Although human protein complexes have been reported to be directly related to viral infection, previous studies have not systematically investigated human-virus PPIs from the perspective of human protein complexes. To the best of our knowledge, we have presented here the most comprehensive and in-depth analysis of human-virus PPIs in the context of VTCs. Our findings confirm that human protein complexes are heavily involved in viral infection. The observed preferences of virally targeted subunits within complexes reflect the mechanisms used by viruses to manipulate host protein complexes. The identified periodic expression patterns of the VTCs and the corresponding candidates could increase our understanding of how viruses manipulate the host cell cycle. Finally, our proposed conceptual application framework of VTCs and the developed VTcomplex could provide new hints to develop antiviral drugs for the clinical treatment of viral infections. http://zzdlab.com/vtcomplex/index.php or http://systbio.cau.edu.cn/vtcomplex/index.php). We hope that the current analysis can provide new insights into the global landscape of human-virus PPIs at the VTC level and that the developed VTcomplex will become a vital resource for the community.Computational analysis of human-virus protein-protein interaction (PPI) data is an effective way toward systems understanding the molecular mechanism of viral infection. Previous work has mainly focused on characterizing the global properties of viral targets within the entire human PPI network. In comparison, how viruses manipulate host local networks  has been rarely addressed from a computational perspective. By mainly integrating information about human-virus PPIs, human protein complexes, and gene expression profiles, we performed a large-scale analysis of virally targeted complexes (VTCs) related to five common human-pathogenic viruses, including influenza A virus subtype H1N1, human immunodeficiency virus type 1, Epstein-Barr virus, human papillomavirus, and hepatitis C virus. We found that viral targets are enriched within human protein complexes. We observed in the context of VTCs that viral targets tended to have a high within-complex degree and to be scaffold and housekeeping proteins. Complexes that are essential for viral propagation were simultaneously targeted by multiple viruses. We characterized the periodic expression patterns of VTCs and provided the corresponding candidates that may be involved in the manipulation of the host cell cycle. As a potential application of the current analysis, we proposed a VTC-based antiviral drug target discovery strategy. Finally, we developed an online VTC-related platform known as VTcomplex (IMPORTANCE Although human protein complexes have been reported to be directly related to viral infection, previous studies have not systematically investigated human-virus PPIs from the perspective of human protein complexes. To the best of our knowledge, we have presented here the most comprehensive and in-depth analysis of human-virus PPIs in the context of VTCs. Our findings confirm that human protein complexes are heavily involved in viral infection. The observed preferences of virally targeted subunits within complexes reflect the mechanisms used by viruses to manipulate host protein complexes. The identified periodic expression patterns of the VTCs and the corresponding candidates could increase our understanding of how viruses manipulate the host cell cycle. Finally, our proposed conceptual application framework of VTCs and the developed VTcomplex could provide new hints to develop antiviral drugs for the clinical treatment of viral infections. Due to its small genome, a virus has to take advantage of the established functions of human cellular proteins to complete its life cycle. Thus, an understanding of the molecular mechanisms involved in human-virus protein-protein interactions (PPIs) is very critical for the design of novel antiviral strategies. To date, numerous experimentally validated human-virus PPIs have been rapidly collected and compiled into multiple host-pathogen interaction databases. Of the multiple databases, HPIDB contains the most comprehensive human-virus PPI data by integrating multiple public resources . Based o2–Proteins often assemble into complexes, also known as molecular machines, to perform a coordinated function. In general, a protein complex is comprised of multiple functionally diversified proteins (subunits)  and serv11–Viral infection is tightly linked with host protein complexes. It has been established that viruses manipulate host protein complexes to regulate host biological processes –16. One –Currently, the available data resources of human-virus PPIs and protein complexes have provided a golden computational opportunity for us to conduct systematic identification and analysis of VTCs. The discrimination of topological and functional properties of viral targets and nontargets within complexes could help us to understand how viruses manipulate host protein complexes. It is already known that a protein complex is a dynamic molecular machine, in which spatiotemporal regulation determines the subunit members and their corresponding functions within a cellular context . TherefoIn addition to providing an in-depth understanding of viral infection, research on human-virus PPIs is beneficial for antiviral drug development , 23. CurIn this work, we revisited the investigation of human-virus PPIs at the VTC level. The overall flowchart that depicts our strategy is shown in P value < 2.2 × 10−16; The current analysis of VTCs was based on hu.MAP, since it is the most comprehensive data resource for human protein complexes. We first collected 4,589 human protein complexes from hu.MAP  and 11,810.1128/mSystems.00303-18.2FIG S1P value of <0.001. Download FIG S1, PDF file, 0.8 MB.Relationship between viral targets and subunits in complexes. (A) Numbers of viral targets within and outside protein complexes for five viruses. (B) Venn diagram showing the overlap between viral targets and subunits in all complexes. Three asterisks indicate a Copyright © 2019 Yang et al.2019Yang et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00303-18.3FIG S2significance < 0.05). Download FIG S2, PDF file, 0.9 MB.Overlap of all VTCs among five viruses. (A) Venn diagram showing the number of overlapping complexes among five viruses. (B) Jaccard indices showing the similarities of VTCs between any two viruses. (C) Proportions of subunits within six complexes targeted by a different number of viruses. These six complexes are significantly targeted by five viruses . We found that the within-complex degree of viral targets was significantly higher than that of nontargets . Therefore, we concluded that our findings are generally robust even when there are variations in data coverage; however, we cannot directly rule out any influence of data coverage on the human-HCV PPI analysis.On the basis of the above analyses of targets and nontargets within complexes, we noticed a data imbalance in the human-virus PPIs among the different viruses. For instance, the number of human-H1N1 PPIs is approximately six times larger than that of human-HCV PPIs. To determine the influence of human-virus PPI data coverage, we performed a down-sampling of the human-H1N1 PPIs and then repeated the above analysis. Specifically, we randomly selected 864 pairs of human-H1N1 PPIs (which is the same number of human-HCV PPIs used in this work) and reanalyzed the preference of H1N1 for targeting housekeeping proteins within complexes as an example to determine the influence of data coverage. The random trial was repeated 1,000 times. The results verified that H1N1 tends to target housekeeping proteins within complexes  is shown in P value = 1.1 × 10−11; P value < 2.2 × 10−16; Subsequently, we focused on the functional distribution of the VTCs. To effectively prioritize the VTCs for each virus, we defined a scoring variable known as VTP value = 6.9 × 10−3; To investigate the dynamic response of VTCs to viral infection, analysis of changes in the host transcriptome resulting from viral infection was conducted. We conducted this analysis only for HIV-1, but it could be extended to other viruses. We first collected known differentially expressed genes (DEGs) during HIV-1 infection from the study by Sherrill-Mix et al. , in whic10.1128/mSystems.00303-18.10DATA SET S3Data Set S3, XLSX file, 0.07 MB.Lists of HIV-1-targeted DECs and all periodic complexes. Download Copyright © 2019 Yang et al.2019Yang et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the The biological significance of the above analysis was further exemplified in two representative complexes . We founP value = 4.6 × 10−9; 1, G1-S, S, S-G2, G2-M, and M-G1), as well as a Mixed group, which contained periodic subunits that were expressed in more than one cell cycle phase, in order to determine whether different viruses tend to target complexes at specific cell cycle phases. We also calculated the Pearson correlation coefficient (PCC) between the proportions of virally targeted periodic complexes and periodic complexes at different cell cycle phases for each virus. Overall, the proportion of virally targeted periodic complexes is similar to that of the periodic complexes expressed at different cell cycle phases  to generate a time series map of VTCs sorted by cell cycle phases. Detailed information about the highly dynamic periodic complexes that are targeted during each phase by five viruses are shown in 1-S transition is considered a major cell cycle checkpoint, during which the host cell verifies DNA integrity. Among the identified periodic complexes that corresponded to this phase, DNA repair complexes such as MSH2-MSH6 (complex ID = 2620) and MSH2-RECQL complex (complex ID = 167) were shown to be targeted by HIV-1 , and the periodic phases that have been reported to be subverted by viruses are involved mainly in cell cycle checkpoints. Considering that regulation of the cell cycle is quite complicated, identification of periodic complexes that are involved in viral infection is far from complete. To identify other potential cell cycle-related protein complexes, Periodicby HIV-1 . Finallyby HIV-1 , while tby HIV-1 . Collect10.1128/mSystems.00303-18.5FIG S4significance < 0.05 and Periodicratio >0.2) were classified into six groups based on specific cell cycle phases . Circles represent human proteins, and V shapes represent viral proteins. Nodes in different colors represent periodic proteins in different phases. Edges in brown represent human-virus interactions. Download FIG S4, PDF file, 1.4 MB.Virally targeted periodic complexes in different periodic phases. Selected virally targeted periodic complexes . Note that the DEGratio reflects the extent of a complex’s response to viral infection and is calculated by dividing the number of DEGs by the total number of subunits in the complex. The distribution of DEGratio values for all HIV-1-targeted DECs is shown in specificity > 2; see Materials and Methods for the definition) to ensure their correlation with viral infections. Finally, we identified 16 druggable complexes with 19 drug targets. Previous studies have already demonstrated that HDFs and immune-related proteins could be used as antiviral drug targets  The complex is a VTC. (ii) The complex is a DEC. (iii) The complex contains at least one known drug target that is specifically expressed in virally targeted tissues