragraph (lines 484 to 491) would seem better placed in the beginning of the 'Scoring' section. The 'Summary' seems more appropriately initiated by line 495.Minor Comments / Grammar:Some of the paragraphs of this review could use transition sentences to switch topics in a coherent fashion.It is highly recommended that the paper be analyzed for grammatical errors and typos.All in all, this review would certainly benefit those who'd like to know more about the available tools to analyze peptide-protein complexes, thank you for your time. No accompanying comment. No accompanying comment.


In life science, protein is an essential building block for life forms and a crucial catalyst for metabolic reactions in organisms. The structures of protein depend on an infinity of amino acid residues' complex combinations determined by gene expression. Predicting protein folding structures has been a tedious problem in the past seven decades but, due to robust development of artificial intelligence, astonishing progress has been made. Alphafold2, whose key component is Evoformer, is a typical and successful example of such progress. This article attempts to not only isolate and dissect every detail of Evoformer, but also raise some ideas for potential improvement. The most common approach to obtain and elucidate protein structures in biological science is x-ray crystallography, by which three-dimensional protein structures are derived based on x-ray diffraction data of individual amino acid residues , structural determination is essential, providing structural information for a deep understanding of individual protein functions and numerous reliable models for comparison and contrast. The availability of a substantial number of protein structures is essential, particularly through the crystallographic approach. This requirement is based on the need for a solid foundation in understanding protein functions and interactions at a molecular level. Achieving these structures through crystallography demands a high degree of accuracy during the crystallization process. A known downside of NMR spectroscopy is the huge number of purified samples needed. Managing the purifying process for large amounts of protein is challenging in some scenarios. There is a long history of protein folding prediction via computational methods. In 1995, the Critical Assessment of protein Structure Prediction (CASP) was founded. Held every 2 years, CASP provides an opportunity for scientists and engineers to test their mathematical models. However, despite decades of work, people have still not found a model to predict protein folding that is close to the ground truth.n = 3) in the CDK20 kinase binding assay  Corpet, , which iAnother important input of Evoformer is amino acids pairs, which allow protein-to-protein information transfer  activation function in equation 4 have an unstable or tiny slope , and makIn the transformer model  is an identical mapping of x and optimal layers learn nothing. So, it makes sure that the shallow layers receive useful information(x) at least.Equation 20 means Layer normalization , keys, values, and gate are all linearly transformed from a specific specie (row-wise). To calculate attention weight, pair bias is added at the end before SoftMax activation is performed. Moreover, gate g is used to filter value representation.Like equation 1, the transformer model  in the transformer model, there is no bias terms since it only has one source of input . However, Evoformer has two totally distinct types of input: MSA and amino acids pairs. Just like a linear function, for instance sq, h, c), keys, values, and gate are all linearly transformed from a specific gene expression (column-wise). There is no pair bias anymore so that column-wise attention looks almost the same as the transformer's self-attention except it has a gate.As mentioned above, column-wise attention is a new technique for specific protein problems. It captures attention across MSA columns. Columns in MSA represent the same gene expression among different species. Supplementary Figure 3   and pairing representation has a shape . To add them up, MSA must be reshaped to the same shape as the pairing representation [Supplementary Figure 5 of the paper  and finally maps to pair representation pij with a linear transformation.To update pair representation There are multiple ways to expand dimensions in this case. For example, we can perform a matrix tile operation on the third dimension. The reason to choose the outer product here is it condenses all the information in MSA in order to reconstruct pair representation.i, j} by receiving all information, its neighbor nodes {i, k} and {j, k} pass according to Supplementary Figure 6 of the paper .The triangular multiplicative update model is analogous to nodes in graph theory. To update the information contained at position {kij and value vij are replaced by kik and vik , bjk is also created and added to inner product affinities as a bias term. In the ending node version, the key kij and value vij are replaced by kki and vki ; bki is also created and added to inner product affinities as a bias term. Now, updating zij not only depends on the dot product similarities of neighbor gene expression but also depends on the third edge bik and bkj  Alphafold2 is not designed to model co-factor-based protein folds. Myoglobin or hemoglobin need a heme to fold and zinc-finger domains are not stable without a zinc ion, so Alphafill is developed to address such issues  One major drawback of AF2, which has a significant impact on the field of biomedicine, is the inadequate quality of transmembrane protein models. Although the confidence indices for transmembrane segments can be reliable, the overall predicted topology of these models is incompatible with their insertion into a membrane bilayer  An inherent constraint of MSA (Multiple Sequence Alignments)-based methods like AlphaFold2 is their reliance on existing knowledge and datasets. While these methods can make educated estimates between known protein structures and potentially even make predictions around those known structures, they struggle to confidently and precisely predict entirely new configurations. Incorporating a molecular dynamics component is crucial to effectively model these novel configurations (Marcu et al., Considering the above limitations, the model should aim to incorporate the effects generated by co-factors. To improve the prediction results of poor-quality transmembrane proteins, it should consider how to enhance the accuracy and reliability of their modeling. Additionally, the model should focus on generalizing to data not present in the existing databases, in order to improve and refine its predictions.Alphafold2 is a complicated system. There are more than 60 pages in the supplementary document of Alphafold2 (Jumper et al., TX contributed to the design and conception of the study. QX contributed to all the details and mathematics of the study. JL contributed by editing and reviewing the manuscript. All authors contributed to the manuscript revision, read, and approved the submitted version.


