De novo protein design is a powerful methodology used to study natural functions in an artificial-protein context. Since its inception, it has been used to reproduce a plethora of reactions and uncover biophysical principles that are often difficult to extract from direct studies of natural proteins. Natural proteins are capable of assuming a variety of different structures and subsequently binding ligands at impressively high levels of both specificity and affinity. Here, we will review recent examples of de novo design studies on binding reactions for small molecules, nucleic acids, and the formation of protein-protein interactions. We will then discuss some new structural advances in the field. Finally, we will discuss some advancements in computational modeling and design approaches and provide an overview of some modern algorithmic tools being used to design these proteins. Binding is one of the most important and fundamental biological processes, underpinning everything from drug interactions to genetic regulation and cytoskeletal formation ,2,3. AltProtein design avoids many of these pitfalls by starting with a well-understood scaffold that has either a modest or no direct relation to natural sequences. Changes are introduced based on chemical principles rather than mimicry, which removes a lot of the potential for mutational side effects that would otherwise bias the results away from the intended chemistry being studied. A simplified approach to protein design is as follows  7]: Fir: Fir7]: There are different approaches to protein design, which vary mainly by how the mutations are introduced. These include directed evolution, redesign of natural proteins and de novo design. In this review we are primarily focusing on de novo protein design, which does not rely on naturally occurring proteins or mimicking sequences from nature, but rather starts from a totally abiological scaffold and introduces mutations based on the chemical principles needed for function and uses either knowledge-based or computational modeling to predict how the protein will fold and reproduce the function of interest. Since the first demonstration of this approach, simple peptides that folded into a four-α-helix bundle ,9, it haThough we are mainly focusing on de novo design we will touch on examples of directed evolution . DirecteIn this review we are looking at the recent progress in protein design efforts towards understanding biological binding, an area very important to fields such as drug design and enzymology. Our primary focus is on papers that have been published in the past three years 2017–2020). In addition to the work of small molecules we will also be looking into DNA binding and the design of protein-protein interfaces. For more in depth perspectives on de novo protein design that both describe the methodology and provide more history and context to the field we suggest the following reviews and perspectives ,19,20,2117–2020. Small molecules are seen throughout biology, from metabolites and cofactors to drugs and therapeutics and protein design has long been interested in understanding these interactions. Here, we will go through some of the recent progress in the field describing binding interactions starting with metal ions, that can be held in place via coordination bonds, and progressing to organic molecules, that must be held in place through non-polar interactions. We will discuss recent work in the field related not just to the binding itself but also to the methods and techniques used to develop the proteins.Metal ions represent the smallest possible ligand that can impart a function. Thorough characterization of natural and model systems have provided many concepts that have been used by protein designers to incorporate transition metals into de novo builds to advance function and obtain new insights into natural proteins. We begin our review on these seemingly simple ligands and the de novo proteins that can bind and use them for catalytic functions.Early work in this field used common metal binding sites grafted into de novo protein interiors ,23, but Recent work has expanded this work to a new series of metals. Paredes et al. formed a protein-titanium complex with the Due Ferri single-chained scaffold (DFsc) . They weRecently, dinuclear Mn clusters to were incorporated into the DF scaffold to develop a model system to study electrochemical Mn reactions . Olsen, Zhang and Lombardi et al. have taken another step closer toward reproducing a full Mn cluster seen in PSII by designing a protein capable of binding a tetra-zinc cluster ,28. TheiIn 2020 Pirro et al. designed a two-domain de novo protein called DFP1 (Due Ferri Porphyrin) which is capable of oxidizing phenol after binding to a synthetic Zn-porphyrin . One of 4S4 cluster inside the core of the bundle. The initial scaffold was selected based on its high tolerance for mutations, so this protein has a lot of potential to reproduce a wide array of redox functions of many natural proteins. This work will be important to expanding the functional library of de novo proteins and provide the ability to finely tune an important functional cofactor with high confidence in the stability of the scaffold.Expanding the work of the DF proteins, Mancini, Nanda, and others developed a protein that bound a larger cubane metallic cluster . They coSelvan and others used rational design to change the function of a different protein scaffold, Cu storage protein (Csp1), into an artificial metalloenzyme (ArM) capable of hydrogenase activity . Their aMutter and coworkers designed a protein that can bind an FeS cluster, a common redox cofactors from biology, and link this metal cluster to electron transfer reactions with other proteins in vivo . They deProtein function is often tied to its ability to change conformations based on the environment. Boyken et al. described a strategy to design pH responsive proteins capable of conformational changes . This stD of 730 µM [Metal-centered cofactors were amongst the earliest molecules bound to de novo proteins owing to their ease of detection and well-described principles for binding. Prevalent among these studies has been Heme B (Fe protoporphyrin IX), which was used to describe a myriad of biological functions in addition to providing the foundation for other small molecule binding designs in the future ,34,35. Of 730 µM . They de2O2 and 120 µM–500 µM H2O2. The detection limit was 4.6 µM H2O2 and quantitation limits for the two datasets were 15.5 µM and 186 µM H2O2. When using HRP as a catalyst the plot shows a nonlinear response in the presaturation phase and was only able to detect hydrogen peroxide in the mM concentration range. The authors stated this assay could be used to monitor the quality of water during and after advanced oxidation processes. Advanced oxidation processes are a viable option for water reclamation for potable reuse.Zambrano et al. provided evidence that a de novo designed miniaturized heme-enzyme, named Mimochrome VI (FeMC6), can be used in hydrogen peroxide assays and even overcame prior natural catalysts in the standard luminol oxidation test . Current3 Zn porphyrin with a 45 nM affinity that remained stably folded at >120 °C and could last for over one year [Non-Fe porphyrins have also been studied with protein design. Polizzi et al. used computational methods to design a 4-helix bundle that tightly bound a tetra-CFone year . This isKodali et al., in their attempts to replicate the light-harvesting abilities of natural systems designed four-helix bundles that bound light-harvesting Zn-porphyrins with nM affinity . BindingThe field of hydrophobic molecules has seen the most advancement as of late as it is arguably the most challenging due to a lack of easy to form ligation bonds clear signals to detect successful binding. Recent progress in this area has led to the creation of a new functions integrating the small molecule ligands.Recently, the Degrado lab has developed a new metric to assist design: the van der Mer , which models the phi and psi angles of the amino acid backbone Cα, and its distance to a chemical groups, rather than atomistically model specific side chain interactions with chemical groups on the target molecule . Their dE. coli where through further testing proved its ability to bind to amantadine. Although the group was not able to create an inducible trimer, they were able to identify two major bottle necks for designing such a system which further research can build upon.The Baker lab has also been influential in designing proteins that can model in very specific hydrogen bonds. Their protein design approach uses Rosetta to design a binding site that locks the ligand into a very specific orientation within the core of the protein. Park et al. used this methodology to create a homo-trimeric protein capable of binding to the small molecule drug amantadine . In 2019E. coli, mammalian, and yeast cells where they were found to fluoresce in vivo.In 2018 Dou et al. successfully created the first de novo designed β-Barrel capable of binding to a small molecule and fluorescing . The twoA significant challenge in the field is discrimination between molecules with similar chemical character, but the need to distinguish one pharmacophore or odorant compound is important in nature and to many chemical industries. Thomas and coworkers have been able to address these issues using a series of designed alpha helical bundle proteins . They inA common theme in many of the protein design papers discussed here is the need for a large pocket that can accommodate many substrates, and to which selectivity can be later programmed in. Certain groups approach this challenge by expanding the number of helices to increase the pocket size, but Caldwell, Haydon, and colleagues chose a different design strategy, they carved out space utilizing a previously designed de novo TIM barrel formed from two dimers; they removed sufficient bulk from buried residues, so a channel was formed . Inside 6 unique sequences. The group characterized variant proteins from the library and demonstrated that many of the variants can withstand different amino acids in its cavity. The protein variants also contained buried polar residues that could be used for catalysis. One of these variants, Syn-F4 was proven to be a catalyst after expressing the sequence in E. coli. Syn-F4 was able to “rescue” another life-sustaining gene from deletion. The authors believe their library can be used to screen and select for novel proteins with different functions not evolved by nature.As an example of how variable libraries can be used to develop cavities and small molecule binding sites, Karas and Hecht developed libraries based on the de novo designed 4-helix bundle protein S-824 . The libBinding of small molecules can also be used to drive further functions. Kang et al. used binding sites to drive chemically induced dimerization . The autIn 2020 Vivek Prakash et al. designed a heterochiral de novo minimal fluorescent protein that can be selectively excited at 342 nm . β- of 90%. These authors believe their design can be used for the creation of implantable devices.In 2020 Ma et al. designed a proton conducting transmembrane protein that exceeded previous proteinaceous systems . The groXu, Baker, and others used protein design to generate a transmembrane pore that could selectively transport K ions and screen out Na or Ca . They usA significant portion of the human proteome encodes for proteins that can bind to nucleic acids. DNA binding proteins (DBP) are responsible