Proteins are essential molecules, that must correctly perform their roles for the good health of living organisms. The majority of proteins operate in complexes and the way they interact has pivotal influence on the proper functioning of such organisms. In this study we address the problem of protein–protein interaction and we propose and investigate a method based on the use of an ensemble of autoencoders. Our approach, entitled  All molecular interactions in a cell have been termed the interactome. Most such interactions involve proteins, which can bind to other proteins or to small molecules. Among these, the interactome of protein–protein interactions (PPI) is of particular significance, as more than 80% of proteins perform their roles not individually, but in complexes  and thusMethods for the identification of PPI can be classified into three categories: in vivo, in vitro and in silico . The firMachine learning methods are among the most popular choices for addressing this problem. Many researchers have pointed their attention in this direction and there are numerous studies approaching PPI that work with protein sequences. Earlier research focuses on “traditional” learning techniques such as Bayesian networks , supportWe propose and investigate a method based on the use of autoencoders with the aim to tackle the underlying classification problem in relation to PPI. The proposed approach, called The main innovation our work brings, compared to the methods previously proposed, resides in the manner in which autoencoders are involved in the process of PPI prediction. While heretofore the main task of autoencoders was feature extraction, the actual classification being performed by an additional classifier  used for encoding relationships between both the class of proteins that interact and the class of proteins that do not interact.In this section we are introducing a binary supervised classifier We decided to use autoencoders in designing positive class) and negative class), where by positive class or to the negative class. Let us denote, in the following, by The PPI problem may be formalized as a binary classification one. Let us consider that we are given two classes From a machine learning perspective, the PPI classification problem may be formalized as learning to approximate two target functions In a supervised learning scenario, the aim is to train a binary classifier on pairs of proteins belonging to both Our Data collection, representation and preprocessing. This stage includes the following steps:i.Collection of data sets which will be used in further training ii.Selection of the set of features relevant for representing the pairs of protein sequences in a vector space model.Training. The set of preprocessed vectors characterizing pairs of proteins prepared at the previous stage will be used for training the AEs and for building the supervised learning model Performance evaluation. This stage refers to the performance evaluation of our predictive model As depicted in The following sections will detail the stages of our approach.p will be represented as a numerical high-dimensional vector p. A pair of proteins k-dimensional representation of proteins p and Let us consider that a protein is encoded by a set For selecting the most appropriate feature-based representation for proteins, we started from two representations extensively used in protein–protein interaction prediction tasks: Conjoint Triad (CT) descriptors ,20,30 an1.CT features [iFeature library [features  can be u library .2.AC features are another type of descriptors which characterize variable-length protein sequences using vectors of fixed size [m properties and a distance xed size . Unlike n et al. . These pn et al. . Since tiFeature which removes non-standard amino-acids from the protein sequences.In the computation of both descriptors we used the default procedure of In this study, we considered a combined feature-based representation for the proteins, obtained by concatenating the AC and CT features computed for a protein sequence, in order to leverage the information captured by both types of features. The chosen method of representation uniformizes proteins, irrespective of their sequence length.As previously described, an autoencoder is trained for each class tion see  for the For training Three architectures are proposed for the autoencoders Joint–Joint architectureOur first architecture receives as input data the representation describing the pairs of proteins and reconstructs the concatenated features corresponding to the protein pairs. A schematic representation of the architecture is depicted in Siamese–Joint architectureGuided by the success of siamese neural network architectures in modeling pair data ,32,33,34p and z and The first such architecture, further referred to as Siamese–Joint, has a shared structure only for the encoder, while aiming to reconstruct the concatenated features of the two proteins. This architecture is presented in Siamese–Siamese architecturez and p and p is obtained from the concatenation of z and The last investigated architecture has a siamese structure in both the encoder and decoder, the weights being shared between the two proteins in a pair. In order to encode information about both proteins in a pair, we obtain a reconstruction for each protein by also using the other protein in the pair. This is achieved by combining the encodings of the two proteins into a common encoding that is used for reconstructing the original proteins. The neural network architectures employed in our study are formed of fully connected layers. For all of the three above mentioned autoencoder architectures, the encoder was formed of two layers of 600 neurons linked to a bottleneck layer of 300 neurons, followed by a symmetric decoder. The SELU  activati library .This section introduces the methodology applied for evaluating the performance of p and p and p, p, p and p and At the testing stage of the previously trained p, p, Let us denote by The probabilities Formulas  and 2)..pr−, FP , TN (true negatives) and FN . The classification of a certain pair of proteins . Based on the values from the confusion matrix, the following evaluation measures are computed [precision, recall or sensitivity, specificity or true negative rate, F1-score or F-score, Area Under the ROC Curve  [Due to the randomness involved in the selecting the training/validation/testing data sets, a val (CI)  of the aThis section starts by describing in HPRD-2007 database. The negative interactions were obtained by selecting pairs of proteins with different sub-cellular localizations [http://www.csbio.sjtu.edu.cn/bioinf/LR_PPI/Data.htm, accessed on 20 May 2020, indicated in [The first data set used in our experiments is Pan’s human protein–protein interactions data set , which cizations . The datcated in .Multi-species data set was proposed by Chen et al. [C. elegans, D. melanogaster and E.coli. The data set has been obtained by combining the three protein–protein interactions data sets originally proposed by Guo et al. [https://github.com/muhaochen/seq_ppi, accessed on 20 May 2020, filtered using 25% and 1% similarity thresholds.The n et al.  and was o et al. . In addiAn overview of the data sets and their number of positive and negative interactions is presented in The data were preprocessed and features were extracted as described in o et al. ,40 for tWe followed the evaluation protocol used in previous studies ,19 and pThe results presented in Despite the low degree of separation between the “+” and “−” classes see , AutoPPIWith regard to the data sets, we observe that our models have good performances, irrespective of data set size and degree of imbalance. This is best noticed when inspecting the three versions of the Multi-species data set, where the AUC measure is above 0.98 for all three, with very small differences between obtained values, with respect to all evaluation measures.Last, but not least, we note very small values obtained for the CI (below 0.01) expressing the stability of our model.sequence ,40,41, psequence , SVMs ussequence , extremesequence , a Latensequence , a stacksequence , a graphsequence , deep nesequence ,46,47 ansequence . From thn et al. , is slign et al.  and achin et al. .accuracy and The comparative results illustrated in proaches , in termaccuracy and F1 performance metrics described in p-value of For verifying the statistical significance of the performance improvement brought by Living beings are complex machinery whose inner gears have evolved to work flawlessly, in ideal conditions. Proteins, the fundamental cogwheels in this gear, perform most of their functions in complexes and thus understanding the way they interact with each other as well as with other molecules represents a step forward in figuring out the mechanisms of life. Through the present work we aim to bring our contribution in this endeavour and to advance the state of in silico methods for solving the problem of PPI prediction.We propose a procedure for the binary classification of protein–protein interactions (positive versus negative) having as focal points two autoencoders that are trained to encode relationships between proteins that do or do not interact. In addition to protein interactions, the input data are represented by protein primary sequences, which are encoded by a combination of two types of descriptors: conjoint triad and autocovarriance. We propose three types of architectures for the autoencoders and evaluate our approach on four data sets including proteins from different species. Experimental results demonstrate the potential of our approach, which proves to be highly competitive in relation to other solutions proposed in the literature. To the best of our knowledge autoencoders have not been exclusively used for predicting PPI so far .protein–protein interaction, it is general and may be applied for other binary classification problems.We note the generality of the binary classification model To further evaluate the performance of