and is not an essential gene. Using these criteria, we mapped expression data obtained during HIV-1 infection of VTCs and identified 61 HIV-1-targeted DECs , and two immune-related proteins (LYN and MYH9). We proposed that these four proteins should be utilized preferentially as potential antiviral drug targets. Following the strategy of drug repurposing, we subsequently attempted to identify potential antiviral drugs based on known drugs. By mapping known drugs in DrugBank or DGIdb  to theseTo facilitate the community, we also built an online platform named VTcomplex to collect and manage VTC-related information, including the known human-virus PPIs for five viruses, human protein complexes, human intraspecies PPIs, potential drug-target interactions, and other key protein information related to viral infection. VTcomplex supports multiple searching keywords, including protein/gene name, UniProt ID, and complex name/ID. Regarding the webpage of a specific VTC, the intraspecies and interspecies PPIs are visualized in network format on the left panel, while other heterogeneous information such as HDFs, innate immune-related genes, and dynamic information about the subunits (DEG and periodic expression data) within this complex is shown on the right panel. An example of a query for HIV-1 Vif protein targeted complexes is shown in 10.1128/mSystems.00303-18.7FIG S6FIG S6, PDF file, 2.2 MB.Flowchart for using VTcomplex. Download Copyright © 2019 Yang et al.2019Yang et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the significance to effectively prioritize the VTCs for each virus. Moreover, we constructed a web portal called VTcomplex to display all relevant information in the form of text and graphics, which will be helpful in obtaining a quick overview of each VTC. To our best knowledge, the developed VTcomplex is the first online platform for VTCs.In this work, through integrating information about human-virus PPIs and human complexes, we conducted a large-scale computational identification and systematic analysis of VTCs. Previous studies regarding the identification and analysis of VTCs often concentrated on an individual virus or several specific VTCs. Thus, the number of VTCs investigated in previous studies has been very limited. For instance, a recent study of VTCs targeted by HIV analyzed only 40 complexes . Thanks The current VTC-based analysis has allowed us to understand human-virus interactions from a unique perspective, which is different from previous analyses based on the entire human PPI network. With regards to network analysis, we focused on the within-complex topological features. VTCs should be regarded as subnetworks in the context of the entire human PPI network. The observation that viruses target human proteins with higher degrees in a complex is consistent with results that have been inferred from the entire network. However, the potential biological meaning may be different. Targeting hubs within the entire network may influence multiple processes, while targeting within-complex hubs may intensively disturb protein complexes. We observed that at the subunit level, viral targets tend to be functionally important proteins, such as housekeeping proteins and scaffold proteins. The topological and functional differences between viral targets and nontargets clearly revealed viral targeting preferences of subunits in VTCs. We found that at the complex level, multiple viruses significantly target complexes related to basic viral self-reproduction, which allows us to catch a glimpse of the common infection mechanisms. Indeed, complexes related to transcription and translation regulation and energy production are significantly targeted by all five viruses. Interestingly, we also found that some virus-specific complexes have similar functions , which s2-M phase), HPV targets CDKN1B (which is highly expressed in the S-G2 phase), and HIV-1 targets both CDK1 and CDKN1B. In addition to the differences in targeted proteins, temporal differences in the expression of targeted proteins indicated that different viruses might manipulate the same complex at different cell cycle phases. Moreover, our temporal mapping of VTCs during the host cell cycle has identified a number of periodic complex candidates whose functional roles during viral infection remain elusive. Undoubtedly, these periodic complexes will provide important hints for conducting hypothesis-driven functional interrogation experiments.To characterize the dynamic properties of VTCs during the host cell cycle, we conducted a temporal mapping of VTCs into different periodic phases. The dynamic properties of the VTCs revealed common periodic complexes manipulated by different viruses and also provided an in-depth understanding of how different viruses subvert phase-specific subunits to manipulate the same periodic complex, which is exemplified by the cyclin B-CDK1 complex  and conducted a comprehensive analysis of VTCs. Regarding the topology and functional features of viral targets in the context of VTCs, we found that viral targets tended to have a high within-complex degree, and they also tended to be scaffold proteins and housekeeping proteins. These differences between targets and nontargets shed light on the potential molecular principles utilized by viruses to hijack human protein complexes. In terms of functional distribution of VTCs, we found that complexes essential for viral propagation were simultaneously targeted by multiple viruses. We characterized the dynamic expression of subunits within VTCs during the host cell cycle, which reflects how viruses manipulate host protein complex assembly. Finally, an online platform for VTCs and relevant information of complex-based antiviral drug discovery was developed. Overall, the current comprehensive analysis of VTCs increases our understanding of the mechanisms of viral infection at the protein complex level, and we hope VTcomplex will become a useful data resource for the community.(i) Human protein complexes and PPIs. A total of 4,659 protein complexes were originally obtained from hu.MAP (http://proteincomplexes.org), which were named by digital IDs from 0 to 4658. Protein subunits with unreviewed UniProt IDs were filtered. Complexes containing less than two subunits were further removed. Thus, 4,588 complexes were retained. Experimentally validated human PPIs were collected from BioGRID  Human-virus PPIs. We first collected experimentally determined human-virus PPIs from HPIDB 2.0 and further removed PPIs containing proteins without UniProt IDs. Our analysis covered five common human-pathogenic viruses, including two RNA viruses (H1N1 and HIV-1) and three DNA viruses . In line with previous studies  Tissue-specific expression data, housekeeping genes, and essential genes. The tissue-specific expression values of human genes were obtained from the GTEx Portal, which collects gene expression information for 53 human tissues  Scaffold proteins, HIV-1’s HDFs, and human innate immune-related proteins. We collected 276 human scaffold proteins from ScaPD  corresponding to a complex targeted by HIV-1. Then, the P value after the Benjamini-Hochberg correction, which was defined as VTsignificance, was used to measure the extent of the complex that was targeted by HIV-1. In general, VTCs for which VTsignificance < 0.05 were considered to be significantly targeted by HIV-1. Note that prioritization of VTCs was conducted for each virus.Taking HIV-1 as an example, we first conducted Fisher’s exact test to calculate the statistical significance  to calculate the within-complex degree for each subunit. Only those VTCs that simultaneously met the following criteria were taken into account. (i) The complexes contained more than two subunits. (ii) The complexes contained both viral targets and nontargets simultaneously. (iii) Both targets and nontargets had at least one interaction. To facilitate the comparison of different VTCs, the within-complex degree of each subunit was normalized by a Z-score within a VTC.Taking each VTC as a subnetwork, we used igraph . Similar to the definition of DEC, a complex with at least one periodic protein was defined as periodic. Moreover, we assigned each periodic complex to a specific periodic phase. Taking the G1 phase as an example, a periodic complex assigned to this phase should meet one of the following two criteria. (i) All periodic proteins within the complex are assigned to G1. (ii) The number of periodic proteins present in G1 is greater than twice the sum of the periodic proteins present in other phases. Otherwise, the complex was classified as Mixed. All periodic proteins and classified periodic complexes are listed in Periodic genes were collected from Cyclebase 3.0  and a respecificity to measure the specificity of the drug target between HIV-1-targeted tissues and other tissues, which was calculated by dividing the mean gene expression value in the targeted tissue by the mean expression value in other tissues.Although HIV-1 infection is cell specific, we used tissues that were most enriched for T cells as the targeted tissues to characterize this specificity. Based on GTEx’s tissue categories, we defined blood, lymphocytes, and spleen as HIV-1-targeted tissues. We used DThttps://www.uniprot.org/uploadlists/) was used to match all of the human or viral gene IDs to UniProt IDs.The online UniProt ID mapping tool (


Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes. Ever since the first protein structure was solved sixty years ago , X-ray cThe structures determined by X-ray crystallography represent an averaged conformation. However, living systems are dynamic and so are their components including proteins. Consequently, the protein structure–function paradigm, entailing the intimate relationship between the three-dimensional structure and function of proteins was expanded to the protein structure–function–dynamics paradigm. As per recent studies ,4, the rWe begin with brief descriptions of some of the most pressing constraints that are encountered while studying biomacromolecules using X-ray crystallography. We then describe some of the recent advances in experimental methods, both in crystallography and otherwise, to remedy some of the limitations. Next, we expound the crucial contribution of computational methods in complementing, as well as supplementing, experimental methods in order to study protein structural dynamics. Finally, we conclude the review with some of the limitations of computational methods and the future prospects.X-ray crystallography has been the most important method for exploring the three-dimensional structure of biological macromolecules over time ,2. It isOne of the first challenges in studying protein structure using crystallography is obtaining good-quality protein crystals. Continued advances in the protocols and instrumentation allowed obtaining X-ray structures of larger systems at higher resolution; however, getting protein crystals that diffract to a high resolution remains a road block that protein crystallographers need to cross . The proRadiation damage to the protein crystals has long been a serious problem in biomacromolecular crystallography. Irradiation of crystals by X-rays causes the generation of free radicals that affect the overall diffraction intensity, and hence, the resolution and quality of structural model . Apart fFurthermore, once the diffraction data are collected, the models created using the data might sometimes