YPEL3 that induces cellular senescence in both normal and tumour cells of humans may show altered expression under the influence of incidental mutations. In this study, we proposed the first structure of Native YPEL3 protein and its five possible deleterious mutants—V40M, C61Y, G98R, G108S, and A131T and predicted their deleterious effects to alter stability, flexibility and conformational changes in the protein. The MD simulation  analysis revealed that the variants V40M, G98R and G108S increased the flexibility in protein, and variant V40M imparted more compactness to the protein.. In general, variants attributed changes in the native conformation and structure of the YPEL3 protein which might affect the native function of cellular senescence. The study provides opportunities for health professionals and practitioners in formulating précised medicines to effectively cure various cancers. We propose in-vitro or in-vivo studies should consider these reported nsSNPs while examining any malfunction in the YPEL3 protein. Identification of these variations and their impact on human health is one of the least explored areas in the field of human genetics. Around 0.12% of genetic variants are predicted deleterious to human health1. These genetic variations, in particular nonsynonymous SNPs (nsSNPs) caused by mutations may affect the structure or function of the protein, by changing the amino acids. However, not all nsSNPs cause damages to the protein. Many of them show disease phenotype and a few of them are neutral2. Therefore, appropriate selection of bio-computational methods, in distinguishing the deleterious nsSNPs from the neutral ones is prerequisite towards predicting the structural and functional consequences of the target proteins. A few earlier studies investigated the pathogenic effects of nsSNPs on the structure and function of various native protein7, and also identified drug resistance mechanism due to point mutations in Mycobacterium tuberculosis8. Rajendran et al.4 investigated that the alterations caused due to the point mutation, P29S in RAC1, increased the rate of cell proliferation leading to a cancerous state. These studies have signified the imperativeness of computational investigations in elucidating the role of nsSNPs and laid foundation to rely on the in-silico analysis to predict cellular mechanism responsible for various genetic disorders. In the present study, we tried to address how the predicted genetic variants affect the structure and functionality of the 'Yippee like 3′ (YPEL3) protein in humans. This protein is encoded by YPEL3 gene which is a member of five closely related paralogues—YPEL1-5 which are named in reference to their Drosophila orthologue9. Baker10 described YPEL3 as a small unstable apoptotic protein (SUAP), which stimulated the removal of IL3 from the myelenoid precursor cell line leading to apoptosis. In addition to this, YPEL3 was also found to be degraded by proteosomes describing it as unstable protein10. The Human YPEL3 gene is located on the short arm of chromosome 16 (16p11.2), having high sequence conservation in wide range of species. In humans, two functional transcript variants of YPEL3 have been identified, i.e. Transcript variant 1 (GeneBank ID: NM_031477.4) encodes for 157 amino acid residue protein of approximately 17.5 kDa. The other transcript variant 2 (GeneBank ID: NM_001145524.1) encodes for a 119 amino acid protein of approximately 13.6 kDa. The transcript variant 1 has an additional 29 amino acids compared to transcript variant 2 at the N terminus. Further, YPEL3 is reported to be a p53 target gene showing growth-suppressive properties like senescence and apoptosis under various circumstances11. Due to inadequate information about the structure and the impact of variants in YPEL3 protein, limited attempts are made to explore the précised medicines facilitating cure for several cancers in the context of mutation driven alteration in the structure and function of the protein. In this study, we determined the structural and functional impact of most deleterious nsSNPs of YPEL3 protein by using the computational simulations and proposed three dimensional structures of YPEL3 protein and its mutants. We also applied several tools to prioritize damaging nsSNPs and assessed the most deleterious one based on stability assessment, evolutionary conservation analysis and post translational modification site prediction. We modelled the 3D structure of both native and mutant YPEL3 protein and analyzed the conformational behavior based on simulations. We believe this study will aid to the ongoing research on cancer genetics and seeking for therapeutic candidates.Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation contributing about 90% of the total human genome polymorphismOf the total 1528 SNPs reported in dbSNP database for YPEL3 gene , 73 missense and 2 nonsense SNPs were mapped in Humans along with their meta information like alleles, chromosomal location and MAF .A total 28 nsSNPs were found consentaneously deleterious with an expected accuracy range (EAR) of 0.87 to 0.72 by the five prediction tools. The tool CADD predicted 59 nsSNPs were deleterious, followed by 50 deleterious nsSNPs by GWAVA, 38 deleterious nsSNPs by DANN, 32 deleterious nsSNPs by FATHMM and only one deleterious nsSNPs was predicted by FunSeq. Table .A total 10 nsSNPs i.e. V40M, R57L, C61Y, G98R, G108S, D114N, E129Q, A131T, A131V and I145T were found consentaneously deleterious by the seven prediction tools. Tool MAPP and PhD-SNP both predicted nine deleterious mutations, while the tool PolyPhen 1 and PolyPhen-2 predicted eight and 13 deleterious mutations. Similarly, SIFT predicted 13 deleterious mutations and nine deleterious mutations were predicted by SNAP. Overall 10 deleterious mutations were predicted by PredictSNP 1 , C61Y (RMSD = 2.222 Å), G108S (RMSD = 2.173 Å), A131T (RMSD = 2.143 Å) and G98R (RMSD = 1.880 Å). While mutants- R57L, D114N, A131V, and E129G did not show any variation with the native structure Table . The topAll the modelled structures of both wild and mutant proteins possessed Z-score significantly different from zero. Wild (− 2.73), V40M (− 3.41), G108S (− 2.15), G98R (− 1.71), C61Y (− 2.85) and A131T (− 3.07) indicating less chances of error and high quality of structures Figure . FurtherFive mutations i.e.V40M, C61Y, G98R, G108S and A131T introduced relatively bigger residue than the wild type. The introduction of the bigger size of amino acids might lead to bumps in protein structure whereas smaller residues might lead to loss of molecular interaction. The change in the charge of amino acids was predicted in G98R . This may lead to a loss of interaction between amino acids of the protein or between protein and other molecules. Mutations i.e. C61Y, G98R and A131T, replaced the native residues by a lower hydrophobic residue which might result in the loss of hydrophobic function on the surface or in the core region of the protein. In mutation G98R and G108S, the torsion angles for the mutant residues were unusual which changed the conformation and the local structure of the protein.The secondary structural analysis revealed the number of residues participating in the formation of secondary structural elements like Turn, Coil, Strand, 3–10 helix, Pi-helix, Alpha helix and Bridges in the wild and mutant protein structures Table . MutatioOut of the top 10 interacting genes predicted by STRING, the WD repeat-containing protein 26 (WDR26) gene showed highest combined score of 0.716 Table  based onMD simulation based on stability, flexibility, compactness and hydrogen bonds monitored the effect of deleterious nsSNPs on wild type YPEL3 and its variants Fig. . The aveThe RMSD analysis of backbone residues analysed the deviation of mutant structures from wild variant based on the convergence of simulation trajectories Figure . All theRMSF analysis depicted a fluctuating trajectory for the wild variant with an average RMSF value of 0.17 nm. In case of mutation V40M, the RMSF value of mutant showed significant increase in the peak from residue 14–75 and rest most of the residues followed the similar pattern as the wild variant except the residue 156 with a higher RMSF value of 0.54 nm. Subsequently, the observed RMSF value for mutation G108S showed higher peaks from residue 25–53 and 73–103 with the wild variant. In case of V40M and G108S, maximum number of residues of mutant showed higher RMSF value in compare to the wild variant revealing increase in flexibility of the protein with an average RMSF of 0.24 nm and 0.22 nm. Further the mutants G98R, C61Y showed differential pattern of RMSF trajectory in compare to wild variant with an average RMSF of 0.21 nm and 0.19 nm contributing to the increased flexibility of the protein. The fluctuation in mutant A131T were in accordance to the wild variant for the majority of residues except for residue 154 to 157 with exceptionally high peak. The RMSF analysis resulted to demonstrate that mutants V40M, G108S and G98R showed significant rise in average value, whereas mutants C61Y and A131T did not show a major change in compare to the wild protein. All these observations suggested that the mutations alter the conformation and change the flexibility of protein Figure . The Rg The h-bond analysis revealed no significant differences in the number of intermolecular hydrogen bonds over the period of 50 ns between the wild and mutants except in case of V40M. For the first 15 ns, a slight decrease in number of h-bonds was observed in mutants G108S, G98R and C61Y. Finally, in mutation V40M, a significant increase in rigidity of protein was observed and so the change in geometry of protein, while other mutations did not impart any such change Figure .2, 96.94 nm2 and 92.73 nm2 in case of mutants V40M, G98R and A131T, respectively in compare to the wild variant with average estimates of 98.39 nm2. In case of mutant G108S, at the start and end of simulation spike in trajectory was