MTUS1) gene acts as a crucial tumor suppressor by inhibiting growth and proliferation of eukaryotic cells including tumor cell lines. Down regulation of MTUS1 gene has been implicated in a wide range of cancers as well as various human diseases. Alteration through nsSNPs can potentially damage the structure and/or function of the protein. As characterization of functional SNPs in such disease linked genes is a major challenge, it is feasible to analyze putative functional SNPs prior to performing larger population studies. Hence, in this in silico study we differentiated the potentially harmful nsSNPs of the MTUS1 gene from the neutral ones by using various sequence and structure based bioinformatic tools. In a total of 215 nsSNPs, 9 were found to be most likely to exert deleterious effect using 7 prediction tools. From which, 5nsSNPs  were selected as potentially damaging due to their presence in the highly conserved region and ability to decrease protein stability. In fact, 2 nsSNPs (S1259L and E960K) among these 5 were found to be individually associated with two distinctive cancers named Stomach adenocarcinoma and Uterine corpus endometrial carcinoma. As this is the first comprehensive study analyzing the functional nsSNPs of MTUS1, the results of the current study would certainly be helpful in future prospects concerning large population-based studies as well as drug discovery, especially developing individualized medicine.The mitochondrial tumor suppressor 1 ( Cancer is known as one of the most dreadful and enigmatic diseases in today’s world. Continuous researches are going on to find out new approaches to combat cancers. Among these, identification and characterization of tumor suppressor genes play a crucial role as the loss of tumor suppressor activity is an important benchmark of cancer .MTUS1 gene that has been reported as a tumor suppressor gene at a chromosomal position 8p22. This gene encodes a family of microtubule-associated proteins named Microtubule-Associated Scaffold Protein 1 which interacts with angiotensin II type 2 (AT2) receptor. Dispersing over 112 kb on the chromosome, the gene incorporates 17 exons, alternative splicing of which gives onto three major transcripts entitled as ATIP1, ATIP3 and ATIP4. Among them the transcripts of ATIP3 entail three variants- ATIP2, ATIP3a and ATIP3b [Our study focuses on d ATIP3b .MTUS1 gene has been reported to be underexpressed in various types of malignancies including ovarian, breast, head-and-neck, pancreas, colon, bladder and lung cancers [MTUS1 gene with hepatocellular and squamous cell carcinoma of the tongue accordingly [Furthermore,  cancers ,5. The rordingly ,7. Ovariordingly .Among different types of mutations single nucleotide polymorphisms (SNPs) account 90% genetic variations in human. SNPs change single base pair in alleles which are the most common form of disparities in DNA sequence ,11.in vitro or in vivo conditions. In this situation in silico approach is a convenient way to distinguish the deleterious SNPs from neutral ones using particular algorithms. The overall effect of polymorphism including functional and structural alterations can also be analyzed through several databases.As human genome contain vast number of genetic polymorphisms, massive investigations are indispensable to explore the significance of each of them along with their association to disease susceptibilities as well as individualized drug designing . HoweverMTUS1 gene and to analyze consequent protein structure and functions. Here, our focus is not only establishing the structural configuration but also finding the association of different cancers with particular nsSNPs. As no effort has been noticed till date, it is the first comprehensive study to analyze the SNPs of the MTUS1 gene systematically which will be helpful in future extensive studies in this regard.In present study, we use public datasets and freely accessible bioinformatics tools to identify the most deleterious nsSNPs of the MTUS1 gene and its protein sequence (FASTA format) was collected from NCBI (https://www.ncbi.nlm.nih.gov/) and UniProtKB (http://www.uniprot.org/uniprot/) respectively. SNPs located in MTUS1 gene were retrieved from dbSNP database (http://www.ncbi.nlm.nih.gov/SNP/) [Data related to human ov/SNP/) .MTUS1 gene; SIFT, SNAP2, Align GVGD, PolyPhen-2, PROVEAN, PhD-SNP and PANTHER were used sequentially.To analyze the functional and structural consequences of deleterious SNPs of https://sift.bii.a-star.edu.sg/) determines the deleterious (probability score <0.05) and tolerated SNPs (probability score ≥ 0.05) based on sequence homology. This prediction helps to analyze the effect of amino acid variation on the phenotypic and functional changes upon protein molecule. The rsIDs retrieved from dbSNP were used as a input query for this server [SIFT (Sorting Intolerant from Tolerant) (s server .https://www.rostlab.org/services/SNAP/) differentiates between effect and neutral variants by scrutinizing a variety of sequence and variant features. For SNAP2, FASTA format of protein sequences was used as input query. The result was obtained as a score, ranges from −100  to +100 (strong effect prediction) that reveals the likelihood of specific mutation to alter the native protein function with expected accuracy [SNAP2 (Screening for Nonacceptable Polymorphisms) (accuracy .http://agvgd.hci.utah.edu/) web server predicts whether missense substitution is deleterious or neutral. This prediction emphasizes on protein multiple sequence alignments and biophysical characteristics of amino acids. It forms a spectrum of classified variants  where C65 is most likely to interfere with function and C15 is less likely to affect [Align GVGD (http://genetics.bwh.harvard.edu/pph2/) was used. As input query for PolyPhen-2; protein sequence, database ID/ accession number and details of amino acids substitution were given to the server. The score of PolyPhen-2 ranges from 0.0 to 1.0 that indicates the particular amino acid substitution as tolerated or deleterious respectively [For exploring the possible effect of an amino acid substitution on the structure and function of protein, PolyPhen-2 (Polymorphism Phenotyping v2) (ectively .http://provean.jcvi.org/index.php) predicts the functional impact of protein sequence variations as ‘deleterious’ or ‘neutral’. FASTA format sequence with substitutions predicted by the SIFT server was used as an input. If the PROVEAN score is ≤ 2.5 the protein variant is predicted to have a deleterious effect, otherwise the variant is predicted to have a neutral effect [By assessing the single amino acid substitutions PROVEAN  (l effect .http://pantherdb.org/tools/csnpScoreForm.jsp) classification system is established on evolutionary relationship, molecular functions and their interactions with other proteins. This tool gives position specific evolutionary conservation (PSEC) scores by estimating alignment of evolutionarily related various proteins. Plain protein sequence, amino acid variants and human organism were used as input query for this prediction [PANTHER cSNP  (ediction .https://snps.biofold.org/phd-snp/phd-snp.html) classifies single point protein mutation as disease-related or as neutral polymorphism based on Support Vector Machine (SVM) method. The required input query was protein sequence, position of mutation and mutated residue [PhD-SNP (Predictor of human deleterious single nucleotide polymorphism) ( residue .http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) predicts the changes in protein stability due to single point mutation. Both protein sequence and protein structure can be used as input query. Upon mutation this tool calculates the sign of the protein stability changes and related Delta Delta G values [I-Mutant Suite  server calculates the evolutionary conservation of amino acid positions in a protein. The evolutionary rate of an amino (or nucleic) acid position is strongly dependent on its structural and functional importance and is computed by using either an empirical Bayesian method or a maximum likelihood (ML) method. It uses a color scheme to present conservation score from 1 to 9 which is classified into variable, average and highly conserved. FASTA format of protein sequence was used as input query [Based on the phylogenetic relations between homologous sequences, the ConSurf (ut query .http://www.cbs.dtu.dk/services/NetSurfP/) server is used to predict the surface accessibility, secondary structure, disorder, and phi/psi dihedral angles of amino acids in an amino acid sequence. The FASTA sequence of MTUS1 protein was submitted to NetSurfP and the output of this server revealed buried and exposed region in protein structure [NetSurfP-2.0 (tructure .https://www3.cmbi.umcn.nl/hope/) assumes the effects of the mutation on the protein structure and the corresponding function. It produces a report, complete with results, figures and animations by collecting and combining available information from a series of web services and databases. Protein sequence and mutation were used as input query for HOPE [HOPE (Have (y) Our Protein Explained) (for HOPE .https://string-db.org/cgi/input?sessionId=croExd7fYyEe&input_page_active_form=single_sequence) database gives an important assessment and integration of protein-protein interaction as well as association from databases of physical interaction and databases of curated biological pathway knowledge. Protein sequence was used as input query for this tool [STRING  (his tool .https://zhanglab.dcmb.med.umich.edu/I-TASSER/) is a hierarchical approach for protein structure prediction as well as a structure-based function annotation. It identifies structural templates from the PDB to provide the most appropriate protein structure and usually provides the top 5 models of the targeted protein. The FASTA sequence of MTUS1 was the input file for this server [I-TASSER (Iterative Threading ASSEmbly Refinement) (s server .https://swissmodel.expasy.org/) is a fully automated protein structure homology-modeling server that uses the updated UniProtKB proteome for target-template alignment. As an input query FASTA sequence was used here. Not only the prediction of structures but also the validation of predicted structures can be estimated through the favored region of Ramachandran plot, QMEAN, and Molprobity score provided by this server [SWISS-MODEL (s server .https://servicesn.mbi.ucla.edu/PROCHECK/) evaluates the stereochemical quality of a protein structure by analyzing residue-by-residue geometry and overall structural geometry [https://servicesn.mbi.ucla.edu/ERRAT/) validates overall model quality by the statistical relationship of non-bonded interactions between different types of atoms based on characteristic atomic interaction. In both cases, predicted models in PDB format was used as an input query [PROCHECK (geometry . ERRAT (ut query .https://prosa.services.came.sbg.ac.at/prosa.php) is also widely used for the refinement and validation of the experimental protein structure. Predicted structures in PDB format was used for the input query for the estimation of the model quality of the protein [ProSA-web ( protein .https://swissmodel.expasy.org/) was used for the structural analysis of the mutated protein. The mutated model was then analyzed in TM-align tool  that gives template modeling-score (TM score) and root mean square deviation (RMSD) value for the comparison of protein structures based on the superimposition of the structures to find the structural similarity. In this server, TM-score ranges from 0 to 1, where 1 denotes a perfect match between two structures. In detail, 0.0 < TM-score < 0.30 means random structural similarity, whereas 0.5 < TM-score < 1.00 means both structures are in the same fold [Point mutation at the particular position in the native protein sequence was made and SWISS-MODEL  is an open-access database which allows exploration, visualization and analyzation of multidimensional cancer genomics data. Distribution of MTUS1 gene mutations in the lollipop plot for mutation frequency in the server was searched to find association of the specific nsSNPs with cancer [cBioPortal (h cancer .https://cansarblack.icr.ac.uk/) is an integrative translational research and drug discovery web resource for oncology. The mutation profile for MTUS1 provided by this database