be suboptimal, particularly for medium- to low-resolution structures. Such uncertainty was explored extensively in protein–ligand systems, where it was found that the model for ligands is often overinterpreted ,11. Since the model of the structure generated using X-ray crystallography is determined as an averaged model from the diffraction data obtained from multiple copies of the protein in crystal, the structural heterogeneity and anisotropy in the atomic motions is usually lost, except for very-high-resolution structures . ConsideCrystals comprise unit cells which are in turn composed of several asymmetric units. As a result of crystallization, the protein molecules in these asymmetric units or neighboring symmetry-related molecules interact, giving rise to spurious interactions that are absent in functional molecules in cells and can be referred to as crystal packing artefacts . DespiteApart from crystal packing, the crystal chemical environment  can also affect the conformation observed in the crystal structure. In human protein kinase CK2, a high concentration of lyotropic salts in the crystallization buffer is known to support the closed conformation of the kinase, which was rarely observed in solution dynamics ,25.Cooling the crystals to a very low temperature  for diffraction data collection—referred to as cryo-cooling—has been an indispensable technique in X-ray crystallography for about three decades now ; currentCrystal structures often contain multiple residues whose coordinates are missing from the model. As per a recent study, about 69% of PDB files were found to contain missing residues . In partIntrinsically disordered proteins (IDPs) or regions of structured proteins with intrinsically disordered regions form some of the toughest targets for structural and dynamics analysis . The higMost biomolecules are surrounded by water within cells. Apart from playing crucial roles in regulating the physico-chemical properties of these molecules, water molecules also participate directly in biochemical reactions ,33,34. UNotwithstanding the challenges mentioned above, crystal structures determined so far provide crucial mechanistic insights into the biological phenomena under observation, and can be used to design further experimental and computational studies to fully characterize dynamics and functional mechanisms. Having described the limitations in obtaining structural and dynamic information using X-ray crystallography, we proceed to review the recent advances in experimental methods that contributed in overcoming these limitations.Several advances were made recently in the field of crystallography to address some of the limitations discussed above. Most of these advances were heralded by path-breaking developments in instrumentation and in experimental protocols.The advent of increasingly high-energy X-ray sources, such as second- and third-generation synchrotrons, dramatically improved the resolution for biomacromolecular crystallography . In addiphenix.rosetta_refine, which combines the crystallography refinement protocol of Phenix [As mentioned in f Phenix  and the f Phenix . Recentlf Phenix  that makf Phenix . FurtherThe two most challenging methodological problems associated with crystallography are growing the crystals and radiation damage due to long-term exposure to high-energy radiation. The advent of X-ray free-electron lasers (XFELs) opened an avenue for solving both these problems ,48. The In addition to crystallographic studies, attempts began extracting the structural information of biological systems in near-native state using XFEL through single-particle analysis of non-crystalline samples ,57,58. AThe effects of cryo-cooling on protein crystal structures are not fully understood. Experimentally, Fraser and coworkers investigated these low-temperature effects extensively ,66. TheyThe recent development of XFEL also provides the opportunity for room-temperature crystallography. Sirerra et al.  studied Structures solved by X-ray crystallography capture only a snapshot or an averaged representation of a dynamic protein. However, these dynamic motions are essential for studying the biological function. Crystal contacts play a role in blocking these internal motions in a protein unless the dynamic region of the protein is located where it cannot interact with its neighboring molecule in the crystal. A new fusion protein method was developed for studying large dynamic motions within protein crystals by minimizing crystal contacts . In thisIn this section, we review some of the biophysical methods that give important information about protein structure and dynamics, which recently gained importance owing to improved experimental and computational protocols. As detailed analyses of each of these methods is beyond the scope of this review, we mention the strengths of each of the methods when compared to crystallography and cite recent reviews that provide details of these methods.NMR spectroscopy is a powerful method for studying not only the structure and dynamics of biomacromolecules in solution, but also ligand binding , proteinHydrogen exchange mass spectrometry (HX-MS) is a comparatively old technique for studying conformational changes in proteins that historically eluded scrutiny through crystallography or NMR spectroscopy. HX-MS quantifies the exchange of amide hydrogen with deuterium in solution and provides information about the tertiary structure and dynamics of the protein . HX-MS iCross-linking mass spectrometry (XL-MS) is an important experimental technique for gaining insight into protein–protein interactions and structures of large macromolecular complexes. Recent years saw considerable development both in the cross-linking methodology and in data processing and visualization . XL-MS Transmission electron microscopy has been used to observe biological systems on the scale of micrometers to tens of nanometers for a long time. However, owing to recent advances in instrumentation, including better electron guns, electron detectors, automated mounting systems, and image-processing software and algorithms , there wSmall-angle X-ray scattering (SAXS) is a powerful and well-established method for studying proteins in solution. It can provide several important parameters about the protein in its native state, including molecular weight, overall shape, and radius of gyration . With reAs described in the previous section, there was immense advancement in experimental methods for studying protein structure and dynamics. However, each method has its own limitations, and computational methods became essential in mitigating these limitations, integrating results from multiple experimental sources and providing a better understanding of protein structure and dynamics. In the following section, we review the computational methods that are crucial in the exploration of protein structure and dynamics.The inability to obtain a protein of interest in considerable quantities, either via expression or purification, still remains one of the first challenges in studying the protein–structure–function relationship. For all such cases where protein structure determination proves challenging, structure prediction remains one of the most sought-after computational methods. With an ever increasing number of known protein structures and the development of better algorithms, there has been a considerable improvement in the quality of model predictions . The most accurate and popular methods of protein structure prediction are still dependent on the availability of the structure for a homologous protein. Homology- or template-based modeling methods involve using the structural information from a sequentially similar region of a homologous protein for which the structure is already known. Template-based methods improved considerably over the years, primarily due to an increase in the number of proteins for which structures are known and due to improvements in sequence-alignment methods .Despite the impressive increase in the number of experimentally determined protein structures, there is still a large gap between sequence space and structure space. Consequently, for a large number of proteins, templates are not available to assist in structure prediction. To understand the structure for such proteins, several ab initio or template-free methods were developed. Although such prediction methods are severely limited with regards to the length of the protein sequence, certain recent advances in reliable residue–residue contact predictions considerably improved their accuracy ,98.Apart from playing a crucial role in understanding the structure and function of proteins difficult to study using experiments, protein structural modeling is routinely used to study regions that are missing from the crystal structures. Modeling the regions of missing coordinates in protein crystal structures forms one of the first steps in protein structural and dynamics studies using varying computational methods.As discussed in Volume maps obtained even from state-of-the-art cryo-EM experiments typically range around 3–4 Å resolution, which is insufficient to obtain atomic level information. Crystallography data, although high resolution, might not represent a functional or solution state. Computational methods integrating information obtained from these two complementary sources can, thus, supplement the overall information and provide novel insight into both large-scale, as well as atomic-level, structure and dynamics. Several methods were developed for fitting high-resolution crystal structures to EM density maps . These SAXS provides information about the overall shape of large macromolecule in solution. This is very helpful in understanding the large conformational changes associated with the function of macromolecules. The low-resolution information obtained from SAXS can be used to reconstruct high-resolution structural models using existing crystal structures ,116,117.Biomacromolecules  of interest can be described at multiple scales of detail. At the small scale, the electron distribution of each atom in the molecule is described, and the dynamics of these atoms are studied using quantum mechanics. Although this approach is extremely useful and accurate in studying small molecules and mapping reactions in localized active-site pockets of enzymes , it becoDespite breakthroughs in hardware and software capabilities of modern computer systems, it is still a challenging task to study protein dynamics in the time range relevant for biological functions, especially for important macromolecules. In order to address this, simplified coarse-grained (CG) models were developed and are used successfully for the last four decades. CG models are simplified representations of biological macromolecules, often treating amino-acid residues, secondary structural elements, or whole domains as single entities. This leads to a dramatic reduction in the degrees of freedom, consequently making these methods computationally much less expensive than molecular dynamics simulations. Structure-based models like Go models that assume a smooth funnel-shaped energy landscape are particularly useful in studying the folding of proteins ,128. InsThe rugged conformational landscape of proteins makes conventional MD simulations vulnerable to be trapped in one of the local, non-functional free-energy minima . In ordeMethods such as replica exchange , umbrellSome other