observed with an average value of 101.44 nm2, whereas in case of G98R overall trajectory was lower than the wild except at the start of simulation. Exceptionally, C61Y showed differential pattern where starting with the lower values, the trajectory significantly elevated higher over the period of time than the wild variant. This indicated that the mutation V40M, G98R and A131T reduced the overall accessible surface area of protein while the mutation G108S and C61Y increased the available surface area for the solvents . In the selected 10 nsSNPs, the ConSurf server predicted 9 highly conserved nsSNPs with a conservation score range from 7 to 9. Further, the substituted positions V40, C61, G98, G108, D114, A131, and I145 were buried with structural significance in protein structure while positions R57, E129 were exposed with E129 having functional significance. Although the mutation V40 showed a conservation score of 2, but this position was taken into account for further analysis since the calculation was performed on less than 6 non gaped homologous sequences. Out of these 10 substitution sites, C61 was the metal-binding site for zinc motifs which was highly conserved site and any change at C61 could have a pathogenic effect and alter the protein function. These variants were further evaluated for PTM analysis.11.Of all the PTM sites present on protein sequence, none of them was common with the nsSNPs location except R57L for methylation. But since the R57L did not show any significant result with respect to the stability of the protein, therefore this PTM site was not considered damaging to the native protein. Further, all 3D structures of YPEL3 protein and its mutants except for R57L, D114N, A131V, and E129G showed a greater degree of variation with native protein structure which signifies structural changes. But among the selected variants, mutant I145T showed high variation with low conservation score in compare to other structures, thus only five structures i.e. V40M, G108S, G98R, C61Y and A131T were explored further. The mutations G98R and G108S replaced Glycine which is one of the most flexible amino acids disturbing the required rigidity of protein. The size difference at position 61 between Cysteine and Tyrosine can disturb the binding of Zinc metal ion. Based on the change in the rigidity, charge and size differences, all selected mutations were predicted damaging to the protein. Secondary structure analysis revealed an absence of Pi-helix in the mutant variants to affect the protein folding and stability. To assess the mutational effect of YPEL3 protein on its biological functional partners, gene–gene interaction analysis revealed that gene WDR26 was the highly interacting gene and DOK7 was the physically interacting gene, and therefore any functional or structural change in YPEL3 could affect the interaction between them. MD Simulation analysis assessed the native behavior of protein in the simulated environment on the basis of stability, flexibility and dimensions of protein as a function of time. The RMSD and RMSF analysis in agreement to each other revealed that the variants V40M, G108S and G98R decreased the stability and increased the flexibility of protein, whereas variants C61Y and A131T showed increase in stability and decrease in the flexibility of protein. The radius of gyration and hydrogen bond analysis revealed that the variant V40M showed higher level of compactness throughout the time, whereas the other variants showed differential level of compactness. The SASA analysis revealed that all the variants changes the native conformation of protein and hence possibly responsible for change in function of protein. Based on the simulation study, we demonstrated that the variants V40M, G108S, G98R, C61Y and A131T imparted changes in the native conformation or structure of the YPEL3 protein in any sense of behavior and hence predicted to affect the protein function and structure in deleterious manner. Kaplan–Meier plotter analysis revealed that the deregulation of YPEL3 gene had antagonistic effects on the survival rate of gastric and ovarian cancer patients. This correlation of YPEL3 deregulation with gastric and ovarian cancer may have an indirect relation with the predicted damaging mutations in YPEL3 gene. The YPEL3 gene was thus found to be an important prognostic marker in case of Gastric and Ovarian cancer patients. It is evident from the results that YPEL3 gene expression was not linked with the gender of the cancer patients as both Breast and Ovarian cancers are gender-specific but only Ovarian cancer patients showed a significant correlation with YPEL3 gene deregulation. However, the expression of YPEL3 gene is regulated by several other genes alsoThe findings of this study will provide clarity in understanding the mutational effect of deleterious nsSNPs on YPEL3 protein and to elucidate their role in different associated diseases. In this study, we utilized highly reliable and widely used computational tools with molecular dynamic simulation analysis and determined five deleterious mutations with both structural and functional consequences on native YPEL3 protein. The results have the potential to pave new insights in drug target identification and Biomarker assessment. However, in-vitro functional assessments are required to ascertain the effects of amino acid change in the native protein. Besides the functional changes, assessment of structural changes due to mutations are also required and therefore 3D crystal structures of the protein is a prerequisite for future studies. Since, the present study investigated the nsSNPs effects on YPEL3 protein, was solely based on the predictions using computational approaches and therefore, experimental validation and comprehensive clinical studies with the inclusion of real time case histories is required for further evaluation and exploring aid to cancer therapies.https://www.ncbi.nlm.nih.gov/gene/83719). The amino acid sequence of protein YPEL3 isoform 2 composed of 157 AA residues was obtained from the Uniprot database (Uniprot ID: P61236). All SNPs along with their metadata like position, residue change and global minor allele frequencies (MAF) were retrieved from NCBI dbSNP22 and then the nsSNPs were filtered out from the function class of database .The nucleotide sequence of the Human YPEL3 gene was retrieved from the GenBank  in PredictSNP2 and nine prediction tools  in PredictSNP1. The prediction results of predictSNP2 were exported to PredictSNP1 along with both neutral and deleterious nsSNPs.We used two bioinformatics tools, i.e. PredictSNP225 and MuPro26. I Mutant uses a support vector machine-based web server for prediction of protein stability based on any single site mutation. This tool uses data from ProTherm27 that is the most comprehensive database of protein mutations derived from experimental data. I-Mutant predicts the change in stability by calculating the change in DDG. It further defines whether these changes increase or decrease the stability of the protein. Negative DDG indicates that protein stability decreases and positive DDG indicates that protein stability increases. Based on the signature of stability change, it gives the Reliability index value  ranging from 0 to 10, where 10 depict the higher reliability. Accordingly, MuPro predicts the effect of a single-site mutation on the stability of protein, based on a set of machine learning programs. The results are based on two machine learning methods, viz., Support vector machine and neural networks. MuPro predicts the effect of mutation on protein stability on the basis of the value of energy change (ΔΔG). It also predicts the sign of energy change using neural networks and support vector machines. This tool is capable of predicting protein stability without the availability of tertiary structure. We submitted protein YPEL3 sequence to these tools to predict the effect of deleterious nsSNPs on the protein structure providing temperature conditions of 25 ºC and ph 7.0.To predict the stability of protein, we used two computational tools i.e. I Mutant 2.028 that provide evolutionary profiles of each of the amino acids in the protein, based on phylogenetic relations between homologous sequences. The tool also predicts the conservation score for each amino acid residue ranging from 1–9, where the score 1–3 denotes variable residues, 4–6 denotes medium conserved and scores 7–9 depict highly conserved residue. These scores indicate the degree to which the amino acids are evolutionary conserved. For this analysis, we first submitted the Uniprot protein sequence of YPEL3 protein in BLASTp against the UniProt database in NCBI and selected those sequences having an identity of more than 50% for further analysis. After the multiple sequence alignment of these sequences, the MSA file was then submitted to ConSurf along with the protein FASTA sequence.To predict the evolutionary conservation of the amino acids in protein sequence, we used ConSurf bioinformatics tool29, Methylation and Ubiquitylation PTM affects the functioning and regulation of the YPEL3 gene. The methylation sites in Human Yippee like 3 protein sequence were predicted using GPS-MSP v.130 and PMeS31 tools. GPS-MSP v.1 predicts mono, symmetry di-, asymmetry di-methylation types specific for arginine residues and mono, di and trimethylation type-specific to lysine residues. It is commonly used in the prediction of potential methylation sites with a threshold value of 0.5. Whereas, PMeS tool identifies methylation sites based on the enhanced feature encoding scheme and supports vector machine. Similarly, the phosphorylation sites at tyrosine, threonine and serine residues were predicted using GPS 5.032 and NetPhos 3.133. Higher value in GPS 5.0 predicts higher chances of residues to get phosphorylated, whereas NetPhos 3.1 uses ensemble approach of several neural networks to predict residue scores with a threshold of 0.5. BDM-PUB34 and UbPred35 are common tools