The Continuous Automated Model EvaluatiOn (CAMEO) platform complements the biennial CASP experiment by conducting fully automated blind evaluations of three‐dimensional protein prediction servers based on the weekly prerelease of sequences of those structures, which are going to be published in the upcoming release of the Protein Data Bank. While in CASP14, significant success was observed in predicting the structures of individual protein chains with high accuracy, significant challenges remain in correctly predicting the structures of complexes. By implementing fully automated evaluation of predictions for protein–protein complexes, as well as for proteins in complex with ligands, peptides, nucleic acids, or proteins containing noncanonical amino acid residues, CAMEO will assist new developments in those challenging areas of active research. One method (AlphaFold2) was able to generate highly accurate predictions even for the most challenging de novo targets. Beyond the CASP community, this breakthrough has implications for the entire field of structural biology: accurately predicting the structure of a single protein chain has never been closer to being considered a solved problem. But far from being the end of structure prediction, this might instead be the beginning of a new era in the 3D modeling of biomolecular structures. Areas that have been limited so far due to the inability to produce sufficiently accurate de novo protein models in the first place, such as the prediction of protein–ligand interactions, large macromolecular complexes and assemblies, or variant effects, might now be within reach of the next generation of structural prediction methods. Independent blind assessment of these techniques will be more than ever required in order to support the development of reliable and reproducible methods. In order to assist the community to tackle those challenges, we are introducing an extension of Continuous Automated Model EvaluatiOn  predict the stoichiometry of the complex; (2) predict the 3D structure of all the components: proteins, peptides, DNA, RNA and ligands, including their orientation and interfaces; and (3) provide per‐residue confidence estimates of the model. This CAMEO category is based on an opt‐in model: participants only receive the target type(s) their method is able to model. This means that a method that only predicts single protein chains can still participate and will receive the targets composed of only one protein sequence, which can be either monomers or homo‐oligomers, while another method by the same group might be designed to predict, for example, complexes of proteins with drug‐like small molecules.In this article, we describe the different types of prediction targets that CAMEO enables in the new category, and estimate the number of expected validation targets for each category based on PDB statistics observed in 2020. One major challenge will be the scoring of the new type of predictions with regard to the actual experimental structures. Wherever appropriate, we comment on scores that are foreseen to be applied to the various prediction types. We are welcoming feedback from the community regarding complementary scoring approaches.22.1http://www.wwpdb.org/files/). Structures containing sequences with unknown residues, starting with caps, or whose type  could not be assigned unambiguously were discarded. Within a prerelease week, amino acid sequences of 30 amino acid residues or longer (“protein”) were clustered with CD‐HITThe prerelease sequences of polymer entities as well as InChI code of nonpolymer ligands were downloaded every Saturday from the PDB2.2Target protein sequences were submitted to two template searches. First, a BLAST+ v. 2.2.31Templates found by BLAST, HHblits and lookup on single chains were aggregated into complexes. A structure was considered to be a template if all the chains of the target structure could be uniquely mapped to the chains of the template structure, and the template structure did not contain any extra polymer chain.2.3Single‐chain predictions were evaluated against the reference structure with the lDDT scoreHomo‐ and hetero‐oligomeric predictions were evaluated with the oligo‐lDDT and QS‐score2.4Based on the “model‐1” prediction results of all public servers, targets were classified as “hard” if the average lDDT was smaller than 0.5, “easy” if the average lDDT was 0.75 or higher, and “medium” otherwise, as described elsewhere.2.5The first models (model‐1) from public servers were harvested approximately 24–30 h after the submission of sequences to 3D servers. ROC AUC, partial ROC AUC (0.0–0.2 FPR), PR AUC, and partial PR AUC (0.8–1.0 TPR) were calculated with an lDDT threshold of 0.6, as described elsewhere.2.6Functional domain annotation was extracted from CATH2.7Structural figures were generated with the Mol* Viewer.33.1Since 2012, CAMEO has been leveraging the prerelease of structures to be published in the upcoming release of the PDB to conduct weekly, blind, fully automated benchmarking experiments. Every Saturday, we download the prerelease data, which contains the sequences of polymer entities, as well as InChI codes of nonpolymer entities contained in the PDB structures to be released on the following Wednesday. We select a set of 20 interesting protein‐modeling targets that are submitted to registered participants, who have 4 days to predict the 3D structure of those targets. We collect those predictions and, upon release of the structures by the PDB on Wednesdays, compare the predictions with the experimental ground truth.The CAMEO evaluation provides a wide variety of scores measuring different aspects of protein structure prediction accuracy, and accordingly does not establish a single unique ranking between the methods. However, some of the scores are featured more prominently on the web site, as we consider them more useful estimations of the model quality. The focus of CAMEO has always been on all‐atom scores to capture the ability of participants to accurately model proteins including biologically relevant protein side chain conformations. In addition, as CAMEO is a fully automated workflow without human intervention, we have been focusing on superposition‐free scores which alleviate the need to manually split proteins into evaluation unitsSince 2016, CAMEOIn 2020, we performed 52 prediction rounds and provided targets to 15 public modeling servers (from nine groups) and 25 development servers . After filtering problematic targets of low or uncertain quality, or targets causing technical issues to scoring tools for formatting reasons, we evaluated and scored 812 targets, 453 of which were oligomeric. Table 3.2Every Sunday, approximately 24–30 h into the evaluation cycle, we collect models that have been already returned by public 3D participating servers. We submitted these models as prediction targets for quality estimation. Throughout 2020, we, hence, collected 8594 models. Results of the evaluation of public servers are shown in Table 3.3The new version of CAMEO extends the scope of the assessment to structures and complexes. Instead of considering every protein sequence separately, a prediction target is now defined as a complete experimental structure with all the chemical entities it contains. In the case of monomeric and homo‐oligomeric protein entries, this would be identical to the current CAMEO‐3D targets and contain only one unique protein sequence. However, for hetero‐oligomeric targets, evaluation is only performed in the context of the whole complex, and no longer as individual isolated protein chains taken out of context. Methods registered to receive hetero‐oligomeric complexes as targets thus receive all sequences of the proteins that form a complex, and are expected to predict the oligomeric structure of the complex. All participating methods receive the sequences of monomeric or homo‐oligomeric targets. This allows establishing a common baseline where all participating servers can be compared with each other on a subset of common targets.In order to select interesting targets for this category, we search for the presence of homologous complexes Figure . CloselyLooking at the data we collected in the 52 prerelease weeks of 2020, 3158 interesting protein structures where no closely related homolog could be found with BLAST were released by the PDB. Among those, 1017 were monomers, 1011 homo‐oligomeric complexes (which cannot be distinguished from monomers from the sequence‐only prerelease data) and 1130 were hetero‐oligomers ; and 113 complexes could similarly be identified with BLAST. These 353 “novel complex” targets are of particular interest, as an accurate prediction would have to successfully predict the assembly mode of the complex, and accurately model the (unknown) interfaces, therefore going beyond the classical reach of homology modeling. Finally for the remaining 212 complexes, no template could be identified by HHblits for at least one of the target entities.HHblits was able to identify homologs in the vast majority (1734) of the 2028 monomeric (1017) or homo‐oligomeric (1011) interesting protein structures contained in the CAMEO target set. We note, however, that 43 of the targets could only be mapped to templates in complex with a different partner. The interfaces are likely to differ from the templates, and therefore we consider these targets as interesting modeling targets for CAMEO. Finally, HHblits was unable to identify a template for 294 of these targets.In order to evaluate the predictions, we are using the same scores as for the homo‐oligomers: oligo‐lDDT, QS‐score, and TM‐score. In addition, other single‐chain scores can be generalized to evaluate heteromers in the same fashion as the oligo‐lDDT score is a generalization of the lDDT score to oligomers. Figure It should be noted that the selection of interesting protein target structures is performed regardless of ligand contents, but nonpolymer ligands are submitted nonetheless to participating servers that support it. Seventy‐six percent of the structures released by the PDB in 2020, and 65% of the interesting protein structures selected in this category, contain at least one ligand. In addition, we are considering specifically selecting interesting ligand modeling targets, which we describe in the following section.3.4Small chemical compounds, which are not part of a polymer chain, are provided as InChI codes and PDB chemical components in the prerelease of the PDB. They are included in the target definition together with the polymer entities for participating servers that support predicting small chemical compounds in complex with proteins. Consequently, in addition to predicting the correct protein structure, predictors are challenged to include the ligands in their models at the correct binding site in an accurate conformation.However, predicting the exact pose of a ligand within a theoretical model remains a challenge which is out of reach for most current protein prediction servers. To specifically facilitate the development of such methods, these should be evaluated separately to the prediction of protein complexes. Therefore, we are proposing a specialized CAMEO category, where easy protein modeling targets (as per the opposite of the definition in the previous section) are selected if