methods use non/quasi-equilibrium sampling. In steered MD , a molecCrystal structures typically portray a single averaged model of protein structure, which loses information regarding the dynamics of the protein not only in solution, but also in the crystal itself. In terms of exploring structural heterogeneity within the protein crystal, there were a few recent attempts to include the ensemble view of the structure in crystal model building. See . HoweverIn these simulations, the entire unit cell is simulated in an environment very similar to the experiment. This makes the comparison between X-ray data and simulations rather informative; however, these are computationally expensive simulations as compared to conventional MD simulations, owing to larger starting structures and delayed convergence . NeverthThe crucial role of computational methods in understanding protein structural dynamics cannot be overstated. In the following sections, we review novel insights gained using computational methods, either independently or in concert with experiments.Biological systems are complex and, often, a multidisciplinary approach is required to understand these complex systems. This is particularly true when it comes to understanding the structure–function–dynamics of large macromolecular machines within cells like ribosomes, proteasomes, spliceosomes, transcription initiation complexes, etc. Until recently, the only experimental method that provided high-resolution information of these macromolecular assemblies was X-ray crystallography. Despite great achievements in determining crystal structures of some of these complexes, the information for most of macromolecular complexes is missing. Recent phenomenal advances in the cryo-EM led to enormous breakthroughs in the understanding of macromolecular structures and dynamics .The ribosome is a macromolecular machine responsible for the synthesis of all proteins inside cells. Owing to its crucial role in all living systems, structure–function studies of the ribosome have gone on for decades, and the determination of an atomic-resolution crystal structure of the ribosome enhanced the understanding of one of the most fundamental biological process . HoweverThe C-terminal domain (CTD) of RNA polymerase II comprises several repeats and is structurally disordered, leaving it inaccessible to most structure determination methods, including X-ray crystallography and cryo-EM. Consequently, despite its important role involving multiple protein–protein interactions during transcription, there was limited structural information detailing the CTD in complex with its cognate binding partners. Using integrative structural modeling, Jasnovidova et al.  determinThe conformational dynamics of the spliceosome, a megadalton-sized machine that processes precursor messenger RNAs to construct coding regions (exons), was studied at atomic resolution to delineate functional motions that were missing from the static structures . ComputaGoing a step further on the length scale, hybrid methods involving cryo-EM, X-ray crystallography, molecular modeling, and molecular dynamics simulations were used to determine the structure and dynamics of a whole viral capsid comprising millions of atoms ,168. TheThere are hundreds of types of post-translational modifications (PTMs) of proteins that were found to be biologically important. Post-translational modifications like phosphorylation, methylation, acetylation, glycosylation, etc. were shown to play major roles in almost all biological processes . These mS-glutathionylation of a particular cysteine residue in Arabidopsis thaliana BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase (BAK1) was explored, and the effect of this PTM on the conformational dynamics reconciled with previous experimental observations [In view of the importance of PTMs in the biological processes, the experimentally derived structural information for such modifications is considerably low . Moleculrvations . Computational methods have contributed to almost every stage of drug discovery for a long time now . StructuVirtual screening entails searching through millions of compounds for their ability to bind to the target protein. Pharmacophore-based screening simplifies this task by extracting a number of electronic and chemical features from a known ligand that can be used to screen for compounds with similar features. An intuitive extension of this approach is three-dimensional pharmacophore-based screening, where not only the chemical features but also the steric features are considered based on available protein–drug structures . ConventComputational methods, particularly molecular dynamics simulations and quantum mechanics simulations, play crucial roles in understanding the role of water in the structure and function of biomacromolecules. Molecular dynamics simulations were used to understand the role of water molecules in protein–DNA binding , enthalpComputational methods for studying protein structural dynamics were instrumental in understanding numerous biological processes and successfully shed light on phenomena that were inaccessible to experimental methods. However, like any other method, they also have certain limitations. In the following sections, we discuss some of the common limitations of the previously discussed computational methods.Results obtained from any computational method are only as reliable and accurate as the parameters used in the development of that method. Molecular dynamics simulations use a combination of functional forms defining the interactions between different particles in the system and a set of predetermined parameters that are used to calculate forces between the particles . Such foThe force fields used to study solution dynamics of proteins were developed to model the behavior of folded proteins. This led to certain insufficiencies in studying intrinsically disordered proteins (IDPs) using the same force fields. Although simple modifications in some parameters of the force field improve the agreement between simulations and experiments, a more thorough evaluation and parameterization of forcefields is required for IDPs ,191. Most of the current force fields use fixed partial charges on the atoms to calculate electrostatic forces. Although these force fields were conventionally successful in simulating the behavior of molecules in homogeneous environments, their performance is limited in conditions where there are variations in local electric fields, and continued efforts are being made to resolve these limitations .While the sampling issues were addressed by several methodological developments as described above, the time scale of MD simulations is ultimately limited by the computational speed. Apart from the MD-specific machine Anton, which can reach sub-millisecond time scales per day , commonlX-ray crystallography has long been the de facto method of choice to study protein structure and function. Despite several challenges and limitations, it continues to remain one of the most crucial sources of structural information. However, the realization of the importance of dynamics and the concomitant improvement in the accuracy and applicability of computational methods to be able to study the functional dynamics of proteins in native-like environments heralded the era of computation-driven structural biology. This was equally matched by the rise of novel experimental methods to elucidate the dynamics of proteins in solution at varying length and time scales. Considering the complexity of biological processes, it is now abundantly clear that a multipronged approach comprising inputs from several different experimental and computational methods is required to understand the important questions. Consequently, hybrid/integrative methods are starting to become the new norm in biomolecular structure and dynamics studies. This is evident from the fact that the Protein Data Bank now has a new field of classification named “hybrid methods”, consisting of structures determined using more than one kind of experimental method. Also, a wwPDB hybrid/integrative methods task force was established in order to formalize the representation, validation, archiving, and publication of structural and dynamics data generated using hybrid or integrative methods ,197. Com


In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets. Gene expression profiles can classify breast cancer into five clinically relevant subtypes. Here, the authors perform an in-depth quantitative profiling of the proteome of 45 breast tumors, and show they can recapitulate the transcriptome-based classifications and identify many potentially antigenic tumour-specific peptides. These original classifications have proven extremely robust and are still widely used to predict prognosis and design therapeutic regimens3.Research efforts over the preceding decades have led to immense progress in our understanding of the molecular heterogeneity of tumors originating in the same tissue, solidifying a long-proposed idea that single effective organ-of-origin specific treatments are not adequate. This realization fostered the need for in-depth molecular characterization to stratify patients into treatment courses that target their individually unique tumors. This principle was first applied to breast cancer when Botstein et al. classified 42 tumors into molecular subtypes based on their mRNA signaturesTM, Oncotype DXTM, and Prosigna RORTM) are not readily available to all patients, and despite progress in the development of pathology-based surrogate PAM50 markers, one out of three patients are still potentially misclassified3.To aid in clinical implementation, a set of 50 transcripts (collectively known as PAM50) were established for the five subtypes  and surrogate immunohistochemistry (IHC) markers  were implemented to partially recapitulate the stratifying and prognostic information garnered in the original studies. However, multigene expression assays .Herein, we present an in-depth quantitative profile of the proteomes of 45 breast tumors, 9 represented from each of the 5 PAM50-based molecular classifications. We demonstrate a remarkable quality of relative quantification by examining protein complex member correlations across tumor samples and are the first to recapitulate the current mRNA-based molecular classifications with an unsupervised analysis of whole-proteome data. We then use the high-quality proteome profiles as a base to interpret multiple layers of systems measurements collected on the same tumors, including those of mRNA expression, genome copy-number alterations, single-nucleotide polymorphisms, phosphoprotein levels, and metabolite abundances. Independent layers of analyses reveal novel immunohistochemical biomarker candidates to more reliably stratify difficult-to-classify patients for treatment options, provide a proteome-based framework to assess prognosis for those straddling treatment class assignments, link immune cell infiltration and tumor extracellular matrix composition to prognosis, and connect molecular classification to metabolic phenotype. Furthermore, the depth and quality of proteome profiling enables application of proteogenomic analyses and the discovery of neoantigens arising from tumor-specific variants of known proteins and regions of the genome previously thought to be noncoding. Finally, the comprehensive data collected in this study are presented as an online resource for