for the prediction of protein Ubiquitylation sites. A balanced cut-off performance selection was used in BDM-PUB, based on Bayesian Discriminant method to predict Ubiquitylation positions. UbPred considers a score of ≥ 0.62 to predict Ubiquitylated residues.A post translational modification is crucial for cell signalling and affects the function of the protein. Based on the previous studies36. The template selected by Phyre2 to predict the 3D models of both native and mutant proteins was c4v30A. The 3D structure of native and mutant proteins were then compared using the TM-Score web tool. The TM- Score is a popular web tool that provides Template modeling scores (TM-score) and Root mean square deviation (RMSD) value on comparison of native and mutant variant protein38. TM-score provides a range of 0–1 where 0 signifies lower structural similarity and 1 signifies higher structural similarity. Likewise higher RMSD value indicates greater variation between native and mutant protein structures39. The mutants having higher RMSD values were submitted to I-Tasser40, which is a very advanced tool for protein structure prediction and analysis. The predicted structures by I-Tasser were verified using ERRAT programme41 which is extensively used in verifying protein structures. Finally, the native structure and selected mutant structures of YPEL3 protein were visualized and superimposed in Chimera 1.10.142.The 3D models of wild and its mutants with most deleterious nsSNPs of YPEL3 protein were generated using Phyre244 and ProCheck tool45. The ProSA tool predicts the overall quality of modelled structure on the basis of Z-Score. If the Z-score of the models lies outside the scores of proteins with similar size, the chance of error increases in the predicted structure. Likewise, ProCheck tool assess the overall quality of model by identifying the percentage of residues in most favored regions, additional allowed regions, generously allowed regions and disallowed regions. Based on these results, the models with Z-score in the range of proteins of similar size and having the disallowed region percentage less than 10% were selected for further analysis.The native and mutant modelled structures were verified using ProSA46. It explained the structural effect of variants on native protein using UniProt and Das prediction servers.The structural change and its effect on protein structure due to variants were predicted by a mutant analysis server i.e. Project HOPE47. It utilizes the atomic coordinates of the modelled structure to assign the secondary structure codes, which is one of the most complex processes in compare to other programmes.To assess the position level variation in secondary structure of wild and mutant proteins, STRIDE programme was used48 and Gene MANIA database49. String predicts gene interaction on the basis of neighborhood, gene fusion, concurrence, co expression, experiments, database, text mining and homology. Based on these parameters, scores are assigned from 0 to 1 to each functional partner genes where 0 denotes lower interaction and 1 denotes higher interaction. Gene MANIA predicts association network and functional interaction based on co-expression, co-localization, pathway, genetic interaction, physical interaction and similarity of protein domains.Interaction and association of YPEL3 gene with other genes was studied using STRING v.11.050-Ubuntu-2020.1-1 version and Linux 4.4.0 package on Intel Core i7 processor, 32 GB RAM system. Structures of wild and mutant proteins were used as a starting structure of MD simulation and the solvation of cubic systems were done at 10 Å radius with point charge SPCE water molecules. Since the structures were found to be positively charged at the physiological pH, three chloride (Cl-) ions were added using “genion” tool of GROMACS to the simulation system in order to neutralise the charge of the systems. These solvated neutral systems were subjected for energy minimization for 500,00 steps using steepest descent algorithm of OPLS-AA/L all-atom force field to relax the structure and ensure absence of steric clashes. Later to control the temperature and compute the electrostatic interaction, Berendsen temperature coupling51 and Particle Mesh Ewald method52 was used. The compressibility range to maintain the pressure at 1 atm was 4.5e − 5 atm. LINCS algorithm was used for constraining the bond lengths for a time step of 2 fs. Finally, MD simulation was performed for 50 ns. Thereafter, a comparative analysis was performed between the wild and mutant structures based on Root mean square deviation (RMSD), Root mean square fluctuation (RMSF), Radius of gyration (Rg), Hydrogen bond and Solvent accessible surface area (SASA) analysis by using gmx rms, gmx rmsf , gmx gyrate, gmx hbond and gmx sasa. All these analysis were represented in the form of plots using XMGRACE program53.Molecular dynamic simulation was performed using GROMACS 2020.111 have mentioned that YPEL3 is under expressed in several types of cancer. So the assessment of the YPEL3 gene on the survival of several cancer types is crucial to Human medical genetics. Kaplan Meier plotter analysis54 is capable to assess the effect of 540,00 genes in the survival of 21 types of cancer using the data of 133,16 patients . The databases used by Kaplan Meier plotter were Gene Expression Omnibus (GEO), European Genome-phenome Archive (EGA) and The Cancer Genome Atlas (TCGA). In this analysis, the probe used for the YPEL3 gene was 223179_at. In this study, the overall analysis was done on 1,764 Breast, 614 Ovarian, 1,926 Lung and 876 Gastric cancer patients. Two groups of patients (High and Low expression level) based on median value for each cancer types and based on this, survival was assessed. A quality control standard was maintained by excluding the biased arrays and a p-value below 0.5 was considered significant.Previous studiesSupplementary Information.


Arabidopsis thaliana. Considering that the application of AraPPISite is limited to one species, it is very natural that AraPPISite should be evolved into a new database that can provide interaction details of PPIs in multiple plants.Protein-protein interactions (PPIs) play very important roles in diverse biological processes. Experimentally validated or predicted PPI data have become increasingly available in diverse plant species. To further explore the biological functions of PPIs, understanding the interaction details of plant PPIs  is necessary. By integrating bioinformatics algorithms, interaction details can be annotated at different levels and then compiled into user-friendly databases. In our previous study, we developed AraPPISite, which aimed to provide interaction site information for PPIs in the model plant http://zzdlab.com/plappisite/index.php) is a comprehensive, high-coverage and interaction details-oriented database for 13 plant interactomes. In addition to collecting 121 experimentally verified structures of protein complexes, the complex structures of experimental/predicted PPIs in the 13 plants were also constructed, and the corresponding interaction sites were annotated. For the PPIs whose 3D structures could not be modelled, the associated domain-domain interactions (DDIs) and domain-motif interactions (DMIs) were inferred. To facilitate the reliability assessment of predicted PPIs, the source species of interolog templates, GO annotations, subcellular localizations and gene expression similarities are also provided. JavaScript packages were employed to visualize structures of protein complexes, protein interaction sites and protein interaction networks. We also developed an online tool for homology modelling and protein interaction site annotation of protein complexes. All data contained in PlaPPISite are also freely available on the Download page.PlaPPISite (PlaPPISite provides the plant research community with an easy-to-use and comprehensive data resource for the search and analysis of protein interaction details from the 13 important plant species. Arabidopsis thaliana, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens and Escherichia coli K12 [. Regarding the known PPI inventory in plants, 36,099 A. thaliana PPIs have been deposited in BioGRID (v3.4.155) [Proteins are involved in most biological processes in cells, and they tend to perform their biological functions in stable or transient complexes rather than in isolation . Therefocoli K12 . Regardi3.4.155) . By contTo improve the coverage of PPIs, a variety of computational methods have been developed to predict PPIs, including interolog mapping , 10, genTo further decipher the molecular mechanisms of PPIs, a key step is to identify interaction domains, motifs and sites associated with PPIs. Some databases have integrated the information of interaction domains and motifs from Protein Data Bank (PDB) , for exaA. thaliana. AraPPISite allows researchers to query the 3D structures, protein interaction sites, DDIs and DMIs of PPIs. Moreover, it displays abundant physicochemical annotations of interaction sites. However, AraPPISite has certain limitations. First, AraPPISite only takes one organism, A. thaliana, into account. Second, AraPPISite only provides protein interaction details of limited experimentally verified PPIs while ignoring the predicted PPIs, which narrows the coverage of AraPPISite. Moreover, the number of experimental PPIs has greatly increased after the publication of AraPPISite. Third, AraPPISite lacks a convenient prediction platform for protein complex structure construction and interaction site assignment, which is not convenient when the query PPIs are not present in AraPPISite. In this context, it is necessary to evolve AraPPISite into a new version that provides interaction details with higher coverage for multiple plant interactomes.In 2016, we developed AraPPISite  to provihttp://zzdlab.com/plappisite/index.php), a free and user-friendly database of plant protein interaction sites. Compared to its precedent