they contain novel ligands that have not been seen in a template.We analyzed the feasibility of this approach on the current data in the PDB. In 2020, we observed 4755 protein targets that would be trivial to solve with comparative modeling but included a combination of nonpolymer ligands never seen before in a template for those structures. Furthermore, 4398 of them contained only homo‐oligomeric or monomeric targets, which would enable many current protein structure prediction servers to participate without having to implement new modeling approaches for protein complexes.Interestingly, 3491 of these 4755 structures contained a known drug from DrugBankTo score these predictions, we will first follow the procedure developed by other ligand benchmarking efforts such as CELPP3.5Accurately predicting the structures of short proteins or peptides has always been challenging for comparative modeling. As a consequence, many protein prediction servers have limits on the minimal length of protein sequences that they attempt to predict. CAMEO has so far taken a conservative approach and submitted targets containing at least 30 amino acids to the participants. In the future, participants will be able opt‐in to also receive peptides with less than 30 residues as targets. These targets are relevant in several areas of research such as host‐pathogen interactions.In order to identify interesting novel targets, we considered a conservative cutoff of 100% sequence identity to a template. In 2020, the PDB released 536 novel structures containing at least one amino acid sequence of less than 30 residues. In 453 structures, such peptides were in complex with a protein or DNA/RNA, making those structures suitable for instance for peptide‐protein docking methods. Eighty‐three structures contained only peptides, either in monomeric, homo‐oligomeric, or hetero‐oligomeric forms, mainly with NMR (Table The interface (QS‐score) and complex (oligo‐lDDT) scores can be used to score protein‐peptide complexes. However, additional scores like those used in the CAPRI experiment,3.6Although several standalone approaches have been developed,Considering a conservative cutoff of 100% sequence identity with previously known structures to identify interesting novel targets, 323 new structures containing RNA were released by the PDB in 2020, and 390 containing DNA. In most of these structures, nucleic acids were in complex with proteins. Just 42 contained only RNA, and 57 only DNA  acid residues which are not part of the 20  standard residues. Traditionally for modeling purposes, the target sequences are canonicalized, that is modified residues are represented by their “parent” or closest canonical amino acid residue. However, this may result in suboptimal models which would not accurately represent the region containing the modification. Posttranslational modifications such as phosphorylations can result in significant conformational changes of the protein structure, which would be impossible to correctly model without knowledge of the modification.As this information is available at the time of prerelease, CAMEO can provide sequences containing noncanonical residues on an opt‐in basis Figure . In thisIn 2020, 444 of the 4323 protein, DNA, RNA, and mixed structures and complexes that we observed contained noncanonical residues Table . We obse3.9https://beta.cameo3d.org/ and is open for registrations. It has been providing targets containing proteins, DNA and RNA to registered servers on a weekly basis since October 2020. Participants can currently choose to receive the nonpolymer ligands contained in these targets as InChI codes or PDB component IDs, as well as noncanonicalized sequences including modified residues. Predictions can be returned in PDB or mmCIF format, and are assessed with a fully automated pipeline including the oligo‐lDDT and QS‐scores. A weekly download of models, reference structures, and assessment results are made available for offline analysis.At the time of writing, the CAMEO “Structures & Complexes” functionality is available as a beta version at Our next steps will be to refine the target selection process, especially with respect to selecting relevant ligand targets as described in the previous sections. We are exploring ways to increase the diversity of the target selection, while ensuring that as many participants as possible receive a common subset of targets in order to make comparisons between servers possible for some aspects of the evaluation. We aim to improve the scoring by providing more diverse scores as described in the previous sections. Most groups developing novel methods have implemented their own scoring workflows locally. We therefore consider at this point the raw data downloads of the prediction results as a crucial service to the community developing specialized prediction methods as it allows including independent blind prediction data in publications describing the new method.4With the extension of CAMEO to the fully automated assessment of prediction of complexes , we aim to encourage and facilitate the development of automated structure prediction servers going beyond the modeling of single chains of amino acids. In this article, we identified several challenging aspects of modeling which we believe will become more active areas of research in the future, and that are suitable for benchmarking with CAMEO. By assessing prediction targets with the same complexity as experimental structures using an “opt in” mechanism for the diverse modeling tasks, CAMEO will assist the development of new methods tackling these specific modeling challenges. As demonstrated by analyzing the PDB releases of the last year, CAMEO will be able to provide a diverse set of challenging blind prediction targets to enable the community to tackle next generation modeling challenges.We welcome feedback from the community on which of these aspects should be prioritized and how various predictions should be numerically evaluated in CAMEO. We encourage methods developers to register to the beta CAMEO server to help testing and evolving these new features according to the needs of the prediction community.https://publons.com/publon/10.1002/prot.26213.The peer review history for this article is available at Table S1 3D servers comparison for “hard” (182), “medium” (427), “easy” (203) and oligomeric (453) targetsᵃ in the 2020 time frame.Click here for additional data file.Table S2 QE servers comparison for all targets (8594)ᵃ in the 2020 time frame.Click here for additional data file.


Cyanovirin-N was observed to interact with the crucial residues involved in the attachment of the human ACE2 receptor. Analysis of the binding affinities calculated employing the molecular mechanics-Poisson–Boltzmann surface area (MM-PBSA) approach revealed that all forms of energy, except the polar solvation energy, favourably contributed to the interactions of cyanovirin-N with the viral proteins. With particular emphasis on cyanovirin-N, the current work presents evidence for the potential inhibition of SARS-CoV-2 by cyanobacterial proteins, and offers the opportunity for in vitro and in vivo experiments to deploy the cyanobacterial proteins as valuable therapeutics against COVID-19.The emergence of COVID-19 continues to pose severe threats to global public health. The pandemic has infected over 171 million people and claimed more than 3.5 million lives to date. We investigated the binding potential of antiviral cyanobacterial proteins including cyanovirin-N, scytovirin and phycocyanin with fundamental proteins involved in attachment and replication of SARS-CoV-2. Cyanovirin-N displayed the highest binding energy scores  with the spike protein, the main protease (M The genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), associated with the COVID-19 pandemic, encodes several structural and non-structural proteins that play pivotal roles in host attachment, infection and replication . Host prpro) and the main protease , the process through which Nsp1-16 are released . Fu. Fupro w protein . Active  protein . In addipro has been reported to be associated with essential mechanisms involved in processing and cleavage of viral polyproteins that facilitate the spread of the virus [pro is now well-equipped to cleave the ubiquitinlike interferon-stimulated gene 15 protein [pro. The binding energy scores have been depicted in pro. Cyanovirin-N displayed the highest binding energy score and was noted to interact with the active site residues Phe69, His73, Thr74, His89, Lys92, Asn156, Lys157, Thr158, Glu161, Asp164, Arg166, Gln174, His175, Leu199, Gln203, Met208 and Gln269 of the SARS-CoV-2 PLpro  binding energy scores than the peptide inhibitors VIR250  and VIR251   . The prepro and PLpro proteins among the cyanobacterial proteins (pro and cyanovirin-N-SARS-CoV-2 PLpro complexes were chosen for molecular dynamics (MD) simulations for a timescale of 120 ns to validate the results of molecular docking and assess the conformational stability of the complexes. A thorough RMSD analysis along the timescale of 120 ns revealed that the complexes were conformationally stable. A detailed RMSD analysis of the cyanovirin-N-SARS-CoV-2 spike complex revealed fluctuations in RMSD values of Cα atoms until 108 ns and attained stability thereafter  of the residues of a protein–protein complex is instrumental in exploring the local changes in a protein chain and assessing its stability on binding another protein ,49. A deproteins B, thus sectively B. The peectively  were relectively B throughpro and cyanovirin-N-SARS-CoV-2 PLpro complexes revealed that they did not experience any change in the number of H-bonds prior to and after the MD simulations of 120 ns. Though the complexes experienced fluctuations during the initial phases of MD simulations and displayed a minimum of 2 H-bonds each, there was a gradual increase in the number of H-bonds  impart stability to a protein–protein complex ,50. The  H-bonds A–C. The ectively A–C at thpro and cyanovirin-N-SARS-CoV-2 PLpro complexes were selected for further estimation of the free energies of binding using the molecular mechanics-Poisson–Boltzmann surface area (MM-PBSA) method. It has been suggested that MM-PBSA provides accurate estimates of free energies of binding of protein–protein complexes and a more negative value indicates stronger binding [pro and cyanovirin-N-SARS-CoV-2 PLpro complexes displayed final binding energy scores of −84.86 ± 1.08 kcal/mol, −62.13 ± 1.14 kcal/mol and −67.69 ± 1.10 kcal/mol, respectively  and PLpro  proteins of SARS-CoV-2 were downloaded from the PDB. The cyanobacterial proteins of interest were also retrieved from the PDB and these included cyanovirin-N , scytovirin  and phycocyanin . The respective viral and cyanobacterial protein structures were prepared for docking analysis by removing the associated water and ligand molecules. Polar hydrogen atoms and Kollman charges were added to the structures using AutoDock tools. The process of energy minimization was achieved using Gromos 96 force field. The refined protein structures were subjected to molecular docking using the GRAMMX server [The high-resolution X-ray diffraction crystal structures of the spike protein , MX server  in lightX server ,32,53,54X server . The comX server  were useX server .Molecular dynamics (MD) simulation is an essential step in validating the results of molecular docking and helps to accurately assess the potential stability of protein–ligand/protein–protein complexes . AntivirAssessment of molecular mechanics-Poisson–Boltzmann surface area (MM-PBSA)-based free energies of binding of protein–ligand/protein–protein complexes in combination with MD simulations provides an apt estimate of the conformational stabilities of protein–ligand/protein–protein complexes . MM-PBSApro and PLpro proteins. Present research findings offer ample scopes to further exploit the potential of these antiviral cyanobacterial proteins as successful inhibitors of SARS-CoV-2 and bolster the global efforts towards the development of novel effective therapeutics against COVID-19.The COVID-19 pandemic, with its rapid transmission and infectivity, has been a global calamity. The current work investigated the binding prospects of the known antiviral proteins from cyanobacteria with the crucial proteins of SARS-CoV-2. Interestingly, all the cyanobacterial proteins, namely, cyanovirin-N, scytovirin and phycocyanin, employed in the present study, displayed high binding energy scores with the SARS-CoV-2 proteins. Robust molecular dynamics simulations and a thorough analysis of the binding free energies based on MM-PBSA method established cyanovirin-N as the most promising inhibitor against the imperative SARS-CoV-2 spike, M


Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena. Obtaining accurate descriptions of protein structures is a fundamental step toward understanding the underpinning of biology. Though there has been continuous improvement in the experimental approaches  spectroscopy, Cryogenic Electron Microscopy (Cryo-EM), and others) for determining protein structures, the gap between the number of protein sequences and known structures is ever increasing. As of March 2021, the total number of protein structures deposited in Protein Data Bank (PDB) [On the other hand, Deep Learning (DL) is becoming one of the mainstream technologies for various scientific application domains, including computer vision , naturalThe Critical Assessment of protein Structure Prediction (CASP) assesses the state of the art in modeling protein structure from amino acid sequence. The first CASP competition, CASP1, was held in 1994. Fast forward, around the time of the CASP12 competition in 2016, contact prediction emerged as the key intermediate step toward accurate structure prediction. For the first time, a deep learning-based method, Raptor-X , achieveAfter the CASP13 competition, some groups continued the distogram prediction efforts. Inspired by the success of AlphaFold  but frusAfter the CASP13 competition, it was also evident that evolutionary information captured in multiple sequence alignments (MSAs) was the most important input for structure prediction, and all research groups have their own in-house methods for generating MSAs. Unfortunately, the problem of generating high-quality alignments, particularly for difficult sequences, stands as a huge challenge. As an effort to build a single pipeline for generating high-quality and deep alignments and also to find remotely homologous sequences in the case of new (difficult) sequences, Zhang’s group developed and released DeepMSA .There have been significant progresses in the field of protein structure prediction, especially those related to free modeling methods that generate structure models without homologous templates. Refer to some excellent reviews ,22 for t1D structures of a protein are residue-wise quantities or symbols onto which some features of the native 3D structure are projected. Some examples of 1D structures are secondary structure assignment, solvent accessibility, etc. The 2D structures of a protein are contact maps and distance maps. There have also been recent review articles highlighting the DL methods in protein structure prediction ,24. TorrIn this article, we will highlight the recent developments in the application of Deep Learning for 3D protein structure prediction with a focus on various steps of the protein structure prediction pipeline. For template-free structure prediction approaches, the important steps are as follows: (i) identification of sequence homologs and generation of multiple sequence alignment, (ii) residue–residue contact prediction and/or residue–distance prediction, (iii) iterative fragment assembly simulations guided by potential or ab initio 3D modeling driven by contacts/distances, and (iv) atomic-level structure refinement and ranking or Quality Assessment of models ,25 as shIn addition, recent advances in microscopy as well as algorithms for image processing have helped Cryo-EM become one of the most widely used techniques for the determination of protein structures and complexes. In this review, we highlight DL-based advances in each step of the protein structure prediction pipeline viz. advances in MSA generation, contact map prediction, protein residue–distance prediction, potentials to guide iterative fragment assembly, models, or quality assessment (QA), advances in overall protein prediction pipelines, and advances in Cryo-EM based protein structure determination and the future outlook for the protein structure prediction field.In this section, we highlight DL-based advances in various steps  of the pThe generation of multiple sequence alignment (MSA) for a query protein is the first step in the majority of protein structure prediction pipelines ,26,27,30Next, we highlight DL-based advances in protein contact map prediction.β-Cβ distance < 8 Å). Residue pairs in contact capture the possible physical interactions between the two residues. A true contact map for a protein with known structure is a 2D map of size “L × L” with 1 s at the locations where the distance is less than or equal to 8 Å and is 0s at all other cells. Here, L is the length of the corresponding protein sequence. Hence, for a protein sequence, predicting a protein contact map is predicting the contact probability for every possible inter-residue pair. From the perspective of reconstruction (building 3D models), not all contacts are equally useful. One technique to classify contacts is to group them into four categories based on how far apart the two residues are in the protein sequence: local (separated by less than six residues in the sequence), short-range (separated by six to 11), medium-range (separated by 12 to 23 residues), and long-range (24+ sequence separation). While local contacts capture secondary structure information, medium-range and long-range contacts are found to be useful for recovering the fold/shape accurately. Hence, the main focus in contact prediction is to predict medium-range and long-range contacts as accurately as possible. In addition, an entire contact map is not required for reconstructing a 3D structure [A pair of amino acids is said to be in contact if the distance between their carbon-beta atoms  is less than or equal to 8 Å  has become the key direction to advance protein structure prediction since the CASP11 experiment. Most recently, Deep Learning has revolutionized the technology for contact and distance prediction since its debut in the 2012 CASP10 experiment.Protein contact map prediction has been deemed to be useful in predicting protein structure for more than a quarter of a century . ProteinOne of the earliest DL-based approaches for protein contact map prediction is DNcon  developeAfter that, as contact map established itself as an intermediate step in most of the successful protein prediction pipelines, a lot of advances have been made in contact prediction using Deep Learning-based approaches. To summarize, some of the earlier Deep Learning-based contact map predictors are DeepCDPred , RaptorXRaptorX-Contact  is a conZhang’s group developed a deep residual convolutional neural network-based approach called ResPre  to prediYang’s group developed MapPred , which uFor each query sequence, MapPred first generates an MSA and then feeds the covariance features derived from MSA into a deep residual neural network. Essentially, a contact map using CCMPred  and DeepDEEPCON  discusseFukuda et al. developed DeepECA , a ConvoKihara’s group recently developed a Generative Adversarial Network (GAN)  based apAs ContactGAN is a contact map refining tool, the input is a predicted contact map (rather than an MSA) and the output is a refined contact map. Essentially, the DL architecture of ContactGAN uses a GAN framework, where generative and discriminative networks are trained with sets of predicted (noisy) and corresponding native (correct) contact maps. The generator network takes a noisy predicted contact map and outputs a refined map, whereas the discriminator network discriminates a generated map and the native map, so that the generator is trained to produce indistinguishable maps from native maps by the discriminator. The generator network is based on ResNet blocks and the Discriminator network is based on CNN. ContactGAN when applied to predict contact maps of CCMPred , DeepCovAs in other fields, the black-box nature of deep learning models has been one of the major hesitation/roadblocks for more widespread implementation/usage of DL-based approaches in Protein Structural bioinformatics. In that regard, the new trend in the field is to develop xAI  approaches. InterPretContactMap  is one oTripletRes , also deThe TripletRes contact pipeline performed quite well in two independent sets of test targets that included 50 non-redundant Free Modeling (FM) targets from CASP11 and CASP12. The major advantage of TripletRes is its ability to learn and directly fuse a triplet of co-evolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes also achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions in the CASP13 experiment in the contact prediction category.Overall, a wide range of features such as precision matrix, covariance matrix, secondary structure, PSSM, sequence profiles, and solvent accessibility have been proven to be important for protein contact map prediction as observed in all contact prediction methods. Similarly, a variety of Deep Learning architectures including ResNet, fully convolutional neural networks (FCNs), GANs, and U-Nets have been implemented by different groups. Among those architectures, we find that all accurate methods use ResNet and its variants. CASP-winning methods such as TripletRes  and RaptInter-residue contact prediction has been widely used for over a decade in the field of protein structure prediction. However, recently, the paradigm shifted toward predicting the probability of distance intervals, also known as ‘distograms’ . After tThe protein inter-residue distance prediction is the prediction of a pairwise distance matrix (2D) from a protein sequence (1D amino acid sequence). It can be compared with the monocular or stereo depth estimation problem in computer vision as shown in Although there were a few previous studies ,59 on prXu’s group during CASP13 implemented ‘distogram prediction’  within tβ–Cβ distances between any pair of residues. Using the amino acid representation of the query sequence and features generated from MSA, the CNN network predicts a discrete probability distribution for every pair. This distribution is found to be similar to