for replication, initiation and regulation of transcription, organization, compaction, and modification of DNA . RNA binIn 2020 Inamoto et al. combined rational design with phage-assisted continuous evolution (PACE) to create MEF, a protein that selectively binds to the E-box motif  where the transcription factor Myc binds . UnregulLebar and Jerala have had success with designing peptides that can act as transcription activators . They deAlthough the field of de novo designed proteins capable of binding to nucleic acids is relatively under researched, there have been advances that use other design methods which may offer engineering insights. Walker and Varani discuss their approach in designing peptides capable of binding to RNA, which focused mainly on structure based peptidomimetics . These aProtein-protein interactions (PPIs) have also been used to extend genetic functions in addition to enzymatic ones. Smith, Savery et al. used PPIs to develop an artificial transcription factor, fusing an RNA polymerase recruitment peptide to a separate DNA binding peptide . Both thEdgell and her coauthors have also used de novo designed PPIs to promote formation of higher order structures that bind DNA . Their wPPIs are growing more complex. A deeper understanding of the advances in core packing and the increased structural stability must also be looked into. This work is included here as the advances in the core structure stability will lead to proteins that can adopt more complex shapes, aided by the stability of tighter cores. We will go through a few illustrative examples that have been recently published.In 2019 Catrina Nguyen et al. investigated the contributions of a fully hydrophobic core and hydrophilic surface to a protein’s thermostability . This waIt is understood that hydrophobic core packing plays a significant role in the thermostability of a protein . It is aIn 2020 Banach et al. tested 4 de novo designed proteins each differing in single mutations with the goal of understanding how a single residue change can lead to complete reorganization of a monomeric 3 α ɑ and 4β + ɑ folds . The desm = 75 °C), Super WA20 (Tm = 122 °C) displays a much higher midpoint. The authors state that stable nanoscale pillars of protein nanobuilding blocks and be used to create new types of self-assembling nanostructures.WA20, a de novo designed 4-helix bundle dimer was recently used to make protein nanobuilding blocks which create self-assembling polyhedral and chain like complexes. In 2020 Kimura et al. made design improvements to WA20 by mutating residues that would lead to an increased stability of both the hydrophobic core and helices. These efforts led to the creation of the highly thermostable protein Super WA20. (SUWA) . CompareA key aspect of protein design is the robustness of the scaffolds. To make minute changes and isolate their effect on function the protein must be able to withstand a series of mutations without misfolding. This is just as important for PPIs, and addressed by Edgell, Woolfson and colleagues . Their wPPIs are also being extended past simple dimer interfaces. Chen, DiMaio, and their coauthors developed a PPI set that extends to whole 2-D arrays . StartinProtein-Protein interactions (PPIs) in nature are immensely important, driving a whole host of chemical reactions and other cellular processes . The fieIn 2019 Robert Langan et al. designed LOCKR a tunable and generalizable protein switch . In thisFurther advancements related to de novo designed protein logic gates were made in October of 2020 when Chen et al. described the design of a 2-input AND, OR, NAND, NOR, XNOR, and NOT logic gates and investigated 3-input OR, AND and disjunctive normal form gates . The resAchieving binding reactions are often the first step toward larger functions. In some cases, those are other signaling events, like the LOCKR system, but in nature binding can also be used to signal the need for metabolic activity. Glasgow, Kortemme et al. have taken these reactions to the next step. They created a protein that undergoes enzymatic activity only when it binds a signaling molecule . To achib562 and subjected it to the same experiments observing similar behavior. They conclude that this treatment of proteins can be used to add a form of allostery to any protein by using surface charge to disrupt the folding, and but retrieving function by balancing the charges to rescue the structure.Linking binding reactions to other signaling events is a significant step. This has been shown with the LOCKR system, using other proteins, but the work of Schnatz, Koder and their colleagues apply this to small molecules . Taking Antimicrobial peptides (AMPs) are short peptides that either disrupt biofilm formation or kill the bacteria off entirely. Though not typically included in discussions of protein design we believe they represent valuable examples of using structural data from proteins to create binding interfaces and engineer rudimentary PPIs.Chevalier et al. published a study where they designed and tested 22,660 de novo mini protein binders and 6,286 control sequences . The min2; GH12, GLLWHLLHHLLH-NH2; and GH16, GLLWHLLHHLLHH-NH2. Minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) were found for all three peptides to 8 cariogenic bacterial strains and one monospecies static biofilm. Of the three peptides tested GH12 had the lowest MIC and MBC at a MIC of 4.0–8.0 µg/mL and MBC of 8.0–32.0 µg/mL making it the most promising candidate of group. In vitro GH12 was shown to have antimicrobial activity against cariogenic bacteria and biofilms along with “little toxic effect” on the viability of human gingival fibroblasts.Wang et al. designed three short α-helix containing antimicrobial peptides called GH8, GH12 and GH16 . The seqIn a study published in 2019 by Charles Chen et al. detailed a new simulation-guided rational design approach that can be used for designing de novo AMPs . The teaBoris Vishnepolsky et al. developed a tool for AMP prediction called the special prediction (SP) tool . An algoMore recently the study of PPIs has been used to develop interventions to the SARS-CoV-2 virus (commonly known as COVID-19), the cause of the worldwide pandemic . VariousLinsky and coauthors used a varied approach to create an effective decoy of the Angiotensin converting enzyme (ACE2) seen on the cell surface . Using sLarue and Sharma take a similar approach to combating this virus . They, aCao and colleagues have taken a similar approach of targeting the ACE2 binding interface with the COVID spike protein’s RBD . In thisProtein design has an active role to play in the fight against cancer by inhibiting the pathways that promote tumor metastasis or other metabolic functions. Below are some recent examples of these studies.Ibara and Bartlett recently used a series of computational programs to perform alanine scanning experiments on a designed helix that binds to MCL-1, a membrane spanning protein often seen in cancer cells ,88. AlanJohannes and Hird also sought to inhibit MCL-1 function through protein design . HoweverFurther work from Fletcher and collages details their efforts to build stronger interfaces to disrupt naturally occurring MCL interactions, both as a cancer therapy and a tool to better understand PPIs . This woIn addition to blocking MCL-1 interactions to fight cancer, PPIs have been used to target p53, a protein implicated in approximately 50% of gene mutations identified in tumor cells. Kamagata and coauthors developed a novel way to design proteins that bind to intrinsically disordered regions (IDRs)-commonly occurring pieces of proteins that lack a defined secondary and tertiary structure . Their aRelatedly, Liu, Xing et al. designed a series of small peptide inhibitors to block epidermal growth factor receptor proteins from dimerizing . SimilarInterleukins are a class of glycoproteins capable of regulating immune response. Designing proteins that are capable of binding to interleukin binding sites will potentially lead to therapeutic candidates. In 2019 Silva et al. designed Neoleukin-2/15 a interleukin-2 (IL-2) and interleukin-15 (IL-15) mimic . NeoleukIn 2019 Grisoni et al. used machine learning to generate 1000 de novo designed anticancer peptides . From thPublished in 2019, Junfeng Shi and Joel Schneider de novo designed a peptide, DVP-1P that is unfolded until it interacts with alkaline phosphatase (ALP), an enzyme that is overexpressed on the surface of some cells including cancer cells . When thMany of the PPIs we discuss are in vivo or designed to be active within a live cell. Monitoring sucess in these environments is difficult due to the dense nature and crowding of biological systems. It is important to develop new methodologies to view and validate these interactions alongside the development of PPIs themselves. Although we have gone through many methods in the papers above, we wanted to highlight a small set of new techniques to monitor PPIs.Zhao and Dmchowski have developed a new way of identifying PPIs in vivo with Xe NMR spectroscopy . In thisE. coli and human cells, which will help groups better detect a variety of PPIs in wildly different systems. They are also maximally functional in anaerobic conditions allowing for probing of PPIs in new environments.Yudenko Gushchin and coworkers have also developed a similar assay that will help probe PPIs in nature, and importantly in anaerobic situations . This asAlongside the many advances in functions and binding and packing interactions the complexity of designs themselves have grown. Improved algorithms are needed to calculate the lowest energy conformations and better determine the ideal folding and interactions. In this section we will go through some recent developments in computational programs, and their applications. For a more comprehensive analysis and perspective on computational protein design we recommend the following articles ,99.The Baker lab has taken a general approach to small molecule binding, creating an algorithm that takes advantage of the structural stability, and diversity, of a naturally occurring protein family, NTF-2. Basanta and coauthors developed an enumerative algorithm that first docks a ligand into a protein cavity, then systematically samples a variety of different amino acid combinations in that cavity to find the most ideal binding partner . OverallStrokach, Kim, and colleagues have also taken an interesting algorithmic approach to protein design . Their pSkalic De Fabritiis and coworkers have also developed an algorithm that can better design binding pockets for small molecules . HoweverLucas and Kortemme have developed a new algorithm that combines PDB screened ligand binding sites with the established Rosetta design programs to create a library of ligand binding proteins that can bind arbitrary small molecules . They weIn May 2020 Sesterhenn et al. created a new design algorithm called Topobuilder meant for creating scaffolds for irregular and discontinuous neutralization epitopes . They prIn 2020 Liu et al. investigated if protein structure prediction could be improved by using loop-specific sampling strategy . The strWe have been showing examples exclusively of proteins being designed to bind to a specific small molecule, nucleic acid, or other protein. However, for context we thought we would highlight a couple of articles that show the opposite approach, matching the ligand back to the protein itself.Interestingly, the search for ligand binding proteins does not have to be led from the protein-side, rather the molecule can be designed from the protein structure. Leal and coworkers did that in a recent paper, targeting the envelop protein of dengue fever . ThroughSimilar to the work of Leal and coworkers, the approach of starting with the binding partner can be used in other ways. Liu and coworkers used this bottom up approach to develop a library of DNA binding sites to generate genetic promoters with variable affinities . By placThere have been many impressive recent advances in de novo design, and the field continues to better understand the mechanisms underlying protein folding and binding of small molecules, DNA, or other proteins. All this work will help biochemists reach the ultimate goal of predicting structure and function of de novo proteins from an amino acid sequence, and generate specific binding interactions for any ligand or binding partner. Protein design has made impressive steps in this regard bringing new logic-functions to proteins , and dev


Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as  The function of a protein can be inferred by analyzing its sequence similarity with other proteins of well-characterized functions. In case of significant sequence similarity, the annotation of a protein with a known function is transferred to the protein with an unknown function. These homology-based methods rely on a comparison between two sequences or between a sequence and a profile hidden Markov model (HMM) or between two profile HMMs –3. The mA profile built from multiple sequences is considered to be more sensitive than a single sequence because it incorporates information from multiple sequences. PSI-BLAST  is one sThe conservation patterns in a multiple sequence alignment of protein families arise from fold-specific and function-specific signals shared across the entire family and unique to the subfamily level respectively. Since a profile HMM is a probabilistic representation of the alignment, therefore, an HMM built from such an alignment is biased to generate a large number of false-positive (FP) sequences. Methods that use pre-classified data consisting of positive and negative training sets to modify emission and transition probabilities using the Viterbi algorithm were used to increase the specificity of the HMMs , 8. AlsoHowever, in case of absence of significant sequence similarity where none of these methods can predict a homolog, genome context-based or non-homology based methods could be used . In geneMycobacterium tuberculosis (referred henceforth as M. tb) strain ATCC 25,618 /H37Rv (NCBI Taxonomic ID:83332), in terms of its enzymatic activities. Such a stepwise systematic approach for the implementation of the discussed methods, as shown in Fig. We proposed two different protocols utilizing information from genome context-based methods for the identification of orphan enzymes referred to as the ‘Hole finding protocol’ as well as identification of candidate proteins for the predicted orphan enzyme referred to as ‘Hole filling protocol’. We used the homology-based method for enzyme annotation i.e. ModEnzA  and contM. tb as a case study. As shown in Fig. M. tb  map to 118 different KEGG pathways; the 56 novel enzymes predicted using the ModEnzA map to 43 pathways. These enzymes predicted by the homology-based method were then used to identify additional putative enzymes using the non-homology based method introduced in this study, ‘Hole finding protocol’, and shown in Fig. M. tb of which 76 are putative local holes, 8 choke points, and 3 high confidence local holes which map to 22, 5, and 5 different pathways respectively  were mapped to the enzyme–enzyme dependency graph (EE graph) generated using the bioreaction database [M. tb or it was an incomplete enzyme  it was excluded from further analysis.To identify missing enzymes  in the database . The EE M. tb enzymes had 1650 neighbors for which the in-degree  and out-degree  were identified. Each of these 1650 enzymes has at least one neighbor which is known in M. tb. As a high confidence filter, we only considered a node as a potential hole/missing enzyme when all the immediate neighbors for that node were present in M. tb. It was discovered that there are 87 such enzymes where all the neighbors were known in M. tb. Of these 87, 3 enzymes had all the neighbors  known in M. tb. These are predicted as ‘high confidence local holes’ in M. tb because they show connections with other known M. tb proteins and hence should be present in the pathogen. These are listed in Table M. tb neighbors.We found that the 646 M. tb, and second that both the neighbors are different enzymes, i.e., it should not be a loop in the graph. Out of these 84 enzymes, 8 enzymes had both the neighbors known in M. tb while the remaining 76 enzymes had either in-degree or out-degree all of which are known enzymes in  M. tb. These were also considered to be putative missing enzymes in M. tb and are listed in Additional file The remaining enzymes were further analyzed to identify probable chokepoints. The chokepoints were identified based on two criteria, first, both the neighbors are known in To identify a candidate protein for these missing enzymes, we have developed the ‘Hole filling protocol’ as shown in Fig. E. coli which is a well-characterized model organism with an extensively annotated genome. All the known proteins from E. coli were ‘knocked out’ one protein at a time, and the candidate set along with the knocked out protein sequence was ranked based on the combined score, ‘S’. The known enzymes for E. coli were obtained by scanning the ModEnzA profiles across all the proteins of E. coli. There were a total of 459 metabolic proteins and 3375 non-metabolic proteins in E. coli  numbers and STRING internal identifiers so that we can retrieve the combined score of association for the neighbors extracted from the EE graph, please refer to Additional file We first validated our hole-filling protocol using all the known metabolic proteins in E. coli. The comparison of our protocol with the various context-dependent methods used is summarized in Fig. E. coli proteins using individual methods of prediction like chromosomal clustering, phylogenetic profile, co-expression, protein fusion, and protein interactions and the rank after combining these methods. Their results ranged from 10% of the knocked-out genes falling within the top 10 candidate proteins using protein fusion to about 60% within the top 10 candidates using a combination of all methods. On the other hand, our protocol showed a 25% increase in prediction for the identification of known metabolic proteins within the top 10 candidates, using the combined score of the functional association from STRING and recalculated using Eq. 1 . We compared this result with a similar work which also reports the rank of proteins based on context-based methods of protein function prediction  using 35Saccharomyces cerevisiae, Dictyostelium discoideum, Arabidopsis thaliana, Drosophila melanogaster, Danio rerio, Salmonella enterica, Shigella flexneri, and Vibrio cholerae. Figure Danio rerio genome, all the other genomes showed that more than 50% or higher proteins were ranked in the top 5 ranks  for this function is found to be experimentally verified by one of the research groups [5.4.2.2 (Phosphoglucomtase) which is known to be one of the important enzymes in polysaccharide capsule formation is not among the list of annotated proteins in the ENZYME database for h groups .M. tb. We have found a significant hit for 1.4.3.4 in the M. tb proteome  which is a very good candidate protein for experimental validation.1.4.3.4 (Flavin containing monoamine oxygenase) is a part of amino acid metabolism  and catalyzes the conversion of aminoacetone into methylglyoxal. The same step can be catalyzed by another enzyme 1.4.3.21 (isozyme) and both of these are so far unknown in The manual analysis and literature survey suggests the importance of the new ModEnzA predictions in These results serve to highlight the importance of ModEnzA in identifying or predicting enzymes that could serve as potential candidates for future validation.In general, homology-based methods are the first choice of methods to be used for function assignment, but in the absence of any similarity with the well known/characterized sequences scope of these methods becomes limited. There are still many orthologs whose functions are not well defined, but they are conserved across multiple organisms , 31. TheMoreover, homology-based methods are biased towards exploring only the molecular functions of proteins rather than providing any information about the context of proteins within the cell. It is well established that proteins do not function in an isolated manner but interact with other biomolecules. Therefore, protein–protein interactions and protein networks are indeed very important to understand the function of a protein. As a result, a new class of in-silico methods came into the prominence to bring to light the cellular function of proteins. Unlike the homology-based methods, these methods rely on the context in which proteins are present within the cell and are known as non-homology/genome-context based methods , 36. TheThe non-homology based methods comprise different protocols based on protein–protein interactions. These interactions include both direct i.e. physical interactions or indirectly such as functional associations derived from methods like Gene Neighborhood, Gene Fusion, Co-occurrence, and Co-expression. Also, protein–protein interactions from various experiments, databases, and text mining approaches provide the necessary information. The metabolic neighborhood also provides a context for the co-occurrence of proteins which can be exploited for finding local holes  and global holes or orphan enzymes (which have no known sequence associated with them). Methods based on sequence similarity cannot be applied to predict the function of such orphan enzymes. Genome context-based methods can be utilized for identifying sequences for these missing enzymes. The information about various context-based methods is available through various protein–protein interaction databases like BIND , BioGridPioneering works in the incorporation of non-homology based method to identify functional links include a Bayesian technique to identify missing enzymes in metabolic pathways , identifIn this work, we highlight the importance of non-homology based methods utilizing the metabolic network context, to predict the function of protein when there is no significant sequence similarity. Such methods are of utmost importance to identify orphan enzymes in a metabolic pathway. We have developed methods (a) to identify missing enzymes in a pathway (Hole finding protocol) and (b) to associate protein sequences to the holes from a set of non-metabolic and hypothetical candidate sequences (Hole filling protocol) by using combined functional association scores retrieved from the STRING database. The identification of missing enzymes in a metabolic pathway requires information about the various reactions in that pathway which in turn is useful for the accurate reconstruction of metabolic pathways. There are various resources providing information for these reactions like KEGG, BKM-React , and theA detailed biochemical experimental validation for each of the predictions is beyond the scope of this work. In context-based methods, the metabolic context of an enzyme is confined to its immediate neighbors both from a reaction base graph as well as an association graph derived from the STRING database. The STRING database, in particular, has