version , PlaPPISite incorporates 12 other plant interactomes. Although the PPI networks and the corresponding interaction sites are mainly inferred from computational methods, PlaPPISite greatly increases the coverage of PPIs with interaction site annotations. Moreover, a convenient prediction platform was integrated into PlaPPISite, in which users could merely submit a pair of protein sequences to obtain the protein complex structure and interaction site information.Here, we present PlaPPISite . The seA. thaliana, which contains 48,607 experimentally verified PPIs, the experimentally verified PPIs of the other 12 plants are rare. To complement the experimentally verified PPIs, genome-wide PPI predictions of these 13 plants were carried out through interolog mapping method [A and B) in one of the plants can be predicted to interact with each other in case an experimentally validated PPI exists between their respective orthologous proteins (A’ and B′) in other species. The protein pair  is also regarded as the interolog template of the protein pair . To obtain high-quality interolog templates for the prediction of plant PPIs, we first collected experimentally verified PPIs of six model organisms, including A. thaliana, S. cerevisiae, C. elegans, D. melanogaster, H. sapiens and E. coli K12, from BioGRID, IntAct, MINT, DIP, TAIR and HPRD [Compared with g method . Brieflyand HPRD   terms, the proportions of subcellular co-localization and the similarities of gene expression profiles were used to assess the reliability of the 13 predicted protein interactomes. As an important gene functional annotation system, GO annotation consists of three categories, i.e., molecular function, cellular component and biological process. It has been reported that two proteins sharing similar GO annotations have higher possibility to interact with each other. We downloaded GO annotations of these 13 plants from the GO database , 37 and It has been established that interacting proteins tend to have the same subcellular localization . Considering that most plants lack proteome-wide subcellular localization information, we predicted the subcellular localizations of proteins for 13 plants through a popular predictive tool called MultiLoc2 , which pG. max, O. sativa, Z. mays, A. thaliana, S. lycopersicum, V. vinifera, B. distachyon, P. trichocarpa and M. truncatula) from the Gene Expression Omnibus (GEO) database [Moreover, transcriptome data were also applied to perform the reliability assessment . Proteindatabase , due to A. thaliana, the number of experimental PPIs is relatively large, and some predicted interactomes have been publicly available, which has allowed us to directly compare our predicted PPIs with some existing interactomes. To this end, we collected 9065 predicted highly reliable A. thaliana PPIs (S-PPIs) from [A. thaliana proteome were removed, and 8358 PPIs were finally retained. To ensure a fair comparison, we selected our predicted high-quality A. thaliana PPIs, which included 38,610 interolog-inferred PPIs whose 3D structures could be built up or DDIs/DMIs could be annotated. In general, the numbers of overlapping PPIs among our predicted PPIs, S-PPIs and experimental PPIs are low, but they are significantly overlapped . Comparatively, our predicted PPIs share a higher overlapping rate with experimental PPIs in comparison to S-PPIs. Collectively, the above direct comparison further suggests that our predictions have a comparable and reasonable accuracy.Regarding the interactome of Is) from , which wA. thaliana, C. reinhardtii, O. sativa and Z. mays were collected from the PDB database, involving 121 non-redundant binary PPIs. Considering that some PPIs may own multiple sets of complex structures from different structure determination sources, the corresponding complex structures with the best resolution were retained. If two interacting proteins could map to multiple chains of the candidate complex structure, the two chains with the largest interaction interface were further selected as the final complex structure of the PPI.A total of 101 experimentally verified complex structures related to Experimental complex structures are not available for most plant PPIs. Following our previous strategy in AraPPISite, two template-based methods (HMPC and PRISM) were further applied to predict the complex structures of both experimentally verified and predicted PPIs, which mainly included three steps, i.e., template selection, monomer modelling and complex modelling.To model the complex structure of an interacting protein pair, we first selected the best homologous template for each protein through BLAST searching  against Moreover, the interaction sites can be retrieved from the experimental/predicted complex structures. Briefly, the residues from two interacting proteins were assigned as interacting sites (residues) if their shortest atomic distance was less than or equal to 4.0 Å. All the interacting residues between two interacting proteins constitute a complete interaction interface.The database construction was based on MySQL 5.5.60 and PHP 5.4.16. The service runs on an Apache 2.4.6 server with the Linux operating system CentOS 7.4. Similar to , a JavaSOur goal is to develop a comprehensive database of plant protein interaction sites that consists of multiple functional modules. It allows users to explore the associations between proteins from a systematic perspective and visualize protein complex structures and interaction sites. In the meantime, it also provides an online prediction platform. Moreover, it allows users not only to access data directly from the online database but also to download the complete data for local use.PlaPPISite provides two ways to obtain the annotation information of PPIs Fig. a. Users Compared to the previous version, PlaPPISite applies the new plug-in NGL to display protein complex structures, which has been widely used in many protein structure databases such as PDB. The utilization of the new plug-in adds a variety of colour schemes and molecular representations, such as backbone and spacefill. Complex structures can be rendered by any colour scheme and molecular representation and viewed from different angles through automatic rotation. In line with the previous version, the detailed interaction sites can be displayed on the complex structure, and the corresponding physicochemical properties are also listed, including bond type, conservation score and changes in Gibbs free energy (∆∆G) Fig. a. MoreovConsidering that the size of each plant interactome in PlaPPISite is large, we only provide subnetwork visualization for each PPI. In brief, for each PPI, PlaPPISite adds a subnetwork, which consists of the first-layer interactions involved in the two interacting proteins , which is in line with our finding. Therefore, the above computational analysis added additional evidence to prove the reliability of the predicted interaction sites. Taken together, our current and previous computational analyses support the reasonable reliability of predicted complex structures and interaction sites.Moreover, we collected 4493 mutated sites of 995  sources , 52 to fPlaPPISite is a freely available public resource that provides abundant PPI details for 13 plant species. At the structural level, PlaPPISite not only includes the 3D structures and interaction sites of experimental/predicted PPIs for 13 important plants but also lists the physicochemical properties and the residue conservation of interaction sites. Moreover, DDI/DMI information are also annotated for those PPIs whose 3D structures could not be successfully constructed. It should be emphasized that the PPI and interaction site information deposited in PlaPPISite may inevitably contain false positives, although we have conducted a series of computational experiments to intuitively provide evidence regarding the reliability of the predicted PPIs and protein complex structures. By taking the potential false positives in mind, we hope PlaPPISite can become an important data platform for accelerating our global understanding of plant interactomes. For instance, it can effectively guide experimental efforts such as mutagenesis to interrogate the functional roles of plant PPIs.Additional file 1: TableS1. The PPI number distribution for the 13 plants in PlaPPISite. Table S2. The number of experimentally verified PPIs of the 13 plants. Table S3. The number of experimentally verified PPIs of six model organisms. Table S4. The GO annotation covrage for the 13 plants. Table S5. The subcellular co-localization proportion for the 13 plants. Table S6. The known mutated information associated with predicted interaction sites.Additional file 2: Figure S1. The distribution of the average cellular component similarities for 1000 random networks and the predicted network. Figure S2. The distribution of the average biological process similarities for 1000 random networks and the predicted network. Figure S3. The distribution of the average molecular function similarities for 1000 random networks and the predicted network. Figure S4. The distribution of the average subcellular co-localization proportions for 1000 random networks and the predicted network. Figure S5. The distribution of the average expression profile similarities for 1000 random networks and the predicted network. Figure S6. Venn diagram showing the numbers of overlapping PPIs among two predicted PPI sets and one experimental PPI set. Figure S7. The primary subnetwork of PPI. Users can export the subnetwork alternatively for further analysis. Figure S8. A prediction platform for complex structure construction and interaction site assignment. (a) The prediction platform interface. Users can submit two protein sequences of a query PPI to retrieve the complex structure and the corresponding interaction sites. (b) A prediction result example. The predicted complex structure and the corresponding interaction sites can be downloaded on this page.