Studies of sets of proteins are a central point in biology. In particular, the application of omics in the last decades has generated lists of several hundreds or thousands of proteins or genes. However, these lists are often not inspected globally, possibly due to the lack of tools capable of simultaneously visualizing the feature architectures of a large number of proteins.https://profeatmap.pythonanywhere.com/.Here, we present ProFeatMap, an intuitive Python-based website. For a given set of proteins, it allows to display features such as domains, repeats, disorder or post-translational modifications and their organization along the sequences, into a highly customizable 2D map. Starting from a user-defined protein list of UniProt accession codes, ProFeatMap extracts the most important annotated features available for each protein from one of the well-established databases such as Uniprot or InterPro, allocates shapes and colors, potentially depending on quantitative or qualitative data and sorts the protein list based on homologous feature content. The resulting publication-quality map allows even large protein families to be explored, and to classify them based on shared features. It can help to gain insights, for example, feature redundancy or feature pattern, that were previously overlooked. ProFeatMap is freely available on the web at: https://github.com/profeatmap/ProFeatMap under the GPL license.Source code is freely accessible at Bioinformatics Advances online. Most omics studies produce datasets involving substantial lists of proteins. A useful approach for examining such lists is to analyze either their protein-associated biological properties as by Gene Ontology  or theirHere, we introduce ProFeatMap, a freely available, user-friendly and interactive web interface. Gathering annotated features directly from well-established databases such as Uniprot , the proProFeatMap is entirely coded in Python (3.9.6). It is freely accessible as a web interface developed using the Dash library (2.0.0), making it compatible with most common web browsers. Maps and corresponding legends are drawn with the pillow library (7.2.0). The program can be run through a website without any account or registration. Alternatively, all scripts are freely accessible on GitHub and can be run locally.ProFeatMap follows four main steps: protein list uploading, features extraction, addition of user numerical values  and map creation and customization. A user guide for each step is found in the help sections of the web interface. A detailed user guide is available within the https://uniprot.org), then extracts the protein features and subsequently determines their occurrences over the entire protein set. Several pieces of information are gathered: features with starting position and length, occurrence, a list of all the associated PDB files, amino acid sequences and sequence lengths. Alternatively, a different database can be selected among InterPro, SMART, Pfam, in which case the Uniprot domains and repeats will be replaced by the annotations of the selected database.According to a user-defined protein list, ProFeatMap automatically downloads the UniProt file for each protein  containing the UniProt Accession codes and the optional unique protein identifiers. Alternatively, a compatible list can be obtained from UniProt by downloading a protein selection through a query or the basket option. Entry lists may also be composed of the UniProt accession codes of various proteins obtained by experimental omics approaches. After extracting feature occurrences, ProFeatMap generates a highly customizable 2D map. By default, ProFeatMap sorts the proteins based on the occurrences of domain, repeat, region and motif features. The number of similar features leads to a similarity value, while the number of differences gives a dissimilarity value. A first sorting by ascending order of the dissimilarities is done, followed by a second sorting by descending order of the similarities. This process results in transitions between different groups of highly similar proteins. It is particularly useful when dealing with large lists of proteins as it likely associates proteins with similar architectures even without any other prior knowledge.The full process can be done through a semi-automatic run in a single click  or by adjusting one or several options .ab initio lists obtained for instance in the context of omics data. To even better appreciate the advantages of ProFeatMap, an example generated for human kinases (with 489 proteins) is visible on In a one-click run, according to a user-defined protein list, ProFeatMap automatically downloads, extracts and maps feature data using all default parameters. It randomly assigns a combination of color and shape to each of the most frequent features displayed on the 2D map . The resSeveral toggle options are proposed to display features in the protein map, including disorder or structural information and coverage, as well as the positions of low-complexity regions or post-translational modifications. Geometric shapes and colors of the features are also fully customizable. Finally, selected features can be colored according to numerical values. These could be any quantitative or qualitative values, either extracted by data mining as the number of ligands, percentage of sequence homology, reads or publications related to a given domain, or obtained experimentally as affinities . ProteinProFeatMap may help identify recurring feature patterns in