was searched for finding specific cancers linked to deleterious nsSNPs [canSAR Black (s nsSNPs .http://gepia.cancer-pku.cn/) is an interactive database that is used to analyze the RNA sequencing expression data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects. It provides expression profiles of a given gene in dot plots or box plots and survival analysis by using log-rank test. For both analysis the name of the candidate gene (MTUS1) was used as input and specific cancer name  was selected [GEPIA  (selected .https://www.mutarget.com/) is a tool based on the TCGA that correlates somatic mutations and gene expression in cancer. Correlations can be analyzed in two ways- The ‘Genotype’ run is for finding changes in gene expression that are related to a specific mutation and the ‘Target’ run is for finding mutations that alter the expression of target genes. The name of the candidate gene MTUS1 was used as input and the specific cancers associated with the significant nsSNPs were selected [muTarget (selected .MTUS1 gene were retrieved from dbSNP database (dbSNP NCBI: https://www.ncbi.nlm.nih.gov/snp/?term=chek2). It comprised a total of 55120 SNPs, out of which 1453 were missense (nsSNP), 3400 non-coding transcript, 558 synonymous, 52194 intronic, 9 initiator codon variant, 1 inframe insertion, 4 inframe indel and 20 inframe deletions  nsSNPs as diseased and rest of 195 (81%) nsSNPs as neutral (PolyPhen-2 speculated 67(31%) as probably damaging, 37(17%) as possibly damaging and the remaining (52%) as benign . PROVEAN neutral  Fig 2)..MTUS1 geBy using 7 Prediction tools , eventually 9 significant nsSNPs  were selected as most deleterious . To inveThe selected 9 nsSNPs were analyzed by I-mutant Suite to reveal the effect of point mutation on protein stability based on free energy change value. Out of 9, 6 variants  were predicted to decrease stability whereas others  were found to increase protein stability .Through the ConSurf web server, all the 9nsSNPs were analyzed to evaluate evolutionary conservation and find putative structural and functional residues. According to ConSurf output, out of 9nsSNPs 7 variants  were highly conserved residues (conservation score of 9) and remaining H1077L and C264Y were predicted moderately conserved (conservation score of 7) and variable (conservation score of 1) residue respectively. Among these 7 highly conserved residues, 5 variants  were predicted as functional and exposed whereas remaining 2 (L1084V and L1143Q) were structural and buried. The result of ConSurf is shown in For the identification of the most deleterious nsSNPs, I-mutant and ConSurf output were compared and scrutinized. 5nsSNPs  were selected as potentially damaging based on this comparison which were subjected to further analysis.Solvent accessibility and stability were assessed for the 5 variants  by NetsurfP. Among these deleterious nsSNPs, 3 variants  and their respective wild variants were exposed to the surface whereas 2 variants (L1084V and L1143Q) and their respective wild variants were buried .HOPE speculated the effect of amino acid substitution on the physical and chemical properties, hydrophobicity, spatial structure, and function of protein . Based oThe backbone, which is the same for each amino acid, is colored red. The side chain, unique for each amino acid, is colored black; (a) S1259L (b) E960K (c) L1084V (d) P503T (e) L1143Q.MTUS1 interacts with Angiotensin II Receptor Type 2(AGTR2), CD274, also commonly referred to as PDL1, Bradykinin Receptor B2 (BDKRB2), Kinesin Family Member 2C(KIF2C), Aurora kinases (AURKB), Leucine Rich Repeat LGI Family Member 3 (LGI3), ELMO Domain Containing 2 (ELMOD2), Polyamine Modulated Factor 1 Binding Protein 1 (PMFBP1), Spermatogenesis Associated 4 (SPATA4), and RIO Kinase 3 (RIOK3)  .The three-dimensional structure of the human MTUS1 protein was modeled using I-TASSER. The top 10 structural analogs in PDB were used as templates for such modeling, from which the topmost template (PDB ID: 6w1sr) covered 93% of the human MTUS1 query sequence. The server provided the top 5 models for the targeted protein though none of them were validated according to the scores of structure validation software due to their poor stereochemical properties .Hence, we used SWISS-MODEL for the three-dimensional structural analysis of MTUS1 protein. Unlike I-TASSER, SWISS-MODEL provided 9 structures and all of them were a partial structure of our targeted protein based on the best-aligned template from UniProtKB proteome. Further analyses were done using PROCHECK, QMEAN, Molprobity, ERRAT, and ProSA programs to estimate the quality of the models , and modFor model 8- Ramachandran favored region was 98.04% by SWISS-MODEL and 95.1% core region by PROCHECK analysis. Molprobity and QMEAN scores were 1.08 and 0.82 respectively. ERRAT showed a higher score of 96.875% and ProSA-Z score was -0.83 for the best-predicted model.In our study, the most suitable model (model number 8) covered the amino acid residues from 1133–1236 and it contained only one mutation (L1143Q) out of the investigated 5 most deleterious nsSNPs. Instead of Leucine, Glutamine was introduced at the position of 1143 in the native protein sequence and SWISS-MODEL was used to determine the mutated structure. While verifying the mutated model quality by SWISS-MODEL structure assessment, PROCHECK, QMEAN, Molprobity, and ERRAT similar scores were found as its native structure .TM-align, an algorithm for protein structure alignment and comparison was used for comparing the structural analog. For mutated structure, we got 0.99982 and 0.05 as TM-score and RMSD value respectively. These values indicate that both the structures are in the same fold.MTUS1 in both cBioPortal and canSAR Black webserver revealed the association of S1259L with Stomach adenocarcinoma (STAD) and the substitution of glutamate at 960 position with Uterine corpus endometrial carcinoma (UCEC). The canSAR black webserver also revealed that S1259L may result in moderate STAD whereas mutation at 960 may result in severe UCEC. However, the remaining nsSNPs were not found in the mutation profile presented by the web servers.The mutation profile of MTUS1 whereas Uterine corpus endometrial carcinoma (UCEC) ensues due to underexpression of MTUS1  results due to overexpression of of MTUS1 .MTUS1. Abnormal expression of MTUS1 displayed association with poor prognosis of patients with STAD and UCEC. In STAD, overexpression of MTUS1 showed shorter survival time compared to patients with lower expression levels. In contrast, UCEC patients with high MTUS1 expression had longer survival time than patients with lower MTUS1 expression  and Uterine corpus endometrial carcinoma (UCEC). The patients were categorized into high-expression and low-expression groups based on the median expression level of pression .MTUS1 as mutated gene showed that for Gastric cancer, mutation in MTUS1 can affect the expression of other genes such as CCNB2, OIP5 etc. This same analysis for Uterine cancer showed that expression of FAXDC2, MYCL etc. altered due to MTUS1 mutation  can change expression of MTUS1. Similarly, expression of MTUS1 can be changed due to the mutation of genes responsible for Uterine cancer . Though the server provides up to top 30 models, we represented expression of two genes for each analysis  is a tumor suppressor gene which has been associated with various cancers i.e. breast, colon, lung, pancreas, bladder and prostate cancer [Mitochondrial tumor suppressor 1  to get an integrated picture of pathogenic SNPs of 9 nsSNPs  as high-Generally, the function, activity and regulation of a protein significantly depend on the structural stability of the molecule. Decrease in protein stability causes degradation, misfolding and aggregation of proteins leading to subsequent dysfunction ,38. To dThe evolutionary conservation profile of a protein helps to determine the severity of a detrimental mutation. The nsSNPs that are located at highly conserved region are more likely to be detrimental than nsSNPs that are located at variable regions . We usedBy analyzing the result of I mutant and ConSurf, we can conclude that out of these 9 nsSNPs, 5nsSNPs  are potentially vulnerable because of their higher conservancy as well as ability to decrease protein stability.Moreover, to analyze the effect of these above-mentioned 5 high-risk nsSNPs on the structure of MTUS1 protein we performed NetsurfP and HOPE. NetsurfP speculates solvent accessibility and secondary structure of the protein . It presDetermination of protein stability is quite difficult as it cannot be predicted by considering a single factor only. Physiochemical properties of amino acid residues such as-polarity, charge, hydrophobicity etc. play crucial role for the overall stability of the protein molecule. It has been shown that protein stability is linked with the presence of hydrophobic residues at its surface area because these residues hinder accessibility of water molecule to the protein . Among tHope server predicted that these 5 highly risky nsSNPs might have damaging effect on the structure of the protein  among whFor the understanding of cellular processes protein–protein interaction network is a vital factor. STRING plays a critical role to filter and assess functional genomics data as well as to provide an intuitive platform for interpreting structural, functional and evolutionary properties of proteins . In the To determine the three-dimensional structure of human MTUS1 protein, structural prediction tools were used due to the unavailability of the PDB ID (Protein Data Bank ID) of this protein. Automated protein structure prediction tool, I-TASSER was used where FASTA sequence of the whole protein was given as an input file and the server came up with the top 5 final structural models. The quality of the predicted models is preliminarily estimated by the confidence score (C-score) given by the server that ranges from -5 to 2 and the highest score depicts the most compatible ones. According to this, the first model (highest C-score = -1.67) was selected as the most suitable structure . As therValidation of experimental models is indispensable to obtain a better quality of targeted protein structure. To ensure it several computational programs- SWISS-MODEL, PROCHECK, QMEAN, Molprobity, ERRAT, and ProSA were used. Among all the verification matrices Ramachandran plot is the most prioritized as it represents the φ-ψ torsion angles of the protein backbone of predicted models. SWISS-MODEL gives the idea about the favored region of the Ramachandran plot and PROCHECK reveals the stereochemical quality of a given protein structure by assessing the Ramachandran plot into different regions—core, allowed, generously allowed, and disallowed region. More than 90% of residues in the core region or in the most favored region can be preferred as a favorable structure .Other computational tools provide scores for the estimation of protein model quality. QMEAN-Z score of -4.0 or below -4.0 indicates the model with low quality and higher score points to the favorable states of the structure . ProSA-ZIn our study, all the 5 models from I-TASSER were invalidated according