been used frequently as a tool for inferring missing protein activities in genomes thereby extending or increasing the protein repertoire of organisms including pathogenic organisms , 49. TheMycobacterium [M. tb H37Rv genome used as a case study in this work and this supports our prediction which classifies this enzyme as a missing enzyme/local hole.We have used the combined scores of functional associations from the STRING database and modified them to be used in an automated protocol for predicting missing enzymes. We carried out a mapping of the predicted enzymes, in this study, to KEGG pathway maps. The 3 predicted local holes map to three different pathways; EC 2.3.2.10 is a part of peptidoglycan biosynthesis, EC 2.1.1.152 belongs to Porphyrin and chlorophyll metabolism while EC 3.2.1.37 belongs to Carbon metabolism. Among these, cases like 2.1.1.152  where there are not enough well-characterized sequences, hole finding algorithms can provide contextual information which in turn could be useful for providing functional annotations. Moreover, since 2.1.1.152 is also known to be present in other strains of M. tuberculosis [M. tuberculosis and therefore it is highly likely that the other two enzymes should also be present in the  M. tb. We noticed that the protocol described in this study identifies the rest two enzymes 1.14.13.83 and 2.1.1.152 as missing enzymes (holes) and interestingly predicts 1.14.13.83 as a choke point and 2.1.1.152 as a local hole, and this makes sense as the first step in the conversion of precorrin-3A to precorrin-6A is catalyzed by 1.14.13.83 . The ‘hole filling protocol’ ranked the proteins Rv2064 and RV0511 as the top-ranked candidates for the enzymes 1.14.13.83 and 2.1.1.152, respectively. A more detailed analysis of these proteins tells us that both are unreviewed entries in UniProt with only electronic annotation . Rv2064 is annotated as Precorrin-3B synthase and which matches with the functionality of the enzyme 1.14.13.83, however, since no experimental evidence is available, this protein is not included in the ENZYME database. We compared the only sequence belonging to the EC 1.14.13.83 in the ENZYME database P21637 (COBG_SINSX) to Rv2064 (candidate protein with rank 1) and found that they both share the presence of the same Pfam domain (PF03460: NIR_SIR_ferr) providing additional evidence for the functionality for the candidate protein Rv2064. Similarly, for Rv0511 which is ranked 1 for the function, EC 2.1.1.152, Precorrin-6A synthase, the presence of the same Pfam domain TP_methylase (PF00590) supports the prediction using our method. These annotations, if experimentally validated, could complete the presence of the chain of enzymes required for the conversion of precorrin-3A to precorrin-6A. The prediction also reflects the ability of our method to pick the best possible candidate for a missing enzyme.It has been discussed in the literature that the Cobalamin biosynthesis pathway is likely to be present in rculosis . The fouThe eight predicted chokepoints are mapped to three pathways; 6.3.3.1 (Phosphoribosyl formylglycinamidine cyclo-ligase), 4.1.1.21 (Phosphoribosylaminoimidazole carboxylase) and 3.6.1.40  map to ‘Purine metabolism’; 1.14.13.83 (Precorrin-3B synthase), 5.4.99.61 (Porphyrin isomerase) and 1.3.3.3 (Coproporphyrinogen oxidase) belong to ‘Porphyrin and chlorophyll metabolism’; 3.6.2.2 (Phosphoadenylylsulfatase) is a part of sulfur metabolism, reduction, and fixation while 6.2.1.28 (DHCA-CoA ligase) do not map to any KEGG pathway map using the FROMP program . AvailabM. tb but is directly connected to 6.3.4.18 (5-(carboxyamino) imidazole ribonucleotide synthase) and 5.4.99.18 (5-(carboxyamino) imidazole ribonucleotide mutase)) both of which are known in M. tb. The protocol predicted 4.1.1.21 as a missing enzyme but on a closer look, we found that the reaction catalyzed by this enzyme occurs in vertebrates during purine biosynthesis but in the case of bacteria two other enzymes are required to carry out the same reaction namely EC 6.3.4.18 and EC 5.4.99.18. This suggests that 4.1.1.21 should not be considered as a missing enzyme in M. tb due to the presence of the other two enzymes 6.3.4.18 and 5.4.99.18, and such cases are exceptions to our protocol.The enzyme 4.1.1.21 (Phosphoribosylaminoimidazole carboxylase) is unknown in Overall, these findings indicate the importance of context-based methods to identify enzymatic activities in absence of significant sequence similarity. The predicted candidates from the hole filling approach listed in Table In this work we proposed, Hole finding protocol, for the identification of local holes in an organism and a downstream protocol, Hole filling protocol, for the identification of candidate proteins for the predicted metabolic holes. The Hole filling protocol uses a scoring scheme that integrates the combined functional scores from the STRING database. Our results signify the importance of such automated protocols for accurately annotating and extending the protein repertoire of pathogenic organisms to identify novel candidates for drug targets which could be experimentally verified in the future. We conclude that the use of non-homology based methods complement the homology-based approaches when used together in conjunction and result in a more complete annotation.M. tb proteome.For any given species, a list of known enzymes can be retrieved using the in-house developed method ModEnzA . The enzThe updated Ma-Zeng bioreaction database , which uThe identification of candidate protein sequences for the predicted missing enzymatic activities (holes) is based on the hypothesis that proteins present in similar pathways are often functionally interacting. Therefore, the known proteins which form the neighborhood of an unknown protein in the same pathway should have very high protein–protein association scores with the candidate sequences. These scores can then be used to rank the candidate proteins to select the best probable proteins for the function. The combined functional association scores were retrieved from STRING (v11.0) which gives higher confidence of prediction than the individual scores. This score was used for all the known interactions between neighbors of an unknown protein (missing enzyme) and a set of candidate proteins.Given a pathway hole (unknown protein) in a metabolic pathway, we can identify its known neighborhood from the EE graph. As shown in Fig. A single candidate protein may interact with multiple neighbors, each pairwise interaction contributing an individual score. In this case, we proposed to combine these pairwise scores in a simple expression of individual combined scores of associations:i is an individual combined score of a candidate protein with a neighboring protein of the missing enzyme iterated over ‘i’ pairs. ‘S’ is the final combined score. It may be noted that the same expression is employed by the STRING database to combine individual channel scores to generate the combined score for a pairwise interaction [where seraction .In the ideal case, we expect to get a rank of ‘1' for a hypothetical protein which could be used as the best possible protein for the desired function. A rank of ‘1' means that the corresponding protein shows maximum association with the known neighborhood of the missing enzymes. Therefore, this protein can be tested for the function of the missing enzyme.Additional file 1: Supplementary Figure 1 Schematic representation of the mapping protocol: EC number to STRING internal identifier.Additional file 2: Supplementary File 1 List of 56 novel enzyme prediction in Mycobacterium tuberculosis H37Rv genome using the method ModEnzA [ ModEnzA .Additional file 3: Supplementary File 2 List of 76 putative missing enzymes that have either in-degree or out-degree, all of which are known in Mycobacterium tuberculosis H37Rv genome.Additional file 4: Supplementary Table 1 A table showing the mapping of predicted 87 missing enzymes/holes  to different pathways using the method FROMP [od FROMP .


Protein interaction is an important data type to understand the biological function of proteins involved in the interaction, and helps researchers to deduce the biological nature of unknown proteins from the well-characterized functions of their interaction partners. High-throughput studies, coupled with the aggregation of individual experiments, provides a global ‘snapshot’ of the protein interactions occurring at all levels of biological processes or circumstances. This snapshot of the interaction network, the interactome, is important to understand the overall events up to the level of comparison between species or pathway simulation, or to find new factors yet undefined in the processes, or to add details to the biological processes and pathways.et al. 2018) contains 28,279 physical protein-protein interactions for the roundworm Caenorhabditis elegans. Among these, 1500 protein-protein interactions have been curated by BioGRID as a collaboration with WormBase. Within the data set, 17,990 protein-protein interactions are unique, and 6,079 unique genes are involved in these interactions. In order to visualize the overall interaction map, a network diagram for all the unique interactions was generated by using the ‘Cytoscape’ program, version 3.6.1   and manual verification. Compared to other databases providing C. elegans protein-protein interaction, WormBase now presents the largest data set, which has 1.72-fold more interaction annotations than IMEx  and 4.51-fold more than BioGRID   (Lee l. 2003) . These nl. 2017) . Most siIn WormBase, protein-protein interaction data can be found as a subclass of physical interaction data in the ‘Interactions widget’ on the gene report page. The Interactions widget provides all types of interaction data related to the gene of interest, such as physical, genetic, regulatory, and predicted interactions. All the interaction data are represented together in a graph created with ‘Cytoscape.js’ and a table. In the table, the gene names of interaction partners (bait-target) in the interaction are displayed along with the publication. The interaction details including the detection method are also captured in the summary and the remark field in the Interactions page. Users can query the data by using the search bar on the WormBase front page or download all the available data files from the WormBase FTP site .C. elegans, budding yeast (Saccharomyces cerevisiae), fruit fly (Drosophila melanogaster), zebrafish (Danio rerio), mouse (Mus musculus) and rat (Rattus norvegicus). Integrated views of interaction data from diverse model organisms will be extremely helpful to build interaction databases for species-to-species comparison, and to establish a disease model quickly based on the database. For the most efficient analysis of the interaction data in WormBase, we are now working on developing a new ‘Venn diagram tool’ and integrating the ‘Gene Set Enrichment Analysis tool’ (https://wormbase.org/tools/enrichment/tea/tea.cgi) into the Interactions widget. We will continue to curate other types of macro-molecular interactions including protein-DNA, protein-RNA and RNA-RNA interactions, as well as newly reported protein-protein interaction data to serve our research community.All the interaction data in WormBase will be available soon at the new information resource for multiple model organisms, the Alliance of Genome Resources . This site will integrate all the interaction data from human and from model organisms All the interactions data are available at the WormBase FTP site.