proteins. An incomplete pattern might be indicative of annotation issues in the protein sequence database. Subsequently, the user may add or remove feature elements collected by further investigation directly in the web interface within the Step #2 of ProFeatMap.fasta format or searching for motifs using regular expressions.ProFeatMap also allows extracting all the sequences of a given feature in The web interface of ProFeatMap is well suited for users with lists of up to 500 proteins. For larger lists or more intensive usages of ProFeatMap, we highly recommend making a local installation. This not only allows lists of several thousands of proteins to be handled, but also gives live feedback on progression or potential errors. Changing the memory parameter also makes it possible to keep progress between steps in your web browser’s memory. Saving and uploading all intermediate files every time the tab is closed would therefore not be necessary anymore. More information on how to install and change this parameter can be found in the user guide.ProFeatMap is a powerful and highly customizable tool to quickly create publication-quality maps displaying domains and other key features along the protein sequences for a set of proteins of interest, and potentially including quantitative or qualitative data. Compared with some conventional motif visualization tools, ProFeatMap does not need nor use any sequence-based predictions. It merely uses feature information accessible in potentially several protein databases. The general overview provided by the representation helps gaining valuable insights into feature organization. The advantages and versatility of ProFeatMap can be even better appreciated when considering either a set of proteins with common features in a unique species, or a set of a particular protein originating from several species. Additionally, it is also suitable for the identification of characteristic elements such as domain patterns, which could prove to be of interest for the project.vbad022_Supplementary_DataClick here for additional data file.


Researchers in the field of mass spectrometry–based proteomics both consume and add to the body of data available in UniProtKB, and this review highlights the information we provide to this community and the knowledge we in turn obtain from groups via deposition of large-scale datasets in public domain databases.The human proteome comprises of all of the proteins produced by the sequences translated from the human genome with additional modifications in both sequence and function caused by nonsynonymous variants and posttranslational modifications including cleavage of the initial transcript into smaller peptides and polypeptides. The UniProtKB database (  •High-quality, comprehensive, free resource of protein sequence and function.•Reference sets of whole-proteome sequence, including human, made available.•Includes isoforms, postprocessed chains, PTMs, variants, and functional domains.•Imports and displays peptide and PTM identifications from public domain repositories.•Access via website, API or ftp, download data in multiple community data formats. UniProt, the world’s leading high-quality, comprehensive, and freely accessible resource of protein sequence and functional information, is used by the global mass spectrometry–based proteomics community to map peptide identifications to proteins. The database supplies details of protein sequence features which may affect their function, such as posttranslation modifications. UniProt in turn takes data which researchers publish or deposit in public domain databases and uses this to increase the information content of relevant entries. Mass spectrometry (MS)-based proteomics researchers aim to isolate, identify, and functionally characterize the protein profile of a cell, tissue, or organism of interest. Scientists may be interested in understanding how this profile changes as cells develop, age, respond to changes in external environmental conditions, or how the application of a xenobiotic, such as a drug, changes cellular behavior at the molecular level. In order for researchers to be able to determine the protein complement of a biological sample, they need to be able to map peptide or protein sequences to a reference resource, which not only enables the required identifications but also links these to the relevant organism and provides details of protein nomenclature, function, and additional sequence features such as posttranslational modifications (PTMs) and functional domains. The UniProt data resource  providesvia comparison to a reference protein sequence database  (Sequence database search engines identify peptides from tandem mass spectra database . The seliProtKB)  is increSaccharomyces cerevisiae, and Escherichia coli, there is a single reviewed entry for every known protein coding gene, for most species complete proteome coverage can only be achieved through a combination of reviewed and unreviewed entries. Reviewed entries contain the sequences of all the protein products encoded by one gene, each with a unique identifier. This includes protein isoforms expressed by that gene along with any shorter protein chain/peptide generated from the parent transcript. Isoforms are defined as sequences that can be generated from the same gene by a single or combination of biological events . Isoforms yet to be manually integrated are maintained in unreviewed UniProtKB/TrEMBL entries but can be viewed in the corresponding reviewed entry on the