to the standard score of validation tools mentioned above . HoweverAll these 9 models were then evaluated for structural validation by PROCHECK, QMEAN, Molprobity, ERRAT, and ProSA programs. According to the standard score of all validation software, model number 8 was selected as the best presumptive structure of MTUS1 protein . This moStructural comparison between wild type and mutant structure was analyzed by TM-align tool. Low TM score and high RMSD value indicate structural dissimilarity whereas we found a high TM-score (0.99982) and low RMSD value (0.05); indicate both the structures are on the same fold . The wilMTUS1. According to this study, more than 30 cancers can occur due to various anomalies  in MTUS1 gene. Interestingly, mutation is the sole reason for several cancers and high frequency of mutation has been observed in Uterine corpus endometrial carcinoma (UCEC) and Stomach adenocarcinoma (STAD) which account 6.99% and 3.86% respectively  and substitution of glutamate at 960 position was linked to severe Uterine corpus endometrial carcinoma (UCEC).MTUS1 against time which provides crucial summary of data that is utilized to calculate measures i.e., median survival time ,46. In tgulation  and downMTUS1. For instance, in case of gastric cancer, the expression of CCNB2, OIP5 etc. genes were found to be altered along with MTUS1 mutation. The CCNB2 gene is involved in cell cycle regulation [OIP5 gene is associated with chromosomal segregation during mitosis [FAXDC2 and MYCL gene’s expression changed due to MTUS1 mutation. The FAXDC2 gene induces megakaryocyte differentiation [MYCL encodes a transcription factor concerned in lung cancer [The muTarget is a tool that is designed to find out not only the genes whose expression are changed due to mutation in the query gene but also those genes whose mutations affect the expression of the target gene. From our analysis, it has been found that the expression of various genes was changed due to mutation in gulation  whereas  mitosis . For Utentiation  whereas g cancer .MTUS1 was used as the target gene, its expression was changed by numerous mutated genes. For instance, the server represented that mutation in TEX15, RECQL4 etc. genes altered the expression of MTUS1 in STAD patients. TEX15 is engaged in normal chromosome synapsis and metabolic recombination during spermatogenesis [RECQL4 encodes a DNA- dependent ATPase which may regulate chromosome segregation [TP53 and ZNF18. Interestingly, TP53 is a tumor suppressor gene [MTUS1 gene. This interaction indicates that aberration in one tumor suppressor may affect other gene of similar category which might worsen the state of the disease [ZNF18 is a zinc finger protein involved in transcriptional regulation [Again, when ogenesis  whereas regation . The expsor gene  like our disease . Howevergulation . Such gein vivo investigation is needed to establish the association with specific cancer in future.Two nsSNPs from the five deleterious nsSNPs have been found to be associated with cancers. It is highly likely that the other three nsSNPs might be involved in different cancers. However, robust MTUS1 gene products play critical roles in various cellular mechanisms and prevent uncontrolled cell growth and proliferation. Consequently, alteration of this gene has been associated with different types of diseases including various cancers.As a tumor suppressor, the in silico analysis of functional SNPs in the MTUS1 gene. We reported 5nsSNPs  as potentially damaging due to their presence in the highly conserved region and ability to affect protein stability. And two of them, S1259L and E960K were found to be associated with Stomach adenocarcinoma and Uterine corpus endometrial carcinoma accordingly. The findings of this study will hopefully provide a guideline in extricating the damaging SNPs which increase the risk of cancers and disease susceptibilities. However, extensive population-based studies along with clinical trials are essential to characterize the effects of these polymorphisms on structure and function of the protein as well as to develop effective and individualized treatment option.This study is the first systematic and extensive S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins’ 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug–target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery. Proteins, large and complex polymers with linear amino acid chains, play crucial roles in cells responsible for constructing and regulating our body. By revealing the structure and contacts of biomacromolecules, we gain a better understanding of their function, thus facilitating the rational drug discovery process. The recent advances in experimental structural biology techniques such as X-ray crystallography, nuclear magnetic resonance (NMR), and cryogenic electron microscopy (cryo-EM) have fueled accurate structure determination ,2,3,4. HProtein structure prediction has become more powerful and accurate with method developments from traditional statistical methods to machine learning (ML) and deep learning (DL) methods ,10,11. AIn this review, we will provide an overview of DL-associated protein structure prediction, related concepts, frequently used DL architectures, and developed methods predicting various PSAs delineating different levels of details of protein structure. The further applications of DTI are of interest and discussed. Finally, current limitations, as well as the advantages of DL-based protein structure prediction upon drug discovery field, will be highlighted.The central dogma of molecular biology states that DNA sequences are transcribed into messenger-RNA (mRNA), and then these mRNA sequences are translated into protein sequences. Searching similar sequences can be used to reveal “homologous” genes or proteins by detecting statistically significant similarity, which indicates common ancestry. This protein sequence, in structural biology, is assumed to determine the three-dimensional structure and function of a protein. It is based on the fundamental observation that similar sequences from the same evolutionary family will typically adopt similar protein structures. Moreover, the structures of proteins are highly conservative in evolution compared with their sequences, and the number of unique structural folds is generally thought to be limited in nature. Thus, tremendous effort has been put into quarrying the relationship between structure and sequence of proteins. As the number of protein sequences is exponentially increasing, while the experimentally verified structures are growing slowly, we expect homology-based contact map prediction and modeling to become far more popular.A protein structure can be defined as one of four levels: primary, secondary, tertiary, or quaternary structures. Primary structure is a linear sequence of amino acids. There are 20 standard amino acids available to form a protein, and each amino acid is connected to the next one via peptide bonds. Primary structure is often introduced as a string of letters, i.e., ‘AESVL…’, as each standard amino acid has a corresponding single-letter code (and three-letter code). This already gives much useful information with respect to protein structure in three-dimensional space owing to the distinctive characteristics of each amino acid. For example, the different hydrophobicity of each amino acid limits the conformation of the protein, and some unique covalent bonds can be formed only between certain amino acids such as cysteine. Many ab-initio protein structure predictions start with this sequence of amino acids, a primary structure.10 helix (G), α-helix (H), and π-helix (I); strands into two types as β-strand (E) and β-bridge (B); and coils into three types as β-turn (T), high curvature loop (S), and any other previously undefined type (C) [Secondary structure defines the form of local segments of proteins. It is normally determined by the hydrogen bond patterns of polypeptide backbone or backbone dihedral angles . The two common secondary structures are α-helices and β-strands. An α-helix is a segment of amino acids where the main chain forms a helix, pointing side-chains outward. Two hydrogen bonds per residue stabilize this helix formation. A β-strand is rather connected laterally where side chains are pointing out perpendicularly to the plane with each successive residue facing the opposite side. This form normally requires a partner β-strand unit for its stability. When used in protein structure prediction, secondary structure normally falls into a three-state or a fine-grained eight-state categorization. The three-state categorization consists of two regular types of α-helix (H) and β-strand (E), and one irregular type of coil region (C). The widely-used eight-state categorization based on the Dictionary of Secondary Structure for Proteins (DSSP) program by Sanders further dissect helices into three types as 3type (C) .Tertiary and quaternary structures elucidate a three-dimensional arrangement of the single and multiple proteins, respectively. They can be represented using the Cartesian coordinates of each atom in three-dimensional space. Owing to the aqueous nature of proteins, the main driving force deciding tertiary and quaternary structure is the hydrophobic interaction among amino acids and water molecules. Thus, proteins tend to possess a hydrophobic core where side chains are buried, avoiding polar water molecules. Such three-dimensional information is deducible when one has the primary structure, secondary structure, and inter-residue CM in hand.In contrast to sequences, which are virtually infinite in number, proteins can take on a finite number of different shapes to carry out their functions in the cell. One can observe stronger structural conservation than sequence conservation; for example, a strong interdependence for polar residues exists at protein core with poor solvent accessibility, but no significant correlation is detected when looking at sequences only . This maDeep learning is a branch of machine learning, utilizing an artificial neural network with many layers embedded, which resembles a human nervous system. Working as universal function approximators, deep neural networks are used to solve various problems: classification, clustering, pattern recognition, predictive analysis, regression, and so on . With thArtificial neural networks consist of nodes in input, output, and hidden layers where each node is connected to nodes in adjacent layers. These connections have distinct weights, and the inputs are processed  at each node. Then, it undergoes the transformation based on the activation function such as sigmoid or rectifier, and the output functions as the input for the next layer. Learning is the process of finding optimal weights that make the neural network behave as desired. Two types of learning are present; supervised learning handles labeled datasets for classifying or predicting purposes, while unsupervised learning handles unlabeled datasets for analyzing or clustering the given dataset. The amount of required training data to build effective deep learning models is dependent on the complexity and the number of features in the training data. To update and optimize the weights, back-propagation is used to calculate the gradient of the loss function that computes the error for each training iteration . When toThe most straightforward and earliest stage example for the deep neural network is the feedforward neural network (FFNN), sometimes called multilayer perceptron (MLP). A perceptron, a single-layer neural network, can only process first-order information to obtain results comparable to those obtained by multiple linear regression. When multiple layers are used, the neural networks can extract higher-order features. In FFNN, information flows in one direction from the input layer to hidden layers, if any, until it reaches the output layer. The network has connections between each node and every other node in the next layer.Recurrent neural network (RNN) contains loops where the output of the layer becomes an input. This looping generates state neurons that enable the network to possess memory about the previous state. Obtaining a future memory is favorable for prediction and is feasible with RNN by introducing a delay, but the prediction rates drop if the delay is too large. To overcome this issue, a bidirectional recurrent neural network (BRNN) has been developed, splitting the state neurons into positive and negative time directions ,32. 