The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low‐throughput studies and large high‐throughput datasets. BioGRID also captures protein post‐translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built‐in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin‐proteasome system, as well as specific disease areas, such as for the SARS‐CoV‐2 virus that causes COVID‐19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens , captures single mutant phenotypes and genetic interactions from published high throughput genome‐wide CRISPR/Cas9‐based genetic screens. BioGRID‐ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads, or via model organism databases and partner meta‐databases.The BioGRID (Biological General Repository for Interaction Datasets,  However, identifying relevant information about gene or protein function with respect to any given process or disease can be a monumental task because of the sheer volume of data contained in the scientific literature and the fact that much of the literature is not open access. Automated extraction of key data elements from publications cannot be easily achieved due to the unstructured free‐form text that comprises most of the biomedical literature. Moreover, relevant data are often only present in nontext elements such as figures, tables, and supplementary information. A fundamental goal of biomedical data curation is to convert text‐, table‐, and figure‐based experimental information from the literature into consistent structured records that can be easily accessed in standardized formats for computational analyses.Saccharomyces cerevisiae.To address these issues, the BioGRID resource was started in 2006 with the focused goal of comprehensively curating all available biological interaction data generated in the budding yeast Due to the vast extent of the human biomedical literature, BioGRID has in part taken a biological process‐ and/or disease‐focused approach in order to build curation depth in critical areas of human biology. These themed curation projects include the ubiquitin proteasome system (UPS), chromatin modification, autophagy, glioblastoma, Fanconi anemia and, most recently the SARS‐CoV‐2 coronavirus that is the causative agent of the COVID‐19 pandemic.In this review, we describe the content and functionality of BioGRID, with an emphasis on the newest developments. We highlight recent interaction curation for the UPS and for the emergent coronaviruses that cause severe acute respiratory syndromes, including SARS‐CoV‐2. We also describe a new extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS), which currently contains over 1,042 annotated CRISPR phenotype screens. The large collection of curated interaction data in BioGRID represents a unified resource for integrative network analyses by computational biologists and enables efficient mining of the biomedical literature by researchers interested in specific genes or proteins.2wiki.thebiogrid.org). High throughput interaction datasets are typically extracted from online supplementary files and converted by curators into a consistent format for upload into BioGRID. The breakdown of protein interactions in BioGRID by the most commonly used experimental methods is shown in Figure All protein and genetic interactions annotated in BioGRID are exclusively derived from expert manual curation of experimental data reported in peer‐reviewed publications. Each experimental result that supports an interaction is assigned by curators to a structured experimental evidence code with an accompanying PubMed identifier. BioGRID uses custom structured vocabularies to describe interaction data, which includes 17 different protein interaction evidence codes  and 11 genetic interaction evidence codes . Detailed curation guidelines for each evidence code method are explained in the BioGRID help wiki  including SGD,BioGRID houses interactions from all major model organisms Figure , Table 1www.ndexbio.org)www.esyn.org),webservice.thebiogrid.org) and BioGRID ORCS REST (orcsws.thebiogrid.org) services provide an open, standardized application programming interface (API) for retrieval of all curated data. These REST services currently allow more than 1,000 different software applications and pipelines to automatically import BioGRID data records. Automated retrieval of BioGRID data is also available through the widely‐used PSICQUIC interface.Several parallel routes enable full access to BioGRID content. For computational biologists interested in a large number of curated interactions, the downloadable file repository holds formatted data files of the most current release as well as archived monthly releases, which are important for future validation or refinement of analytical methods. Downloads can be customized as needed to create complex interaction datasets that may be visualized using tools such as Cytoscape (cytoscape.org),3Regulated protein interactions governed by PTMs underpin virtually all cellular responses to developmental cues, environmental signals, and stress.thebiogrid.org/project/2), which classifies interaction partners as kinases, phosphatases or accessory subunits.Protein kinases and phosphatases have evolved to control cellular responses to myriad different signals, such that protein phosphorylation is the predominant reversible protein modification.In another example of particular interest, BioGRID has annotated approximately 383,700 nonredundant ubiquitin modification sites mapped on more than 54,900 human proteins, as well as 40,000 unique sites mapped to more than 3,900 budding yeast proteins. E3‐substrate relationships that have been demonstrated with in vitro enzymology evidence are also captured as protein interactions in BioGRID using the evidence code biochemical activity with ubiquitination noted as the modification. Within the UPS themed project, sites of ubiquitin modification identify ubiquitinated substrates in particular contexts and can help to establish substrate‐E3 relationships.4As currently more than 19 million publications are retrieved from PubMed with the search term “human,” curation of the entire corpus of literature that contains potential human gene/protein interactions is a wholly impractical task even when narrowed by additional search terms. To partly circumvent this issue, BioGRID has taken a focused project‐based approach to curation of human protein interactions, allowing for curation of manageable collections of high impact data. BioGRID themed projects represent central biological processes with disease relevance such as chromatin modification, autophagy and the UPS, or diseases of interest including glioblastoma, Fanconi Anemia and COVID‐19. These projects begin with the generation of a core gene/protein list by curators, which entails literature review for evidence of protein function, consultation with domain experts, and inspection of relevant annotated functional domains and GO pathways. These gene lists are then used to identify papers containing relevant biological interactions, which are captured in BioGRID using the appropriate interaction evidence codes Table . As simpA significant sustained curation effort underpins the current largest themed project at BioGRID, namely curation of the protein, genetic, and chemical interactions of the UPS. The conjugation of ubiquitin to substrate proteins controls the stability, localization, and/or function of much if not most of the proteome.5www.biorxiv.org) and medRxiv (www.medrxiv.org) prior to peer‐reviewed publication, BioGRID has altered its curation protocols to allow data from preliminary reports. Once preprint data is published in a reviewed journal, the data is re‐curated and replaces the preprint data in BioGRID. To expedite curation of newly identified SARS‐CoV‐2 host protein interactions, an in‐house text analyzer developed by BioGRID has enabled rapid triaging of preprints from nonpeer reviewed sources. Expansion of this project to other related coronaviruses of global health interest, SARS‐CoV and MERS, also allowed for the curation of relevant interactions with viral protein orthologs. As of October 2020, BioGRID has reviewed over 1,300 published papers and preliminary reports resulting in the capture of over 20,000 coronavirus interactions. These interactions are displayed on the relevant BioGRID protein summary pages and through a new COVID‐19 Coronavirus Project page (thebiogrid.org/project/3/covid-19-coronavirus-project.html). For example, interactions of the BRD2 chromatin reader with the SARS‐CoV‐2 Envelope (E) protein and ORF14 proteinThe emergence of the Coronavirus Disease 2019 (COVID‐19) pandemic in early 2020 led to a critical need to identify new therapeutic treatment options. To assist in this urgent drug discovery effort, BioGRID pivoted curation toward the capture of coronavirus‐related interaction data to help identify potential drug targets and lead compounds for drug repurposing. In March 2020, BioGRID began curation of interactions related to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV2), the causal agent of COVID‐19.6BioGRID is one of the few resources that combines manually curated chemical‐protein interaction data with relevant protein and genetic interactions. Database integration of chemical‐target and biomolecular interaction networks can help guide drug repurposing, which is faster and more cost‐effective compared to de novo drug development.www.drugbank.ca),www.chemspider.com), BindingDB (www.bindingdb.org),www.wwPDB.org).To facilitate network‐based approaches to drug discovery, BioGRID has integrated manually curated chemical–protein interactions to allow for the display of small molecule‐target data in relation to protein and genetic interactions. BioGRID chemical interaction records use a minimal unified set of fields that allow interoperability between multiple resources, as well as a standard downloadable format for computational analyses. The minimal records for each small molecule interaction are displayed in a chemical view available on the Result Summary page for each protein or gene. The minimal record structure has been designed to allow for efficient import of data into BioGRID from various other drug interaction resources, including DrugBank  that have been engineered to bridge substrates and E3 ubiquitin ligases and thereby induce the targeted degradation of specific proteins of therapeutic interest. These bifunctional small molecules include PROTACs (protein‐targeting chimeric molecules), HaloPROTACs, and SNIPERs (specific and nongenetic IAP‐dependent protein erasers), as well as molecular glue compounds such as the IMiDs (immunomodulatory imide drugs).As of October 2020, the entire chemical‐protein interaction dataset in BioGRID, including the unique collection of UPS inhibitors and BVLs, comprises over 28,000 chemical‐protein interactions involving approximately 5,300 bioactive compounds curated from more than 9,300 publications  that direct the nuclease to specific sites in the genome have revolutionized genetic screens in human cell lines and other animal model systems.orcs.thebiogrid.org) to house and distribute comprehensive collections of CRISPR screen datasets curated from the literature. Given that CRISPR nuclease formats, sgRNA libraries, experimental methods and scoring algorithms vary substantially from one publication to another, BioGRID curators use a custom record structure called MIACS  to capture salient screen parameters and metadata.53 BioGRID‐ORCS curation is updated on a regular basis and currently includes 1,042 CRISPR screens from 114 distinct publications representing more than 60,000 unique genes across three species  in over 670 cell lines .BioGRID will continue to maintain existing data collections and to improve curation throughput by text‐mining approaches. New themed curation projects underway include Alzheimer's disease,Rose Oughtred: Conceptualization; data curation; methodology; supervision; writing‐original draft; writing‐review and editing. Jennifer Rust: Conceptualization; data curation; methodology; writing‐original draft; writing‐review and editing. Christie Chang: Conceptualization; data curation; methodology; writing‐original draft; writing‐review and editing. Bobby‐Joe Breitkreutz: Conceptualization; methodology; resources; software; visualization; writing‐original draft; writing‐review and editing. Chris Stark: Conceptualization; methodology; resources; software; visualization; writing‐original draft; writing‐review and editing. Andrew Willems: Conceptualization; data curation; methodology; writing‐review and editing. Lorrie Boucher: Conceptualization; data curation; methodology; supervision; writing‐review and editing. Genie Leung: Data curation. Nadine Kolas: Data curation. Frederick Zhang: Data curation. Sonam Dolma: Data curation. Jasmin Coulombe‐Huntington: Methodology. Andrew Chatr‐aryamontri: Conceptualization; data curation; methodology; writing‐review and editing. Kara Dolinski: Conceptualization; funding acquisition; project administration; supervision; writing‐original draft; writing‐review and editing. Mike Tyers: Conceptualization; funding acquisition; project administration; supervision; writing‐original draft; writing‐review and editing.The authors have no conflict of interest to declare.