the breast cancer research community to explore and to test new hypotheses within their areas of expertise (14. LC-MS/MS-based protein quantification was performed as described in the 12.Nine patients classified into each of the five PAM50 subtype groups were selected from the Oslo2 study cohort to ensure tumor diversity is represented (denoted Oslo2 Landscape cohort) Fig. 13,14. LCIn all, 13,997 protein products of 12,645 genes were identified at a 1% protein false-discovery rate (FDR) based on 248,949 identified unique peptides Fig. . The sub16 and Andromeda in MaxQuant) and performing reverse phase protein lysate assays (RPPA) on sections of the same tumors. Both spectral search methods yield similar protein identifications  and HGFAC (activates HGF) are in the MS-based protein correlation group associated with blood plasma, indicating a possible pathway of activation through phosphorylation of proteins of group 2.RPPA was performed with phosphorylation-specific antibodies against 41 known cancer-related regulators of cell signaling to explore their impact on the quantified tumor proteome. Hierarchical clustering of phosphoprotein correlation profiles (RPPA-quantified phosphoprotein abundance to MS-quantified protein abundance) divides phosphoproteins into four distinct groups  abundance profiles (Supplementary Methods), producing six consensus core tumor clusters (CoTC) , identifies three tumor clusters that resemble CoTC1 , CoTC3 , and CoTC6   Fig. . CoTC asTC) Fig. . UnsuperThe CoTC groups, composed of PAM50-classified basal-like tumors (CoTC1 and 2), are distinguished by differential expression of immune markers, E2F and MYC targets, along with G2M checkpoint-related proteins Fig. . The lum20. Overlapping pursuant classifications with those based on an independent measurement of cellular function is emblematic of the value added by proteome-based profiling.Finally, tumors displaying marked glycolytic characteristics, as determined by stratification based on profiled metabolite abundances, are all members of CoTC6 Fig. . Deplete21, the physical nature of component interactions or their tightly related functions may exacerbate this effect. Indeed, proteins with known interactions, as reported by Biogrid or CORUM, have more correlated abundances. Moreover, these elevated correlations are substantially more distinguished at the protein as opposed to the transcript level  of proteins marked by high variance across the Oslo2 Landscape cohort in a manner that minimizes edge length . CoTC3  and CoTC4  tumors highly express luminal proteins, yet are distinguished by a small immune response network outpost enriched for function in the interferon alpha response   Fig. , suggest24) and normal-like tumors , co-elevation of EGFR and MET appears to be confined to ductal carcinoma in situ (DCIS) regions for a subset of normal-like tumors  and do not appear dependent on protein/mRNA half-life , average protein precursor area, or number of peptide spectral matches (PSMs)  are not as highly correlated with transcript quantity as are groups of soluble metabolic  and signaling/proliferation-related  proteins ent Fig. .Highly proliferative tumors  have a tendency to have more correlated proteomes and transcriptomes than lowly proliferative tumors   in breast tumors30. Thus, we explore whether variance across proteomes in the Oslo2 Landscape cohort can be accounted for by CNAs.Much of contemporary thinking labels cancer as a disease of the genome, and gene copy-number alterations (CNAs) are known to be associated with expression of their corresponding transcripts  at an elevated rate .The attenuation of CNA effects may be explained by regulatory mechanisms at the protein level. To investigate, genes are split into lowly and highly attenuated groups based on a gaussian mixture model of protein abundance and mRNA expression correlations to CNAs. The gene subset with high mRNA–CNA and low protein–CNA correlations are defined as highly attenuated Fig. , and accate Fig. ; suggestHER2 amplicon, while loss effects influence estrogen signaling  based on cis associations of CNAs and mRNA expression of 619 genes. Of those that we quantified at the protein level in the Oslo2 Landscape cohort, 83% overlap with the set of genes having significant copy number to transcript or to protein abundance associations as determined by the linear regression method implemented by Curtis et al.30 . Spectra matching known peptides and multiple genomic regions are filtered out along with single amino acid variants (SAAVs) not meeting stringent verification criteria imposed by SpectrumAI12 Fig. .Fig. 7Pr33 verify that many of these peptides are transcribed and translated in independent breast tumor cohorts  , or corresponding to un-annotated alternative gene translations  Fig. ; two-thirts Fig. , while C36 and not identified in MS data from normal tissue37  were predicted to bind MHC class I were predes Fig. . Tumor sdes Fig. . These pdes Fig. .Identifying antigen targets in the proteome, as opposed to the transcriptome, may streamline development of immunotherapies, because the immune system is activated by protein fragments displayed on the MHC. These high-confidence identifications demonstrate the prominent role proteogenomic analyses of high-quality proteome spectra will have in the push towards individualized medicine.29, including MAP3K1, AKT2, FOXA1, ERBB2, and CDKN1B, are amongst proteins identified with SAAVs  Fig. , have a Molecular characterization has progressively stratified breast cancer patients into more disease-type specific cohorts; with the first clinical manifestation being the adoption of immunohistochemical evaluation of ER, PR, KI67, and later HER2 expression as primary determinants of treatment regimens. Though interpretation of marker expression and administration of targeted therapies denotes a vast improvement over sole reliance on staging and grade, variances in specimen preparation and a heavy reliance on human judgment along with technological advances in measuring gene expression fostered development of unbiased whole-transcriptome profiling as an accompaniment.3 and may prevent patients from receiving chemotherapy who do not stand to benefit38. Furthermore, mRNA profile-based stratification has defined a normal-like subtype that clinical markers alone do not recognize.Importantly, unbiased analyses of thousands of gene transcripts largely recapitulates pathological marker classifications, inspiring confidence that they could further delineate patients responsive to targeted therapies. Indeed, transcript profiling of gene panels selected to represent the variance across tumor subtypes is recommended for assigning treatment courses for patients with early stage ER+ /HER2− tumors5. However, to date, unbiased proteome profiling has not resolved a consistent variance with mRNA profiling across known breast tumor subtypes. This has largely been accredited to the unreliability of mRNA as a surrogate for protein abundance. However, given unbiased mRNA profiling distinguishes function and differential responses to therapy; adherence to the tenets of the central dogma dictates proteome profiles would distinguish the same differences while layering additional insights. In fact, our findings suggest expression profiles of prognostic mRNA panels stratify breast tumors based on known biological variances partly because the selected transcripts are reliable protein surrogates.Proteome characterization has provided valuable insight on CNA effects and their attenuation at the protein level11. The reliability of protein quantification is attested to by the remarkable correlation of protein complex members, and suggests protein co-function may be inferred by high cross-tumor correlation. From here, we layer additional insight over the breast cancer landscape by further analyses of proteomes and parallel systems measurements. Such insights include identifying a definitive separation of basal-like tumors based on immune components, postulating regulatory control of characteristic cancer hallmark genes by phosphorylation of specific groups of regulatory proteins, revealing the glycolytic preference of proliferative tumor subtypes, and determining copy-number alterations may be attenuated by protein-level regulatory mechanisms, such as ubiquitinylation and subsequent degradation in the proteasome. Furthermore, we validate the often ambiguous mRNA-based normal-like subtype, both with an unbiased approach and the identification of MET-EGFR coexpression as a biomarker; potentially for the DCIS component.Herein, we present a landmark study, which is the first to recapitulate known function-enriched stratifications of breast tumors based on unbiased analyses of proteome profiles. We attribute this to the proteome coverage and high-quality quantification resulting from the reduction in peptide fraction complexity accomplished by HiRIEF separation12 and identify proteins in individual tumors, with high confidence, corresponding to un-described gene variants, noncoding regions, or regions thought to be noncoding due to poor genome annotation. We postulate protein products of undescribed gene variants and noncoding regions are the consequences of cancer genome instability, and that they are strong candidates of tumor-specific targets for immunotherapies.With an eye toward advancing to individually tailored therapies, we apply our recently developed proteogenomics pipelineAs breast tumors are continuously revealed to be individually unique diseases, considerations of molecular profiles will become paramount in selecting from available treatment options and developing new ones. Though mRNA profiling has been initially dominant in this role, the “landscape” study presented herein demonstrates the instrumental contribution analyses of the quantitative proteome will have moving forward. Patient stratification based on high-quality proteome MS data is marked by consistency with multiple systems level and immunohistochemical readouts, underscoring the utility of a multi-faceted approach to translate systems level findings into effective therapeutic strategies.www.breastcancerlandscape.org.We have created a user friendly and easily accessible data portal with analysis tools to ensure that this rich dataset can be explored by the research community, available at: 39, and peptides were separated using immobilized pH gradient-isoelectric focusing (IPG-IEF) on narrow range pH 3.7–4.9 and 3–10 strips11. Peptide fractions were separated using a 3000 RSLCnano system and analyzed using a Thermo Scientific Q Exactive. MSGF + Percolator in the Galaxy platform was used to match MS spectra to the Ensembl 75 human protein database16. Protein identifications were limited to 1% protein FDR40. Proteogenomics was performed as described by Branca et al. and Zhu et al.12. See Tumor samples from Oslo2 cohort were prepared for MS analysis using a modified version of the spin filter-aided sample preparation protocolFurther information on experimental design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Reporting SummaryPeer Review File


