Structure-based stability prediction upon mutation is crucial for protein engineering and design, and for understanding genetic diseases or drug resistance events. For this task, we adopted a simple residue-based orientational potential that considers only three backbone atoms, previously applied in protein modeling. Its application to stability prediction only requires parametrizing 12 amino acid-dependent weights using cross-validation strategies on a curated dataset in which we tried to reduce the mutations that belong to protein–protein or protein–ligand interfaces, extreme conditions and the alanine over-representation.G measures, as well as better receiver operating characteristics and precision-recall curves. Our method is almost anti-symmetric by construction, and it performs thus similarly for the direct and reverse mutations with the corresponding wild-type and mutated structures. Despite the strong limitations of the available experimental mutation data in terms of size, variability, and heterogeneity, we show competitive results with a simple sum of energy terms, which is more efficient and less prone to overfitting.Our method, called KORPM, accurately predicts mutational effects on an independent benchmark dataset, whether the wild-type or mutated structure is used as starting point. Compared with state-of-the-art methods on this balanced dataset, our approach obtained the lowest root mean square error (RMSE) and the highest correlation between predicted and experimental ΔΔhttps://github.com/chaconlab/korpm.Bioinformatics online. Mutations can affect the stability and function of proteins and eventually cause diseases. Moreover, improving the thermostability of enzymes or antibodies can be crucial for various applications in protein engineering. To study how amino acid substitutions affect protein stability, differences in Gibbs (un)folding free energy  structures, and The success of KORP is rooted in the consideration of the 6D nature of the residue interactions into a full joint probability distribution as:G = Gunfolded − Gfolded). Here, we define G as the unfolding free energy of the protein, and positive values of Gwt, and the mutant Gmut by summing G values using cross-validation experiments as detailed below.Following the same strategy used to successfully adapt this potential to the evaluation of protein–ligand interactions  kcal/mol.Experimental temperature between 10 and 40°C and pH between 5 and 9, to avoid extreme conditions. When multiple experiments were available for the same mutation, we selected the one closest to standard conditions , or excluded them if the average difference in ΔΔG experiments are far from optimal. Since protein–protein interactions may affect the free energy estimation, we select only monomeric structures, according to the PDB Biological Assembly, or based on literature information when available.Available monomeric structure in the PDB with resolution better or equal to 2.5 Å. The identification of the oligomeric state from the crystal structure and its correspondence with actual ΔΔNo known direct interaction with ligands, since protein–ligand interactions at the mutation site are likely to affect the free energy change. A mutation is discarded if the distance between any pair of heavy atoms in the mutated residue and ligand is smaller than 5 Å.We also discard entries with incomplete backbone atoms coordinates and cases outside the two-state folding model.Ssym blind test dataset , and 27% are stabilizing (ΔΔG > 0), and more than one-third involve alanine. To have a more balanced dataset, we limited the number of entries per pairwise amino acid mutation to a maximum of 15 by removing mainly destabilizing mutations involving alanine. With this data reduction, we aimed at alleviating the alanine over-representation while avoiding losing stabilizing mutations and protein families. The resulting balanced subset , while cysteines, tryptophanes and prolines remain underrepresented. Moreover, the majority of the entries belong to a few protein families, i.e. the 10 most populated protein families include around 60% of the total of the mutations.The initial curated dataset  includesd subset  includesDespite the relatively small number of different proteins available, low levels of sequence identity between training and test sets are desirable to avoid overfitting and are mandatory for machine learning approaches. This problem complicates fair comparison between methods and can lead to over-optimistic results . In the training phase, we minimize mean absolute error (MAE) between predicted and experimental ΔΔG using the BOBYQA optimization algorithm from NLopt  (https://github.com/chaconlab/korpm). CartddG, and to a lesser extent FoldX, predicted some destabilization cases with extreme ΔΔG values, likely due to hard collisions. To keep the consistency in the comparison we reduced this anomalous behavior by assigning a value of −8 kcal/mol for all predicted values lower than this cutoff.We compare our approach with eight representative ΔΔSsym .Two separate datasets were used for performance evaluation and comparison with other stability prediction methods. Firstly, the balanced dataset ture see . Secondlture see . After tG (RMSE), as well as statistical measures of the performance of a binary classification test: sensitivity (TPR), specificity (TNR), precision/recall (PPV), accuracy (ACC) and Matthew’s correlation coefficient (MCC). In addition to single value measures, the receiver operating characteristic (ROC) or precision-recall (PRC) plots, and their area under the curve. PRC is more illustrative of the classifier performance with unbalanced datasets  and 2 indicates a completely incorrect prediction. Finally, the correlation coefficient between direct and reverse predictions in the Ssym dataset gives an estimation of the methods’ anti-symmetric bias.The performances were evaluated using common measures employed in the field, including the PCC the mean absolute error between predicted and the experimental ΔΔdatasets . We alsoSsym dataset are summarized in G values . Overall, these values, which are very similar to those obtained in the cross-validation experiments , and another with the stricter criterion of excluding from the training set all proteins presenting at least 25% sequence with any of the Ssym proteins (blue line). The resulting curves are practically identical, which confirms the absence of significant overfitting with distinct protein families. The high fraction of true positives (i.e. correctly predicted stabilizing mutations) among positive predictions depicted in PRC plot confirms the goodness of the approach  the otherwise unstable apo-protein  to the mutant (PDB ID: 8PTI) protein . KORPM cIn summary, despite the expected limitations in handling flexibility, ligand binding, protonation states or SS-bonds, the good overall results confirm the ability and robustness of our approach for the estimation of protein stability.Ssym dataset for comparison with multiple state-of-the-art methods, even though some of those methods may have included mutations from this dataset in their training process, while we rigorously excluded them. Exceptions are DDGun3D which is an untrained method, and ThermoNet and PoPMusicS which also employed Ssym as a blind test.The comparison between stability prediction methods is difficult because of the different training/testing and validation subsets adopted and the inherent experimental uncertainties. Here, we used the G predictors on this particular balanced benchmark including both direct and reverse mutations. KORPM obtains the lowest RMSE and MAE, and the highest PCC values. For classification purposes, the ROC and PRC plots and their corresponding area under the curves provide a model-wide evaluation independent of arbitrary threshold definition . The case of Dynamut2 is particularly striking, as this predictor yields the lowest RMSE on direct mutations . Overall, these results are rather similar to those obtained on the direct mutations of Ssym, with the notable exception of FoldX, which performed significantly worse on S461 . As before, the largest outliers for KORPM correspond to a disulfide bond  and a salt bridge .Another comparative assessment was performed using the S461 dataset, obtained by curating the S669 recently compiled by dictions , DDgun3DSsym dataset, excluding all of its mutations from the training and cross-validation test. Even though some methods may have incorporated many of such mutations into their training datasets , KORPM achieved the lowest RMSE and highest PCC between predicted and experimental ΔΔG, as well as superior ROC and PRC curves. In the S461 dataset, our method remains consistently close to optimal with respect to the various performance measures: top ROC curves at 0.19 distance with respect to the best PopMusicS RMSE.Here, we successfully address the challenge of predicting stability changes upon mutation with a simple sum of pairwise energy terms that depend on the interacting amino acids and their relative position and orientation of three backbone atoms per residue in the native structure. To reduce overfitting, we did not add additional features to our potential, as it is common for several other methods, and we fitted only 12 amino acid-dependent weights using cross-validation procedures. The relatively low variance of those fitted parameters indicates that the method is robust to variations in the training set. We obtained similar performances whether allowing mutations in homologous proteins to be split between the training and validation sets or not, which further suggested the absence of significant overfitting. As a blind test set for comparison with the state-of-the-art methods, we adopted the Ssym mutations from the training set. ACDCNN has top results on reverse mutations while maintaining good results on direct cases including the S461 dataset. In contrast, methods like Cartddg, FoldX and Dynamut2 perform very well on direct mutations, which may be more relevant for some types of real-case applications, but generally worse on reverse mutations, as has been previously noted , the difficulty to account for changes in protonation states or disulfide