the true distances. Then, the predicted backbone torsion angles and pair-wise distance between residues are combined to form a protein-specific potential. Finally, the gradient descent on protein-specific potential is applied to obtain the final protein model.AlphaFold  is a proProSPr  is an unAnother seminal work that championed the use of distogram is trRosetta (transform-restrained Rosetta) , which iKihara’s group developed a DL-based method called AttentiveDist  for protAll distogram prediction methods use ResNet as the core deep learning architecture. The RaptorX method  was rankAn ideal distance prediction algorithm should predict exact physical distances on the entire distance map accurately . AlthougIn this work, the author has developed a framework called Protein Distance Net (PDNET)  that conDing et al.  developeXu’s group also developed a Deep Learning-based method  to prediRealDist  is a purCheng’s group developed DeepDist , which iContact and distance predictions have been widely used as the intermediate steps toward protein structure prediction. The quality of the 3D structure prediction depends on the accuracy of such contact and distance predictions, which has been shown in the recent 13th and 14th CASP experiments. Therefore, it is important to assess and evaluate such predicted contacts and distances. To evaluate predicted contacts, web servers such as EVAcon  and ConEThe methods discussed above represent the recent developments in real-valued protein distance prediction. While most of them use deep residual networks (ResNets), methods based on generative adversarial networks (GANs) and Attention Networks have also been proposed. The results of the methods such as REALDIST , DeepDisRanking of models, Quality Assessment (QA), and refinement are some of the integral steps in the protein structure prediction pipeline. For example, in I-TASSER , once thBhattacharya’s group recently developed a distance-based model quality estimation method called QDeep . One of Xu’s group developed ResNetQA   using Deep Learning  with feaBaker’s group recently developed a DL framework called DeepAccNEt  that estThe necessity for mitigating error and increasing the accuracy of the predicted 3D protein structure has led to the development of methods for the refinement of 3D models. The methods discussed here represent the most recent developments in the field of quality assessment in the estimation of model accuracy (EMA). QDeep  uses ResIn this section, we highlight the recent advances in overall protein structure prediction pipeline using Deep Learning. Mainly, we will discuss recent Deep Learning-based improvements in some of the most successful protein structure prediction pipelines as evidenced by CASP13 and CASP14 and some end-to-end DL-based approaches for protein structure prediction.Here, we highlight DL-based advances in some of the most successful protein structure prediction pipelines.AlphaFold  is a proSimilar to AlphaFold, trRosetta (transform-restrained Rosetta)  is a newUsing similar ideas as trRosetta, Gray’s group expanded the ideas to predict antibody structure prediction and developed DeepH3 , which iRaptorX is also one of the successful protein structure prediction pipelines developed by Xu’s group. The most recent version of RaptorX  also incCheng’s group, the primary developer of MULTICOM (another protein structure prediction pipeline), has also incorporated recent advances in DL-based approaches to improve the MULTICOM  protein Given a target protein sequence, initially, for the template-free modeling, MULTICOM generates MSA and then the MSA is used to calculate residue–residue co-evolution features that are passed to the DNN of DeepDist to predict the inter-residue distance map. Then, the MSA and predicted distance map are used to generate ab initio models tools . Finally, MULTICOM EMA predictors are used to rank the models. The new MULTICOM was ranked 7th out of all the predictors in protein 3D structure prediction.Yang Zhang’s group also has been incorporating DL-based advances in the I-TASSER pipeline. Especially, during CASP13, the group released C-I-TASSER and C-QUARK programs , based oEssentially, as in previous iterations of I-TASSER, the C-ITASSER pipeline consists of the following steps. (a) Given a protein sequence, the sequence is threaded using LOMETS, and at the same time, MSA is generated using DeepMSA. (b) Template fragments are created from the threading templates, which are then subjected to structure assembly using Replica-Exchange Monte Carlo (REMC) guided by the potential calculated from the improved contact map created using NeBcon (that incorporates other DL-based contact prediction approaches). Finally, the models are clustered using SPICKER, and the cluster centroid is chosen and subjected to structure re-assembly and finally, Fragment-Guided Molecular Dynamics (FG-MD)-based refinement is applied to obtain the final model.In CASP13, the average TM scores of the first models produced by C-I-TASSER and C-QUARK were 28% and 56% higher than those constructed by I-TASSER and QUARK, respectively. Detailed data analyses showed that the success of C-I-TASSER and C-QUARK was mainly due to the increased accuracy of Deep Learning-based contact maps, as well as the careful balance between sequence-based contact restraints, threading templates, and generic knowledge-based potentials. In CASP14, I-TASSER also incorporated a distance matrix, but the details are yet to be released.There have been some advances in some of the most successful protein structure prediction pipelines such as RaptorX, I-TASSER, Rosetta, MULTICOM, and AlphaFold. These advances can be mostly attributed to the improvement in one or more stages of the prediction pipeline due to the use of DL-based approaches. Especially, for all of these pipelines, these advancements have been integrated into the original pipeline and are available as web servers or a standalone version of the tools provided in Github.As in other scientific fields, there have been some attempts to get rid of human-engineered steps in the protein structure prediction pipeline. It has to be noted here that the existing approaches contained a number of modules, which were each trained separately even though they were using DL, whereas an end-to-end deep learning system would be trained as a single integrated structure with a system of sub-networks coupled together. In that regard, end-to-end DL approaches use sequence as an input and map to protein structures end-to-end. In this section, we review a few end-to-end DL-based approaches for protein structure prediction.NEMO , which sAnother seminal work for the end-to-end Deep Learning approach for protein structure prediction is based on AlQuraishi’s work . Based oAlthough the details of AlphaFold2 (recent version of AlphaFold ) have noIn CASP14, AlphaFold2 outperformed other methods and produced remarkably accurate models that compelled organizers to declare the protein structure prediction problem for single-domain proteins to be solved and that there seems to a be a lot of enthusiasm about the possibility of solving the protein structure prediction problem to some extent in the community.Although existing approaches were using DL, these approaches contained a number of modules, each trained separately, whereas an end-to-end deep learning system is trained as a single integrated structure with a system of sub-networks coupled together. In that regard, end-to-end DL approaches use sequence as an input and map to protein structures end-to-end. Recent developments of end-to-end deep learning systems such as NEMO, AlQuraishi’s Recurrent Geometric Network, and AlphaFold2 for protein structure prediction represent the most remarkable advancements in the field. Co-evolutionary-related sequences, primary sequences, and PSSMs are used to predict contact map and eventually protein structure. AlQuraishi’s end-to-end model  predicteOwing to the significant progresses in the past decades for protein structure determination by Cryo-electron microscopy (Cryo-EM), it has evolved into one of the effective tools in structural biology. Cryo-EM is a Nobel prize-awarded technology that provides 3D maps of protein structures. The number of Cryo-EM maps deposited in EMDB as of March 2021 is ≈15 K , and theFor a recent review on modeling molecular structures from density maps of different resolutions, please refer to Alnabati et al.  and MalhThere are intermediate steps  involved in the construction of high-resolution 3D Cryo-EM maps. Among them, single particle picking is a critical step that involves the picking of single-particle two-dimensional projections from thousands of 2D micrographs. Here, we will describe some advances in DL-based approaches for various steps in Cryo-EM based protein structure determination mainly: single-particle picking, back-bone prediction, secondary structure prediction, all-atom prediction for protein complex, and EM density map generation/refinement.Cryo-EM micrographs contain 2D projections of the particles in different orientations. As a large number of single-particle images must be extracted from Cryo-EM micrographs to form a reliable 3D reconstruction of the underlying structure, particle picking is one of the critical steps and is often regarded as a bottleneck for automated structure determination from a Cryo-EM map. Recently, Deep Learning-based approaches have been emerging for single particle picking. We summarize briefly some of the existing DL-based approaches for single particle picking.DeepPicker  is one oPIXER  is a parCASSPER  is anothMicroGraphCleaner  is anothAlthough not based on DL-based approaches, it is worthwhile to mention AutoCryoPicker here. Based on three stages: image processing, particle clustering, and particle picking, AutoCryoPicker is an automated, unsupervised approach for single particle picking in Cryo-EM micrographs. One of the salient features of AutoCryoPicker is that this approach is based on an unsupervised ML algorithm: no labeled training data is required, which is sometimes hard to create in case of Cryo-EM .For Cryo-EM images at lower resolutions such as 5–10 Å, one of the significant challenges in determining protein structures in Cryo-EM is the prediction of (location of) backbone. In this regard, recently, Si et al. developed a C-CNN (Cascaded)-based approach that comprises multiple CNNs, each predicting a specific aspect of a protein’s backbone structure viz. secondary structure elements, backbone structure, and c-alpha atoms and then combining the results to predict complete map . On a beIt is still challenging to detect the secondary structure of a protein using Cryo-EM images when the spatial resolution of Cryo-EM images is at the medium level (5–10 Å). Li et al.  proposedRecently, Kihara’s group at Purdue developed a DL-based approach called Emap2Sec  for deteHere, we briefly explain the DeepTracer approach for the all-atom structure of a protein complex.One of the challenges in deriving the structure of a protein based on its 3D Cryo-EM map is to be able to fit the atoms to the EM map, which has to be done manually or performed using template-based or homology modeling methods. In that regard, automatically and accurately determining the molecular structure from a Cryo-EM map is an important