E. coli membrane proteome, including 243 that are newly discovered. While EVcomplex2 does not require available structures, coevolving residue pairs can be used to produce structural models of protein interactions, as done here for membrane complexes including the Flagellar Hook-Filament Junction and the Tol/Pal complex.Increasing numbers of protein interactions have been identified in high-throughput experiments, but only a small proportion have solved structures. Recently, sequence coevolution-based approaches have led to a breakthrough in predicting monomer protein structures and protein interaction interfaces. Here, we address the challenges of large-scale interaction prediction at residue resolution with a fast alignment concatenation method and a probabilistic score for the interaction of residues. Importantly, this method (EVcomplex2) is able to assess the likelihood of a protein interaction, as we show here applied to large-scale experimental datasets where the pairwise interactions are unknown. We predict 504 interactions de novo in the  Escherichia coli cell envelope proteome.Our understanding of the residue-level details of protein interactions remains incomplete. Here, the authors show sequence coevolution can be used to infer interacting proteins with residue-level details, including predicting 467 interactions de novo in the  For instance, even in the well-studied proteome of Escherichia coli, less than half of probable protein interactions have been identified4 and less than 9% have been structurally characterized, even accounting for similarity to 3D structures of complexes in related species. Hence there is continued interest in computational approaches that can accelerate the discovery of protein interactions in 3D at atomic resolution.A longstanding goal of molecular biology is to determine the three-dimensional structure of protein interactions at atomic resolution. However, despite the ‘resolution revolution’ in cryo-electron microscopy7 and computational methods10 to identify which proteins interact within an organism to scale, but the only computational methods able to determine both interactions and their precise, residue-resolution interfaces are based on coevolution. Coevolutionary methods such as EVcouplings12 and others13 have been successful in determining 3D structures by leveraging the vast corpus of natural sequences using probabilistic graphical models to infer candidate pairs of interacting residues. These coupled positions can be sufficient to fold single proteins13 and RNAs14, without the use of homologous structures, and even resolve protein interaction interfaces de novo16. However, in the previous work that resolved interacting residues between proteins, coevolution was typically benchmarked on fewer than 100 examples and limited by using co-operonic proteins or phylogeny to identify sequence pairs. Therefore, de novo prediction was limited to select, small datasets or individual runs on a webserver18. While this work was in preparation, new work was published that used proteome-scale coevolution to provide a dataset of 804 candidate interactions in E. coli19. Though these predictions are valuable, the methodology is not available for queries of new protein pairs or new large-scale predictions (see Results for detailed comparison).There have been many experimentalE. coli, compared to previous methods that rely on genomic position (~10%). As proof of principle, we present results on a new benchmark set, which allows us to assess the specificity and sensitivity of the method. As a case study, we predict 504 interactions involving cell envelope and membrane proteins, which are some of the most challenging to determine experimentally. We provide docked models of these complexes, some of which are of particular biological interest and are discussed in more depth . We also provide predicted protein interactions at residue resolution for an AP/MS dataset where the precise details of interactions are unknown, as well as residue-level predictions for a Yeast Two-Hybrid dataset. To demonstrate the potential for eukaryotic complexes, we also show successful predictions for eukaryotic-exclusive complexes including the human spliceosome.Here we address the challenges of concatenation and statistical scoring to provide a new method that integrates information from residue-level scores to determine whether two proteins interact and the residues involved in their interaction Fig.  without E. coli. Of the estimated 10,000 protein interactions in E. coli, approximately 3,946 have been observed4, involving about 50% of all E. coli proteins. Of these protein-protein interactions, 9% have an experimentally determined structure or can be inferred trivially from solved homologous interactions19, hence leaving a large fraction of interactions unknown or without structural resolution  have high-quality monomer alignments and are therefore amenable to EVcomplex2 (Methods). We verify that these alignments are of high quality by testing the precision of the top ECs for those monomers with an experimental structure (either in E. coli or another organism), finding that 78% have reasonable precision of the top ECs  . This increases the number of protein interactions that can be tested in E. coli will interact4  at both predicting interacting proteins and identifying interface residue contacts   Fig. , for a tE. coli cell envelope as well as the details of their interacting residues, including 243 interacting pairs not previously observed experimentally , 262 of which would have been blindly identified using our stringent protein interaction scoring threshold  of complexes found to interact by Yeast Two-Hybrid, we find at least one EC above the 80% precision residue interaction threshold (and 198 complexes have three or more ECs) . We predict interaction and residue contacts for the known interaction between TolA and TolB27 as well as residue contacts and a likely interaction of TolR and TolB in the membrane28. For CpoB, which interacts with the Tol system and the cell division septum site29, we provide a molecular model of its interaction with TolB.We constructed an atomic model of the Tol/Pal system Fig. , a molecThe prediction of protein interactions using coevolution is not restricted to bacterial genomes. However, predicting protein interactions using coevolutionary methods in eukaryotes is difficult due to the smaller number of sequenced genomes and higher numbers of paralogous genes compared to bacteria. We used our positive benchmark set, which contains structures from all domains of life, to assess the prospects of EVcomplex2 for predicting protein interactions at residue resolution in eukaryotes (Methods). Of the 1,675 protein pairs in our dataset, 977 are found predominantly in eukaryotes (more than 90% of concatenated sequences are eukaryotic in origin) . We find that 42% of proteins in the human proteome can be aligned with medium sequence diversity in at least one domain, and 20% of proteins can be aligned with the high diversity cutoff used for E. coli in all of their domains. These represent approximate upper and lower bounds on the number of proteins that would be amenable to coupling-based analysis with any concatenation method. Of the 3 × 109 possible interactions in the human proteome, this means that between 4% and 17% of protein interactions are currently accessible to EVcomplex2.We assessed the current prospects for predicting whether proteins interact and their residue resolution on the 74,449 proteins in the human proteome, by aligning their constituent PFAM domainshttps://marks.hms.harvard.edu/ecolicomplex/) or by downloading the source code (https://github.com/debbiemarkslab/EVcouplings)33.In this work, we demonstrate a method for inferring protein interactions at residue-level detail from genomic sequences alone. We provide a probabilistic scoring method, calibrated on large, non-redundant datasets, which can be used to determine the probability of interaction of two proteins. If the protein interaction is known or inferred by the protein interaction score, the residue interaction score can be used directly to determine residues in contact at the interface. These methods can be freely accessed via our website  and passed through the RCSB Protein Data Bank with the following query: “Sequence Length is between 30 and 1200 and Resolution is between 1.0 and 5.0 and Representative Structures at 30% Sequence Identity.” IDs were mapped to UniProt42 identifiers using the SIFTS database43 (download date 2/20/2018). Only complexes with at least two chains that map to different UniProt identifiers were kept. Single-chain and fusion proteins were removed because they may have non-specific interface contacts not found in nature.We built a dataset of non-redundant heterodimeric protein-protein interactions with resolved crystal structures. A list of all heteromeric structures in the PDBePISA was downloaded32 annotated in that protein, which we call the PFAM set for that protein. We then consider a protein-protein interaction as unique if and only if the interacting proteins constitute a pair of PFAM sets not yet seen in our database, yielding 4154 non-redundant pairs of interactions. We then consider only contacting pairs of proteins, defined as a protein pair with at least 20 pairs of residues with a minimum atom distance less than five angstroms. Because each unique interaction can be represented by many crystal structures, representative crystals were chosen based on the number of other interfaces present in that crystal – i.e., complexes with more subunits were prioritized. We removed any cases where the two proteins map to the same chain, where the two proteins share any PFAM domain, or where either of the proteins was less than 30 amino acids in length, resulting in a final list of 1,675 protein complexes with known interfaces 19, and proteins from membrane protein export apparatuses : “BamABCDE Outer Membrane Protein Assembly Complex”, “TatABCE protein export complex”, “SecD-SecF-YajC-YidC Secretion Complex/Sec Translocation Complex”, “Sec Translocation Complex/SecYEG translocase”, “Sec Translocation Complex”, “GspC-O secreton complex.” The final dataset included 3987 protein pairs  were downloaded from UniProt. For both the positive benchmark set and E. coli genome set, the entire length of every protein was used for sequence alignment. Alignments were constructed using jackhmmer version 3.1b246 with five iterations against the UniProt database (downloaded February 2018), as implemented in the EVcouplings software package. Alignments were considered to have sufficient coverage if at least 80% of the columns were less than 50% gaps. A range of bit-scores were tested . For each alignment, the Neff was calculated by downweighing each sequence by the number of other sequences with more than 80% identity. The alignment with the largest Neff that had 80% coverage of columns was selected for each protein. Proteins from the E. coli genome were deemed eligible for EVcomplex analysis if their monomer sequence alignment had at least 80% column coverage and Neff/L > = 2.5, after characterizing the precision of monomer protein couplings across a range of sequence alignment diversities . We consider these contacts to be artifactual due to their very high scores relative to known interactions. Finally, we do not consider proteins from E. coli where there is a non-traditional amino acid annotated in the UniProt sequence, because these can indicate pseudogenes. To ensure high-diversity, low gap alignments for inference, we removed from consideration concatenated sequence alignments with Neff/L less than 0.2 or coverage of columns less than 80%.To avoid analyzing protein pairs where the two proteins are homologous, we implemented four different quality control metrics. First, we remove all pairs of