2D-BConvolutional neural network (CNN) often encompasses three types of layers: convolutional, pooling, and fully connected layers ,36. CNN Graph deep learning models enjoy attention from numerous application domains thanks to their structural consistency to the native graph-structured data. Graph convolutional network (GCN), a generalization of the convolutional operator upon non-Euclidian structured data, contains several spectral or spatial convolutional layers . Its uniTo improve our fundamental understanding of biological phenomena, protein structures and their contacts shed light on their mechanism of action, possibly assisting with drug design. Based on the co-evolution analysis and deep learning methods, protein structure prediction methods have made significant progress in recent years by using multiple sequence alignments (MSAs) of the target protein and its homolog. A combination of the architectures mentioned above is widely used in this type of protein structure prediction methods. One famous example would be a combination of bidirectional RNN and CNN (BRNN–CNN) . In thisProteins and their functions are distinguished by their structures in numerous aspects, but the rate of discovering protein structures has been much slower than the rate of sequence identifications owing to the cost and complexity. Therefore, protein structure predictor has become one of the most efficient and high-throughput tools in Bioinformatics to handle flooding known sequence data with developing methodologies such as statistical, ML, and DL methods. The feature used in the predicting process is known as PSA; it contains simplified information to ease the computing process and is used as an intermediate step to estimate the full protein structure. One dimensional- (1D-) and two-dimensional- (2D-) PSAs have enjoyed a great amount of attention, where secondary structure, solvent accessibility, or intrinsic disorder is mainly described as 1D-PSA, and CM or the detailed version of CM (multi-class CM or distance map) is expressed with 2D-PSA. Several DL applications have been developed for 1D- and 2D-PSA predictions, becoming more accurate owing to expanding of the availability of sequence and structure data.3 score) or eight-state percentage accuracy (Q8 score), which is defined as the percentage of correctly predicted secondary structure residues.The most fruitful feature among 1D-PSAs is the secondary structure, the very first step for the full protein structure prediction from the sequence. Two main classifications are available: three-state categorization into α-helix, β-strand, and coil region, or eight fine-grained categorizations, which further segregate the previous three states (vide supra). The earlier stage methods have used sequence data solely as input sources, but later, evolutionary information and physicochemical properties were involved in enhancing the prediction accuracy . The accOne of the earliest servers available for secondary structure prediction would be JPred developed by Cuff et al. . The ser3 score in their latest versions with deeper neural networks and better algorithms. Considering the explosive advancement in reliability for Q3 score with DL methods, it might not take too long until the theoretical limit of 88–90% is attained.Taking other 1D-PSAs into account along with secondary structure and considering physicochemical properties, as well as evolutionary information, helped to enhance the overall accuracy. DESTRUCT, proposed by Wood and Hirst, iteratively used cascade-correlation neural networks upon both secondary structure and torsional angles . The iteOne special kind of 1D-PSA targets disordered regions of proteins. Many proteins contain intrinsically disordered regions (IDRs) that are highly flexible. Having multiple structures available, IDRs are involved in assembling, signaling, and many genetic diseases . TherefoWith the information gained from 1D-PSAs in hand, one might need 2D-PSAs to fully construct the three-dimensional protein structure. Recent endeavors for 2D-PSAs are focused on CM and multi-class CM, both expressing the closeness between residue pairs in a protein. CM takes a binary 2D matrix structure of N × N, where N is the length of the protein sequence, assessing each residue pair as 1 (presence) or 0 (absence) for matrix elements based on the user-defined threshold Euclidean distance . Multi-class CM is expressed in a 2D matrix, but the matrix elements are quantized in detail, categorized into more than two states. The importance of this CM for protein structure prediction is directly shown in estimations; an early study estimated that one could assemble a structure model within 5 Å RMSD from the native structure if N/4 long-range protein contacts are known, and another study estimated that one contact per twelve residues allows for robust and accurate protein fold modeling ,67.The CM itself definitely provides useful information on the given protein’s spatial organization, but one should note that CMs often contain transitive noise coming from “indirect” correlations between residues. Methods for direct correlation analysis are used to remove this noise such as mutual information (MI), direct coupling analysis (DCA), and protein sparse inverse covariance estimation (PSICOV) ,69,70. DMany groups have developed CM predictors utilizing multi-stage deep neural networks. The previously introduced Distill server also provides the CM predictor named XX-Stout . The devRecently, in 2019, DeepCDPred was developed, which includes a multi-class CM predictor exploiting distance constraint terms . The autOne of the main goals of structural bioinformatics is to unravel the relationship between the individual amino acids that make up a protein and the corresponding 3D structures, i.e., to distinguish the relationship between genotype and phenotype. A breakdown in this relationship may allow us to clarify the role of certain proteins, such as the binding to specific targets or catalyzing novel chemical reactions, providing insights into biological advances and drug discovery. Several experimental techniques for structure determination exist and have been continuously developing. Decades of theoretical work has attempted to predict protein structures from their amino acid sequences and, in some way, 1D- and 2D-PSA predictions are also the efforts to excel this 3D structure prediction. Significant progress has been achieved on this problem thanks to the rapidly growing number of available sequences and the application of global statistical methods. Deep learning has become the dominant technology to predict protein structures based on contact or evolutionary maps.https://predictioncenter.org/index.cgi) is a biannual competition with global collaborative efforts designed to evaluate the state-of-the-art techniques in protein structure prediction. The algorithm for tertiary structure prediction can be subdivided into the following: homology modeling, which utilizes a known structure with a similar sequence as a template ; fold recognition, which is also called protein threading (templates required); and de novo structure prediction, which is template-free modeling. Recent advances in DL-related techniques have been increasing the accuracy of contact distance prediction and residue–residue co-evolutionary analysis and, finally, in the past several years, significant progress has been made in template-free protein structure prediction as well as template-based modeling [CASP . Analyzing this covariation may help identify directly contacting residues in 3D conformations, functional residues in substrate binding, or residues involved in protein–protein interactions. As discussed in the earlier section, CM is a bi-dimensional matrix coding the absence/presence or the probability of contact between residue pairs in a given protein. The values near the main diagonal in a CM are trivial because these are the ones from adjacent amino acid pairs . The most relevant information in a CM is located far from the main diagonal. Analyzing elements far from the main diagonal may give useful information about structural properties and spatial details of the protein backbone. Hence, these contacts or multi-class contact maps can provide information concerning the spatial organization of the protein and can be used to improve the quality of the predicted tertiary structure. In the case of a typical globular protein, nearly 90% of all residue pairs are expected to be non-contacting, so that only a small portion of inter-amino acid distances should be accurately employed as constraints to direct structure determination. Advanced deep learning techniques have shown promise in predicting accurate residue–residue contacts. In order to increase the accuracy of CM, one may need to consider several key factors such as deep learning techniques, reliable MSA, distance distribution prediction, and domain-based contact integration.AlphaFold (Google DeepMind), the latest hot trend in this field, was first released at CASP13 (2018) and has evolved to AlphaFold2 at CASP14 (2020) ,84. DL wAlthough the AlphaFold series were more prominent than other competitors during the two CASP experiments, various other prediction programs were developed based on deep learning and showed significant progress. For example, RaptorX  is a serOne of the popular and successful approaches to protein structure prediction is homology modeling, which relies on two principles: (i) the amino acid sequence determines the protein’s typical fold or 3D structure, and (ii) the 3D structure is somehow preserved with regards to the primary sequences. Using known structures of homologous proteins that have a certain degree of sequence similarity, it is a quite convenient and effective way to build an initial model. However, the problems in the weak sequence–structure