problem.Information about the macromolecular structure of protein complexes and related cellular and molecular mechanisms can assist the search for vaccines and drug development processes especially in the current scenario of the COVID-19 pandemic. To obtain such structural information, the authors developed DeepTracer , which iDeepTracer has a Deep Convolutional Neural Network, specifically U-Net, at its heart to allow for fast and accurate structure prediction and predicts four pieces of information: the location of amino acids, location of the backbone, secondary structure prediction, and amino acid types. The modified U-Net architecture in DeepTracer has four separate U-Nets, one for each structural aspect of the molecule . The pre-processing step that prepares the Cryo-EM maps to be fed into the neural network primarily consists of obtaining the voxel.Researchers applied DeepTracer on a previously published set of 476 raw experimental Cryo-EM maps and compared the results with a current state-of-the-art method. The residue coverage increased by over 30% using DeepTracer, and the RMSD value improved from 1.29 to 1.18 Å. DeepTracer determines the all-atom structure of a protein complex based solely on its Cryo-EM map and an amino acid sequence with improved accuracy and efficiency compared to previous methods.Although understanding of physiological processes that proteins are involved in requires the exploration of conformational landscapes of protein complexes, structural biology until recently has been strongly driven by a static-centered view of protein architecture . One of To understand the mechanisms underlying the biological function of protein, it is essential to elucidate the 3D structure and dynamics properties of the protein. Various studies have been performed to understand/characterize dynamics of proteins. Cryo-EM is a powerful tool for the investigation of protein structures including analysis of their dynamics .As protein adopts variable conformations in the specimens in Cryo-EM single-particle analysis, it can be inferred that the dynamics properties of the proteins are ‘hidden’ in the reconstructed Cryo-EM maps. Based on this hypothesis, Okuno’s group at Kyoto University developed DEFMap (Dynamics Extraction From cryo-EM Map) , which iHere, we briefly describe two tools for the generation/refinement of an EM map.Zhang’s group recently developed EMRefiner , a MonteIn addition, Zhang’s group also developed DEMO-EM , an autoKihara’s group at Purdue recently developed a GAN  -based apWith the recent advancements in the Cryo-EM technology, Cryo-EM is becoming a leading technology for determining the protein structure. There are intermediate steps  involved in the construction of a high resolution 3D Cryo-EM maps. Here, we described some DL-based advances in various steps such as single-particle picking, back-bone prediction, secondary structure prediction, EM density map refinement, and all-atom prediction for protein complex for Cryo-EM based protein structure determination.We summarize all the methods described in the article in We are at an exciting era in terms of protein structure prediction approaches especially due to the advancement in the field made possible by using Deep Learning. As discussed earlier, especially end-to-end Deep Learning approaches are probably going to be some of the more exciting developments in the future. Although we are likely to see advances in every aspects of protein structure pipelines over the next decade or so, we envision that the most advances will be in the following arena.All protein structure prediction methods use multiple sequence alignments (MSAs) as input, and it is what seems to be driving the improvement in the accuracy of the predicted structures. Zhang’s group developed and released DeepMSA  as an efDuring the CASP14 conference in 2020, after DeepMind presented that AlphaFold2 does not use distograms or real-valued distance maps as intermediate steps for structure prediction, many researchers questioned if contact/distogram/real-valued distance maps were a wrong direction in the first place. However, since DeepMind extracted distance map predictions to investigate and explain what their models were learning, many others suggest that distogram prediction real-valued distance prediction can be extremely useful to ‘explain’ what the deep learning models have learned. During the conference, some also suggested that the distogram/distance predictions could be useful when predicted between domains. In the future, if distograms/distance maps are gradually understood to be a passage to understand what deep learning models learn, methods such as DISTEVAL  will alsAs stated by the organizers of CASP, the single-domain protein structure prediction problem is solved to some extent. In this new scenario, we are likely to see new trends in DL-based approaches for the prediction of multi-domain protein structures. In that regard, correct domain boundary assignment from sequence is a critical step toward accurate multi-domain protein structure prediction. Recently, Zhang’s group developed FUpred  to prediAs in other fields, the black-box nature of deep learning models has been one of the major hesitation/roadblocks for more widespread implementation/usage of DL-based approaches in Protein Structural bioinformatics. In that regard, the new trend in the field is to develop xAI (explainable AI)  approach


Infectious diseases caused by novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e. signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts.We developed DeepViral, a deep learning based method that predicts protein–protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction.https://github.com/bio-ontology-research-group/DeepViral. Prediction results for 14 virus families are available at https://doi.org/10.5281/zenodo.4429824.Code and datasets for reproduction and customization are available at Bioinformatics online. Infectious diseases emerging unexpectedly from novel and reemerging pathogens have been a major enduring public health concern around the globe  and targMycobacterium tuberculosis . Another method based on protein sequences .Interactions between hosts and pathogens were obtained from the Host Pathogen Interaction Database  . The phehttps://www.uniprot.org/uploadlists) on March 6, 2020. The Gene Ontology annotations of human proteins (release date 2020-02-22) were downloaded from the Gene Ontology Consortium (The phenotypes associated with human genes were collected from the Human Phenotype Ontology (HPO) database (nsortium . Human Pnsortium  and filtnsortium .https://www.ebi.ac.uk/ols/ontologies/ncbitaxon).To add background knowledge from biomedical ontologies of phenotypes and GO classes, we downloaded the cross-species PhenomeNET ontology , and 1000 columns representing each position of the amino acid sequence.N is the total number of predictions, ty and py is the true label and predicted likelihood of y.To predict the likelihood of an interaction between a pair of proteins, we trained the network as a binary classifier, to minimize the binary cross-entropy loss defined below, We implemented our model using the Keras library and performed training on Nvidia Tesla V100 GPUs. The phenotype model consists of a fully connected layer with the feature embeddings as input. The sequence model, adapted from DeepGOPlus , is a coWe started with the biological hypothesis that phenotypes (i.e. symptoms) elicited by viruses in their hosts can act as a proxy for the underlying molecular mechanisms of the infection, and therefore may provide additional information to the prediction of potential PHIs for novel viruses.To generate feature embeddings for human proteins and virus taxa, we applied a recent representation learning method DL2Vec , and their protein functions from the Gene Ontology (GO) database  database  have rarely been compared with methods designed for intra-species (e.g. human) PPI prediction. To compare with the existing sequence-based methods for both intra- and inter-species PPI prediction, we evaluated DeepViral and RCNN  cross validation: at each run, one virus family in our positive set was left out for testing, 20% of the remaining families for validation, and the rest 80% for training. The objective of the LOFO cross-validation is to evaluate the model under a scenario in which the novel virus emerges from a novel virus family—in our study, ‘novel’ is defined as the situation in which we have no or very little knowledge about its protein interactions and the molecular functions of the viral proteins.n is the sample size and σ is the standard deviation. Additionally, the mean ranks of the true positive proteins were provided as a more interpretable metric: for each viral protein, we ranked all of the 16 627 human proteins in Swiss-Prot (with a length limit of 1000) as its potential interaction partner based on the prediction score and obtained the mean ranks of the true positives.Instead of using ‘dissimilarity-based negative sampling’ to construct a balanced dataset, we sampled our negatives from all the possible pairwise combinations of human and viral proteins, as long as the pair did not occur in the positive set. Essentially, we treated all ‘unknown’ interactions as negatives. As the dataset was at this point unbalanced with more negatives than positives, we evaluated the model with the area under the receiver operating characteristic (ROC) curve . A high With the newly constructed dataset, we further evaluated the existing methods as well as the variants of DeepViral, under the scenario in which a novel virus (from a novel family) emerges and no previous knowledge (except about its protein sequences and the phenotypes elicited in its hosts) is known.We compared DeepViral with two existing state-of-the-art methods based on protein sequences: Doc2Vec + RF . However, the phenotypes associated with viral infections obtained from PathoPhenoDB focus on hallmark phenotypes of viral infections that can be used to discriminate between infections of different viruses and thereby de-emphasize the phenotypes resulting from general immune response , or with known interactions from its taxonomic relatives (LOSO). However, even the best performing model, i.e. DeepViral jointly trained with phenotypes and sequences, has only been able to rank the known true positive proteins up to a mean rank of 3156 out of all 16 627 human proteins in the LOFO evaluation. While the mean rank is sensitive to predictions at a low rank see , future DeepViral has several limitations that can be addressed by future work. One is the scarcity of training data for inter-species PPIs. This challenge may be addressed by transfer learning on the much larger intra-species PPI data available for humans and other model organisms. We also did not utilize other types of PHIs outside virus–human interactions in our current study, such as those of other hosts, e.g. plants and fishes, and other types of pathogens, e.g. bacteria and fungi; both may provide further insights in PHIs and the mechanisms underlying viral infections. In particular, in zoonotic diseases, information from PHIs in animals (if available) may be used to identify or suggest interactions that occur in human hosts (btab147_Supplementary_DataClick here for additional data file.