The core of the Translocatome database is the manually curated data set of 213 human translocating proteins listing the source of their experimental validation, several details of their translocation mechanism, their local compartmentalized interactome, as well as their involvement in signalling pathways and disease development. In addition, using the well-established and widely used gradient boosting machine learning tool, XGBoost, Translocatome provides translocation probability values for 13 066 human proteins identifying 1133 and 3268 high- and low-confidence translocating proteins, respectively. The database has user-friendly search options with a UniProt autocomplete quick search and advanced search for proteins filtered by their localization, UniProt identifiers, translocation likelihood or data complexity. Download options of search results, manually curated and predicted translocating protein sets are available on its website. The update of the database is helped by its manual curation framework and connection to the previously published ComPPI compartmentalized protein–protein interaction database (http://comppi.linkgroup.hu). As shown by the application examples of merlin (NF2) and tumor protein 63 (TP63) Translocatome allows a better comprehension of protein translocation as a systems biology phenomenon and can be used as a discovery-tool in the protein translocation field.Here we present Translocatome, the first dedicated database of human translocating proteins (URL:  Subcellular localization of proteins is essential in spatial and temporal organisation of biological processes such as signalling pathways enabling their separation into organelles . TransloProtein translocation is a process which refers to the alteration of a given protein's subcellular localization. However, this phenomenon has no unified definition, and the word ‘translocation’ may also refer to gene translocation or RNA translocation at the ribosome. In this work we define protein translocation as a systems biology phenomenon, which refers to the regulated movement of a protein of a given post-translational state between subcellular compartments. Translocation changes the interaction partners and leads to altered function(s) of translocating proteins. There are certain processes  which were all collected based on related publications containing experimental evidence. Altogether Translocatome contains 13 066 human proteins, which were selected from the compartmentalized protein–protein interaction database  using the inclusion criterion that every protein needed to have at least one experimentally validated subcellular localization. By the application of the well-established gradient boosting machine learning tool, XGBoost , type of experimental evidence, affected signalling pathway(s) and pathological properties. The core of the Translocatome database is the 213 manually curated human translocating proteins ( XGBoost  we predihttp://translocatome.linkgroup.hu/coredata). With the manual curation process involving the judgement of 3 independent experts we aimed to collect detailed and experimentally validated information about every entry extracted from peer reviewed publications  and link(s) to peer-reviewed article(s) describing the experimental evidence of translocation,initial and target localizations of the translocating protein,interacting partners and biological functions ,translocation mechanism,the used detection method,protein structural information on translocation mechanism,disease group, exact disease involved and pathological role,signalling pathways affected.The core data of Translocatome is the extensively curated set of 213 human translocating proteins (see Core Data at the website: We used the UniProt naming convention  for prot(a) with experimentally proved diffuse, multi-compartmental distribution, (b) with exclusive single-compartment localization, (c) docked to DNA/RNA, (d) embedded in membranes or (e) attached to the cytoskeleton . The process was based on our previous work . DCS varies between 0 and 1, having increasing values if the protein has more curated data. The score is calculated and normalized after weighting all the available data, where those related to translocation have a higher weight . TES values were rescaled to the interval  by min–max normalization using Equation (The XGBoost machine learning method gave every protein of the Translocatome database a Translocation Evidence Score (TES) that is proportional with the translocation probability of the given protein. For each protein we computed TES using Equation 1)\docu\docu(1)\entclass1pt{minimaThe larger the TES value, the greater the probability of translocation. As a numerical example, suppose that ‘protein A’ has 20 neighbours (degree) in the human interactome and its UniProt record contains only two GO terms, ‘animal organ morphogenesis’, and ’cytoplasm’. Then the predicted translocation evidence score of ‘protein A’ is −0.497 + 2.675 + 1.353 = 3.531. The value is then normalized using Equation  1133 high-confidence translocating proteins having a TES value higher than 0.6167; (b) 3268 low-confidence translocating proteins having a TES value between 0.6167 and 0.4487, as well as (c) the residual 8665 proteins having a TES value lower than 0.4487, which were considered as non-translocating  213 manually curated translocating proteins, (ii) 160 physiologically translocating manually curated proteins (the positive training set), (iii) manually curated non-translocating proteins (the negative training set), (iv–vi) high-, low-confidence and non-translocating protein sets, as well as (vii) the whole protein set and (viii) its protein–protein interaction network. These sets of proteins can be downloaded in a comma separated .csv format. Besides these pre-defined sets users can also download the results of their search queries as a tabulator separated file . Examples and explanations of the output formats are available in As part of the user-friendly interface, various search functions were developed. We provide an easy to use quick search function (with UniProt AC autocompletion) which can be used to find protein families or a given protein. The advanced search option creates the possibility to search for more elaborate sets of proteins filtered by their localization, UniProt identifiers, Translocation Evidence Score or Data Complexity Score. The web interface provides eight pre-defined protein sets as download options covering https://rubyonrails.org) framework. The MCF website follows the hierarchical model-view-controller design pattern to ensure the separation of the data layer from the business logic and the user interface. The MCF stores all the data of the Translocatome and provides them to the front-end of the Translocatome website after expert review. To ease usability an end-user documentation is available as tutorials, detailed descriptions and location-specific tooltips in the HELP menu on the site (http://translocatome.linkgroup.hu/help). Further details of design and implementation of the database are summarized in To allow the development of the Translocatome database as a community effort a manual curation framework (MCF) was designed. MCF uses the same MongoDB database as the Translocatome site, with a user interface developed in the Ruby on Rails 4.2 ((A) were already included in the manually curated 213 translocating protein set ; (B) were previously shown to be translocating proteins but have not appeared in our Core Data of 213 proteins collected from keyword-based searches  have not been described as translocating proteins yet, but from the literature we can conclude that their translocation is probable (one protein: TP63); (D) there is no information in the literature about their translocation (one protein: PRKRA). Proteins of categories (C) and (D) are good candidates for further experimental studies verifying their translocation.The Translocatome database is the only current dedicated collection of human translocating proteins. With its Translocation Evidence Score (TES) for 13 066 proteins it helps the identification and experimental validation of novel translocating proteins. To demonstrate the prediction efficiency of Translocatome we assessed the first 40 proteins with the highest TES values. Table The best hit of the XGBoost algorithm, the PTEN protein is a part of the manually curated 213 translocating proteins. As its second best hit, the XGBoost algorithm correctly predicted NF2 (Merlin) as a translocating protein, since NF2 in its dephosphorylated form indeed translocates to the nucleus . NF2 is Out of the 25 proteins listed on Table With the above examples we demonstrated that the XGBoost machine learning algorithm  is able The existing MoonProt  and UniPIn summary, Translocatome offers a unique dataset of 213 specifically collected human translocating proteins listing the source of their experimental validation, several details of their translocation mechanism, local compartmentalized interactome as well as their involvement in signalling pathways and disease development. In addition, it provides translocation likelihood values (as Translocation Evidence Scores) for 13 066 human proteins identifying 1133 and 3268 high- and low-confidence translocating proteins, respectively. The assembly of the Translocatome database  and an advanced search to find sets of proteins filtered by their localization, UniProt identifiers, Translocation Evidence Score or Data Complexity Score. The web interface provides eight pre-defined protein sets as download options and a possibility to download the search results. End-user documentation is available as tutorials, detailed descriptions and location-specific tooltips in the HELP menu of the site.http://translocatome.linkgroup.hu. Translocatome is a community-annotation resource, which is helped by its manual curation framework (MCF). MCF allows the users to build in their own experimentally verified translocating proteins. Translocatome will be updated and upgraded annually for minimum 5 years. The Translocatome database is connected to our previously developed, compartmentalized protein–protein interaction database (ComPPI, Translocatome is available at  ComPPI, . Thus thWe plan to resolve current Translocatome limitations, such as extending the database to other species than humans. Future plans include the extension of positive and negative datasets and localization-based network visualization. Translocating RNAs play a key role in subcellular regulation as well, but their role is even more complex and mysterious. We plan to extend our database and add translocating RNAs, to fill out this gap. The improvement of the data not only means, that Translocatome will have more proteins or more detailed information. In this process the whole database will be updated meaning that the XGBoost machine learning will reappraise the data and provide more even accurate predictions based on the updated data.In conclusion, the Translocatome database introduced here provides the first dedicated collection of 213 translocating human proteins including their interaction partners in the different subcellular localizations. Importantly, Translocatome gives a Translocation Evidence Score to more than 13 thousand human proteins allowing the assessment of their translocation probability. All these features are accessible in a user-friendly manner. The Translocatome database allows a better comprehension of protein translocation as a systems biology phenomenon, and can be used as a discovery-tool of the field. Since translocating proteins become more and more important therapeutic targets ,7 Translhttp://translocatome.linkgroup.hu.The Translocatome database of human translocating proteins can be accessed freely at Supplementary DataClick here for additional data file.