similarities, aligning sequences with structures, modeling of rigid body shifts, and accurate conformations of loops and side chains, as well as detecting errors in a model, are still recognized to date. The combination with DL-based approaches recently appears to outperform conventional methods, accomplishing a significant improvement in model accuracy. The CASP13 and 14 results demonstrate that the complex mapping between amino acid sequence and 3D protein structure can be effectively learned using a neural network and generalized to previously inaccessible cases.The homology modeling is generally performed by the following steps: (i) identify and select the eligible templates, i.e., other homologous proteins with known 3D structures , and JackHMMer, among others); (ii) multiple sequence alignment  ,92; , which is a succeeding step for protein structure prediction. For both template-based and template-free methods, QA is followed by structure predictions to measure the divergence from the natively folded protein structures. Since CASP7(2006), QA has been categorized for competition to develop methods for assessing the quality and correctness of protein structure models . EarlierThe identification of the physical interactions between new drug candidate molecules and their biomolecular targets is an essential part of designing new drugs. A computational approach with the ability to predict novel drug–target interactions (DTIs) can be utilized in lieu of costly and time-consuming procedures with conventional screening methods. A number of machine learning and deep learning approaches based on ligand-based and target-based approaches have been employed to predict binding affinities to save time and money in drug discovery efforts. In addition, the large chemical and genomic spaces present greater challenges as multiple drugs can be associated with multiple targets. In the perspective of medicinal chemistry, neural networks have been used in compound classification, QSAR studies, and the identification of drug targets and drug molecule’s binding modes. A variety of machine learning techniques are being used to take advantage of the large volume of complex high-dimensional information to predict interaction patterns. Here, we discuss the recent advances in DL-based DTI prediction, especially the cases relying on their 3D structural aspects.A deep-learning approach has been applied to lead optimization in combination with traditional in silico drug discovery approaches. Wallach et al.  (AtomNetThe accuracy of traditional docking modules and scoring functions can be also improved in combination with DL approaches. Morrone et al.  combinesThere have been significant advances in predicting protein CMs from the MSA of homologous proteins by analyzing the signals associated with co-evolution. Combining suitable DL methods has become a powerful framework to disentangle the underlying relationships between sequences and structural elements, leading to better drug design based on the target structures. This review covered the current trends in the protein structure prediction field, especially state-of-the-art techniques combined with deep learning architecture for contact map prediction. DL has just started to be applied to the biomolecular structure, but showed a successful strategy in the prediction field. It is fascinating and encouraging that the current DL-based techniques provide a significant advance; however, it does not mean that they ultimately “solved” the protein folding problem. Protein folding is guided and accelerated by local interactions that are rapidly formed, driving the further large-scale folding or assembly. In the folding process, some proteins need helpers like chaperones or neighboring domains. The current prediction methods do not say about this process, and we still do not have a solution to the problem of protein folding mechanism or pathway. Moreover, some proteins have floppy, ‘intrinsically disordered’ parts in their structures rather than well-defined forms. These disordered parts can act as a functional unit. DL-based approaches also showed high performance on the prediction of these regions, but they do not interpret the functional mechanism of these floppy regions. Thus, we need to develop DL approaches that will be able to address some of these caveats as well.It is evident that the drug discovery research will continue to make progress with the learning-based approaches to explore the structures of biomacromolecules and the vast chemical space modulating these targets. In some cases, owing to the inherent limitations of data-driven research, it may be difficult to construct reliable models because of the lack of high-quality data sets. However, we believe this limitation could be overcome by incorporating expert domain knowledge and continuously growing high-quality data sets. We also expect that homology-based contact map prediction and modeling becomes more prominent to improve accuracy at a large-scale prediction problem.


The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes.Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm.http://datasetmachat.drugdesign.fr.All data are available online at Bioinformatics online. Proteins are macromolecules involved in most biological processes. Protein structures have been classified based on their backbone conformation and evolutionary history . MoleculThe evaluation of the performance of shape retrieval methods in the literature is performed classically during the SHREC community benchmark  . Apart from the Protein hierarchical level, the dataset contains sub-classes along two hierarchical sublevels. The Species hierarchical level contains 54 classes corresponding to the different species. The Domain hierarchical level is composed of 241 classes corresponding to the initial SCOPe classification. For each structure of the dataset, the solvent excluded surface (SES) (Set ce (SES)  was compce (SES)  with defSet  3D-Surfer, the protein global surface information is represented with 3D Zernike Descriptors (3DZD), mathematical moment-based invariants of 3D functions  (The am (VFH) .CE  (tension)  represenDeepAlign   within 15 Å in the reference structure are computed. The lDDT score is computed as the average of the four fractions of conserved distances between the defined atom pairs ij with a growing tolerance of 0.5, 1, 2 and 4 Å, respectively.MMLigner , First-tier (FT), Second-tier (ST) and Mean Average Precision (MAP). The Precision-Recall plot draws the recall To evaluate the computation time of the shape retrieval methods, we considered the sum of the runtimes required to compute (i) the method’s descriptor for the largest SES, (ii) the method’s descriptor for the smallest SES and (iii) the distance between two SES. All calculations were performed on an Intel Core i7-6700HQ CPU@2.60 GHz with 32 GB of RAM.i.e. distant surficial homologs), we compared the dissimilarity matrices to a sequence aligning matrix. Clustal Omega  on the hierarchical protein shapes set Protein, Species and Domain. 3.1.1The precision-recall curves in 3.1.2Protein hierarchical level to the Species hierarchical level, the orthologous proteins are separated into disjoint classes. The overall performance decrease for the shape retrieval methods, except for VFH in FT and MAP . We observe a flattening of the precision-recall curves  for the al level . Regardi3.1.4The runtimes are presented in α (or backbone) atoms are taken into consideration; they are sensitive to the changes of orientation between the domains of multi-domains proteins; models bearing unrealistic structural features  are not penalized . From the biological function point of view, 3 out of the 6 pairs share different biological functions. The pairs b, f and e, respectively associate a calcium-binding protein and an electron binding protein; a calcium-binding protein and a metal-binding protein; a metal-binding protein and an electron transport protein.Shape retrieval methods allow to compare protein structures regardless of their sequences, secondary structures or fold. We identified in set n shapes  sharing We compared the distances obtained for these protein pairs with the distances obtained within distant surficial homologs retrieved from the literature B, . Using tructures , CE retrructures ].The performance of the shape retrieval methods were evaluated on the hierarchical dataset of protein shapes  and cryo-Electron Tomography (cryo-ET) where detected macromolecular shapes can be identified using shape retrieval methods  or a high similarity between the objects to be compared , shape-based methods, due to the abstract protein molecular surface representation, may be better suited to the blind classification of large datasets including highly heterogeneous protein structures , such asi.e. proteins with similar molecular surface shapes and low sequence identity, is of a major interest. It underlines the usefulness of shape retrieval methods and beyond, tackling protein structure comparison through their molecular surface shape instead of their backbone orientation, especially when structural methods fail to identify such similarity. Shape retrieval methods could be used to identify proteins with similar molecular surfaces despite a low sequence identity which could be beneficial to the protein structure prediction community, notably in threading where folds could be enriched with surface shapes. Similarly, these methods may be beneficial to the protein structure classification where reference databases still require manual intervention from human experts. With this in mind, shape-based methods may enrich the pool of methods available for protein comparison able to retrieve proteins with similar shape but different topologies  on a protein shapes dataset. On this dataset, 3D-Surfer and PANORAMA outperformed Shape-DNA and VFH. On a selected example displaying large conformational changes , all shape retrieval methods displayed a reasonable performance in recognizing their different conformations within the dataset.Different structure comparison methods were used as a reference in this study . TM-Align slightly outperformed shape retrieval methods in the retrieval task, but failed in tracking the large conformational changes of the calmodulin. For the calmodulin case, the superposition-free structure comparison method lDDT outperformed all the other evaluated methods in identifying the different conformers of calmodulin. We also identified six pairs of distant surficial homologs that could be used for future studies on protein surficial similarity search. Finally, shape retrieval methods were associated with larger computational costs compared to classical structural alignment methods but this additional cost is still compatible with the treatment of large structural datasets. Geometric learning methods could be beneficial here since their computational cost seems to be lower.This work confirms the interest of protein molecular shape as a higher-level description of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners, (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm.btab511_Supplementary_DataClick here for additional data file.


Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface. Protein–protein interactions (PPIs) lie at the heart of the machinery of life. Numerous cell mechanisms, such as metabolic pathways, transport or immune response, rely heavily on protein interaction networks ,2,3. As Experimentally, a wealth of data regarding the protein interactome can be acquired from proteomics ,12. Atomdocking, where one attempts to determine the structure of a protein complex starting from its individual partners. Established in 2001, the CAPRI  initiative has fostered significant developments in docking and scoring methods [assembly category in order to combine both worlds, i.e., tertiary and quaternary structure prediction methods [In parallel with experimental techniques, in silico approaches have also evolved to provide a promising complementary strategy in both predicting the interaction partners ,18,19 an methods ,24,25. I methods ,27,28. I methods ,32,33,34 methods ,38,39,40To aid the development of such tools, in this review, we first focus on the recent strategies established to investigate protein–protein interfaces in a dynamic perspective  simulations remain a first-choice tool in understanding the dynamics of biomolecular assemblies ,42. MD-pcroscope  offered croscope  and divecroscope .The efficient sampling of the rugged conformational landscape of protein interfaces is an expensive process. Despite advances in hardware technologies, the computational cost of simulating long time scales (multiple microseconds) to escape local energy minima can still present a limiting factor for many systems. These considerations led to the development of enhanced sampling strategies, focusing only on the interface sampling over time . For exaIn docking, most approaches treat the interacting protein partners as rigid bodies during the first exploratory steps. The backbone flexibility is only addressed at later refinement stages. In these approaches, whereas side-chain flexibility can be addressed to a certain extent, the backbone flexibility remains a costly challenge . To reduAnother option when trying to address the interface flexibility with a reduced computational cost is to use elastic network models (ENM) ,69,70. EENMs are also commonly used to perform normal modes analysis (NMA) and investigate the collective dynamics in a protein structure. For example, Liang et al.  studied Next to the portrayed computational tools, several biophysical techniques can provide information on the conformational heterogeneity of the interaction partners. The structure and dynamics of interface residues, or the lifetime of local contacts, can be obtained via X-ray crystallography, NMR, cryoEM, or FRET . NMR andFor example, Kharche et al. investigated the interaction between the CXC chemokine receptor 1 (CXCR1) and its cognate chemokin, interleukine-8 (CXCL8) . Here, KInterestingly, coevolutionary data retrieved from the sequence of interacting proteins can also bring precious information when investigating the dynamics of protein–protein interfaces, as they can be used to guide the assembly formation . Direct Various tools are now available that have been tuned to process all-atom MD trajectories. These tools are developed to gauge changes in covalent (intra) and non-covalent (inter) interactions, as well as changes in the intrinsic dynamics of a biomolecular complex. These analysis tools usually come together with the simulation package used to create the relevant MD trajectory. In the case of GROMACS, for example, a vast collection of gmx scripts allow the user to calculate changes in interfacial backbone-related terms , hydrogen bonds, salt bridges, specific interfacial distances, buried surface area, interfacial water molecules, and RMSDs/RMSFs . The corIf the user requires an analysis that is beyond what is provided by these widely used simulation engines, then they can refer to cross-platform analysis tools, such as MDTraj and MDAnalysis . Both tohttps://mmb.irbbarcelona.org/3dRS/, accessed on 10 January 2022, ref. [As introduced above, to visualize MD analysis results, VMD has been widely used. In their very complete review on the visualization of biomolecular interactions , Agamenn22, ref. ). In the22, ref. ,106. TheThe dynamics of interfaces reflect fundamental properties of the protein–protein assembly such as the interaction specificity, the stability of the complex, or the kinetics of association and dissociation ,108. NotAddressing protein dynamics is a central problem in predicting protein–protein interactions, as the formation of a protein assembly is likely to induce conformational changes in the interacting partners. As a consequence, many docking algorithms will use conformational ensembles of the interacting partners to produce dynamically relevant structural models for the protein complex. Even so, these final complex models are still considered rigid objects during their quality assessment and ranking, and the dynamic properties of the generated interfaces are rarely investigated after docking, probably because of the high computational cost associated with a follow-up simulation at the atomic scale.stability patches, i.e., residues presenting a reduced local mobility, and that their location overlaps with the protein–protein interface. Once the binding sites have been located on the protein surface, their dynamics can play a key role for biomolecular recognition. Fuchs et al. [One key issue when attempting to model protein–protein interactions is the accurate location of the binding patches on the partner surfaces. To reveal the binding sites through the lens of protein dynamics, Kuttner and Engel used steered MD simulations to investigate protein backbone dynamics . They shs et al.  performes et al.  performeIn a “pre-docking” perspective, the protein dynamics can also be accounted for using alternative conformations of the partners. The PRISM  tool ,34,122 uWhen analyzing the dynamics of interfaces, a first source of information can be obtained via docking simulations since these methods extensively sample possible binding geometries. Even rigid-body sampling can provide a useful overview of the interface properties. In its selection process, the ClusPro server explicitly includes the relative sizes of the clusters of lowest-energy predicted structures, identified during Fast Fourier Transform sampling . Inclusirigid, i.e., when the conformational changes induced by the binding process lead to a root mean square deviation (RMSD) that is below 1 Å, the protein interface remains a dynamic object, and trajectories from short (50 ns) MD simulations can be used to study the effect of the solvent on the complex surface. For example, Visscher et al. used this approach to show how the hydration layer contributes to the complex stability [MD simulations performed after docking are mostly used for the refinement of the interfaces. In this case, though, the algorithms use very short time scales to save time. For example, the HADDOCK ,137 proctability .Recently, research groups began using all-atom MD simulations to investigate the stability of structural models produced by docking to assess their quality. Radom et al.  used shoMD simulation of protein complexes can also help identify the residues playing a part in partner selection in the signaling pathways. To this end, Van Wijk et al.  investigInterestingly, numerous studies on protein complexes that use MD simulation do not focus on the interface dynamics per se, but on other properties. For example, MD can be used to determine the binding free energy in the complex. Hou et al.  performeFinally, NMA has been coupled with several docking approaches to model the conformational changes that occur within the partners upon binding. Schindler et al. developed iATTRACT , where tfuzzy complexes, where one or both partners in the interaction will retain some disorder [Intrinsically disordered proteins (IDPs) or regions (IDRs) can be found in nearly a third of the human proteome , and IDPdisorder ,154, invdisorder ,155. IDPdisorder   dataset, which comprises over 1700 trajectories, from atomistic MD simulations of soluble monomeric proteins [https://submission.gpcrmd.org/home/, accessed on 30 November 2021) [https://bioexcel-cv19.bsc.es/#/, accessed on 30 November 2021), which provides web access to atomistic-MD trajectories for macromolecules involved in the COVID-19 disease. However, we are still missing similar initiatives for protein–protein assemblies. This is of importance, as such initiatives play a key role for the development of artificial intelligence and machine learning (AI/ML)-based approaches, since they rely on the availability of large datasets. The year 2021 has shown very promising work using AI/ML strategies for the prediction of protein assemblies [The dynasome, i.e., the ensemble of mobility patterns that can be presented by a protein, has been considered a central element in our understanding of proteins for over a decade, as it was often described as the missing link between structure and function . This leproteins . Numerouer 2021) , or specsemblies ,172, thusocial network in the cell.As high-performance computing facilities now render long simulations increasingly accessible worldwide, it is high time for us to address the fourth dimension of macromolecular assemblies, namely time, and start investigating their dynamic properties in a more systematic fashion, if we want to decipher the protein


Protein protein interactions (PPIs) are essential to most of the biological processes. The prediction of PPIs is beneficial to the understanding of protein functions and thus is helpful to pathological analysis, disease diagnosis and drug design etc. As the amount of protein data is growing fast in the post genomic era, high-throughput experimental methods are expensive and time-consuming for the prediction of PPIs. Thus, computational methods have attracted researcher’s attention in recent years. A large number of computational methods have been proposed based on different protein sequence encoders.Notably, the confidence score of a protein sequence pair could be regarded as a kind of measurement to PPIs. The higher the confidence score for one protein pair is, the more likely the protein pair interacts. Thus in this paper, a deep learning framework, called ordinal regression and recurrent convolutional neural network (OR-RCNN) method, is introduced to predict PPIs from the perspective of confidence score. It mainly contains two parts: the encoder part of protein sequence pair and the prediction part of PPIs by confidence score. In the first part, two recurrent convolutional neural networks (RCNNs) with shared parameters are applied to construct two protein sequence embedding vectors, which can automatically extract robust local features and sequential information from the protein pairs. Based on it, the two embedding vectors are encoded into one novel embedding vector by element-wise multiplication. By taking the ordinal information behind confidence score into consideration, ordinal regression is used to construct multiple sub-classifiers in the second part. The results of multiple sub-classifiers are aggregated to obtain the final confidence score. Following that, the existence of PPIs is determined by the confidence score. We set a threshold S. cerevisiae and Homo sapiens. Through experimental verification, our method outperforms state-of-the-art PPI prediction models.We applied