Because of the key relevance of protein–protein interactions (PPI) in diseases, the modulation of protein-protein complexes is of relevant clinical significance. The successful design of binding compounds modulating PPI requires a detailed knowledge of the involved protein-protein system at molecular level, and investigation of the structural motifs that drive the association of the proteins at the recognition interface. These elements represent hot spots of the protein binding free energy, define the complex lifetime and possible modulation strategies. Here, we review the advanced technologies used to map the PPI involved in human diseases, to investigate the structure-function features of protein complexes, and to discover effective ligands that modulate the PPI for therapeutic intervention. Many relevant human pathologies, including cancer, neurodegenerative and infection diseases, are the result of abnormal protein–protein interactions (PPI) that alter the mechanism of molecular recognition and the affinity of binding partners under a given set of conditions . Proteinin vivo . These technologies identify efficiently direct physical interactions between two proteins. However, they require proteins to be expressed at non-endogenous levels and often are not able to capture all interactions involving intermediary or scaffold proteins. Another high-throughput proteome-scale mapping uses epitope tags fused to bait proteins . Protein associated with the bait are then identified and characterized using affinity purification or co-fractionation followed by mass spectrometry  the development of FRET-based high-throughput screenings in living cells , which is often used as intracellular assay for PPI and their dynamics in cells . Non-radng cells ; 2) a phng cells ; 3) a FRng cells .The results of gene screenings and proteomics are reported in different online databases, including interactions across thousands of published studies and experimental techniques. The classification of PPI is made on the base of different parameters, including protein domains, type of interactions, identification of involved species and detection methods .http://thebiogrid.org) is an interactome dataset that includes nowadays nearly 120,000 interactions among nearly 15,000 proteins, and different cell-line-specific interaction networks obtained from proteomics approaches. It is the most comprehensive experimentally derived model of the human interactome to date. It comprises 5,522 baits in human colon cancer cell lines (HCT116) (www.interactome-atlas.org) is a server that integrates human data of genome, transcriptome and proteome, enabling cellular function to be studied within most physiological or pathological cellular contexts, with more than 64,000 PPI (https://string-db.org) is a database of known and predicted PPI. The interactions include direct  and indirect  associations, from knowledge transfer between organisms and other databases; 4) IntAct (https://www.ebi.ac.uk/intact) of PPIs derived from individual user submissions and literature observations; 5) Interactome3D (http://interactome3d.irbbarcelona.org) a web service for the structural annotation of PPI networks; 6) APID  a protein interactomes data server; 7) Pathway Commons that merge information of both pathways and interactions (http://www.pathwaycommons.org); 8) BioGRID (https://thebiogrid.org) is a biomedical interaction repository of proteins, genetic and chemical interactions, with data compiled through comprehensive curation efforts; 9) I2D is a database that aims to facilitate experimentation using experimental and predicted PPI for five model organisms and human (http://ophid.utoronto.ca/iid).Among these, 1) BIOPLEX ((HCT116) . Other d,000 PPI ; 3) STRIhttp://www.rcsb.org/pdb) (http://dunbrack2.fccc.edu/ProtCiD/Search/Uniprots.aspx) is a data resource for structural information on protein interactions. It provides clusters of interfaces of full-length protein chains and functional domains as a means of identifying biological assemblies, showing information on four types of interactions: protein–protein interactions at the chain level, protein–protein interactions at the domain level, domain–peptide interactions, and also the interactions of domains with nucleic acids and ligands. Within the ProtCID server, the PDBfam database contains 8636 protein domain families (Pfams) present in the PDB , single particle cryo-electron microscopy (cryoEM) and other methodologies, contained within the (10) Protein Data Bank (PDB) (org/pdb) . The 11) the PDB .http://hdock.phys.hust.edu.cn) (https://www.rosettacommons.org)   and 13) ons.org) . While sThe most important lesson learned from the successful design of compounds against PPI is the value of quality structural information describing the protein interaction and involved binding motifs . ExpandiXRD has a dominating role in solving biomolecular structures at atomic resolution. The volumetric data obtained by this technique (electron density map) describes the position of atoms of the protein-protein complex of interest. When the structure of the individual protein partners is solved, the structure of the entire complex can be uncovered and modelled using a combination of complementary approaches that include mutagenesis, chemical cross-linking and analytical ultracentrifugation using computational methods . Solving50–logP or logD; values > 5 are considered favorable for in vivo activity) (2), with compared to the average surface size of small molecule-protein when targeting single proteins   . The req,000 Å2) .Most of the structures reported in the PDB archive (∼90%) have been determined using XRD, including those of protein-protein complexes. Another technology, single particle cryoEM is starting to become instrumental in structure determination of macromolecular complexes, for targets that are either very hard to produce in large quantity or almost impossible to crystallize . The thrProtein-protein complex formation may be eventually studied using Nuclear Magnetic Resonance (NMR) spectroscopy . The croIn recent years, some PPI-modulating compounds have entered clinical studies, and few of them have been successfully approved for treatment of diseases, in particular for cancer . The comHigh-throughput screenings can yield useful starting points for chemical optimization . BecauseThe rational design of small molecules to target specific PPI strongly benefits from a better understanding of how such compounds bind at the interfaces of the complex. Highly sensitive technologies, including X-ray Crystallography (XRD), surface plasmon resonance (SPR), nuclear magnetic resonance (NMR), isothermal titration calorimetry (ITC) and mass spectrometry (MS), are frequently used for this purpose . X-ray cSpecific compounds targeting PPI can be designed to inhibit/dissociate or to stabilize/enhance the protein-protein complex . When a Small molecules. These compounds should cover generally a large surface area and make many hydrophobic contacts to affect PPI, thus can face pharmacokinetic issues. The small molecules are in principle more suitable for tight and narrow PPI interfaces . The development of these compounds is strongly based on PPI structural information. They work binding to a protein interface mimicking the partner protein (2). They are susceptible to hydrolysis and require stabilization processes  to prolongate their half-life  terfaces . An innoterfaces . 2) Stru protein . Generalalf-life . Finallyecessity . Using secessity . Resultsvaccines  (Hoelzgevaccines .Despite the successes in clinic for different protein interaction systems involved in diseases, the results of the recent years have highlighted that some PPI are indeed difficult to tackle. Whether a system can be effectively modulated depends on the structural features of the protein-protein complex, and on the possibility to study the system using different biophysical methodologies. Among these, X-ray crystallography and single particle cryo-EM, will continue to be central to successfully assess the structural and mechanistic details of interaction events at atomic resolution, and essential for the development of powerful modulators, be they small molecules, structural peptides, or antibodies. Coupled with further understanding of the nature of identified interactions by genetic and proteomics approaches, the structure determination of PPI will provide a direct path to the development of novel therapeutics targeting these challenging systems. The next future is likely to face the need of further technology innovations in the field. Bifunctional and covalent inhibitors might be fundamentally new ways to target specific PPI systems .


Cycline-dependent kinase 4 (CDK4), an enzyme of the cycline dependent or Ser/Thr protein kinase family, plays a role in cell cycle progression (G1 phase) by phosphorylating a tumor suppressor protein called pRB. Alteration of this enzyme due to missense mutation/ nonsynonymous single nucleotide polymorphisms (nsSNPs) are responsible for various types of cancer progression, e.g. melanoma, lung cancer, and breast cancer. Hence, this study is designed to identify the malignant missense mutation of CDK4 from the single nucleotide polymorphism database (dbSNP) by incorporating computational algorithms. Out of 239 nsSNPs; G15S, D140Y and D140H were predicted to be highly malignant variants which may have a devastating impact on protein structure or function. We also found defective binding motif of these three mutants with the CDK4 inhibitor ribociclib and ATP. However, by incorporating molecular dynamic simulation, our study concludes that the superiority of G15S than the other two mutants (D140Y and D140H) in destabilizing proteins nature. Melanoma, particularly malignant melanoma, is a form of skin cancer caused by pigment-producing cells called melanocytes. Although the etiology of the disease is fully complex including different types of genomic alteration in some genes, e.g. MC1R, CDKN2A, and CDK4, nonfunctional CDK4 is the cardinal hallmark of the disease cutaneous malignant melanoma-3 (CMM3). Furthermore, a vast majority of human tumors are now believed to be occurred due to the deregulation of the CDK4/6–cyclin D–INK4–RB pathway . HoweverThe gene (CDK4) positioned on the long arm of 12(q) chromosome is 5 kb long and has 8 exons, one of which is a non-coding exon , 11. TheThe daunting task of cancer studies is to discover the effects of SNPs specifically nonsynonymous single nucleotide polymorphisms (nsSNPs) or missense mutations in the coding sequence of tumor suppressor genes. A single nucleotide polymorphism, or SNP, is known as a single nucleotide variation  at DNA level. SNPs (Single Nucleotide Polymorphism) were also estimated to be responsible for more than 90 per cent of sequence variations in the human genome . HoweverIn silico) based on the biochemical intensity of the amino acid substitution, as well as the protein sequence and/or structural information. Based on this information, various in silico approaches have been developed recently to sort out the functional malignant nsSNPs in the candidate protein by utilizing different prediction algorithms. Functional nsSNPs in various genes like PPARG [Current advances in human genome science have yielded a wealth of evidence showing tens of millions of human genetic variations across populations, including