proteins where the first protein contains a PFAM domain that is found in the second protein. Then, we remove all pairs where our structure comparison protocol found overlapping hits to the same chain of the same PDB structure. Next, we remove all pairs that display high-scoring coevolution along a diagonal between the two proteins , hits composed of more than 50% gaps were filtered from the alignment, and sequences with homologs more than 80% identical were downweighed to compute Ni and j are within 5 amino acids, were not included for further analysis. For analysis of raw ECs, the Z-score of each inter-residue EC was calculated using the distribution of all inter-residue ECs. The EVcomplex score was calculated by dividing each inter-protein EC by the absolute value of the minimum inter-protein EC score (for the raw EVcomplex score), and then normalizing for the Neff/L of the concatenated alignment (for the EVcomplex score)15. The EVcomplex2 score is calculated by inputting the couplings scores and additional features into the model described under “Detecting inter-protein residue contacts”, for predicting residue interactions when the protein interaction is presumed known, or by inputting the coupling scores and additional features into the model described under “Detecting protein interactions” for predicting protein interactions.Raw EC scores, sometimes called the Corrected Norm (CN) score, were calculated by performing the average product correction (APC) on the Frobenius Norm scores output by plmc. Diagonal ECs, where positions hmmsearch version 3.1b246. For E. coli monomer alignments, the top 20 were selected. For the validation dataset, all hits above a bitscore threshold of 0.2 L were selected. Once structures were selected for comparison, the minimum of the nearest atom inter-residue distances across all structures were used for comparison to ECs11.To identify experimental structures for comparison of evolutionary couplings, the monomer sequence alignments were searched against the PDB database using 50 with a probe size of 1.4 Å and standard van der Waals radii51. If multiple structural hits were found for each protein, we take the mean relative solvent accessibility across all structures, to avoid biases caused by incomplete structures.Relative and absolute solvent accessibilities were calculated using DSSP (CMBI version by M.L. Hekkelman/2010-10-21)C for each column i in the concatenated sequence alignments were calculated as one minus the normalized entropy for the column i,fk is the frequency of the k-th amino acid in column i.The conservation i and j are in contact (distance < 8 Å), given their EC score and additional residue-level features. This model for residue interaction was trained on the positive benchmark dataset, which includes 560 protein complexes for which the concatenated sequence alignment has sufficient coverage and diversity. Because most of the inter-protein ECs are noise and do not correspond to real contacts16, we take the top ten inter-protein ECs as input to the model. Residue pairs where one or both residues were missing in all structures were removed from the dataset because their distance is unknown. This resulted in 5,344 residue pairs with a defined minimum atom distance, 26% of which were within 8 Å.We trained a logistic regression classifier to predict the probability that two residues 52, using the LIBLINEAR solver and L1 regularization. Regularization weight of 0.05 was chosen to maximize a tradeoff between recall and precision using ten-fold cross-validation on the training dataset.140 (25%) of the 561 complexes had all of their ECs held out and used for testing. Models were trained using the Logistic Regression classifier in Scikit-learn v. 0.23.515; the Z-score of the corrected norm (CN) score of all inter-protein ECs; the conservation of the more conserved for the two residues; the relative rank of the inter-protein ECs; the maximum of the intra-protein EC enrichment11 for the two residues; the percent of residues in both positions that were hydrophobic (amino acids AVILMC), hydrophilic (amino acids STNQ), bulky (amino acids WFY), or charged (amino acids RHKDE) (4 separate features); and the combined hydrophilicity score at both positions. The features chosen for the final model based on non-zero weights in the L1 regression were the EVcomplex score12, the conservation of the more conserved for the two residues, the relative rank of the first inter-protein EC, and the maximum of the intra-protein EC enrichment for the two residues11  to select features and regularization weight, chosen to maximize a tradeoff between recall and precision using ten-fold cross-validation. The final model was trained with L2 regularization using the LIBLINEAR solver in Scikitlearn v. 0.23.5.Models were trained using the Logistic Regression classifier in Scikitlearn v. 0.23.5The protein pairs from the negative benchmark set that were excluded from the training and test set were used to assess the false-positive rates expected when the model is applied to large datasets.Science, 2019 Supplementary Table S319. All protein pairs with a crystal structure listed were extracted from their Supplementary Table E. coli identified in the co-evolution screen”). 331 of 339 protein pairs were successfully run using our pipeline, with the remaining 8 excluded due to technical issues or pathologies of the protein pair , periplasmic, outer membrane-bound , and extracellular. Proteins annotated as “membrane related” but without a specific location were excluded. Each of the four compartments was tested against itself and against proteins in the physically adjacent compartment(s): inner membrane versus periplasm, periplasm versus outer membrane, and outer membrane versus extracellular.Localizations of 53 to reduce the effect of crystallization in a complex that may bias the docking procedure. Restraints-based docking was done using HADDOCK (High Ambiguity Driven biomolecular DOCKing) v2.254 and CNS55. This allows restricting the possible interaction search space using a more sophisticated treatment of conformational flexibility. The top five inter-ECs scores in both structure-aware and structure-free regression models were selected. Unambiguous distance restraints were applied on C-beta  with an effective distance of 5 Å with an upper and lower bound of 2 Å. Default HADDOCK protocol was done starting with a rigid-body energy minimization (500 models), followed by semi-flexible refinement in torsion angle space (100 models), and ending with further refinement in explicit water (100 models). Upon binding, small conformational changes can be accounted for by HADDOCK through explicit flexibility during the molecular dynamics refinement. The resulting models were scored using the default HADDOCK scoring function, and the top ten best-scoring models, as well as the top models from RMSD clusters, are reported. To evaluate docking of our benchmark set where the structure of the interaction is known, we calculated Ligand-RMSD (L-RMSD) as well as Ligand centroid rotation angle and displacement in comparison to the reference structures.Side chains of all monomers were perturbed and optimized using SCWRL442 accession numbers were mapped against the NCBI taxonomy database56 We extracted the domain of life for each sequence in the database and calculated the percent of sequences in each alignment that were eukaryotic in origin.For all concatenated sequence alignments that comprise the positive benchmark set, the UniProt46 for five iterations at multiple length-normalized bit scores between 0.1 and 0.5 using the 2017 Uniref100 release42. Pseudo likelihood maximization was then used to compute evolutionary couplings scores for each possible pair of positions in the query.For estimating the number of possible human interaction pairs suitable for our analysis, we used a set of EVcouplings runs computed for all ~16 K PFAM domains in 2017 (PFAM release 30.0). A representative sequence from each domain family was chosen, and alignments were built for each sequence using jackhmmerWe downloaded the set of 74,449 unique translation products annotated by UniProt human reference proteome UP000005640 (dated 12/2/2019). Each entry was then cross-referenced against the PFAM 32.0 release to identify component domains. Using the set of runs described above, we then identified how many domains in each protein had an alignment with high coverage  and a sufficiently diverse set of sequences (effective number of sequences per residue > = 1 or 5). These calculations were then used to report the number of human proteins amenable to EVcomplex reported in the text.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting Summary


Computationally predicting the interaction of proteins and ligands presents three main directions: the search of new target proteins for ligands, the search of new ligands for targets, and predicting the interaction of new proteins and new ligands. We proposed an approach providing the fuzzy classification of protein sequences based on the ligand structural features to analyze the latter most complicated case. We tested our approach on five protein groups, which represented promised targets for drug-like ligands and differed in functional peculiarities. The training sets were built with the original procedure overcoming the data ambiguity. Our study showed the effective prediction of new targets for ligands with an average accuracy of 0.96. The prediction of new ligands for targets displayed the average accuracy 0.95; accuracy estimates were close to our previous results, comparable in accuracy to those of other methods or exceeded them. Using the fuzzy coefficients reflecting the target-to-ligand specificity, we provided predicting interactions for new proteins and new ligands; the obtained accuracy values from 0.89 to 0.99 were acceptable for such a sophisticated task. The protein kinase family case demonstrated the ability to account for subtle features of proteins and ligands required for the specificity of protein–ligand interaction. Discovery of the biologically active substances and their target proteins is a crucial stage in drug development. Computational prediction of the interaction between the proteins and drug-like ligands significantly decreases the time consumption and costs required for experimental studies.The approach known as SAR (structure–activity relationship) allows for prediction of the biological activities of a compound from its structural features ,2. The SOvercoming the mentioned limitations, the researchers investigate the combined space of protein and ligands. It allows for building large-scale predictive models . Such anSmall-molecule ligands’ description uses the well-known techniques developed for SAR investigations . ProteinGeneralized estimates do not account for the impact of individual amino acid residues. However, the experimental studies show examples when the individual amino acid residues crucially influence the protein affinity to a ligand ,25. In sSeveral approaches provide predictive models for the restricted set of target proteins and multiple ligands. In extreme cases, mutant variants of the same display various affinities to the compounds ,13,17,22We suggest the method that predicts protein–ligand interactions from the amino acid sequences classified by the protein affinities to ligands. Description of amino acid sequences calculated from a segment-to-segment comparison of the test and training sequences allows avoiding problems with the incorrect matching of aligned residues . The earTherefore, the binary coefficient 1 and 0 were used to define the protein belonging to one of two clear classes presenting the targets interacting and not interacting with a given ligand, respectively. In this study, we also applied the fuzzy classification of proteins. All proteins from a training set were referred to the class of interacting targets with different belonging coefficients. Belonging to the class of non-interacting proteins is defined in the same manner. These “fuzzy” coefficients adopted values in the range from 0.0 to 1.0. They were calculated from ligands’ structural features with the PASS (prediction of activity spectra for substances) method, successfully used in many SAR studies ,31. Our http://www.way2drug.com/proteochem_dataset.php).We tested our program on five protein groups: G protein-coupled receptors (GPCRs), protein kinases, ligand-gated ion channels, voltage-gated ion channels, and nuclear receptors. The characteristics of training sets collected according to the below-described procedure see  are presAccording to protein groups, interaction parameters , and cutoff values (1 and 10 µmol), the collected data formed several datasets.As described below, each protein–ligand pair in a training set got the interaction index equal to 1 (interaction) or 0 (no interaction). Thus, we composed up to six sets for each protein group .