website as a result of automated gene-centric mappings. To create a UniProtKB/Swiss-Prot entry, UniProt curators firstly examine the protein sequence and correct, where necessary, for errors caused by sequence annotation issues such as erroneously selected initiation sites or frameshifts. They identify peptide chains generated by posttranslational cleavage of the parent transcript. These chains may be associated with protein location and activation and are generated by the removal of a signal or transit peptide or be bioactive peptides with defined biological activities. Positional features described within the UniProt entry, such as binding sites, PTMs, and amino acid variants, are mapped to a selected representative  sequence which is the entry displayed by default on the website and in most download formats. All sequences  are distributed in FASTA format as described in the ‘UniProtKB consists of reviewed UniProtKB/Swiss-Prot entries to which data have been added by our expert biocuration team and unreviewed UniProtKB/TrEMBL entries that are annotated by automated systems. While, for a limited number of species including Human, i.e., a proteome and are supplemented by proteomes sequenced and/or annotated by groups such as Ensembl (versus the standard distribution of protein count expected for completeness in comparison to a cluster of closely related organisms (https://www.uniprot.org/help/assessing_proteomes). We additionally supply the assessment of the genome assembly status imported from the source of the genome assembly and annotation .The main source of protein sequences in UniProt is the translation of nucleic acid sequence submissions to the International Nucleotide Sequence Database Collaboration (INSDC) source databases . Increas Ensembl , the Nat Ensembl , VectorB Ensembl  and Worm Ensembl . Viral p Ensembl  for vert Ensembl . We alsohttps://www.uniprot.org/proteomes/). The most commonly accessed proteome is that of human, which is described in more detail below.A subset of these proteomes are then selected as reference proteomes, by either community choice or computational methods based on sequence clustering by similarity, to represent a spectrum of well-studied model organisms and other organisms of interest for biomedical research and phylogeny. Proteomes can be searched and downloaded from a specific section of the website . Ensembl sequences that are absent from UniProtKB are imported into UniProtKB/TrEMBL. These entries are flagged as part of the proteome and have an Ensembl cross-reference. On the website they can be viewed as part of the UniProt reviewed entry for that gene and are described as “Computationally mapped potential isoform sequences”. Both canonical and all isoform sequences are available for download  which enables users to convert between identifiers from an increasing number of resources to download the generated identifier list or simply to access a batch of UniProt entries to download or work with on the website.Users may have used a sequence database other than UniProtKB to make their initial peptide to protein identifications and mappings and may subsequently wish to map to UniProtKB to access the richness of data within this resource. UniProt provides an accession number mapping service  and a short, largely free-text description. Moving forward, the free-text descriptions will increasingly be mapped to ontology/controlled vocabulary terms to enhance the machine-readability of these data. As a first step toward this goal, the UniProt biocurators have replaced free-text descriptions of biologically relevant ligands which are crucial to protein function, including activators, inhibitors, cofactors, and substrates, with stable unique identifiers from the ChEBI  ontology  making kHistorically, large-scale datasets and most computational predictions have been mapped to the UniProt reviewed entry, rather than integrated into the downloadable version of the entry, as it was not trivial for the user to differentiate between manually reviewed and automatically generated data. However, the adoption by UniProtKB of evidence tags sourced from the Evidence and Conclusion Ontology  now makee.g., a Tyr to Phe mutation) or mimicked , in addition to studies by MS. Less common modification, where such tools are not available, may therefore currently be underrepresented in the database. These manually annotated PTMs have been used as a gold-standard set by MS proteomics researchers to assess the efficacy of their own pipelines to identify PTMs on a large scale. Users may access additional information on specific PTMs by using the cross references to specialist resources supplied in many entries.The presence of chemical modifications, added enzymatically to the protein backbone at various points during the life-cycle of a protein, can alter the function of a molecule, change its conformation and/or its subcellular location, and affect its stability and longevity. PTMs can provide a rapid mechanism for changing function, such as switching an enzyme activity "on" and "off" or causing a protein complex to disassemble. UniProt expert curators have mapped literature-described PTMs to protein sequences for many years, not only capturing details of the amino acid position and the chemical change  but alsoThe presence of conserved sets of amino acids in related protein sequences can give information about protein function, its location in the cell, for example membrane topology, and additional molecules to which they may bind, such