bonding, as well as the interferences from protein–ligand or protein–protein interactions.By taking a close look at KORPM outliers, we identified some expected limitations which are also common to most approaches for predicting ΔΔG data itself. Firstly, the most critical point is that the amount of data is limited, with no more than a few thousand mutations in a few hundred distinct proteins. The variability in terms of protein folds, functions or amino acid nature is also rather small, and no more than a third of currently available mutations are stabilizing. Furthermore, the data are far from homogeneous as it includes measurements made with different experimental methods, and in different environmental conditions , which creates a numerical variability that can go way beyond the experimental errors. To definitively improve and properly cross-validate protein stability prediction methods, it would be mandatory to increase the amount of mutational data by orders of magnitude.We showed that KORPM has comparable or better performances than competing methods but such comparisons between methods should be always considered with a certain degree of caution. Besides the difficulty to identify overfitted methods without purely novel and non-redundant data, there are still many sources of uncertainty in the available experimental ΔΔG predictions. Notably, if the mutation is located at a protein–protein interface or protein–ligand binding site, it becomes a different prediction problem, related to the binding affinity. And of course, since experimental research is typically focused on the protein’s biological activity, functional and interaction sites are often chosen as targets for mutagenesis. Even if the ΔΔG measurements are explicitly performed on the apo form, active sites tend to have properties distinct from the rest of the protein, with significantly more options for stabilizing the fold via mutations, but often at the cost of functionality (G for 51 mutants of β-glucosidase around its binding site (G measurement. Note also that the experimental characterization of the oligomerization state and the correspondence of the PDB structure is totally ignored in the current databases.Secondly, there is no genuine and perfect correspondence between the available crystallographic structure and the soluble structure in the conditions of the experiment. Potential changes of the oligomeric state, presence or absence of a particular ligand, conformational changes or partial aggregation are critical but largely ignored intrinsic limitations of ΔΔG is the change of the free energy difference between the native and non-native or unfolded state of a protein, but the effect of the mutation on the free energy of the non-native state is usually neglected. As an exception, the DDGREM contact potential (Thirdly, the very definition of the target quantity, the stability change upon mutation, is not without ambiguity. The scope of most predictors is limited to a simple two-state folding model. Any deviations from this model, or occurrences of higher-order transitions with multiple intermediates, are bound to generate inaccuracies, and this information is rarely well translated into database annotations. Furthermore, most predictors are essentially blind to the different types of contributions that participate in the change in free energy. Notably, entropic contributions are critical, but very rarely taken explicitly into consideration. Some methods based on effective energy functions may implicitly include solvent entropy but do not account for changes in the conformational entropy of the protein. On the other hand, ΔΔotential , which iG experimental data orphans of 3D protein structures.Despite this realistic view of the limitations of the quality and availability of data, and in consequence of our ability to properly assess predictive methods, there is room for future improvements. The higher throughput afforded by new deep mutational scanning methodologies (In summary, we believe that our approach constitutes a very valuable addition to the field, thanks to its simplicity, its natural robustness against overfitting, and its good balance between efficiency and accuracy. These characteristics make it likely that further advances in prediction performances could be achieved by combining/integrating it with other methods, or with some of the energetic functions or features considered by those methods. Future work should address merging our structural base potential with complementary evolutionary information derived from multiple sequences that have been proven useful to predict mutations. Other weaknesses of the proposed method such as protonation and disulfide bonding could be alleviated with ad hoc modules for adding effective correction factors. Meanwhile, KORPM offers a costless alternative to estimate protein stability changes upon mutation, with an accuracy comparable to or better than currently available methods.btad011_Supplementary_DataClick here for additional data file.


Protein-protein interactions (PPI) are pivotal to the numerous processes in the cell. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of the interacting structures and physiochemical propertiesof interacting interfaces and the of forces interactions. The interaction interface of obligatory protein-protein complexes differs from that of the transient interactions. We have created a large database of protein-protein interactions containing over100thousand interfaces. The structural redundancy was eliminated to obtain a non-redundant database of over 2,265 interaction interfaces. Therefore, it is of interest to document the analysis of these interactions in terms of binding sites, topology of theinteracting structures and physiochemical properties of interacting interfaces and the offorces interactions. The residue interaction propensity and all of the rest of the parametric scores converged to a statistical indistinguishable common sub-range andfollowed the similar distribution trends for all three classes of sequence-based classifications PPInS. This indicates that the principles of molecular recognition are dependent on the preciseness of the fit in the interaction interfaces. Thus, it reinforces theimportance of geometrical and electrostatic complementarity as the main determinants for PPIs. The cellular milieu where proteins perform their function is crowded. However, the spatial and temporal preciseness of interactions is rarely violated. This specificity and accuracy of the interacting proteins determines the fate of cells -3.Prote2 of interaction site formajority of heteromeric protein complexes. Bahadur et al. (2004) reported the abundance of aliphatic and aromatic residues and deficit of charges residues (except for Arg) in homodimeric interfaces in conformation with previous studies [The PPI manifests in formation of various types of complexes . In protein-protein complexes, theinteraction of two or more proteins is very specific and is usually characterized by only a small subset of their surfaces -15. Only studies ,29,30. W studies . On theIt has the protein-protein complexes (PPCs)as reported in PDB with their structural classification based onSCOPe (version 2.06). It harbors over 32000 X-ray crystallized structures of PPCs with structural resolution better than 2.5 Å. The informationabout these PPCs is available in the form of atom contact pairs wherein two atoms belonging to two different protein chains of a PPC were considered to be in contact if the intervening distance between them was less than the sum of their van der Waals radiiplus 1 Å as a tolerance factor. We utilized the entire PPInS and its non-redundant form (NRDB) database for our study.To study the influence of homo or heterodimeric nature of the proteins in PPIs, sequence similarity between the protein chains involved in the PPII was calculated using BLAST . Based oNot all the amino acid residues favor their occurrence on the protein surface, some prefer to stay in the protein core thereby avoid or does not contribute much in protein complexation ,33-34. The binding nature of proteins is determined from physicochemical, structural, and evolutionary properties of their constituents favouring the non-covalent interactions with partner protein(s). Such properties bring two molecules closer, influence them forbiological interactions, and define the destiny of the PPCs. Knowing the implications of protein binding sites analysis in protein engineering, all of the 223,714 PPIPs from PPInS and 4,530 PPIPs from NRDB were examined with respect to various PPI siteparameters. While analyzing a PPIP, if an atom from a PPIP was seen interacting with more than one atom of the partner PPIP, its contribution in PPII formation was considered only for once.The hydrophobic residues are reported in abundance of PPI sites . The kinThe solvation free energy of amino acids from the interacting protein also influences its kinetics, hence we calculated the solvation free energy (ω) of PPIP by linear summation of the average individual contributions of the interacting atoms(Eq. 4 and 5 - see PDF). The solvation free energy scale for amino acids given by Wimley et al., 1996  was usedThe protein-protein interaction patches are generally very small region on the protein surface withvery specificcertain structural / thermodynamical features. We determined the size of such interaction interfaces by summing up the difference of thesolvent accessible surface area (SASA) of the atoms in the bound and unbound forms. (see PDF)Contrary to the ASA,the depth index of an amino acid indicates the extent to which an amino acid is buried in the protein core. The location of amino acids in proteins is determined using their solvent accessibility. An amino acid is said to exist onprotein surface if the sum of solvent accessibilities for all of its constituting atoms is a non-zero value. While the amino acids with zero solvent accessibility are considered to be buried in the protein core .The depThe protrusion index studies the topology of the interface site and gives the measure of how much dense is the neighborhood of an atom on the protein surface .The protprincip function of the SURFNET [princip, an