(a)A new ligand for a target with a known ligand spectrum based on the chemical structure comparisons;(b)A new target for a ligand with a known target spectrum based on the protein sequence comparisons;(c)A new protein–ligand pair if both objects were uncharacterized. It includes a comparison of ligand structures as well as a comparison of protein sequences.Commonly, three predictive tasks are considered in predicting protein–ligand interaction: To simulate all situations and test our approach, we implemented three scenarios .In all cases, the prediction accuracy was evaluated with ROC (receiver operating characteristic) analysis based on the leave-one-out cross-validation procedure. This technique provides building the dependence curve of true positive vs. false positive rates at different cutoffs of accuracy scores. The area under curve (AUC) is commonly used as accuracy estimate .aP and iP  showed the high ligand prediction accuracy with the average AUC value of 0.95 with small variation. Thus, our approach revealed the stable sequence features required for the ligand specificity.Predicting the ligand-specificity of protein kinases resulted in the less accurate estimates, displaying the average AUC value of 0.84 with significant variation from 0.69 to 0.94. Using the frame of 30 residues somewhat increased the predictive power compared with the seven-residue frame. It seems to relate to the more distant residue interacting. The stricter cutoff of 1 µmol conditioned the significantly more accurate prediction, which is consistent with results obtained for the kinases’ ligand prediction.The three remaining protein groups presented a relatively small number of sequences and ligands. However, predicting the ligands for these groups displayed high efficiency . The aveIn general, the results obtained in the second scenario are close to the results published in our previous study, which used the same methods and provided prediction accuracy comparable or exceeding those of other methods with the “Gold Standard” set .aP) and non-interaction (iP), as calculated in the first scenario. This technique ;Testing the isolated protein ;IC50, Ki, Kd as interaction parameters;Excluding the mutant proteins.To select reliable and suitable data, we formed the MySQL queries with the following limitations:https://www.rdkit.org) at default parameters. According to the limitations of the PASS program [The ligands’ structures were normalized with the RDkit package , voltage- and ligand-gated ion channels.The data retrieved for the same interacting pair often presented the results obtained by different research groups at different experimental conditions. Therefore, we developed an algorithm that provided the integration of such data. According to protein–ligand interaction parameters, we divided the dataset composed for each protein group into three subsets: IC50, Ki и Kd, if enough data were available. These parameters adopted precise (such as 0.1 µmol) or interval values . In all three cases, we established two cutoffs, 1.0 and 10.0 µmol, and considered the protein and ligand as interacting if the parameter did not exceed the cutoff. As a result, each protein–ligand pair got the binary index designating the interaction (1) or non-interaction (0). As the calculation of this interaction index affected the dataset size, three mentioned subsets were divided into two parts, resulting in up to six subsets for each protein group if data were available.In the presence of both fixed and interval values, only fixed parameters were accounted for if even interval values contradicted fixed ones. The interaction index adopted a value of 1 if the data median did not exceed the cutoff and 0 otherwise;In the absence of fixed values, the protein–ligand pair was not considered if interval values put in non-intersected areas  or the parameter values were at the interval limited at two ends ;In the case of a few intervals limited below , the index adopted 0 if the maximal edge value was higher than a given cutoff; otherwise, the protein–ligand pair was excluded;In the case of a few intervals limited above , the index adopted a value of 1 if the minimal extreme value was less than the cutoff; otherwise, the protein–ligand pair was excluded.We calculated the interaction index values according to the following rules:http://www.way2drug.com/proteochem_dataset.php.The datasets are available online at Each target protein in the training set should have at least three established ligands, providing an adequate PASS program prediction. Each ligand should have at least three established targets, providing the correct ROC analysis.aP and iP scores, which are probabilities of interaction and non-interaction between target protein and ligand, respectively.The PASS method was developed for predicting the biological activities of drug-like organic compounds . This meIn this study, we simulated the situation with uncharacterized ligands and sequences using the belonging fuzzy coefficients calculated by the PASS program. Comparing the results obtained with fuzzy and binary coefficients showed the usefulness of the suggested approach.Using the PASS program, we obtain the fuzzy classes, including all training set ligands, introducing Pa and Pi as coefficients belonging to the class of ligands of a particular target protein. It allows predicting affinity for the protein–ligand pairs with non-defined interaction spectra for both the protein and compound. Most training data have no assignment because of the absence of experimental results, and they are usually considered conditionally negative examples. However, some of them can present positive but non-detected examples. Using the weight coefficient based on structural similarity enables accounting for hidden positive examples.K) results in the segment scores R calculated as follows:F is the segment length, i is the position of a sequence Q, h is a shift between sequences, sim  is a similarity of matched residues from Q and K sequences accordingly to a chosen metrics (residue identity in this study).Our approach uses the positional scores calculated by segment comparison between the test sequence and all training sequences . The segp equals the maximum of R values obtained for all segments containing p at all shifts.Finally, the score for each position The positional scores obtained with all training sequences are integrated to estimate the possible protein–ligand interaction.thk training sequence gets two coefficients ka and kb, which, respectively, weight the protein ability and inability to interact with a given compound C. In other words, these values define a priori belonging a protein to the classes of ligand interactors and non-interactors.Commonly, proteochemometrics studies aim at the solution of two tasks. One of them is an estimation of belonging the protein to the class of ligand targets. Another one is an estimation of belonging the compound to the class of protein ligands. The incompleteness of experimental data significantly decreases the number of bound protein–ligand pairs included in the training set. We suggest overcoming this limitation by introducing weight coefficients based on compounds’ structural peculiarities see . Each ktEstimating the potential protein–ligand affinity uses the naïve Bayes according to the technique described earlier for chemometrics methods .Q should be compared with N training sequences. The integrated score for each position of Q is calculated as follows:pkS is a score for the position p obtained by comparison with the thk sequence.During the prediction procedure, the test sequence pt values are average on all m positions of the test sequenceThe 0t presents weights of ability and inability to interact with a ligand without accounting for sequences similarity:The a priori value C is estimated as follows:Finally, the possible interaction of a test protein with ligand B(C) varies from −1 (no interaction) to +1 (interaction). The null value shows the undetermined result.The estimate aP and iP, reflecting the thk protein’s affinity to a test ligand, substitute values ka and kb into Equations (3) and (5).Our previous study used binary coefficients of protein–ligand interaction and non-interaction to predict new target proteins for ligands with known target spectra . This stOur study’s principal aim was to develop the method for predicting the protein–ligand interaction based on the protein sequences and ligand structures. Defining targets with enough narrow sets of experimentally detected or predicted ligands as well as ligands with narrow set of targets is promised approach to obtain selective drug candidates.http://www.way2drug.com/proteochem_dataset.php).To correctly test our approach, we collected the datasets with various parameters of interaction. The applied procedure overcame difficulties caused by the ambiguity of initial information. The collected data are available online (We implemented fuzzy coefficients weighting the interactions of target proteins and small molecule compounds to solve this task. The PASS software provided the reliable probabilistic estimates of ligand affinity to targets. Applying these values as weight coefficients, we modified the earlier prediction method using the binary prediction coefficients. Comparing the results obtained with the previous and new program versions showed the expected accuracy decrease with fuzzy coefficients. However, the estimates calculated in the second case were quite acceptable with the most dataset. Generally, we defined the more suitable conditions of each protein group’s predictive procedure by testing our method with various parameters and the best results for GPCR, protein kinases, ligand-gated ion channels, voltage-gated ion channels and nuclear receptors were 0.92, 0.89, 0.96, 0.93, and 0.99, respectively . Indeed,Protein kinases displayed the lowest estimates. These results seemed to relate with smaller structural differences in these target proteins and ligands, causing the less effective recognition of interaction specificity. High accuracy was detected with binary and fuzzy coefficients for a few datasets . The correspondence between the protein and ligand features shown in our study spoke in favor of cooperative using protein and ligand description, as simulated in the third scenario.Our approach is suitable for the broad applicability domain in proteochemometrics with different degree of correlation between the phylogenic relations and ligand specificity. In case of protein kinases demonstrating the weak dependence of that kind, our method provided prediction of ligand specificity with acceptable accuracy. It suggests that our method is suitable for not yet considered protein targets and their ligands, given the enough representative data sets.