This provides essential information for drug discovery, both to evaluate targets and design new potential therapeutics.The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a rapidly growing infectious disease, widely spread with high mortality rates. Since the release of the SARS-CoV-2 genome sequence in March 2020, there has been an international focus on developing target-based drug discovery, which also requires knowledge of the 3D structure of the proteome. Where there are no experimentally solved structures, our group has created 3D models with coverage of 97.5% and characterized them using state-of-the-art computational approaches. Models of protomers and oligomers, together with predictions of substrate and allosteric binding sites, protein-ligand docking, SARS-CoV-2 protein interactions with human proteins, impacts of mutations, and mapped solved experimental structures are freely available for download. These are implemented in SARS CoV-2 3D, a comprehensive and user-friendly database, available at  The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first detected in late 2019 in Wuhan . Since then it has spread dramatically, infecting over 40 million people, with over 1 million deaths reported to date and causing major health and economic challenges globally . High geMN908947.3) (https://www.ncbi.nlm.nih.gov/nuccore/MN908947.3)and was shown to be a single strand of 29.9 kb, with a 3′ poly (A) region and 5′-methylguanosine cap. The entry of the virus into the host cell is facilitated by the viral Spike protein, which includes a receptor-binding domain that recognizes the ACE-2 receptor on the surface of the human respiratory epithelial cell. Upon entry of the SARS-CoV-2, a single positive strand of RNA is released into the cytoplasm of the host cell [The full genome of SARS-CoV-2 RNA was released in March 2020 ; these proteins also assemble to form the replication-transcription complex RTC . The secThe SARS-CoV-2 virus, like other coronaviruses, is an obligate pathogen that uses the translation machinery of host cells for viral gene expression. It has been established that the Nsp1 viral protein of SARS-CoV-2 disrupts gene expression by inhibiting translation and promoting mRNA degradation in the host cells . Recent Various viral proteins are being studied as potential drug targets and candidates for vaccine development. Attempts to therapeutically modulate the S glycoprotein include developing antibodies against ACE2 and S protein subunits, and identifying small molecules to abolish the S-ACE2 interaction , 14. Thepro and PLpro) on the other hand are Nsps, which cleave the viral polyproteins pp1a and pp1b, resulting in the formation of 16 Nsps that perform a range of important functions [The viral proteases  , but alsrculosis  and Mabebscessus , which iHere, we describe a novel, extensively annotated SARS-CoV-2 3D database. We focus on structures of all gene products and their higher order assemblies, i.e. homo- and hetero-oligomers, and trans-membrane regions, as well as ligand and metal-ion interactions, with acceptable assessment score. The SARS-CoV-2 3D database provides a web interface that is user-friendly and easily accessible, so that end-users can navigate, inspect and download the 3D structural proteome data, visualize modelled oligomeric complexes, analyze pockets of modelled structures and investigate SARS-CoV-2 human protein interactions, mutations and protein–ligand docking.MN908947.3, were compared to structures in the Protein Data Bank (PDB) [All SARS-CoV-2 modelled structures built using MODELLER  version nk (PDB)  using PSnk (PDB) , which rnk (PDB) , which rnk (PDB) , which unk (PDB) , and thenk (PDB)  was usedhttps://doi.org/10.5281/zenodo.3384945). For example, Nucleoprotein (N) has been solved experimentally in separate PDB entries; PDB ID: 6M3M is a monomer covering residues 49–174, and PDB ID: 6WZQ is a homodimer covering residues 252–364. Initially, we have built the unsolved missing regions between positions 1 and 49 using (PDB ID: 5NP3) as a template and regions between the 174 and 252 using structures from PDB IDs: 6K12, 1F15 as templates. All the regions were assembled to obtain a full protomer, followed by aligning the modelled protomer to the experimentally defined homodimer to obtain the full homodimeric modelled structure. The homodimeric S protein has been modelled in both open and the closed conformational states (https://academic.oup.com/bib).The homo-oligomeric models were built manually and automatically via the ProtCHOIR pipeline .P-value, hydrogen bonds, salt bridges and disulphide bonds. Furthermore, the MolProbity was used as quality assessment to validate the quality of modelled structures. The MolProbity log-weighted single value is a score derived from combination of multiple features such as percentage Ramachandran not-favoured, clashscore and percentage bad side-chain rotamers.After obtaining the final model, side-chain energy minimization implemented in Foldit  was usedWe have not modelled proteins, such as Nsp5 and Nsp16, which have full structural coverage define by X-ray, cryo-EM and NMR experimental approaches. Structures were downloaded from the RCSB PDB, and saved as biological assemblies. We have implemented the Fpocket into our final models in order to identify potential ligands, and allosteric binding sites. The pocket ranking is based on the possibility of binding small drug-like molecules.G) between wild and mutant forms (ΔΔG = ΔG wild − ΔGmutant). PROVEAN [G values, are displayed in the tables corresponding to each protein on the website.In order to understand the impacts of mutations on overall protein stability, where there are experimentally solved protein structures, Nsp3, Nsp5, Nsp9, Nsp12, Nsp13, Nsp15 and S, we utilized mCSM-Stability , mCSM-PP PROVEAN  and I-Mu PROVEAN  are sequ PROVEAN , DeepDDG PROVEAN  and MAES PROVEAN  use the  PROVEAN  was used PROVEAN  utilizes PROVEAN  implemenet al. [A list of high-confidence viral-human protein–protein interactions determined through tandem affinity purification mass spectrometry was obtained from Gordon et al. . UNIPROTet al.  identifiet al. ), resultet al.  via the et al. , and theet al. . The topThe following Nsps and their corresponding Protein Data Bank (PDB) files were screened using the FDA approved drug library of 1930 compounds extracted from the eDrug 3D web-resource .(i) Nsp3 (PL Proteinase)—PDB Id 6XAA.(ii) Nsp5 (3CLPro/Main Protease)—PDB Id 6XMK.(iii) Nsp12 (RNA dependent RNA polymerase in complex with nsp7 and nsp8 cofactors)—PDB Id 7C2K.(iv) Nsp14 (Guanine N7 methyltransferase). At the time of virtual screening, the crystal structure of nsp14 of SARS-CoV-2 was not solved experimentally. Hence, the crystal structure of nsp14 of SARS-CoV-1 virus with PDB Id 5C8S has been used in the virtual screening experiments as this protein has 95% amino acid sequence identity to that of SARS-CoV-2.(v) Nsp15 (Uridylate specific endoribonuclease)—PDB Id: 6XDH.(vi) Nsp16 (2′-O-methyltransferase)—PDB Id: 6WKQ.(vii) S protein subunit 1 PDB Id: 6VSB.The above-mentioned Nsps were selected for virtual screening as most of them were identified as potential drug targets in SARS-CoV-2. The proteins and the ligands were prepared using protein preparation wizard  and LigpThe front-end of the web interface is implemented in HTML5, CSS, Bootstrap 4.5, jQuery and Font Awesome library to add icon functionality. All the tables are stored in PostgreSQL server and Express.js, a web application framework for Node.js used in the back-end to query the stored tables in PostgreSQL. We used the Embedded JavaScript template (EJS) as a template engine that dynamically generates the final HTML. The dynamic sunburst chart was created using the Plotly.js library, and the network graph interaction was created in Data Driven Documents (D3.js) package. To facilitate programmatic access to the SARS-CoV-2 3D database we have constructed a stored model, protein–protein, and PDB tables that have data available through RESTFUL APIs. The data are returned in a JSON object that is easily parsable by other software.pro) Nsp3, with 1945 residues, whereas the smallest is Nsp11 with 13 residues. The modelled structures exist as monomers, or as homo- or hetero-oligomeric complexes. The ligands and cofactors bound to the structures are retrieved from selected experimental structures of homologues. There are 15 proteins where experimentally determined structures have a mean sequence coverage is 81.41%, whereas the coverage for the modelled structures is 97.5%. The mean average of the quality assessment MolProbity score for our models is 2.52 with standard deviation of 0.65, the lowest and highest values are 0.85 and 3.47, respectively.We have built oligomeric models with almost full sequence coverage for the 21 proteins that’s partially solved or do not have structures deposited in the PDB . The lonIn this section, we illustrate some of the modelled structures listed in The Nsp14, conserved throughout the CoV family, plays fundamental roles in the viral replication/transcription complex. Structurally, it has two domains, the N-terminal exoribonuclease (ExoN) and the