equation of plane was derived by employing the coordinates of the interacting atoms in the PPIP. Following this, the root-mean-square deviation (RMSD) of interacting atoms from the derived plane was determined anddesignated as the planarity index of the PPIPs.The protein binding sites are flat and circular in shape . The cal SURFNET  wasusedThe overall trends of parameteric score distribution wereapparently very similar. Hence, the statistical aspect of the data was explored. The distributions of PPIP parametric scores  for each PPI site parameterfrom all three PPII classes were taken into the consideration. For each PPI site parameter, the mean and standard deviation of parametric scores were calculated with respect to each PPII class separately. Thereafter, for each PPII class, p-value describingthe statistical significance between the parametric score of three PPII classes was calculated using two-tailed ANOVA test.We examined the PPIPs derived from experimentally determined PPCs in terms of various physicochemical and geometrical properties. Two datasets viz. PPInS and NRDB where interaction sites were demarcated based on the inter atomic distance between theconstituents of two protein chains of the PPCs, were considered for the study. The collection of ACPs between two protein chains of a PPC were referred as the PPII while the collection of atoms involved in PPII from each interacting protein chains weretermed as the PPIPs. All the PPIIs from PPInS and NRDB were categorized into three separate classes viz. LSS, MSS, and HSS by looking at the sequence similarity between the protein chains involved in formation of PPII under consideration (Table 1 - see PDF).Out of total 111,857 PPIIs in PPInS, around 73% of PPIIs were formed by the protein chains sharing HSS. Around 25% PPIIs were formed by the protein chains sharing LSS, and only 2% PPIIs were results of the interaction between protein chains sharing MSS. ForNRDB, the values for PPIIs with HSS, LSS, and MSS were around 62%, 32%, and 6%, respectively. This showed the presence of homodimers in abundance. The possible reasons for this may be the fact that the origin of life started with interactions in absolutelyhomologous proteins. However, through the course of evolution, perturbation in the genomic code might have caused the formation of heterologous protein complexes. Similar findings have been reported by  .The facThe RIP was calculated separately for all three classes of PPIIs  of NRDB . The propensity scores that we have obtained are quite similar to those which were earlier proposed -22.The The result obtained by analyzing the PPIPs from all the three PPII classes  of the PPInS and NRDB dataset are given in Table 3 and 4(see PDF), respectively. The parametric scores for all the PPIPs from LSS, MSS, and HSS are showngraphically in supplementary figures S2-S7 (see PDF)enmasse. In Table 5(see PDF), the statistical analysis of PPIP parametric scores is presented in terms of mean, standard deviation, and p-value . The statisticalanalysis was carried out considering all three PPII classes of both the datasets separately as well as collectively.The PPIPs were analyzed with respect to hydrophobicity, solvation free energy, depth index, size of the interacting patch, protrusion index, and planarity. It is pertinent to mention that on analyzing PPIPs from PPInS, initially, a significant differencebetween the parametric scores (obtained with respect to each PPI site parameter) of the three PPII classes was observed (Table 3 - see PDF). However, on removing less than 1% PPIPs  from each PPII class, the cumulative parametric scoresfor depth, protrusion, and planarity index of PPIPs from all three classes of PPII reduced down to the identical ranges. Similarly, the cumulative score for solvation free energy and hydrophobic content of PPIPs also were also seen converging to a commonsub-range. The possibility that these trends were on account of proportional redundancies of PDB, we looked for the same patterns in the NRDB.The analysis of PPIPs from NRDB considering both the cases, i.e. analysis of all PPIP from each PPII class and analysis of PPIPs after removing less than 1% statistical outlier from each PPII class, also showed the similar trends for the parametric scores foreach PPI site parameter (Table 4 - see PDF). Here, the range of parametric scores for each PPIP parameter (except for the planarity) showed the greater variability. After removal of outliers, only the PPIPs from LSS and MSS class found to converge into a commonsub-range for parametric scores. However, this could be explained by the methodology of database creation. At the time of NRDB creation, for each pair of SCOP superfamily pair, the PPII selected as a part of NRDB was the one with maximum number of ACPs among allthe PPIIs sharing the same SCOP superfamily pair. Therefore, it was obvious for the larger PPIPs to possess relatively higher cumulative hydrophobicity, solvation energy, and other parametric scores. But, as the planarity index of PPIPs is not much dependent onthe size of interacting patches (i.e. PPIPs), the cumulative parametric score obtained for planarity for each PPII class, confined to a common sub-range.2 less than HSS and 1300 Å2 morethan MSS. The effect of size can be seen for the rest of the parameters except for planarity index. Planarity score was an average measure of RMSD of PPIP constituent atoms from the best-fit plane, hence this value was almost size independent. The hydrophobicityshowed the largest variation, with range for values corresponding to HSS (-1 - 93) was almost 3 times that of LSS (-1 - 36) and MSS (0 - 31). The parametric range for cumulative depth and protrusion index for HSS (Depth: 0 - 28 and Protrusion: 0 - 150) was almosttwice that of the other two classes. The upper and lower meniscus for solvation free energy of HSS (-29.0 kJ·mol-1- 32 kJ·mol-1) was very broad as that of LSS (-6.0 kJ·mol-1 - 21 kJ·mol-1) and MSS (-5.0 kJ·mol-1- 23 kJ·mol-1). In Supplementary Figures 8-13(see PDF),the trend of parametric values obtained for around 99% of the PPIPs from each of the three PPII classes of both PPInS and NRDB datasets with respect to six PPI parameters are shown.The size of PPIPs varied for each of these classes  and it was least for MSS and highest for the HSS. The maximal tail size of the interacting patch of LSS was almost 1300 ÅThe hydrophobic analysis of PPIPs from PPInS and NRDB revealed that even though the range of hydrophobicity values of PPIPs differed among HSS, MSS, and LSS , the distribution of the parametric scores followed almost the same pattern. Thestatistical analysis of parametric score distribution with respect to mean, standard deviation, and p-values (Table 5 - see PDF) support the hypothesis that the cumulative hydrophobic content of PPIP from all the PPII classes is significantly same. And, this wasapplicable for PPIIs from both the datasets. The hydrophobicity score for the PPInS is multi-peak, whereas the same obtained from NRDB is single-peak. This apparent difference is perhaps due to the high order of redundancy in PDB for PPI complexes with therelatively larger contribution of hydrophobic effect in the binding energy. As the hydrophobicity is a major contributor to the formation of protein crystals ,50, thusThe range of solvation free energy values of PPIP does not differ amongst HSS, MSS, and LSS  as the graphs follow an identical pattern with statistical similarity among the parametric scores of different PPII classes (Table 5 - see PDF).This was true for NRDB too. This similarity is perhaps due to the uniform nature of PPI, which is also one major reason that solvation-energy based prediction tools for PPI and drugability studies are more successful -52.InteThe distribution range of the depth index of PPIP also does not differ amongst the three classes of PPInS and NRDB both, as seen in Figure 4(see PDF) where the graphs follow an identical pattern. The depth index of the PPInS is the same as obtained for thePPIPs from NRDB with single-peak with similar shoulders. The statistical analysis of cumulative depth distribution for each PPII class of the both of these datasets was also significantly same (Table 5 - see PDF). The very low value of cumulative depth indexshows that the atoms involved in PPIPs are on the surface. This may be due to the effect of interacting surface induction. The majority of PPIPs have identical cumulative depth index and this fact is not influenced by the extent of redundancy in the datasets.Irrespective of the structural/functional class of the protein, the cumulative depth index of the PPIP remains more or less constant. This is another indication that irrespective of the homo/heterodimers, the physicochemical and structural parameters governingPPI occupy the same value-space.2. This is similar to earlier reportswhich reported the size of the interfaces as small as ~800 Å2[2 for heterodimers, 550 to 4718 Å2 for homodimers, and 423 to 2361 Å2 for transientcomplexes. In our study too, the interfaces from PPInS dataset are reported with size upto 6500 Å2 ) while in NRDB interaction interfaces are reported to be between 112 Å2 to 8400 Å2per interacting partner. However, most of the PPIIs from PPInS and NRDB were seen covering protein surface up to within 800 ± 400 Å2 and 1200 Å2 ± 400 Å2,respectively inconformity with earlier studies [The size of interacting patches has a direct relationship with the van der Waals (vdW) forces of interactions. Greater the size of PPIP, larger is the vdW interaction energy of PPIPs. In earlier studies ,53,a lis ~800 Å2.In anot studies ,22. In FThe protrusion index or the compactness of neighborhood of interacting residues has been studied by some groups  and it h2), the deviation of interacting atoms from the derived plane was found in the range of 0-8 Å. Maximum number of PPIPs were reported withdeviation of 1-5 Å which is analogous to the previous findings [The planarity analysis of PPIPs revealed the clear independency of the type and size of protein in terms of their binding site preferences . For both the datasets, PPInS (which isrepository of over two hundred thousand PPIPs) and the NRDB  as reported inprevious studies. On analyzing PPIPs from