The African swine fever virus (ASFV) has severely influenced the swine industry of the world. Unfortunately, there is currently no effective antiviral drug or vaccine against the virus. Identification of new anti-ASFV drugs is urgently needed. Here, an up-to-date set of protein–protein interactions between ASFV and swine were curated by integration of protein–protein interactions from multiple sources. Thirty-eight swine proteins were observed to interact with ASFVs and were defined as ASFV-interacting swine proteins. The ASFV-interacting swine proteins were found to play a central role in the swine protein–protein interaction network, with significant larger degree, betweenness and smaller shortest path length than other swine proteins. Some of ASFV-interacting swine proteins also interacted with several other viruses and could be taken as potential targets of drugs for broad-spectrum effect, such as HSP90AB1. Finally, the antiviral drugs which targeted ASFV-interacting swine proteins and ASFV proteins were predicted. Several drugs with either broad-spectrum effect or high specificity on ASFV-interacting swine proteins were identified, such as Polaprezinc and Geldanamycin. Structural modeling and molecular dynamics simulation showed that Geldanamycin could bind with swine HSP90AB1 stably. This work could not only deepen our understanding towards the ASFV-swine interactions, but also help for the development of effective antiviral drugs against the ASFVs. African swine fever virus (ASFV), the causative agent of African swine fever (ASF), is an enveloped and double-stranded DNA virus with genome size ranging from 170 kbp to 194 kbp . ASFV maVaccine and antiviral drugs are believed to be the best tool for prevention and control of viral infection and spread . UnfortuAlthough most antiviral drugs target the viral proteins, in recent years several studies have attempted to develop antiviral drugs which targeted the host proteins . CompareSeveral studies have investigated the protein–protein interactions between ASFV and swine. Some important interactions were listed as follows: the viral DP71L protein interacts specifically with and activates protein phosphatase 1 (PP1) ; the virThe protein–protein interactions between ASFV and swine proteins were compiled from three sources . First oSecondly, 20 protein–protein interactions between ASFV and swine were obtained from the literature. This was achieved by firstly searching the PubMed database by the key word “ASFV” in the title or abstract on 29 December 2018, which resulted in 630 abstracts. Then, each abstract was manually screened based on whether it contained protein–protein interactions between ASFV and swine, and 117 abstracts were retained. Finally, the full texts of the manuscripts corresponding to these abstracts were read carefully and 20 extra protein–protein interactions between ASFV and swine were compiled from these articles.e-value smaller than 0.001, coverage greater than 40%, and sequence identity greater than 30%, were considered as homologs of ASFV proteins. The swine proteins which interacted with the hits were predicted to interact with the ASFV proteins. The obtained three protein–protein interactions overlapped with those from Viruses.STRING  which interacted with swine proteins based on the database of Viruses.STRING. Then, 159 ASFV proteins encoded by BA71V, which were downloaded from NCBI RefSeq database , were bls.STRING .All the swine protein–protein interactions were downloaded from STRING database  on 8 Jandegree and betweenness, respectively. The shortest path length between two proteins in the protein–protein interaction network was calculated with the function of shortest.paths.The igraph package (version 1.2.2)  in R wasThe network was visualized with the help of Cytoscape (version 3.7.1) .enrichGO and enrichKEGG in the package “clusterProfiler” (version 3.6.0)  terms and KEGG pathway enrichment analysis for the ASFV-interacting swine proteins or the ASFV infection-associated swine proteins were conducted with functions of n 3.6.0)  in R (verichment .Candidate drugs were predicted with the help of DrugBank (version 5.1.2) . The proHomo sapiens (PDB code: 1YET). Sequence alignment showed that the identity between swine HSP90AB1 and human HSP90AA1 was 92.3% in the Geldanamycin-binding domain (208 residues). Besides, only amino acid substitutions but no gaps were observed in the alignment. The highly similar and gap-free alignment indicated that the predicted structure is reliable. In addition, 1YET is the complex structure of Geldanamycin and HSP90AA1, which allowed us to transfer the binding conformation of Geldanamycin from 1YET to the predicted structure of swine HSP90AB1. To validate the binding conformation between Geldanamycin and swine HSP90AB1, molecular dynamics (MD) simulation was performed for 10 ns using GROMACS  ASFV-interacting swine proteins were found to interact with 4,184 other swine proteins. The latter 4,184 swine proteins were defined as ASFV infection-associated swine proteins. The ASFV-interacting swine proteins together with ASFV infection-associated swine proteins formed a protein interaction network with 9,305 non-redundant interactions , includi3.5 and 59.2, respectively, whereas these values for the ASFV-interacting swine proteins were 144, 104.6 and 58.7, respectively, and they were 120, 104.2 and 58.8, respectively, for the ASFV infection-associated swine proteins , a drug used for treating hyper-calcemia and Rifabutin (DrugBank ID: DB00615), a antibiotic with potent antimycobacterial properties, were observed to both target the ASFV-interacting swine proteins and ASFV proteins. Both of them (highlighted in red-edge) were approved for use, suggesting their potential use for treating the ASFVs.Some drugs were observed to have strong specificity on the ASFV protein or ASFV-interacting swine proteins, such as the Hydroxyurea (DB01005), Infliximab (DB00065), Adalimumab (DB00051) and so on. Hydroxyurea specifically targeted F778R. It is an antineoplastic agent that inhibits DNA synthesis through the inhibition of ribonucleoside diphosphate reductase. It may be used to inhibit DNA synthesis of the ASFV virus, thus blocking the proliferation of the virus. Infliximab specifically targeted TNF and is primarily related to inflammation control and neurological indications. It may be used to block the necrosis during the ASFV infection.Homo sapiens.Some drugs were observed to target multiple ASFV-interacting swine proteins, such as Geldanamycin (DB02424), Polaprezinc (DB09221) and Andrographolide (DB05767). For example, Geldanamycin could target the HSP90AA1, HSP90AB1 and HSP90B1, all of which played a central role in the swine protein–protein interaction network and swine-virus protein–protein interaction network . This suVaccines and antiviral-drugs are considered as the most effective tools for fighting against viruses. Unfortunately, nearly all attempts to develop vaccines against ASFVs have failed to induce effective protection . TherefoIn the era of systems biology, a large amount of protein–protein interactions have been accumulated, including the virus-host protein–protein interactions. This study compiled an up-to-date protein–protein interaction network between ASFV and swine. Analysis of the network could help identify possible associations between viral activities and host defense strategies, which may facilitate development of potential therapies by disrupting host-virus interactions . SeveralBoth the ASFV-interacting swine proteins and ASFV infection-associated swine proteins were enriched in the functions of cell death, apoptosis or necroptosis. This suggested that these processes may play an important role in viral infections. Previous studies have shown that ASFV infection induced TNF-alpha production which further induced apoptosis in the infected cell . ActuallLots of drugs were predicted to target the ASFV-interacting swine proteins or ASFV proteins. Several strategies could be used to select the candidate drugs. For specificity, the drugs with high specificity on the ASFV-interacting swine proteins or ASFV proteins could be selected, such as the Hydroxyurea and Infliximab; for broad-spectrum effect, the drugs which targeted the ASFV-interacting swine proteins with high degrees in the swine protein–protein interaction network, such as Polaprezinc, or those which targeted multiple ASFV-interacting swine proteins, such as Geldanamycin, could have a large influence on the protein–protein interactions between swine and ASFV. Two drugs, that is, Gallium nitrate and Rifabutin, were observed to target both the ASFV-interacting swine proteins and ASFV proteins. They could also be used for broad-spectrum inhibitory effect against ASFV infections.Most antiviral drugs target the viral proteins. Drug resistance frequently appears due to rapid mutation of viruses. On the contrary, the drugs targeting the host protein may have the advantage of stable effect since the host proteins generally evolve far slower than viral proteins . BesidesThere were two limitations to this study. Firstly, the protein–protein interactions between swine and ASFV are far from complete. The ASFV encodes more than 150 proteins . Only 16In conclusion, this study curated an up-to-date set of protein–protein interactions between swine and ASFV as far as we know, and identified the ASFV-interacting swine proteins which were vital for viral infection. The ASFV-interacting swine proteins were observed to play a central role in swine protein–protein interaction network, and also took part in interactions between swine and several other viruses. They could be taken as potential antiviral-drug targets. Several drugs were predicted to target the ASFV-interacting swine proteins and ASFV proteins. They could be helpful for further development of effective drugs against the virus.10.7717/peerj.8855/supp-1Supplemental Information 1MM: contains Van der Waals, electrostatic interactions, and net non-bonded potential energy between the protein and inhibitor; Polar: polar solvation energy; Apolar: non-polar solvation energy; Total: total energy.Click here for additional data file.10.7717/peerj.8855/supp-2Supplemental Information 2vdw refer to Van der Waal energy, ΔEelec refer to electrostatic energy, ΔEmm refer to total potential energy between the protein and ligand (ΔEmm = ΔEvdw + ΔEelec), ΔEpol refer to polar solvation energy, ΔEapol refer to non-polar solvation energy and ΔEbinding refer to the total binding energy (ΔEbinding = ΔEmm + ΔEpol + ΔEapol).ΔEClick here for additional data file.10.7717/peerj.8855/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.8855/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.8855/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.8855/supp-6Supplemental Information 6Click here for additional data file.