C-terminal N7 methyltransferase domain. The Nsp14-Nsp10 complex is essential for viral replication and transcription, and disruption of the complex decreases replication fidelity . The fulThe E protein, 75 amino acids long, is one of the smallest transmembrane proteins in SARS-CoV-2. The lack of E protein not only reduces viral loads but also budding of vesicles from the plasma membrane. Structurally, the E protein consists of three regions: N-terminal, transmembrane and C-terminal. The template selected for modelling the homo-pentameric E protein is PDB ID: 5X29, the NMR structure of the SARS-CoV-1 E protein . The modThe M protein, one of the most abundant type III glycoproteins in coronavirus particles, is located in the viral envelope between the S proteins and facilitates the virus budding . KnowledThe Nsp1 plays an essential role in suppressing gene expression of the host cell via association with the ribosome. Nsp1 in SARS-CoV-1 inhibits cellular anti-viral defence mechanisms via partial shutdown of the innate immune system, so facilitating viral replication , 61. NspWe modelled structures for 308 experimentally confirmed viral-human protein–protein interactions. Viral infections can, at one level, be viewed as a perturbation of host–protein interaction networks. Structural modelling of these complexes should lead to improved understanding of how SARS-CoV-2 manipulates and disrupts cellular processes. Furthermore, most antiviral development programs focus on inhibition of viral proteins , resultiwww.drugbank.ca) with a link on the web interface.Top hits, scored by Glide XP  and MM/GSingle nucleotide mutations leading to amino acid changes can have a significant impact on protein structure and function. In order to understand the structural impact of mutations on the viral proteins, we used several tools, such as mCSM-Stability, mCSM-PPI, Provean, Maestro, I-Mutant and DeepDDG, to estimate the change in folding energy between mutant and native forms. We performed a saturation mutagenesis substituting each amino acid in the protein sequence on experimentally validated viral protein structures to gain a comprehensive view of the most stabilizing and destabilizing amino acid substitutions. The mutations that had, on average, the most destabilizing effect on local and global protein stability are reported. Destabilizing mutations in the vicinity of enzyme active sites or protein–ligand/protein interfaces can negatively impact necessary interactions for the viral life cycle. For example, in our analysis, several amino acid positions in different proteins were largely destabilizing regardless of the substituted amino acid. These residues were primarily located internally inside hydrophobic-rich protein domains. The most destabilizing mutated residues for S protein were found buried within the subunit 1 C-terminal and N-terminal receptor binding domains . The most destabilizing mutations for the main protease were predicted near drug binding sites  , while thttps://sars3d.com/. The website has a ‘Navbar’ including the help page, which shows in detail how the database can be accessed programmatically through the RESTFUL API. The first page with a jumbotron identifies four features of the SARS-CoV-2 3D database: oligomeric modelling, binding-site prediction of modelled structures, mutation analysis, and protein–protein and protein-ligand docking. For simplicity, the database can be queried in two ways: through a table on the right that contains the gene identifier, or through a sunburst viewer on the left that contains the gene identifier (The website can be accessed from HTTPS URL: entifier .The results page gives a brief description of the queried gene and links to different modelling pipelines, such as SWISS Model , I-TASSEThe Models/PDB table contains a target model structure that can be viewed in the Mol* viewer and downloaded locally or programmatically through a RESTFUL API. Also, it contains other information such as templates used to build the model, MolProbity score, PDB coverage, model coverage, sequence length, oligomeric states, oligomeric interfaces and model information.GenBank [There are now multiple online databases specific for genomic data, including GenBank  containiGenBank  containiGenBank  containiGenBank  and SCOPGenBank  databaseGenBank  is conceM. tuberculosis database originally established in 2015, and Mabellini for M. abscessus established in 2019. Since building our models, we have noted the databases such as Coronavirus3D [Two 3D structural databases, developed by our group using in-house pipeline tools such as Vivace , are Choavirus3D , which aThe approach described here in the SARS-CoV-2 3D database includes not only domain or protomer structures but also multi-domain structures, homo- and hetero-oligomers, transmembrane proteins, and ligand and cofactor binding for the full SARS-CoV-2 proteome. We generated protomer structures with good sequence coverage and possible oligomeric states from selected templates. All the models in the SARS-CoV-2 3D database have good quality scores and exhibit known folds, therefore providing a reliable basis for multiple purposes, such as protein–ligand docking, protein–protein docking and mutation analysis. We have reviewed all solved experimental structures from PDB, we have mapped protein–protein interactions between SARS-CoV-2 and human proteins, considered protein–ligand docking of proved anti-viral FDA drugs, and predicted the impacts of mutations using a diverse set of tools.Modelling the relative orientations of individual domains in multi-domain gene products remains a challenge in the field of comparative modelling. However, over the past decade, the resolution revolution in cryo-EM has proved able to provide reliable data for multi-domain and multi-component systems. Modelling the interactions of the proteins of SARS-CoV-2 with human proteins through identification of conserved amino acids located on interfacial regions is a possible solution for improving docking protein–protein interaction. We have included several methods to assess the impacts of mutations in order to indicate uncertainties and help identify false-positive predictions. We intend to update new experimentally solved structures from PDB as they become available and address all aspects of new data in future updates of SARS-CoV-2 3D.SARS-CoV-2 3D database, a comprehensive resource for the SARS-CoV-2 proteome, is based on 3D structures, using either experimentally solved structures or through comparative computational modelling based on structures of close homologues. Since the SARS-CoV-2 proteome is relatively small with 25 gene products, we have extended the methodology, used to construct our previously published databases such as Mabellini, and Chopin, to include models of transmembrane proteins, multi-domain proteins, and homo- and hetero-oligomers manually as well as using our recently developed software ProtCHOIR that is designed to build homo-oligomeric assemblies. While structural models for SARS-CoV-2 have been developed by multiple tools such as SWISS MODEL, I-TASSER and AlphaFold, our SAR-CoV-2 3D database is the first resource to our knowledge integrating not only the SARS-CoV-2 proteome model but also protein–protein interactions, mutation analysis, and protein–ligand docking, all in a user-friendly manner.We have built an entirely new website based on Node.js with new functionality such as protein–protein interactions 2D viewer to visualize human SARS-CoV-2 interactions, UniProt viewer for sequence annotations, sunburst to navigate and query the proteome, MolStar viewer to render the 3D structure and a RESTFUL API.The goals of the SARS-CoV-2 3D database are to bring together the most important data that help the drug discovery process by modelling unsolved proteins structures, often as oligomers, exploiting protein–protein interaction data that have recently been published. Furthermore, Fpocket is used to predict the potential ligand binding sites and potential allosteric sites. The SARS-CoV-2 3D database is regularly updated with new mutation data and a range of sequence and structure-based methods are exploited to analyze the impacts of mutations. With the likely future availability of new templates, the model structures will be updated to increase the model accuracy. Efforts are in process to add new annotations such as pathway analysis and to model further protein–protein interactions.Supplementary information is available for this paper.SARS-CoV-2 3D is a comprehensive database for COVID-19 bringing together computational and experimental 3D structures on one platform that provides essential information for drug discovery.SARS-CoV-2 3D database includes protomers, homo-oligomers, hetero-oligomers, and transmembrane modelled proteins, including with ligands and cofactors.SARS-CoV-2 3D includes predictions of allosteric and small ligand binding sites prediction.SARS-CoV-2 3D includes protein–protein interactions between SARS-CoV-2 and human proteins.SARS-CoV-2 3D incorporates protein–ligand docking and saturation mutagenesis.Ali_SARS_CoV-2_supplementry_bbaa404Click here for additional data file.