our method to predict PPIs on data sets  Proteins , 2 are cA large amount of high-throughput experimental methods have been applied to predict the PPIs from protein complexes, such as Yeast double hybrid screens , tandem Shen et al.  introducS. cerevisiae and Homo sapiens data sets.Notably, confidence score could be regarded as a kind of measurement for PPIs. The higher confidence score one protein pair gets, the more likely the protein pair interacts. Thus in this work, we propose a novel method, called ordinal regression and recurrent convolutional neural network (OR-RCNN), to predict PPIs by confidence score. The method could be concluded into two parts: (1) an encoder of protein sequence pair based on recurrent convolutional neural network (RCNN), and (2) PPI prediction model based on ordinal regression. In order to deal with the protein sequence pair in a better way, two RCNNs with shared parameters are introduced here. Each RCNN encodes one of the protein sequence pair into an embedding vector, which integrates multiple convolution layers with pooling and bidirectional gate recurrent unit (GRU) layers with concatenate, so as to extract the local features and sequential information more accurately. An element-wise multiplication is then done on the two embedding vectors, encoding them into one novel embedding vector. Till now, the encoder of protein sequence pair is presented, which encodes a pair of protein sequences into one embedding vector aggregating multi-granularity features. In order to predict PPIs, we predict the confidence score of the embedding vector first. The ordinal information is hidden behind confidence score by means of artificially setting the ordinal sub-intervals of confidence score. To this end, the concept of rank is used to show the ordinal information, and each sub-interval is corresponding to a rank value. To efficiently use the ordinal information, ordinal regression is applied here. Based on it, the prediction problem of confidence score is transformed into a series of binary classification problems. Some multi-layer perceptrons are utilized in the binary classification to get the ordinal information about rank. Later, all the ordinal information are aggregated to get the final confidence score of the protein pair. Finally, whether the interaction between protein pair exists is determined by its confidence score. Experimental results show that our method could boost the performance of PPI prediction on both the S. cerevisiae and Homo sapiens. Then, we give the experimental setting. Finally, the experimental results are presented.In this section, we first introduce two PPI data sets S. cerevisiae and Homo sapiens are both from STRING database . F. F27]. FOur method only differs in the RCNN unit for the bidirectional GRU layer. Our method use the concatenate operator , insteadht4 (Eq. ), the coFinally, the embedding vectors for protein sequences K non-overlapped sub-intervals. The first sub-interval is expressed as k’th (K sub-intervals are ranked in an increasing order. Then, the label k (k’th sub-interval. Above all, Suppose the value of confidence score for a protein pair falls in the interval D. Since the ordinal information of each data k’th (k. To this end, we divide the whole training set D into two subsets: the positive class k, and the negative class k, and then relabel them byk’th sub-classification problem. Obviously, the D with different divisions. It would contribute to getting better classification performance and can effectively avoid the over-fitting.Now, we begin to train the prediction model with data set gression , 37 is aL,k, otherwise, it is no more than k.While training, the sub-classifier function . It solvNow, we summarize all the ordinal information from each sub-classifier Finally, we predict PPI of protein pair


D. discoideum proteins have no GO annotations and no obvious similarity to any annotated protein across diverse organisms. We have been systematically examining these unannotated protein sequences using software that predicts a protein structure and then compares the predicted structure to known structures.Using Gene Ontology annotation in any aspect or using any evidence code, we found that approximately 14% percent of predicted  D. discoideum is an excellent model organism for many mammalian processes and disease states, large portions of its genome that could be useful for research remain uncharacterized. For instance, of the 11,088 protein coding genes in the D. discoideum genome that are not annotated as transposable elements or pseudogenes , the predicted amino acid sequence of 1,514 proteins are not similar enough to any known protein to receive a GO term, although many initiatives for experimental, large scale manual, and electronic annotations exist .Although et al., 2010; Yang et al., 2015; Zhang et al., 2017). I-TASSER generates secondary and tertiary structural predictions for a given protein, and then compares the predicted tertiary structure to known structures from the Protein Databank (PDB) . I-TASSER can then provide structure-based annotations for the protein, including gene ontology terms, possible ligand-binding, and enzymatic activity predictions. The 3D structures produced by I-TASSER can also be visualized and compared to Protein Databank proteins to, for instance, compare the active site of a protein in the PDB to a possible active site in the structure predicted by I-TASSER.An alternative way to predict the function of an amino acid sequence is to generate a predicted protein structure, and then compare the predicted structure to protein structures generated by X-ray crystallography or NMR. A commonly used program that does this sort of structure prediction and then structure comparison is I-TASSER  . I-TASSER predicted that AprA has similarity to the human secreted peptidase DPPIV, examination of the predicted AprA structure showed a very close match in both the amino acids and position of the amino acid side chains in one domain to a catalytic triad in the DPPIV structure, and DPPIV and AprA turned out to have very similar properties .As an example of this approach, we previously found a D. discoideum proteins where there was no significant similarity to a known protein by GO or BLAST . Some of the I-TASSER identifications were to disparate proteins. For instance, the top structural analogs for the protein produced by D. discoideum gene DDB_G027895 included the BC component of the ABC complex from Yersinia entomophaga (TM score: 0.936), TedB2-TccC3 toxin from Photorhabdus luminescens (TM score: 0.872) and teneurin 2 partial extracellular domain (TM score 0.332). Another example of a protein with multiple possible similar proteins is encoded by DDB_G0275975; I-TASSER predicted that this protein has structural similarity to both antifreeze proteins and ABC transporters.Over the course of a year, a group of 10 undergraduates was able to generate I-TASSER-based annotations for ~220 predicted 3Rs), the proteins encoded by DDB_G0276051, DDB_G0277495, DDB_G0270742, and DDB_G0274535 all have predicted structural similarity to the Clostridium difficile toxins A and B, DDB_G027561 encodes a protein with predicted structural similarity to an influenza RNA-dependent RNA polymerase, DDB_G0276755 encodes a protein with predicted structural similarity to a toll like receptor 3 ligand binding domain, DDB_G0276921 encodes a protein with predicted structural similarity to a pilin proteins from the gram-positive bacteria Streptococcus Pyogenes, and DDB_G0276485 encodes a protein with predicted structural similarity to integrin cell adhesion proteins. In addition, some annotations revealed possible gene duplications. For instance, the proteins encoded by DDB_G0278559, DDB_G0278561, DDB_G0278565, and DDB_G0278553 all have predicted structural similarity to the MU2 Adaptin subunit in multiple species, and an examination of the chromosome location of these genes showed that they are all are near each other on chromosome 3.Other predicted proteins showed a single major similarity. For instance, the protein encoded by DDB_G0277585 has a predicted structural similarity to the ligand gated calcium channel inositol-1,4,5-triphosphate receptor  on the predicted amino acid sequence to ensure that it lacked significant sequence similarity to other known proteins. Once the BLAST search also returned no significant hits, the protein was then submitted to the online I-TASSER algorithm (https://zhanglab.dcmb.med.umich.edu/I-TASSER/). The I-TASSER analysis is time-intensive, and returns results in 4-7 days. Annotations were formatted primarily using the I-TASSER report and any information obtained from the preliminary BLAST search. Following the BLAST information, the top 3 PDB threading templates used by I-TASSER, top 5 predicted Protein Databank analogs, 3D structures, possible enzymatic activity, ligand binding, and GO terms were described in each annotation. A threading template is an amino acid sequence with a known fold that the LOMETS algorithms in I-TASSER use as a guide when generating 3D structures for the query protein. In contrast, the Protein Databank structural analogs are structures generated using X-ray crystallography and/or NMR which show similarity to the I-TASSER models of the query protein .We have begun to use I-TASSER to predict the possible function of the 1,514 predicted The confidence measures in each annotation include the TM-score, the normalized Z-score, and the C-score. The TM-score measures topological similarity between a protein structure generated by I-TASSER and a pre-existing structure from Protein Databank. When comparing structures, the TM-score calculation weights smaller-distance mismatches less strongly than larger-distance mismatches for higher sensitivity. The TM-score has a range of 0 to 1, and a TM-score > 0.5 indicates a highly confident structural prediction. The normalized z-score is a measure of sequence alignment between the query protein and a threading template, which uses the number of conserved residues to determine whether the template is a good target. A normalized z-score > 1 indicates a good template. The C-score is a measure of predicted structure quality that ranges from -5 to 2, and a C-score > -1.5 typically indicates a correct fold. The C-score calculation accounts for both RMSD (Root Mean Square Deviation) of the predicted structure and normalized z-score of the template.et al., 2013) to predict catalytic sites and has been extended to integrate functions from UniProtKB , Enzyme Commission ), and GO. The sequences of EC and GO were then mapped to BioLiP structural entries. In the Zhang group publications , the authors provide metrics of several annotation aspects including GO, and they used a GO-score (0-1) cutoff at 0.5. This cutoff is a sensitive approach for GO annotations from I-TASSER.In addition to I-TASSER information, UniProt, PFAM, Interpro scan, and STRINGdb searches, as well as Kyte/Doolittle hydrophobicity plots, were used to enhance annotations. PFAM and Interpro provide information on protein superfamily membership and conserved functional domains for a protein query while STRINGdb compiles a web of linkages to the protein of interest such as predicted co-expression. Combining the information from these resources, each annotation aims to provide insight into a protein’s structure, function, and relevance to further research. I-TASSER utilizes the function database BioLiP (Yang, Roy