PPInS, initially, a significant difference between the parametric scores (obtained with respect to each PPI site parameter) of the three PPII classes was observed. However, on removing less than 1% PPIPs  from each PPII class, the cumulative parametric scores for each PPI site parameter from all three classes of PPII reduced down to the identical ranges. The analysis of PPIPs from NRDB considering each PPII class (with and without outliers) also showedthe similar trends for the parametric scores for each PPI site parameter, however, with greater variability (except for the planarity). As the PPIPs in HSS class were relatively larger in size, the resulting cumulative score could not get aligned with the scoresfrom LSS and MSS wherein they were found to converge into a common sub-range after outlier removal. However, overall, the objective analysis of PPIPs  with respect to all PPI sites parameters showed the similartrends. This indicates that the principles of molecular recognition among proteins are not driven by their sequence / structural similarity and reinforces the importance of geometrical and electrostatic complementarity as the main component for PPIs.The proposed work was conceptualized by MK and executed by VK and MK. The data was analyzed by VK, AM, TG and MK. The manuscript was written by VK, AS and MK. All authors have read and approved the manuscript prior to submission.PPInS - Protein-protein interaction sitesbaseNRDB - Non-redundant databaseACP - Atomic contact pairPPII - Protein-protein interaction interfacePPIP - Protein-protein interacting patchRIP - Residue interface propensityLSS - Low sequence similarityMSS - Moderate sequence similarityHSS - High sequence similarityPDB - Protein Data BankPPC - Protein-protein complex


Recently developed methods to predict three-dimensional protein structure with high accuracy have opened new avenues for genome and proteome research. We explore a new hypothesis in genome annotation, namely whether computationally predicted structures can help to identify which of multiple possible gene isoforms represents a functional protein product. Guided by protein structure predictions, we evaluated over 230,000 isoforms of human protein-coding genes assembled from over 10,000 RNA sequencing experiments across many human tissues. From this set of assembled transcripts, we identified hundreds of isoforms with more confidently predicted structure and potentially superior function in comparison to canonical isoforms in the latest human gene database. We illustrate our new method with examples where structure provides a guide to function in combination with expression and evolutionary evidence. Additionally, we provide the complete set of structures as a resource to better understand the function of human genes and their isoforms. These results demonstrate the promise of protein structure prediction as a genome annotation tool, allowing us to refine even the most highly curated catalog of human proteins. More generally we demonstrate a practical, structure-guided approach that can be used to enhance the annotation of any genome. More than 20 years after the initial publication of the human genome, the scientific community is still trying to determine the complete set of human protein-coding genes. Although the number of genes is converging around 20,000, we do not yet have agreement on the precise number. The true number of different isoforms of human genes – variations due to alternative splicing, alternative transcription initiation sites, and alternative transcription termination sites – is even less certain. Currently, the major human gene annotation databases each contain well over 100,000 protein-coding transcripts , but theAlthough the functions of many human genes are known, elucidating gene function remains a complex and time-consuming task. Given that at least 92% of human genes express more than one isoform , and thaIn a similar vein, the use of RNA sequencing (RNA-seq) to detect gene expression also provides clues to function: if a transcript is consistently expressed in multiple samples, it is more likely to be functional than one for which little expression evidence can be found. Genes may encode multiple transcripts that fold into distinct isoforms with well-defined functions , but recThis study explores a fundamentally new line of evidence that can be used to investigate protein function: computational prediction of three-dimensional (3D) structure. The recently developed AlphaFold2 system can automatically predict 3D protein structure with accuracy that often matches far more time-consuming laboratory methods , allowinIn a recent effort to create a single consensus annotation of all human protein-coding genes, two of the leading human genome annotation centers created the MANE (Matched Annotation from NCBI and EMBL-EBI) database , a high-Here, we describe our use of protein folding predictions from ColabFold , an openUsing CHESS, a large set of transcripts assembled from nearly 10,000 human RNA-seq experiments, we identified all protein-coding gene isoforms that were 1000aa or less in length . The 233,973 transcripts at 20,666 gene loci that fit this description encoded 127,398 distinct protein isoforms, and we predicted structures for all of them. Additionally, we included 3302 structure predictions for proteins with length >1000aa from the AlphaFold Protein Structure Database  for isofIn total, 22,644 transcripts  encoded an isoform that scored a higher pLDDT than the isoform encoded by the corresponding MANE transcript. However, many of these higher-scoring transcripts encoded relatively short, often low-expressed, likely non-functional fragments of larger proteins. Therefore, we incorporated filters based on RNA-seq expression data when determining which higher-scoring isoforms appeared superior to MANE isoforms (see ‘Filtering MANE comparisons’). Based on the combination of RNA-seq evidence and protein foldability, we identified 940 unique alternate isoforms at 632 loci  which appeared to have a more stable structure than the annotated primary isoform. Data for these 940 alternate isoforms can be found in isoform.io.Gene identifiers for all predicted protein isoforms as well as pLDDT scores and evolutionary conservation data from mouse can be found in To illustrate the improvements in human gene annotation that can be obtained using accurate structure prediction, we describe a small set of proteins, selected from O-methyltransferase  is responsible for the final catalytic step in the production of melatonin, a critical hormone in sleep, metabolism, immune response, and neuronal development  while RefSeq is missing this isoform. The MANE version of this gene is 373aa long and appears in the CHESS (CHS.57426.2), GENCODE (ENST00000381241.9), and RefSeq (NM_001171038.2) gene databases. The predicted structures of both isoforms are shown in The CHESS and GENCODE gene databases contain a 345aa isoform of ASMT according to AlphaFold2 corresponds to the biologically active version of the protein. The score we use for these comparisons is the pLDDT score, which has been demonstrated to be a well-calibrated, consistent measure of protein structure prediction accuracy (We hypothesized that the highest scoring isoform of accuracy . A pLDDTASMT was expressed at a level of 327 transcripts per million (TPM), while the 373aa isoform from MANE was expressed at 34 TPM, nearly 10 times lower, supporting our hypothesis that the higher scoring 345aa isoform is functional.Because melatonin is primarily synthesized within the human pineal gland at night, we quantified ASMT isoform expression using RNA-seq data from a previously published experiment that used tissue extracted from the pineal gland of a patient who died at midnight . In thisASMT protein creates an unstructured loop that bulges out from the primary structure. The alternate isoform, missing this ectopic exon, closely matches the experimentally determined ASMT X-ray crystal structure of the biologically active protein. Furthermore, as reported in S-adenosyl-L-methionine and to synthesize melatonin. Thus, the structural comparison, the expression evidence, and a melatonin synthesis activity assay all combine to support our hypothesis that the 345aa isoform represents the primary biologically functional isoform of ASMT.Further evidence for the functionality of the 343aa isoform is shown in CRYGN) is a highly conserved member of the crystallin family of proteins, responsible for the transparency of the lens and cornea in vertebrate eyes  and GENCODE (ENST00000644350.1), and it was also present in RefSeq v109 (XM_005249952.4) but was removed in the next release, v110.As shown in CRYGN’s dimer-like characteristic, with two structurally similar domains as shown in Both the MANE and alternate isoforms are exactly the same length despite having different C-terminal sequence content. Visual comparison of the predicted structure for the alternate isoform reveals a marked improvement in the structure in the β domain and a clear recovery of CRYGN in human to determine how it recovered its functional shape despite losing its original stop codon. We found that the common vertebrate four-exon structure of CRYGN has changed to a five-exon structure in humans, as shown in CRYGN sequence outside of primates.Encouraged by the substantially improved folding of this alternate isoform, we examined the intron-exon structure of TXNDC8, alternatively PTRX3) is a testis-specific enzyme responsible for catalyzing redox reactions via the oxidation of cysteine from dithiol to disulfide forms  reveals a very close structural correspondence between the two proteins, with a predicted root-mean-square deviation (RMSD) of 0.8 Å. TXNDC8 from the CHESS annotation, as well as the 3D alignment of CHS.56446.14 to its orthologs in cow and mouse. Given its substantially higher pLDDT score and near-perfect structural conservation in other species, the CHESS transcript appears to be a much better candidate for the canonical form of this protein. The MANE isoform, because it is missing multiple key structures, may represent a non-functional product of transcriptional noise.In stark contrast to the poorly folded MANE isoform , an