Quantitative and qualitative analyses of cell protein composition using liquid chromatography/tandem mass spectrometry are now standard techniques in biological and clinical research. However, the quantitative analysis of protein–protein interactions (PPIs) in cells is also important since these interactions are the bases of many processes, such as the cell cycle and signaling pathways. This paper describes the application of Skyline software for the identification and quantification of the biotinylated form of the biotin acceptor peptide (BAP) tag, which is a marker of in vivo PPIs. The tag was used in the Proximity Utilizing Biotinylation (PUB) method, which is based on the co-expression of BAP-X and BirA-Y in mammalian cells, where X or Y are interacting proteins of interest. A high level of biotinylation was detected in the model experiments where X and Y were pluripotency transcription factors Sox2 and Oct4, or heterochromatin protein HP1γ. MRM data processed by Skyline were normalized and recalculated. Ratios of biotinylation levels in experiment versus controls were 86 ± 6 (3 h biotinylation time) and 71 ± 5 (9 h biotinylation time) for BAP-Sox2 + BirA-Oct4 and 32 ± 3 (4 h biotinylation time) for BAP-HP1γ + BirA-HP1γ experiments. Skyline can also be applied for the analysis and identification of PPIs from shotgun proteomics data downloaded from publicly available datasets and repositories. Wide practical application of liquid chromatography in combination with mass spectrometry has been observed recently in proteomics ,2 and meHowever, information on protein composition is not sufficient to fully understand the mechanism of cell function. The quantification of protein–protein interactions (PPIs) in vivo can be a useful extension in research since more than 80% of proteins do not function separately, but rather interact and participate in the formation of stable or transient complexes . These pMethods based on a combination of affinity purification (AP) or tandem affinity purification (TAP) and mass spectrometry (MS/MS) have now become standard for the identification of protein partners ,14,15,16Enzymatically catalyzed proximity labeling is an alternative to immunoprecipitation and biochemical fractionation for the proteomic analysis of macromolecular complexes and protein interaction networks . In thisOn the other hand, the proximity utilizing biotinylation (PUB) method is based on the use of humanized wild-type biotin ligase BirA fusions. The wild-type BirA uses biotin and ATP to generate biotinoyl-AMP ,23. WildBoth methods are based on similar principles; this is the in vivo creation of a permanent covalent mark on one of the proteins of interest or partners interacting with them, which allows us to bypass the limitations imposed by the extraction and purification stages. Ultimately, results will be obtained with much fewer false-positive and false-negative protein identifications compared to traditional methods such as IP-MS/MS or TAP. The result is the facilitation of the bioinformatic part of data analysis.The aim of this work was to use the Skyline program to process the results of experiments on the quantitative analysis of PPI using the proximity utilizing biotinylation (PUB) method.Escherichia coli protein biotin ligase  *\(.*\), Description parse rule > [^] *\(.*\), Taxonomy source—none, Sequence report source—FASTA file, Full-text report source—None, Number of threads—automatic, Use memory mapping?—Yes, Lock to memory?—No.On mascot server Analysis of data, including Building a Spectral Library in Skyline, Configuring Transition Settings, Populating the Skyline Peptide Tree, Importing Raw Data into Skyline and Subsequent Filtering, and data processing and calculation of biotinylation levels are described in In this study, the Skyline program was used for the first time to analyze results obtained by using a proximity utilizing biotinylation method based on expression in mammalian target cells BAP-X and wild-type BirA-Y protein conjugates . Peak areas of biotinylated BAP were used for the estimation of PPI, while peak areas of propionylated BAP on MRM chromatograms were used for the recalculation and normalization of data between different samples. This program allowed for fast processing of raw data, the calculation of peak areas, and provided the output file in CSV format, which is convenient for subsequent analysis on Microsoft Excel.Skyline was also used to analyze data on protein–protein interactions and proximities obtained by using mutant biotin ligase BioID . These rOverall, the Skyline program offers an advantage in that it provides a good graphic representation of data and reduces analysis time. This protocol could be applicable, not only to BAP, but also to other synthetic peptides which are absent in NCBI or SwissProt databases.


Increasingly, consumers are moving towards a more plant-based diet. However, some consumers are avoiding common plant proteins such as soy and gluten due to their potential allergenicity. Therefore, alternative protein sources are being explored as functional ingredients in foods, including pea, chickpea, and other legume proteins. The factors affecting the functional performance of plant proteins are outlined, including cultivars, genotypes, extraction and drying methods, protein level, and preparation methods . Current methods to characterize protein functionality are highlighted, including water and oil holding capacity, protein solubility, emulsifying, foaming, and gelling properties. We propose a series of analytical tests to better predict plant protein performance in foods. Representative applications are discussed to demonstrate how the functional attributes of plant proteins affect the physicochemical properties of plant-based foods. Increasing the protein content of plant protein ingredients enhances their water and oil holding capacity and foaming stability. Industrially produced plant proteins often have lower solubility and worse functionality than laboratory-produced ones due to protein denaturation and aggregation during commercial isolation processes. To better predict the functional performance of plant proteins, it would be useful to use computer modeling approaches, such as quantitative structural activity relationships (QSAR). Many consumers are shifting toward more sustainable, healthy, and ethical diets, for example, flexitarian, vegetarian, and vegan diets . As a reSoybean proteins and wheat gluten are two of the most widely used plant-based ingredients in traditional alternatives to animal products . HoweverTherefore, it is essential to understand the functional properties of these new plant-based proteins and compare them to the more widely used soy and gluten proteins in commercial products and animal-based proteins. The purpose of this paper is to provide an overview of the critical factors impacting the functional properties of plant-based ingredients, with an emphasis on emerging plant proteins such as those isolated from chickpea, pea, lentil, and faba bean. These key factors include cultivars, genotypes, extraction methods, drying methods, protein level, and preparation methods . In addition, current methods to characterize plant protein functionality are highlighted, including water and oil holding capacity, protein solubility, emulsifying, foaming, and gelling properties, and their limitations are discussed. The impact of the total protein content of emerging plant proteins on their functional performance is also discussed to provide insights into the most suitable ingredient forms to use in different commercial applications. Additionally, we discuss a series of analytical tests to better predict plant protein performance in foods.By reviewing the available literature, most previous studies have focused on characterizing the functional performance of individual plant proteins, using analytical tools that vary across studies. As a result, it is often difficult to compare the performance of proteins from different sources directly. For this reason, we critically focus on the most common methods currently used to test the functional properties of plant proteins and highlight the ones we believe are most suitable for providing quantitative information that can be compared between studies. Studies highlighting the properties of different legume proteins published from 2007 to 2021 are included. Also, several older articles are included in this review as they propose the original methods to evaluate the functional properties. Articles were searched from Web of Science and Google Scholar using keywords: “pulse protein”, “plant protein”, “plant-based foods”, “legume”, “pea”, “faba bean”, “chickpea”, “lentil”, “soy”, “protein isolate”, “functional properties”, and “extraction methods”, “drying methods”, “gelling properties”, “protein solubility”, “emulsifying properties”, “foaming properties”. Two authors independently searched and selected the papers included in the manuscript. The selection of included articles was done according to the titles and abstracts. Only articles available in English were included.Many kinds of plant proteins are available as functional ingredients in foods, including those derived from cereals, legumes, oilseeds, and algae . Among tMost plant proteins are composed of albumin and globulin fractions . DiffereFor lentil proteins, the water-soluble fraction reported significantly varied results among cultivars, with red lentil Fırat and green lentil Pul II having the highest contents of around 70 g/100 g . MoreoveFor chickpea proteins, many researchers have compared the functional performance of those derived from the Kabuli and Desi cultivars. Within the Kabuli type, different cultivars exhibit differences in their water absorption capacity and foaming properties. For instance, Sarı-98 chickpea protein reported a higher water absorption capacity (23%) and foaming capacity (18%) than other cultivars . In a reIsoelectric points of isolates from different pea protein report similar results being in the range of pH 4.6 to 4.9 . Their wMoreover, this study suggests a synergistic effect of extraction method and cultivar type on the functional performance of pea proteins, including their water holding capacity, foaming capacity, foaming stability, and emulsifying properties. For example, CDC Meadow isolates had a relatively high-water holding capacity when extracted by salt extraction, but a low one when extracted by micellar precipitation. This demonstrates the importance of optimizing both cultivar-type and extraction method to obtain good functional performance in plant protein ingredients.Vicia faba L. genotypes were reported to have similar functional properties )Ingredient functionality often varies from batch to batch due to changes in biological origin, isolation conditions, or processing conditions. Often, the proteins are denatured and/or aggregated during the production process, which negatively impacts their performance and reduces consumer acceptance. Moreover, plant protein ingredients may contain various kinds of impurities that impact their performance in an unknown way. In the future, it will therefore be important to develop more consistent and gentle processing operations to produce high-quality plant protein ingredients for specific applications. Another factor holding back the development of plant proteins is the lack of standardized methods to characterize their functional properties. Different researchers use different analytical methods, which makes direct comparison of the results on plant proteins challenging. In the future, it will therefore be important to develop a suite of standardized methods that all researchers can use to compare plant proteins under similar conditions. This will enable the best plant protein candidates to be selected for particular applications. Finally, more research is required to establish a better understanding of the relationship between the molecular and functional attributes of plant proteins, as this would allow food manufacturers to identify and select proteins that can produce the functionality required in a particular application. Important advances are being made in this area using modern computational methods. Finally, any plant-based food product must have the desirable sensorial and nutritional properties, which requires the utilization of appropriate sensory methods and an understanding of their gastrointestinal fate.