SNPs. Study of these variations can provide a framework for analyzing the relevance of these variations in disease susceptibility as well as formulating newer treatments. However, analysis of all these substitutions with the help of laboratory-based techniques is time-consuming, costly, and laborious. An effective alternative to this is the use of methods  [Uniprot ID: P11802) [The SNP data of CDK4 gene was adopted from the NCBI  database dbSNP (ov/snp/) . The seq P11802) .st filtration webservers to predict malignant nsSNPs in the CDK4 protein coding region. SIFT; SNAP-2 and PROVEAN yield results in either tolerated or harmful categories whereas PON-P2 allocates variants into 3 groups: pathogenic, neutral, or unknown classes. SIFT algorithms (https://sift.bii.a-star.edu.sg/), being the first choice in SNP characterization, are often used to predict harmful nsSNPs in almost all computational work [http://provean.jcvi.org/index.php) tool applies delta alignment scores based on the variant version and reference of the protein sequence. In PROVEAN, malignant nsSNPs were characterized by a comparison of the score below the threshold value of −2.5 [https://www.rostlab.org/services/snap/), is focused on advanced machine-learning and neural network based approach to classify the harmful nsSNPs and the functional impacts  of these variants in protein. To predict the alteration in protein function (gain or loss) it utilizes functional and structural annotations, sequence, and evolutionary qualities of the queried protein. SNAP2 identifies with high accuracy whether the mutation is neutral (ranges from -100 to 0) or effect (ranges from 0 to +100) [http:/structure.bmc.lu.se / PON-P2/), predicts the mutations of harmful amino acids by means of machine learning algorithms. PON-P2 splits the results into 3 classes: pathogenic, neutral, or unidentified [http://fathmm.biocompute.org.uk/), which employs Hidden Markov Models (HMM vector) to assess the functional impact of both coding variants, i.e. non-synonymous single nucleotide variants (nsSNVs), and non-coding variants in the human genome [Distinguishing the malignant and benign forms of SNP from an ample data pool is an intricate process. Nevertheless, a huge number of computational bioinformatics webservers have recently been developed to discern the effect of single amino acid polymorphism or SNPs in a protein. We used those webservers in this analysis to predict the damaging/deleterious/pathogenic SNPs as malignant mutations in the CDK4 gene. Because analyzing the harmful variants by all these different algorithms of webservers added reliability by cross-checking the predicted results; we utilized PROVEAN, SIFT, SNAP2, FATHMM, PON-P2 and Predict SNP as a 1nal work . To idennal work . To pred of −2.5 . Anotherto +100) . PON-P2 entified . Anothern genome .https://loschmidt.chemi.muni.cz/predictsnp/) can predict neutral or harmful SNPs by integrating these eight predictive tools e.g. MAPP, PANTHER, nsSNP Analyzer, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The SNP benchmark dataset in predict SNP is trained on 43,000 unbiased mutations. Thus, by incorporating all the different servers listed above, predict SNP can deliver a significant consensus result with high precision of throughput. The result provided by the predict SNP server falls under two categories, e.g. harmful or neutral, where both categories hold a specific confidence score established on consensus outcome of all the eight different servers mentioned before [st filtration.Finally, the Predict SNP server  for further sort out the most malignant nsSNPs from CDK4 protein.In the http://www.modpred.org/) was used to identify the Post translational modification sites (PTM) of each residues in CDK4 protein [ModPred webserver ( protein . PTM sithttps://consurf.tau.ac.il/) was employed to predict the conservation of each residue of a protein in the evolutionarily conserved region [ConSurf webserver  to predict the protein stability due to the alteration of the non-synonymous residues in the CDK4 protein. If a new residue decreases protein’s stability then this substitution can be clarified as malignant. I-Mutant is a "SVM" approach focused on machine learning for predicting the free energy of a protein due to the specific substitution [https://www.ics.uci.edu/~baldig%20/%20mutation.html) (supporting vector-based machine approach) along with I-Mutant for stability prediction to emphasize the final results [We also utilized I-Mutant and Mupro webserver  was used to produce the mutant and native protein’s 3D model structure [https://zhanglab.ccmb.med.umich.edu/ModRefiner/) was used for energy minimization and refinement of the mutant proteins to increase their structural accuracy [https://servicesn.mbi.ucla.edu/PROCHECK/) for validating the designed proteins [Amino acid substitution has a structural impact on the mutant protein and can alter the protein’s stability and functions . As a result, after identifying the final nsSNPs from 2tructure . Furtheraccuracy . We haveproteins . In the proteins .https://www3.cmbi.umcn.nl/hope/) to characterize the substitution impact of residues in a protein. This server collects structural information including sequence annotations and tertiary protein structure which are readily accessible in the Uniprot database. Project Hope server offers valuable information on the structural changes between the residues of native and mutant proteins by integrating this information [After modeling of the native and mutant protein models, we utilized three different webservers to predict the structural effect of the final nsSNPs in the protein structure. First of all, we utilized the Project Hope server (ormation . The prohttp://webclu.bio.wzw.tum.de/stride/) to evaluate the secondary structural elements of the native and mutant proteins [After substitution impact analysis, secondary structure analysis is conducted to predict secondary structure of the mutant and native protein models e.g. alpha helices, beta sheets, and coil regions. We used the STRIDE program   for superimposition of the mutant proteins with native. TM-align combine the TM-score rotation matrix and Dynamic Programming (DP) to predict the best structural alignment between protein pairs. The server also measures the total RMSD score of the superimposed mutant and native protein models [Finally, structural deviations of the mutant models compare to the native was evaluated by calculating the Root Mean Square Deviation (RMSD) score after superimposition. We used the TM-align  and ATP. Ribociclib can compete with ATP for binding in the ATP binding cleft of the proteins (CDK4/CDK6) and thus inhibit its activity [Molecular docking is an activity , 49.https://bioinfo3d.cs.tau.ac.il/PatchDock/) as molecular docking tool for docking analysis [We used the PatchDock server  solvated withSPC/E (Simple Point Charge) water molecules and periodic boundary conditions wereoptimized to carry out the simulations. Two sodium ions were added to the system to neutralize it. To achieve a consistent conformation, the energy minimization was carried out using 5000 iterations of steepest descent algorithm. Constant number of particles, temperature, and pressure (NPT) ensembles through Parrinello–Rahman biostat and constant number of particles, volume, and temperature (NVT) ensembles through Berendsen thermostat with no pressure coupling were used to equilibrate the energy minimized systems for 100 ps at a constant temperature of 300 K and a constant pressure of 1 atm.Finally, the systems were subjected to a MD simulation of 100 ns. From the simulation trajectories, RMSD (Root-mean-square deviation), RMSF (Root Mean Square Fluctuation), SASA (Solvent-Accessible Surface Area), Rg (Radius of gyration), and number of Hydrogen bond analysis were carried out. The resulting plots for these analyses were created using the XMGRACE  program.The comprehensive workflow and webservers used in human CDK4 to identify the malignant SNPs are summarized in .DbSNP database holds all the necessary information about CDK4 gene polymorphism. The dbSNP database with CDK4 reported total 2118 SNPs where 852 were found in the intronic region, 239 are nsSNPs (missense), 152 are synonymous, and the rest belongs to other categories. In our study, only the nsSNPs were exposed to further investigation since their interruption may encode altered structure or function of the protein.1stfiltration process which is given below.To sort out the Malignant nsSNPs (Deleterious/Pathogenic/Damaging) from 239 nsSNPs, first of all we utilized six different webservers  as a Initially PROVEAN server was used to evaluate the 239 nsSNPs of CDK4 protein. A total of 112 nsSNPs from this analysis were found as malignant. After we selected those 112 nsSNPs for screening via other databases . Accordi2ndfiltration process.After identifying the malignant nsSNPs from six different webservers, we intersect the prediction of all webservers based on their results and scores. Thus, by combining the algorithm of all six different webservers, finally, eight nsSNPs were identified as highly malignant based on their predictive consensus scores  and sele2ndfiltration process, first of all we utilized the ModPred webserver to predict the Post translational modifications (PTM) sites of all residues of CDK4 protein. ModPred webserver results in only four proteolytic cleavage sites  among the eight highly malignant nsSNPs. However, rests of the four residues were found to have no function in Post translational modification of the protein . Evolutionary conserved protein residue is usually considered as malignant when compared with residues in non-conserved position. Analysis of ConSurf webserver indicated highly conserved nature of the eight nsSNPs with highest conservancy score e.g. 9  were likely to decrease protein stability. Rest of the three nsSNPs  can increase stability of the protein predicted by these webservers  which scored highest in all webservers. So, we selected these three nsSNPs for further structural based analysis  which scored highest amongst all nsSNPs retrieved from dbSNP database. However, for the same codon positions, somatic mutations are not found so far in D140. But, For G15, two somatic mutations (G15R and G15C) have been documented in COSMIC  database. COSMIC represents the world’s largest and most comprehensive resource for exploring the impact of somatic mutations in human cancer [1st and 2ndfiltration of this study. Interestingly, after analysis, our mutation G15S showed almost similar prediction with G15R and G15C in all the webservers  to identify the structural impact of the three nsSNPs  in CDK4 protein.th position may disturb this domain and can abolish its function. Furthermore, Project Hope server reaffirmed that the native residue is located on the surface of the protein; mutation of this residue can disturb interactions with other molecules or other parts of the protein. Glycine is also extremely flexible and this flexibility may be essential for the proper functioning of the protein. Further in case of both D140Y and D140H mutants; by replacing tyrosine and histidine residue in place of aspartic acid at the 140th position in the protein kinase domain of CDK4 protein can trigger similar unstable conditions predicted by Project Hope server. Moreover, the wild type residue is more hydrophobic than the mutated