Molecular recognition features (MoRFs) are short protein-binding regions that undergo disorder-to-order transitions (induced folding) upon binding protein partners. These regions are abundant in nature and can be predicted from protein sequences based on their distinctive sequence signatures. This first-of-its-kind survey covers 14 MoRF predictors and six related methods for the prediction of short protein-binding linear motifs, disordered protein-binding regions and semi-disordered regions. We show that the development of MoRF predictors has accelerated in the recent years. These predictors depend on machine learning-derived models that were generated using training datasets where MoRFs are annotated using putative disorder. Our analysis reveals that they generate accurate predictions. We identified eight methods that offer area under the ROC curve (AUC) ≥ 0.7 on experimentally-validated test datasets. We show that modern MoRF predictors accurately find experimentally annotated MoRFs even though they were trained using the putative disorder annotations. They are relatively highly-cited, particularly the methods available as webservers that on average secure three times more citations than methods without this option. MoRF predictions contribute to the experimental discovery of protein-protein interactions, annotation of protein functions and computational analysis of a variety of proteomes, protein families, and pathways. We outline future development and application directions for these tools, stressing the importance to develop novel tools that would target interactions of disordered regions with other types of partners.  •MoRFs are short protein binding regions that undergo disorder-to-order transitions•Machine learning is the main methodology used to derive MoRF predictors•MoRFs can be accurately predicted from protein sequences•Predictors available as webservers are significantly more cited•Predictors that target other types of disorder functions should be developed Recent P20963), which is one of the key players in the adaptive immune response. These MoRFs were annotated using the structures of protein-protein complexes from the Protein Data Bank (PBD) [Proteins with IDRs are particularly important in the context of protein-protein interactions (PPIs). Hub proteins, which are defined as proteins that interact with a large number of proteins in the PPI networks, are enriched in IDRs when compared to the other proteins . This stnk (PBD)  that areA recent computational study has analyzed abundance of MoRFs in 868 complete proteomes, showing that estimated 21% of IDRs in Eukaryota and 29% in Bacteria and Archaea have MoRFs  and shorWhile some of the MoRF predictors were mentioned in the context of a couple of recent articles that discuss prediction of functions of IDRs ,74, they2MoRF predictors identify putative MoRF regions in an input protein sequence. We also contrast MoRF predictions with results of two methods that target the prediction of a more generic set of disordered protein-binding regions (DPBRs), which cover MoRFs and other disordered protein binding domains that are longer than 25 residues . The res2.1Over a dozen MoRF predictors was developed during the last 14 years. The first method, α-MoRFpred ,96, whicThe second predictor, Retro-MoRF , was pubThe first publically available computational tool that covers prediction of all MoRF types is MoRFpred ,87. ThisWhile only three predictors were developed between 2005 and 2012, 11 methods were published over the next seven years. The MoRF predictors are made accessible to the community in two ways: as webservers and/or standalone code. The webservers are arguably easier to use and they are more suitable for less computer savvy users who want to perform ad hoc predictions for a limited number of proteins. The only requirements for the webserver users are to have access to the Internet and to have a modern web browser to connect to the website of the webserver. The predictions are calculated on the server side and the results are returned via email or/and the web browser window. Nine out of 14 MoRF predictors offer this option. We note that the webserver for one of these methods, MFSPSSMpred, is no longer available. The source code option requires the end users to run the predictions on their own hardware. This could be attractive in situations when large datasets of proteins must to be predicted and when the end users would like to embed a given MoRF predictor into a larger bioinformatics pipeline. The source code is available for nine of the 14 MoRF predictors. We note that six methods, including MoRFCHiBi , DISOPRE2.2We also briefly discuss several related computational methods that target prediction of disordered protein-binding regions (DPBRs), short linear motifs (SLiMs), and semi-disordered regions.DPBRs cover MoRF regions and longer disordered protein-binding domains. There are currently two predictors of DPBRs: ANCHOR ,41,93 anSLiMs are short sequence motifs in eukaryotic proteins that are associated with protein binding events. While most SLiMs are localized in IDRs, approximately 20% of them are associated with protein-protein interactions in structured regions . A colleSemi-disordered regions are the regions that are predicted midway between being disordered and structured; i.e., they are predicted with 50% probability to be disordered . Recent 3Various MoRF predictors use different predictive models, different types of information extracted from the input sequence, and different training datasets. This results in different predictions for the same input sequence, where some methods are expected to be on average more accurate than others.As we mentioned before, the MoRF predictors are trained using datasets of proteins with MoRF regions located within putative IDRs. A representative set of 11 MoRF predictors uses the same putative IDR-based training dataset from . These pThe relatively low predictive performance of fMoRFpred  can be eInterestingly, a majority of the results are located at or above the diagonal line in 4MoRF regions are highly abundant across all domains of life. They have unique sequence signatures that facilitate the development of accurate computational predictors of MoRFs. These predictions were used to assist experimental discovery of PPIs, generate putative protein functions, and facilitate computational analysis of a variety of proteomes, pathways, and protein families. We survey a comprehensive collection of 14 MoRF predictors. Our study reveals that the development of these methods has accelerated in recent years, resulting in the release of eight tools in the last three years. MoRF predictors rely primarily on machine learning-derived predictive models that are generated using training datasets where MoRFs are annotated using putative IDRs. We demonstrate that these computational tools are well-cited and that most of them are available as convenient to use webservers. Our analysis also shows that they produce accurate predictions on test datasets that use both putative and experimental annotations of disorder. We highlight the empirical observation that they accurately identify experimentally annotated MoRFs in spite of the fact that they were trained using datasets with putative annotations. The most accurate methods are meta-predictors but they also require the longest runtime. On the other hand, the fastest method, fMoRFpred, is shown to generate the least accurate results.Our survey reveals that the underlying predictive models are rather homogeneous, as they almost always use the SVM model. This is true for all methods that were published in 2018. With the advent of deep learning models in bioinformatics , we beliWe stress the fact that IDRs carry out many cellular functions that require interactions with a wide range of partners. IDRs are involved in protein-protein, protein-DNA, protein-RNA, protein-lipid, and a variety of protein-small ligand interactions. Numerous examples of these interactions are available in the DisProt resource ,78. A suFinally, recent research advocates the development of quality assessment scores for the disorder predictions . These sThe authors declare no conflicts of interest.


While belonging to the nanoscale, protein machines are so complex that tracing even a small fraction of their cycle requires weeks of calculations on supercomputers. Surprisingly, many aspects of their operation can be however already reproduced by using very simple mechanical models of elastic networks. The analysis suggests that, similar to other self-organized complex systems, functional collective dynamics in such proteins is effectively reduced to a low-dimensional attractive manifold. These pMolecular structures and equilibrium conformations of almost all proteins are known. They are determined through a combination of X-ray diffraction experiments, cryo-electronic microscopy and other techniques, and can be found in the Protein Data Bank (PDB). Moreover, conformational dynamics of proteins is sufficiently well reproduced in all-atom molecular dynamics (MD) simulations. Hence, it may seem that just some special MD simulations have to be performed in order to unveil the operation mechanisms of protein machines. In practice, severe difficulties are encountered if one attempts to do this.All-atom MD simulations of proteins are extremely demanding in terms of the computation time. Even with the most powerful supercomputers, the dynamics of a protein can be traced only up to microsecond times. The best achievement so far has been that, by employing special hardware and for a very small protein, a trajectory of a millisecond duration could be obtained . This isWithin the last century, a substantial progress in understanding large complex systems has been made (e.g. ). It is Furthermore, two kinds of mathematical models for complex systems can be distinguished. The ‘realistic’ models are used to provide accurate quantitative predictions for a particular considered case . Typically, they would include many variables and parameters, and their results still need to be interpreted and understood. On the other hand, ‘simple’ mathematical descriptions for complex systems are also broadly employed. The intention of such reduced models is not to yield accurate quantitative predictions, but rather to help in understanding of the principal mechanisms involved .Good examples of both approaches are provided by brain research. There exist realistic models for chemical and electrical processes within a neural cell. However, they are complicated and therefore used only for single neurons or small populations of them. Large-scale modelling is instead performed by means of greatly simplified models of neural networks. In such networks, a neuron can even be treated as an automaton with just a few states. Thus, actual physical and chemical processes are not resolved. Nonetheless, simple neural network models play a fundamental role in understanding of the brain.constructive biology aimed at principal understanding of how various biological functions emerge )A limitation of simple elastic network models, considered in our review, is that they cannot provide for partial unfolding and refolding, i.e. for ‘cracks’ in a protein ,72. The It cannot be accidental that elastic networks already allow one to reproduce principal features of conformational dynamics in protein machines. However, these models remain so far phenomenological and their relationship to the underlying all-atom dynamical descriptions still has to be elucidated. On the other hand, further analytical and numerical investigations are also needed to get better general understanding of nonlinear dynamics in elastic networks.


The protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein’s healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function.features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation.Recent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape The proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction. Proteins are ubiquitous biological macromolecules (biomolecules) found in nearly all processes that maintain and replicate a living cell. These biomolecules are inherently dynamic, switching/interconverting between shapes/conformations with different potential energies. These interconversions regulate interactions of a protein with different molecular partners and by doing so regulate allosteric signaling, catalysis, and other cellular processes that occur on 0.1−10Å length scales and nanosecond-seconds time scales /[x− max(x)] to permit a correlation-based analysis. Figure Leveraging wet-laboratory characterizations of activities of H-Ras variants, we now conduct the following analysis. We compare the extracted landscape descriptors (from the reconstructed landscape of each variant) to the biochemical parameters reported in , 31. SpeFigure GAP-catalyzed hydrolysis (P8) is another biological activity of H-Ras on which our analysis provides further insight as to how it is impacted by mutations. Table Finally, Table δ2, m, and n0) can be leveraged and tuned to control the computational demands at the expense of possibly missing small basins. If the landscape has a shallow hierarchical organization, the computational demands of the BDR method will be low.As this paper shows, the described methods can be utilized to reveal basins and saddles in an underlying energy landscape. If the focus is on expediently capturing and analyzing saddles, the SDR method is more appropriate, as it employs a rigorous definition of a saddle point. In contrast, the BDR method employs a working definition of saddles and identifies them after extracting the hierarchical organization of basins in the landscape. Doing so is more computationally demanding, and the majority of the computational budget is devoted to identifying basin boundaries via the alpha convex hull. The specific characteristics of basins in a landscape impact performance. For instance, if the landscape is very rugged and contains many basins within basins, the BDR method will spend significant time in extracting this organization. Parameters (such as in-silico models of how mutations impact function, as well as elucidate the role of specific conformational states and state-to-state interconversions in key biological activities.Summarizing a protein energy landscape via its basins and saddles, as proposed in this paper, is an interesting venue via which one can pursue relating protein energy landscapes and mutation-driven alterations of landscapes to protein function and dysfunction. Specifically, the analysis presented in this paper allows relating over a dozen mutations of an enzyme key to human biology and health to biological activities via dynamics, validating prior dry- and wet-lab work and revealing novel mechanisms via which mutations percolate to dysfunction. The results presented here are promising and suggest the approach of an exciting stage of landscape-driven enquiry of the relationship between dynamics and function, where one can compute and mine landscapes of protein variants to learn This paper has presented two novel methods that automate the reconstruction and analysis of energy landscapes. In particular, the proposed methods organize the information hidden in evaluated samples and reveal central local structures of a landscape, such as basins and saddles that underly the spatio-temporal dynamics. Our focus on such local landscape structures is due to the role that they play in regulating the equilibrium dynamics of a protein and the insight that they confer on how mutations alter function via altering dynamics.Finally, it is worth noting that the proposed methods are general and applicable to any set of evaluated samples that populate the state space of a dynamic system. In the strict context of molecular modeling research, the proposed methods can be valuable in analysis of results obtained by tertiary or quaternary structure generation methods, such as template-free protein structure prediction methods, as well as protein-ligand binding and protein-protein docking methods. Analysis of the underlying landscapes populated by decoys representing computed tertiary or quaternary structures can be leveraged to identify thermodynamically-stable and/or semi-stable structural states and possibly advance research in recognition of the native unbound or bound state of a molecule.


Network science-based analysis of the observability of dynamical systems has been a focus of attention over the past five years. The maximum matching-based approach provides a simple tool to determine the minimum number of sensors and their positions. However, the resulting proportion of sensors is particularly small when compared to the size of the system, and, although structural observability is ensured, the system demands additional sensors to provide the small relative order needed for fast and robust process monitoring and control. In this paper, two clustering and simulated annealing-based methodologies are proposed to assign additional sensors to the dynamical systems. The proposed methodologies simplify the observation of the system and decrease its relative order. The usefulness of the proposed method is justified in a sensor-placement problem of a heat exchanger network. The results show that the relative order of the observability is decreased significantly by an increase in the number of additional sensors. The placement of sensors significantly affects the performance of identification, state estimation as well as fault detection and isolation (FDI) algorithms. The goal-oriented placement of sensors for dynamical systems is a challenging task . ParametNetwork representation-based structural controllability and observability analysis of dynamical systems is a hot topic in literature ,14,15,16When the observability analysis of the system is performed on a properly defined network, the problem concerning the observability analysis is reduced to a reachability problem . In someThere is an almost infinite number of objectives that can be used to define a sensor placement problem . In mostIn this paper, to ensure observability and minimize the relative order of dynamical systems, two methodologies for sensor placement are introduced. The presented methods combine the metaheuristic SA optimization method with clustering. SA is chosen as it exhibits excellent levels of performance with regard to graph-represented combinatorial optimization (CO). The use of simulated annealing is strengthened by the fact that the relative degree of the system can be interpreted as a path in a network, and simulated annealing models a random walk on the search graph. Based on this concept, SA was applied to place micro-hydropowers in a water supply network  and netwk-medoid clustering problem. This concept has been utilized in Ref. [As sensors practically group the observed state variables, sensor placement can be considered as a special  in Ref. , where fk-medoid clustering problem. The fundamental idea of the paper is that instead of considering the distance between the cluster centers and the clustered objects, a goal-oriented sensor-placement algorithm can be derived by the introduction of a problem-relevant objective function into the scheme of the CLASA algorithm. The second novelty of the paper is that the random search of the resulted algorithm is fine-tuned by calculating the selection probabilities based on network distance-related fuzzy membership functions.The Clustering Large Applications (CLASA) algorithm  developeThe structure of the paper is the following. In In A nonlinear MIMO system can be represented by state-space model:N, the number of actuators by M and the number of sensors by K, then the matrices The model can be linearized and the resultant linear approximation represented by a linear state-space model :(3)x˙(t)i is defined as For each pair of output When the aim of the observer is to estimate the effect of the j-th state variable and the nearest sensor, As S is determined by minimizing the following cost function:As the generation of a balanced placement of sensors is of interest, the set of sensor nodes Besides the minimisation of the relative order, the observability of the system should also be ensured. Based on the Kalman rank criterion, a linear dynamical system is said to be structurally observable, if and only if the observability matrix nk(O)=N) .V represent the state variables and the edges j-th and i-th nodes in the directed network Based on the state-transition matrix of A see . In the E is determined by E is defined by As directed graphs can be represented as bipartite graphs, the maximum matching of the related bipartite graph was studied ,17. The e system . As the location .i-th element is non-zero, so In order to generate the output matrix As the number of sensors which can be allocated without any restrictions is k-medoid clustering problem, where the centroid elements are the sensor nodes and the members of the clusters are the observed state variables.To optimize the relative order and smoothness of these orders, the objective function (Equation ), which c-Medoid Clustering method (GDFCM) [c-Medoid Clustering method (GDFCM) [To determine the locations of the additional sensors two approaches are proposed inspired by Clustering Large Applications based on Simulated Annealing algorithm (CLASA)  and the  (GDFCM) . In the  (GDFCM) .In both algorithms, firstly the i, whose dynamics can be seen in Equation  can be seen in Algorithm 1. The algorithm possesses the following inputs and parameters: Equation , β∈Equation , K+ reprAlgorithm 1 Pseudo code of the modified CLASA algorithm (mCLASA).1:proceduremCLASA can be seen in Algorithm 2. The algorithm possesses the following inputs and parameters: G denotes the network representation of the system, The pseudo code of the suggested Geodesic Distance-based Fuzzy Algorithm 2 Pseudo code of the Geodesic Distance-based Fuzzy c-Medoid Clustering with Simulated Annealing algorithm (GDFCMSA).1:procedureGDFCMSA. HENs are widely studied dynamical systems because the complexity of interlinked heat exchangers requires advanced process monitoring and control algorithms. The network topology of the studied HEN is shown in  process , which rFollowing the detailed analysis of the problem with regard to the placement of sensors in the HEN, three other dynamical systems will be analyzed to illustrate the applicability of the methods on larger examples. These benchmark examples are not typical control-relevant problems as they are used to study model-reduction algorithms , but theThe parameters of the resulted case studies are summarized in As random search-based techniques were evaluated, each result was evaluated based on 100 independent runs of the algorithms. The parameters of the algorithms were the same in each scenario: Equation , as can In all the examples, firstly the To evaluate the robustness of the random search-based algorithms, the results of 100 independent runs were visualized in boxplots to show the distribution of the optimized cost functions. As can be seen in It is visible that in all cases both the cost functions and the relative orders of the observed state variables decrease significantly as the number of sensors increases, so a relatively small quantity of additional sensors can cause a noticeable improvement in terms of the relative order compared to the observer designed only to ensure observability. It can also be pointed out that additional sensors  as their positioning is based on a geodesic distance-based fuzzy membership functions. Simulated annealing is applied by both algorithms to minimize the cost function, which in turn minimizes the maximum and average of the relative orders of the system to generate a balanced placement of sensors.Additional sensors are placed into the system based on the CLASA algorithm by the mCLASA algorithm. The placements of additional sensors is further improved by the Geodesic Distance-based Fuzzy A slightly better solution is provided by the GDFCMSA algorithm than the mCLASA method at the expense of some additional computational resources for the evaluation of the membership degrees. Solutions to massive problems are generated by both methods over a short period of time, which illustrates the applicability of these methods in an industrial setting.Although this paper deals with the placement of sensors, the proposed methodology can be applied to control configuration design as well, since this is the dual problem of the design of the output configuration.