as metals or cofactors . In UniPUniProt curators capture detail on individual genetic variants, including RNA editing events, capturing the amino acid change, the name of the variant , when available, and the effect(s) of the variation on the protein, cell, or complete organism. Variant interpretation and classification are in line with the American College of Medical Genetics guidelines and additional recommendations by the ClinGen sequence variant interpretation workgroup . Similarhttps://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/docs/International_Protein_Nomenclature_Guidelines.pdf. Gene names are separately added by the appropriate genome nomenclature committees, but UniProt curators are in constant communication with these groups to keep naming consistent, where possible.While lists of accession numbers may be an efficient way of storing the results of a proteomics experiment, they tell the researcher nothing about the function of a protein and do not enable the grouping of members of the same protein family. UniProt curators are responsible for ensuring that proteins are consistently named, thus enabling literature searching and entry retrieval. A good protein name is unique, unambiguous, can be attributed to orthologs from other species, and follows official gene nomenclature where applicable, while indicating protein function. Commonly used synonyms are listed within an entry, but the recommended name is that which describes the protein to the fullest extent. The guidelines followed to generate the recommended protein name are available at e.g., alternative initiation, alternative promoter usage), developmental stage, disease, domain, ligand, molecular function, PTM, and technical term . UniProtKB Subcellular location and Disease sections are also described using fully documented controlled vocabulary terms. Catalytic activities in UniProtKB are annotated using Rhea, a reference vocabulary of biochemical reactions (https://www.rhea-db.org)  . Rhea us-db.org)  to descrFunctional annotations for specific isoforms and polyprotein cleavage products are captured when these differ from those of the canonical reference protein in an entry. These are stored separately in the record to enable computer accessibility, and the representation of these data has been enhanced on the UniProt website .e.g., subcellular locations, EC number assignments, keywords) are used for computational mappings of terms to less well-studied proteins. All annotations (currently for >160 million proteins) are linked to UniProtKB identifiers by the GO Annotation team and available in this form for download from the QuickGO database (www.ebi.ac.uk/QuickGO/) and are also visible in the relevant UniProtKB records.For a fully computationally accessible description of the molecular function(s) of a protein, the process(es) in which it plays a role and the cellular component(s) it is located in, most researchers use the Gene Ontology (GO) , a machiUniProtKB provides residue-level mappings between entries from PDB and the corresponding sequences in UniProtKB, with a dedicated UniProt curator manually linking cases where the sequence has mutated beyond the point where a computational match can be made . This inhttps://github.com/ebi-webcomponents/protvista-uniprot). Many of these described features are now also directly embedded into the entry page.The ability to visualize a number of different sequence features co-occurring on the same regions of a protein sequence can give the user important insight into the mechanism of action of a particular protein or an understanding of how a disease variant causes a change in the function of that protein. For example, a disease variant may occur in the active site of an enzyme, suggesting the change in amino acid may result in a change or loss of catalytic activity. An experimentally induced positional mutation may confirm the existence of a PTM, give an indication of protein function or cellular/organism phenotype observed as a result of that change. Mapping of that feature to a 3D structure, experimentally generated or predicted, may give additional information as to the effect of a change to the protein sequence or by a chemical modification caused by a PTM. The ProtVista viewer  has beenIn addition to supporting the MS community through annotation of protein sequence and function, UniProt additionally accesses data from public domain proteomics data repositories and integrates proteomics data through a series of peptide import pipelines, using this information to inform and enhance relevant entries.via the proteomics endpoint in the Proteins application programming interface (API). This pipeline is currently under review, with the intention of updating the quality metrics to bring these more in line with those published by the Human Proteome Project  proteomic PTM data are available with plans for Plasmodium falciparum, Arabidopsis thaliana, mouse (Mus musculus), and human (H. sapiens) to follow shortly.The PTMeXchange project aims to establish a reproducible pipeline to import large-scale PTM-focused proteomics datasets. This collaborative project between UniProt, PRIDE, PeptideAtlas, and the University of Liverpool facilitates filtering, re-analysis, and site-based confidence scoring of PTM-specific public proteomics datasets from significant model organisms . Filterihttps://github.com/ebi-uniprot/interaction-viewer) which is also available for local installation. Links to stable protein complexes curated by the Complex