alternate isoform assembled as part of the CHESS project (CHS.56446.14) and RefSeq (NM_001364963.2) has a pLDDT score of 96.9, an improvement of 40 points. Inspection of the alternate transcript  reveals TXNDC8 shown in TXNDC8 isoform appears non-functional, lacking several key structures. In addition, isoform 12 in All isoforms of Assemblies of RNA-seq experiments typically reveal thousands of un-annotated gene isoforms, as was illustrated by the use of GTEx to discover more than 100,000 new isoforms when building the original CHESS gene database . The appIL36B, alternatively IL1F8, FIL1-ETA, or IL1H2) mediates inflammation as part of a signaling system in epithelial tissue. The pro-inflammatory properties of IL36B have been implicated in the pathogenesis of psoriasis  averaged a pLDDT of only 50.2, a score indicative of near-complete folding failure, while an alternate isoform in CHESS (CHS.30565.4) and RefSeq (XM_011510962.1), shown in IL36B orthologs in 1517 organisms including mouse, rat, and Hawaiian monk seal. In addition, expression of the MANE isoform was observed in only 1 sample in the GTEx data with an expression level of just 0.01 TPM, while the CHESS isoform was found in 775 samples with a much higher expression level of 8.4 TPM  is required for stable expression of glycosylphosphatidylinositol (GPI)-anchored proteins , with a pLDDT of 87.9. The coding sequence of this isoform exactly matches the sequence of the assumed biologically active protein . On average, across all 31 tissues in the GTEx data, five times more spliced reads supported skipping that exon, as in CHS.7860.59, rather than including it.VEGFB), a growth factor implicated in cancer and diabetes-related heart disease  encodes the freely soluble protein VEGFB-186, while the alternate isoform  encodes the sequestered protein VEGFB-167. Additionally, VEGFB-186 is present as a full-length cDNA clone (MGC:10373 IMAGE:4053976) in the Mammalian Gene Collection  seemed to contain a poorly folded region similar to CHS.56446.6, a low-scoring human protein. Further inspection of TXNDC8 in the mouse genome revealed that the primary RefSeq transcript contains a misfolding fifth exon, while the alternate RefSeq transcript skips the misfolding exon, similar to the functional human isoform, shown in Rodentia to an unnamed protein product. In contrast, a BLASTP search of the third exon present in all mouse transcripts, homologous to the missing exon in the human MANE transcript, revealed significant hits to at least 31 homologs outside Rodentia.While examining the evolutionary conservation of the 3D structure of Rodentia. Folding this alternate mouse transcript revealed that these additional N-terminal amino acids fail to form any confident predicted protein structure. As a result, none of the three transcripts annotated in mouse fold into the highly conserved structure of TXNDC8 in human. Predicted structures and exon alignments for isoforms in human and mouse are shown in Unlike the functional human isoform, however, the RefSeq alternate mouse transcript is annotated with a start site 26 codons upstream of the translation initiation site annotated in the human orthologs. A BLASTP search of the 26 amino acid additional sequence resulted in zero significant hits outside TXNDC8 in mouse should be similar to the human isoform in 3D structure. Based on our observations, this similarity might be realized if the mouse alternate isoform simply started at the downstream start site that matches human, yielding a 105aa protein rather than the 131aa protein that is currently annotated. Folding the coding sequence of the alternate mouse transcript, minus the 26 N-terminal amino acid residues, revealed a predicted structure remarkably similar to both human and cattle TXNDC8. TXNDC8. Remarkably, the predicted average RMSD between aligned heavy atoms of the putative human and mouse proteins is just 0.83 Å. For reference, the atomic diameter of one carbon atom is 1.4 Å.We hypothesized that a truly functional isoform of TXNDC8 transcription, we aligned 8 gigabases of RNA-seq cDNA from a mouse testis sample (SRR18337982) to the GRCm38 reference genome using HISAT2  the nucleotide and amino acid sequences; (2) the predicted structure, as a file that can be viewed in a standard structure viewer such as PyMOL  (3) the In this analysis, we demonstrated the ability to improve protein-coding gene annotation by predicting 3D protein structures. We searched tens of thousands of predicted structures of alternate isoforms of human genes and identified a subset that appear to fold more confidently than the isoforms found in MANE, a recently developed ‘universal standard’ for human gene annotation . We founIn the illustratory examples described here, we provide biological and evolutionary context for cases where an alternate human isoform appears clearly superior in structure to its canonical protein. Given the many additional high-scoring transcripts that we identified , we expeWe expect computational protein structure prediction to become an indispensable tool for future transcriptome annotation efforts. Still, the functionality of many proteins may not be revealed by structure prediction alone. Cases where substantial portions of a protein fail to form a stable structure, such as intrinsically disordered proteins, were not examined here. Additionally, non-functional isoforms may achieve a higher pLDDT simply due to the omission of small, yet functionally important intrinsically disordered regions. In these cases, it is necessary to contextualize results with both expression data, when available, and evolutionary sequence conservation analysis. If a low-scoring region is highly conserved across species or is consistently expressed, this still provides a strong indication of function regardless of foldability.Although we restricted our analysis to whole-protein comparisons, comparing local structural portions of a protein, potentially near shape-sensitive ligand binding sites , may enaThough the complete sequence of the human genome has been revealed , the annWe folded all transcripts in the CHESS annotation less than 1000 amino acids in length. Similar to the initial effort to fold the human proteome , the lenhttps://colab.research.google.com) with Python version 3.8.15.To generate the 940 protein isoforms in We selected isoforms that, when compared to the MANE isoform for the same gene, scored at least 5% higher as measured by pLDDT and were at least 90% as long. Additionally, to capture cases where the MANE transcript might be missing functional sequence elements, we selected alternate isoforms that were at least 5% longer than the MANE isoform, that had equal or higher pLDDT scores, and that were assembled in an equal or higher number of GTEx samples. Finally, to capture cases where an alternate isoform might be functional despite being substantially shorter than the MANE protein, we selected isoforms at least 50% as long as the MANE protein where the alternate isoform scored at least 5% higher and was assembled in an equal or higher number of GTEx samples. After applying these filters, we observed that in some cases, a processed pseudogene  containehttps://www.ncbi.nlm.nih.gov/refseq/MANE. Annotations from the CHESS v3.0 database were retrieved from http://ccb.jhu.edu/chess. Additional CHESS annotations came from an unpublished set of transcript assemblies created as part of the process of building CHESS v3.0; these were assembled from approximately 10,000 GTEx RNA-seq experiments across 31 tissues using StringTie2  annotations were collected from https://www.gencodegenes.org/human/.Transcript annotations for MANE, CHESS, RefSeq, and GENCODE were retrieved from the following sources. The MANE v1.0 database was downloaded from NCBI at ringTie2 . TranscrAll visualizations and 3D protein structure atomic alignments were performed in PyMol  version RNA-seq data were downloaded from NCBI for run SRR5756467 from BioSample SAMN07278516, a pineal gland from a patient who died at midnight. A detailed summary of the experimental protocol used to generate these data can be found in NCBI BioProject PRJNA391921. Isoform-level quantification was performed using Salmon  version RNA-seq data were downloaded for run SRR18337982 from BioSample SAMN26725167, a tissue sample from the testis of a control mouse. A detailed summary of the experimental protocol used to generate these data can be found in NCBI BioProject PRJNA816862. cDNA reads were aligned to the mm39 reference genome using HISAT2  version We assessed the conservation of GT-AG intron boundaries between CHESS human transcripts and transcripts from the GRCm38 mouse reference genome. Data for the human-mouse alignment was extracted from a 30-species alignment anchored on GRCh38 that was downloaded from the UCSC genome browser . We used This study applies AlphaFold to the CHESS selection of transcripts with the goal of generating predicted 3D protein structures and a quality measure of folding, the pLDDT score. From these data, the authors build a database for result exploration, documented by several examples, including proteins, where the authors propose the pLDDT score as a measure of presumed superior biological functionality over other isoforms. These results will be highly relevant for anyone working with proteins that occur in different isoforms. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Volker Dötsch as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Anna Poetsch (Reviewer #2).Thank you for submitting your article "Structure-guided isoform identification for the human transcriptome" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) Using the representative isoform from the MANE database the authors provide 5 exemplar genes whose MANE representative isoform when translated yields a protein structure with a lower pLDDT score than a non-representative isoform, suggesting that the protein stability of the representative isoform is suspect. The authors clearly explain that not all isoforms encode a well folded functional protein. Thus, it would be helpful for the authors to provide a clearer indication of how common the non-exemplar representative isoforms in the MANE database may have a lower pLDDT score. Armed with this a false positive and negative estimate could be provided.2) The authors evaluate the single representative isoform selected for each human protein coding gene in the MANE database. In addition, the authors provide a list of isoforms with the highest pLDDT score for each human gene. However, it would be useful for the authors to provide a section that speaks to the confounding conditions and sequence features are understood to be confounding in evaluating the use of pLDDT scores. For example, since evolutionary conservation and synteny considerations of isoform sequences are an important consideration in evaluating the potential utility of an isoform, what would the authors recommend if the pLDDT scores are elevated but one or more of the other considerations are less than ideal?3) The study is based on the elegant idea to aid genome annotation through 3D structure prediction. This is a very powerful approach that allows large-scale data generation for functional interpretation. This approach appears technically sound and well executed . However, in my opinion, the authors could make more use of the potential of their approach. From the big-data start, they seem to directly restrict themselves to interesting examples. I am missing a global analysis that shows the bigger picture of their results. Given that they have generated structures from 90,415 isoforms, each associated with a pLDDT score, conservation scores, length, expression levels and other quantifiable data listed on page 18. I would wish for a comprehensive analysis of these data and their potential before applying the focus on a few (admittedly very nice) examples.4) One of the weak spots of such an analysis is the relationship between foldability and functional relevance. Disordered regions would imply reduced relevance due to poor pLDDT scores, which may be a misleading conclusion. While this may be a problem difficult to solve with this approach, it still needs to be addressed and discussed throughout the paper and particularly as part of the global analysis, not just in the context of examples.Reviewer #1 (Recommendations for the authors):The manuscript is well written and speaks to an important and timely issue concerning the number how to annotate and evaluate isoforms for each gene. As the number of isoforms for a genome continues to increase it will be helpful to provide some logic to distinguish among the isoforms.Using the representative isoform from the MANE database the authors provide 5 exemplar genes whose MANE representative isoform when translated yields a protein structure with a lower pLDDT score than a non-representative isoform, suggesting that the protein stability of the representative isoform is suspect. The authors clearly explain that not all isoforms encode a well folded functional protein. Thus, it would be helpful for the authors to provide a clearer indication of how common the non-exemplar representative isoforms in the MANE database may have a lower pLDDT score. Armed with this a false positive and negative estimate could be provided.The authors evaluate the single representative isoform selected for each human protein coding gene in the MANE database. In addition, the authors provide a list of isoforms with the highest pLDDT score for each human gene. However, it would be useful for the authors to provide a section that speaks to the confounding conditions and sequence features are understood to be confounding in evaluating the use of pLDDT scores. For example, since evolutionary conservation and synteny considerations of isoform sequences are important consideration in evaluating the potential utility of an isoform, what would the authors recommend if the pLDDT scores are elevated but one or more of the other considerations are less than ideal?Finally, the authors are encouraged to provide readers with a reasoned argument that the addition of structure-guided isoform considerations is more than an incremental advancement in the annotation of the human transcriptome. Essential revisions:1) Using the representative isoform from the MANE database the authors provide 5 exemplar genes whose MANE representative isoform when translated yields a protein structure with a lower pLDDT score than a non-representative isoform, suggesting that the protein stability of the representative isoform is suspect. The authors clearly explain that not all isoforms encode a well folded functional protein. Thus, it would be helpful for the authors to provide a clearer indication of how common the non-exemplar representative isoforms in the MANE database may have a lower pLDDT score. Armed with this a false positive and negative estimate could be provided.We have updated our analysis to include all CHESS 3 isoforms <= 1000aa (rather than the previous limit of 500aa) and have lifted over all CHESS 3 matching predictions from the AlphaFold Protein Structure Database, which increases our maximum length limit to 2699aa. This resulted in >98% of all human loci containing at least one protein structure prediction, which we hope makes the following additions to the global analysis more truly global.We have rewritten the “Scoring the transcriptome” section of the results to include a more comprehensive explanation of the comparison between MANE and the higher-scoring isoforms. To clarify how commonly the non-exemplar isoforms  outscore the canonical isoform, we added a count of the number of isoforms that score a higher pLDDT vs. their associated MANE isoform. However, even with RNA-seq, protein structure prediction, and evolutionary conservation evidence, we do not believe we can create a gold standard for which transcripts are truly functional, and thus we cannot provide an unbiased estimate of false positives and negatives for MANE. Many of the hundreds of isoforms identified in Table S3 represent poorly-studied proteins where the additional experimental evidence used to further analyze our Exemplary Predictions simply does not exist. We believe that our addition of more explicit numbers, as now provided in the updated Results section, provides a fair comparison to MANE despite lacking formal false positive and negative estimates.2) The authors evaluate the single representative isoform selected for each human protein coding gene in the MANE database. In addition, the authors provide a list of isoforms with the highest pLDDT score for each human gene. However, it would be useful for the authors to provide a section that speaks to the confounding conditions and sequence features are understood to be confounding in evaluating the use of pLDDT scores. For example, since evolutionary conservation and synteny considerations of isoform sequences are an important consideration in evaluating the potential utility of an isoform, what would the authors recommend if the pLDDT scores are elevated but one or more of the other considerations are less than ideal?We have added a more thorough explanation to the “Scoring the transcriptome” section on why certain isoforms may achieve a high pLDDT but still be non-functional. Due in part to the problem of short protein fragments (now mentioned in the results), which sometimes get misleadingly high scores, our set of filtered transcripts is based on a combination of foldability and RNA-seq expression evidence rather than foldability alone. We hope this clearer description of the reasoning behind our methods, alongside the examples which incorporate additional experimental evidence, may show future researchers how to handle cases where considerations are less than ideal, e.g. ASMT lacking expression data in GTEx because it is expressed primarily in the pineal gland which was not sampled. Our overall goal here is not to provide a definitive solution to the problem of determining which isoforms are functional , but rather to provide a type of field-guide and associated genome-wide database which can be used as resource in future efforts.3) The study is based on the elegant idea to aid genome annotation through 3D structure prediction. This is a very powerful approach that allows large-scale data generation for functional interpretation. This approach appears technically sound and well executed . However, in my opinion, the authors could make more use of the potential of their approach. From the big-data start, they seem to directly restrict themselves to interesting examples. I am missing a global analysis that shows the bigger picture of their results. Given that they have generated structures from 90,415 isoforms, each associated with a pLDDT score, conservation scores, length, expression levels and other quantifiable data listed on page 18. I would wish for a comprehensive analysis of these data and their potential before applying the focus on a few (admittedly very nice) examples.We have added Figure 1, a transcriptome-wide view of the relationship between pLDDT vs. protein length and GTEx expression to the results. The figures shows a lack of a clear linear relationship between pLDDT and length or expression, which implies that protein structure prediction provides an orthogonal source of useful information for annotation. This is also noted in the updated manuscript.4) One of the weak spots of such an analysis is the relationship between foldability and functional relevance. Disordered regions would imply reduced relevance due to poor pLDDT scores, which may be a misleading conclusion. While this may be a problem difficult to solve with this approach, it still needs to be addressed and discussed throughout the paper and particularly as part of the global analysis, not just in the context of examples.This is a great point. Many functional proteins are either entirely disordered or contain intrinsically disordered regions, and protein folding will inherently fail to provide useful information in these cases. We have added a section as part of the global analysis in the results which quantifies how often we observe this in our data. Additionally, we have expanded our Discussion section to include a more thorough description of how we believe this problem should be approached by researchers looking at protein foldability in context with expression and evolutionary evidence.


Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures:  With increases in computing power, code scalability, and advancement of theoretical methods, physics-based computational tools such as molecular dynamics and molecular/quantum mechanics can be used for the reliable representation of PLI and prediction of accurate binding free energies4. However, these methods are computationally expensive and are limited to a number of protein–ligand complexes5. This limits their routine use in high-throughput virtual screening4 for the discovery of novel hit candidates and lead optimization6 for a given protein target7. Molecular docking has been used to predict binding affinity and estimate interactions with reasonable computational cost15; however, its accuracy is relatively low as it uses heuristic rules to evaluate the scoring function.Accurate prediction of protein–ligand interactions (PLI) is a critical step in therapeutic design and discovery. These interactions influence various molecular-level properties, such as substrate binding, product release, regio-selectivity, target protein function, and ability to facilitate potential hit identification, which is the first step in finding novel candidates for drug discovery16. There has been significant effort to develop deep learning models that predict PLI18, and other biophysical properties20 that are critical for therapeutic design but cannot be predicted through physics based modeling21. The greater understanding of PLI enabled by deep learning can help in the estimation of properties such as activity, potency and binding affinity22. However, several technical challenges limit the use of deep learning for modeling protein–ligand complexes and accurate prediction of properties. The first challenge relates to the limited availability of protein–ligand 3D data and the second challenge focuses on the appropriate representation of the data (domain knowledge), specifically in terms of the comprehensive 3D geometry representation. Structure-based methods have the advantage of producing results that can be interpreted but are limited by the number of available samples21. Circular fingerprints, generated by encoding localized structural and geometric information, have long been a cornerstone of cheminformatics23. The flexibility of fingerprints has created new avenues for molecular computational research, including the increased implementation of graph-based representations to include domain awareness25. Molecular graph representations provide a way to model and simulate the 3D chemical space while retaining a wider range of structural information. In that context, Ragoza et al. implemented a deep convolutional neural network (CNN) that operates directly on 3D molecular graph input, similar to the AtomNet model previously implemented by Walloch et al.27. Other approaches such as Graph-CNN developed by Torng et al. use unsupervised autoencoders to leverage sequence-based data that is more abundant but also costly in terms of structural accuracy28.The use of deep learning has revolutionized the healthcare system in recent years29. That approach has been generalized and improved through the formulation of graph attention (GAT) networks30. GAT architectures operate on the importance of a given node, leading to improved computational efficiency and accuracy31. Lim et al. showed the implementation of such an architecture to capture PLI, which provides a baseline for the development of more robust models32. Chen et al. proposed a bidirectional attention-driven, end-to-end GNN to predict PLI and enable biochemical insights through attention weight visualization1. Predicting the activity of a protein–ligand complex is a binary classification problem. Reshaping the problem to focus on affinity creates a regression problem of heightened complexity. The existing deep learning models that predict the binding affinity or other biophysical and biochemical properties such as 222133. However, most of the methods use sequence-based data for proteins and SMILES representations for interacting ligands. For example, DeepDTA22 and DeepAffinity21 use SMILES strings of the ligands and amino-acid sequences of the target proteins to predict the affinity. MONN33 is a multi-objective sequence-based neural network model that first predicts the non-covalent interaction between the ligand and the residues of the interacting target and then the binding affinities in terms of Graph-based representations extend the learning of chemical data to graph neural networks (GNNs). Monti et al. designed a mixture model network (MoNet) that enables non-Euclidean data, such as graphs, to be learned by CNNsIn this contribution, we formulated two GNNs based on the GAT architecture by incorporating domain-specific featurization of the protein and ligand atoms ; we consolidated these features into a single atom-level feature, hybridization, which describes the bonding properties of an atom.The distinct featurization of our GNNs reduces the feature size of the protein, which can contain a large number of atoms, and enhances the feature size of the ligand, which is typically a small molecule. This shift reduces redundancy in the protein representation and focuses computational resources on the improved atomic representation of the ligand. While the Graph-CNN developed by Torng et al. also involved a large number of ligand features, more than half were at the bond level34 and PDBbind36, which are described in further detail below. The datasets consist of protein–ligand complexes and their docking affinities, while some samples include experimentally derived binding affinities. Prediction is based on accessing ligands as active (active-interact molecules or positive) or inactive (set of decoys or negative) depending on whether the ligand is able to bind with the protein, as described in more detail below. Table Machine learning models for training PLI prediction require data on target proteins, ligands/compounds, and interactions between them. In this work, our goal is to improve the degree to which the graph-based model can be generalized while also maintaining accuracy. We accomplish this by enlarging the dataset used to train our model and including a variety of targets. We collected and curated protein–ligand complexes from two public datasets, DUD-ETo confirm that these two datasets offer diversity in terms of both target protein and different functionality of the ligand, we computed pair similarities between the targets and ligands, which can be found in the supporting information Figure 34. The dataset was originally designed to test benchmark molecular docking programs by providing challenging decoys, but some have noted that the dataset suffers from limited chemical space and biases38. In their analysis of the DUD-E dataset, Chen et al. generated a docked subset38, which we use here. Ligands from the experimentally verified ChEMBL dataset were designated as positive, while the generated decoys and their docked structures were considered negative. These files were parsed into individual standard database formats (SDF) files for each ligand and corresponding docking pose.The DUD-E dataset consists of pairs of experimentally verified active complexes and property-matched inactive pairs, called decoys39. The target proteins were pulled from the original DUD-E set, cleaned of water molecules, and matched with their designated ligands. Targets were screened with a homology test to understand the diversity of targets present in the dataset, and none had a similarity greater than 90%. Each complex was processed to crop the target protein and retain atoms within a distance of 8 Å from the ligand. Because the DUD-E dataset is biased towards inactive complexes due to the large number of decoys, equal counts of active and inactive samples were collected in regards of a target with no consideration of a specific complex. A complete 1:1 active-to-inactive ratio was not always achieved; however, the imbalance was found to be minimal and to have no affect on performance. Samples from the experimentally verified ChEMBL dataset were labeled as active, while decoy samples were denoted with the key word ZINC.Ligands with a molecular weight above 500 Da were removed, and the docked structures were converted to a machine-readable format using RDKit36. Binding poses for a refined set of the protein–ligand complexes were generated by docking calculations40. A 90% homology test was run on this set, which resulted in a small number of proteins being removed because of a high level of similarity. As with the DUD-E dataset, the docked structures were converted to a machine-readable format using RDKit. The root-mean-square distance (RMSD) was used to label ligands as positive if they maintained an RMSD less than or equal to 2Åcompared to the original crystal structure, and negative if the RMSD was greater than or equal to 4Å. Molecules with RMSDs between these thresholds were removed. The viable molecules and their target proteins were processed into a dataset of protein atoms cropped within a distance of 8Å. As