residues in both cases and the wild-type residue forms a salt bridge with several surrounding residues including R101, K142 and R142. But after mutation the charge differences in mutants may disturb the original residue’s ionic interaction and may cause loss of the salt bridge bonding with other surrounding residues. The stereo chemical conformation of wild type and mutant residues predicted by Project HOPE server are shown in Project Hope server’s structural effect analysis showed the replacement impact of serine residue substituted by glycine in the catalytic protein kinase domain of mutant G15S protein. This domain is essential for binding with ATP and other protein-protein interactions . However substitution of the buried glycine residue with serine at the 15Further, Secondary structure analysis of native and mutant proteins through STRIDE program showed significant alteration in the average secondary structure of the mutant proteins compared to the native . The majFinally, we superimposed the 3D structure of native and mutant proteins to obtain more clear vision of the structural deviation caused by these nsSNPs. The superimposed structure of the native with mutant proteins demonstrated a significant deviation in the mutant structures compared to the native protein CDK4 . Total RProtein-ligand docking analysis was carried out with ribociclib and the tertiary structure of the modeled proteins (Native and mutant both) of CDK4. Alteration in mutated protein structure may affect the binding interaction of ribociclib with CDK4 protein. Studies on CDK4 also revealed some specific residue to be more frequent for binding in the ATP cleft naturally e.g. Ile12, Val20, Ala33, Val77, Phe93, Glu94, His95, Val96, Gln98, Asp99, Thr102, Glu144, Leu147, Ala157, Asp158 whereas Glu144 is the key binding residues which contacts with the terminal phosphates of ATP . RibocicTo further evaluate the ATP binding affinity of the mutant models we docked ATP with native and mutant proteins which resolved significant differences in binding affinity. Docking analysis of ATP with Native protein by Patchdock server revealed lowest Patchdock score of 4382 where the highest score was observed in mutant G15S e.g. 5822. Even if the other two mutants also showed larger patchdock score than native which were 4836 for D140Y and 5114 for D140H. This difference indicated the significant binding improvement of the ATP with the mutant protein models. However, residual bond information analysis of the ATP-protein complex further resolved same compelling result as ribociclib where, ATP binds at the same binding pocket of native CDK4, mutant D140Y and D140H models . HoweverTo determine the interaction of native and mutated residues with surrounding residues non-bonding interaction were further analyzed. Analysis resolved consequent reduced interactions with the surrounding residues only in the Tyrosine residue at 140th position . This reThe effect of nsSNPs on the protein structure of CDK4 was analyzed by molecular dynamics simulation. After the dynamics simulations of the wild-type and other variants of the protein, analyses of the simulations were done for RMSD, RMSF, Rg, SASA and Hydrogen bond numbers.From the overlapping graph  of the sThe trend was followed in RMSF analysis also. RMSF values of a simulation illustrate the flexibility of a protein structure. G15S variant again showed the greatest deviation particularly in the 240 to 260 residues range. All the other variants also showed higher flexibilities compared to the wild type in the simulation .Rg values of a simulation. In this case, variant D140H showed the biggest variations of them all. It showed reduced number of Rg values throughout the simulation meaning a more compact structure than the others. Rg values of the D140Y remained close to the wild protein during this simulation. Variant G15S showed slightly higher values than the wild protein  could greatly aid in targeting of disease-associated mutations by filtering out the most likely pathological variants from a large pool of SNP datasets.Diverse experimental studies have been carried out over the past few years by assessing the relationship between drug response and nsSNPs for cancer treatment. In an apprehending analysis Chambers et al. reported the involvement of nsSNPs in the alteration of protein structure and function . Wang anThere are however huge quantities of SNPs that have been detected in human CDK4 genome to date. But all SNPs may have no functional impact on the protein or might have the consequence of low frequency that is not feasible for analysis. The present research, which uses various computational tools, focused on illuminating certain novel CDK4 nsSNPs that may have biological significance for the protein. And yet no single bioinformatics method can be sufficiently accurate to find out the entire blueprint of the functional significance of every gene polymorphism. Therefore, a broad collection of complementary bioinformatics methods have been applied in the current research.st filtration) for identifying nsSNPs in CDK4 protein of the CDK4 gene. The algorithms used in those six servers were theoretically distinct from each other and thus the comparison scores of all those resources would guarantee high prediction reliability. Ultimately eight nsSNPs from 239 nsSNPs of the CDK4 gene were recognized as highly malignant and assigned for further downstream filtration analysis (2nd filtration).We used six different extensively used computational servers, notably SIFT, PROVEAN, FATHMM, SNAP-2, PON-P2 and Predict SNP in the first line up sorting methods  for post-translation protein modification sites prediction, participation in proteolytic cleavage was seen only in four nsSNPs. Finally, we used the I-Mutant and MU-Pro as protein’s stability prediction servers in the 2nd filtration process. These side-by-side intersect evaluations in the 2nd filtration process deduced the eight nsSNPs to three  for further analysis of the structural impact on the CDK4 protein.Typically, conserved residues of a protein are more crucial because of the supposition that they are involved in the stability or folding of proteins . We usedst and 2nd filtration, 3D structures of CDK4 proteins (native and three mutants) were designed to obtain the structure level alterations attributable to these adverse polymorphic modulations. To resolve the structure level alteration in protein e.g. structural impact, alteration in secondary structure and structural deviation analysis; again three different web servers were utilized respectably Project HOPE, STRIDE and TM align. The G15S mutant results in substitution from Glycine (a non-polar amino acid) to serine (a polar amino acid) in the protein kinase domain of the protein. Project HOPE server estimated that this larger size mutated residue (serine) is likely to disrupt the interaction with other protein residues in the neighboring region as the native residue (glycine) is located on the surface of the protein. In addition, Project HOPE evaluated the substitution of the buried aspartate residue with tyrosine and histidine at the protein’s 140th position leads to a loss of hydrophobicity in the protein’s core. This loss of hydrophobicity may cause reduction in the ionic interaction with surrounding residues. Thus, these abnormal changes in protein could lead to the loss of thermodynamic stability during aggregation and folding [After sort listing the nsSNPs from 1 folding .In addition, secondary structure analysis of the mutant proteins compared to native found major alterations in alpha helix, beta turn, beta sheet and coil regions. For example, in all the mutants; residues of A2-S3 have been completely converted from 310-helix to coil. However, the most prominent modification was found in the mutants G15S and D140H where the secondary structures of residues L161-S166 were fully converted to coil from alpha helix.Also, the superimposition of mutant model structures with native structure showed more RMSD deviation (1.32 Å) in the G15S mutant than the other two mutants. This metadata concludes that the mutant G15S has more superiority in alteration of the native protein’s 3D structure.In silico method which is particularly used in drug designing and vaccine designing approaches to resolve the binding status between a drug/vaccine with its corresponding macromolecules [th position of the protein.Molecular docking analysis was conducted to further emphasize the supposition as to which mutants have more negative impact on the protein. Molecular docking is a popular olecules , 64. Ribolecules . HoweverWe also studied the non-bonding interaction of mutant residues with neighboring residues at the point of mutational position. Results showed reduced interaction only in D140H mutant whereas the other two mutants (G15S and D140Y) showed increased interaction within 4A ° surroundings residues.Finally, MD simulation analysis was done to assess the behavior of the modeled proteins in a simulated environment based on RMSD, RMSF, Rg, SASA, and Hydrogen bonding. The RMSD analysis revealed the unsteady nature of all the variants compared to wild type. RMSF analysis revealed the increased flexibility of the variants. In both cases, G15S variant showed the greatest deviations. Rg analysis found that the variant D140H had the highest level of compactness throughout the time over all the other variants, whereas G15S showed expansion compared to the wild type. In SASA analysis, all three variants showed great degrees of variability over the wild type and are possibly responsible for changes in protein-protein interactions. Hydrogen bond number analysis did not show a great deviation in the simulations, but variable numbers of Hydrogen bonds were seen to be forming throughout all the time.Based on simulation studies, it can be hypothesized that the variants G15S, D140Y, and D140H induced changes in the native conformation of the CDK4 protein in some behaviors and are therefore expected to have a negative impact on the protein’s role and structure.in silico study only focuses on the broad collection of useful data which is not a definitive conclusion. Therefore, more and more experimental studies are required to confirm these computational findings. However, the present study will aid in shortening the expensive experimental cost of identifying disease-associated SNPs in CDK4 protein as we have computationally sort listed the SNPs. So, these final three mutants  specifically G15S could be a malignant nsSNPs which was the aim of this research. So, findings of this study could be a blueprint for malignant nsSNPs identification in CDK4 gene for further clinical and experimental-based study. We believe that this study will help to differentiate malignant SNPs associated with CDK4 altered patients in future genome association studies.However, the main objective of this research is to deduce the relationship between the nsSNPs and their impact on the protein-protein interaction, stability, folding and catalytic function analysis. After a concomitant structural and sequence-based study, we found the G15S mutants as the strongest malignant mutant among the three suggested mutants. Besides the S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S1 Table.(DOCX)Click here for additional data file.S2 Table.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.