Portal , or to run specific, sophisticated queries e.g., spectral library search tools). PEFF includes support for encoding known sequence variants, PTMs, and proteoforms and enables search engines implementing this format to query the exact positions of known PTMs and variants. The UniProt PEFF format currently describes variation and sequence data and is available through the Proteins API service (https://www.ebi.ac.uk/proteins/api/doc/) for 31 species. An extension to include PTM data is planned. UniProt personnel are also actively collaborating with the HUPO-PSI and the Consortium for Top Down Proteomics who are developing a notation format called ‘ProForma’ , a unified format for protein and nucleotide sequence databases developed by the HUPO-PSI (Human Proteome Organization-Proteomics Standard Initiative) to be used by sequence search engines and other associated tools  enables access for approximately 800,000 users per month totaling nearly 9.5 million page views (November 2022), so needs to be reliable, responsive, and usable on all platforms. With this in mind, we regularly assess the existing website in terms of performance and have undertaken a full release of a new version in July 2022. This has been iteratively designed and created through a user-centered design process and now separates the front-end (Web user interface) from the back-end (API). This improves responsiveness while remaining scalable and eases maintenance. The new design includes improved search and navigation using a caching logic to support both simple and complex popular queries. Once a search has been completed, search results can be downloaded in predefined FASTA, XML, RDF/XML, and text formats and also a customizable tab-separated or Excel format  into the search field and the tool quickly finds all UniProtKB sequences that match 100% with the query sequence . Searchehttps://www.ebi.ac.uk/proteins/api/doc/) (A) does not require users to be fluent in a programming language and instead provides a multiquery search format (B) that allows users to intuitively query the database and return a readable and detailed response to queries. Users can tailor their search queries within the multiquery search form sections to restrict their search criteria by categories such as species, genomic location, peptide uniqueness, or even by source database. Following each query, a sample request code in multiple programming languages, such as Java, R, and Python, is provided that gives the user a programmatic query with which to recreate their search via their own desktop terminal (C). Additionally, for those not looking to recall their search results programmatically, a URL is provided that directs them to their results on a webpage. All data provided by the Proteins API are freely downloadable in a range of formats (D). Data derived from sources external to the UniProtKB are currently only available programmatically through the service.The UniProtKB Proteins API  providespi/doc/) A does noh format B that alterminal C. Additi formats D. Data dUniProt website RESTful URLs can be bookmarked, linked, and used in programs for all entries, queries and tools available through the website and a Java library is also provided that enables a stable remote API for programmatically accessing UniProt data.https://sparql.uniprot.org) allows users to perform complex federated queries that combine RDF data from UniProt with that from other SPARQL endpoints. For example, it is possible to query for ligand-binding site annotations in UniProtKB using identifiers, names, synonyms, and chemical structure data from ChEBI.All UniProt data are available in RDF (Resource Description Framework), a core semantic web technology which enables researchers to perform analytic query operations without knowing the underlying database structure. The UniProt SPARQL service (via the ‘Download latest release’ link located on the UniProt.org homepage) provides the most frequently requested datasets in a range of file formats . The additional manually curated isoform sequences that are described in the UniProtKB/Swiss-Prot section are available in a separate FASTA file (uniprot_sprot_varsplic.fasta.gz). Our FTP directory also includes expanded FASTA sets, containing both the canonical and manually reviewed isoform sequences, for all reference proteomes.The UniProt FTP site (accessible UniProt plays a pivotal role in support of MS proteomics science, collecting, standardizing, and organizing protein sequence data and creating a reference framework for multiscale data integration and analysis. From the provision of FASTA and PEFF files at the whole proteome level which enables search engines to operate to enabling the annotation of proteins with biological function, UniProtKB is a crucial data management tool, working with the community to ensure the best possible service is supplied. Members of the UniProt team actively collaborate with proteomics standards bodies, including HUPO and the HUPO-PSI, to both input into the development of standards, formats, and controlled vocabularies and to implement these in UniProt whenever appropriate. Additionally, UniProtKB is working with data resources such as the members of the ProteomeXchange, PTMeXchange, and the IMEx Consortium to develop data import pipelines, enabling the output of the MS proteomics community to inform and enhance protein sequence and function data.Large-scale data are an asset when converted to knowledge  and the The authors declare no competing interests.