PDBbind data were significantly limited, all available samples were used. This resulted in a greater inclusion of negative samples than positive samples.The PDBbind dataset contains experimentally verified protein–ligand complexes from the Protein Data Bank41 and manually curated antiviral data were used to generate the docked complexes with each target. Ten docking poses were calculated for each complex with the qvina docking program42 through a custom non-covalent pipeline. The docking data was parsed and cropped to the 8 Å threshold in the same manner as the DUD-E and PBBind datasets. Similar to PDBbind, positive and negative samples are determined with RMSD. Protease data were largely directed into the training set while the other targets, with the exception of on non-structural protein, were directed to the test set. This variety allows the inclusion of the critical targets of SARS-CoV-2 viral life cycle in both the training and the test sets.The SARS-CoV-2 dataset is composed of seven protein targets: Mwww.ibscreen.com). All ligands were fed as an input to the our GNN models paired against MThe IBS dataset is created using 486,232 synthetic compounds from the IB screening database  PDBbind2016, (2) PDBbind2018, and (3) PDBbind2019For the PDBbind2016 dataset, we prepared various train-test splits, which are summarized in Table We focus on the For the PDBbind2016 data, all crystal poses with an experimentally measured 2 is obtained as a result of non-covalent inhibition of MCULE-5948770040 compound with M2. When a protein–ligand complex had multiple measured We also considered an independent dataset associated with SARS-CoV-2 targets. To prepare this dataset, we used crystal structures of SARS-CoV-2 main protease  for the protein and ligand. To this end, we would like to understand the effects of the number of protein targets and the number of protein–ligand complexes per target; therefore, we train the In addition to making sure the model can be generally applied, we are interested in developing a model that does not require the docked structure to be known before inferences can be made. We performed the same experiment using varying numbers of DUD-E targets and complexes on our To assess the ability of our GNN models to be generally applied, we produced three test sets of varying similarity to the training set Table . Our basWe then assessed the model’s ability to identify top-scoring 3D poses in each protein–ligand complex from our PDBbind repository described in the dataset preparation section. The ’best’ docked pose is quantified as having an RMSD of less than 2 Å with respect to the crystal structure. In this analysis, we measure not only the ability of the model to identify an active protein–ligand pose but also its ability to identify the best pose among multiple docked poses. Figure In each rank, both the see Fig. .47.In addition, we tested our models on datasets that include molecules from ChEMBL for DUD-E targets and SARS-CoV-2-target-specific data. Additional ChEMBL data were collected for a small subset of DUD-E targets extracted from the initial test set and implemented without docking for the We next investigated the performance of the see Fig. . For thePredicting the binding affinity of a protein–ligand complex plays a critical role in identifying a lead molecule that binds with the protein target; however, the experimental measurement of protein–ligand binding affinity is laborious and time-consuming, which is one of the greatest bottlenecks in drug discovery. On the other hand, half maximal inhibitory concentration  PDBbind2018-docked, (2) PDBbind2018 crystal, and (3) PDBbind2016 crystal structure dataset. Since most of the deep learning models that we compare in this section are trained on complexes from the PDBbind2016 database, training our models on the same dataset helps to obtain a comparison with the previous models49, which is a 3D CNN for protein–ligand affinity prediction that combines the 3D voxelization with atom-level features. Among all the results, the best performance is achieved by the First, we assessed the performance of the GNN models trained using PDBbind2018 data docked and crystal-only data as shown in Table 50 and FAST48 models. Our analysis on the PDBbind2016 core set shows that the 48, while 48. Out of all the combinations of the general and refined sets, we achieved the best performance with the In addition, for non-docking experiments, we used the PDBbind2016 general and refined datasets for training and the PDBbind2016 core set for testing. We assessed the performance of our methods for predicting the affinity through comparison with the Our results on the PDBbind2018 and PDBbind2016 datasets suggests that with our proposed GNN frameworks, we achieve top performance compared with prior deep learning methods for binding affinity prediction while preserving the spatial orientation of the protein and ligand. While To investigate the generalizability of our GNN models in predicting the binding affinity of unseen and novel targets, we compare the performance of our GNN and various models on the PDBbind2019 structure-based evaluation dataset Table . The PDBFinally, to expand the scope of our experiment, we also trained our model on a physics-based docking affinity score. We refer to these models as Docking Binding Affinity (DBA) models. We compare our model’s performance against the physics-based docking on the two DBA datasets. Our results suggest that, with a correlation score of 0.79, the 21, DeepDTA22 and MONN33. While these methods were trained purely on the protein sequence and 2D SMILES representation of the ligands, our model is novel in that it considers the 3D structures of the protein and ligand to predict As a next step, we tailored our GNN models to predicting 21, DeepDTA22, and MONN33 were trained on BindingDB data, which has nearly 10 times the amount of data than that available for our dataset. In addition, we observed improvement in the Pearson correlation coefficients in predicting The overall performance of the GNN model is relatively low compared to the existing methods listed in the Table To assess how critical the learned protein and ligand representations are for predicting 2. Interestingly, Our extensive analysis on the SARS-CoV-2 MIn this work, we devised graph-based deep learning models, These implementations enable us to leverage the vast amount of 3D structural data of both the target protein and ligands, and interactions between them which is crucial for activity, potency, and affinity prediction to accelerate in silico hit identification during early stages of drug design. The goal of our extensive study is to generalize the graph-based models by incorporating domain-aware information, features, and biophysical properties and by utilizing a large amount of data including a variety of targets. The test accuracy for 50 with highest prediction correlation coefficients. Using PDBbind2016 data, we achieved Pearson correlation coefficients of 0.66 and 0.65 on experimental affinity prediction and 0.50 and 0.51 on 51, the GNN model performance can be further improved.Our GNN models were further modified for regression tasks to predict binding affinities and Our model is unique and novel in that it considers the 3D structures of the protein and ligand to predict affinity and https://github.com/PNNL-CompBio/pf-gnn_pli.The Supporting information is available with Table (S1–S8) and Figures (S1–S7)) detailing data, models, hyperparameter optimization, Performance Metrics for Classification and Regression GNN Models, IBS molecule properties, and Docking Binding affinity Data and Results used in this study. Description about hyper-parameters and the trained Supplementary Information.


LEP protein, which is vital for the evolutionary adaptation of sheep. Our findings demonstrate that the five nsSNPs reported in this study might be responsible for sheep's structural and functional modifications of LEP protein. This is the first comprehensive report on the sheep LEP gene. It narrow downs the candidate nsSNPs for in vitro experiments to facilitate the development of reliable molecular markers for associated traits.Leptin is a polypeptide hormone produced in the adipose tissue and governs many processes in the body. Recently, polymorphisms in the LEP gene revealed a significant change in body weight regulation, energy balance, food intake, and reproductive hormone secretion. This study considers its crucial role in the regulation of the economically important traits of sheep. Several computational tools, including SIFT, Predict SNP2, SNAP2, and PROVEAN, have been used to screen out the deleterious nsSNPs. Following the screening of 11 nsSNPs in the sheep genome, 5 nsSNPs, T86M (C → T), D98N (G → A), N136T (A → C), R142Q (G → A), and P157Q (C → A), were predicted to have a significant deleterious effect on the LEP protein function, leading to phenotypic difference. The analysis of proteins' stability change due to amino acid substitution using the I-stable, SDM, and DynaMut consistently confirmed that three nsSNPs (T86M (C → T), D98N (G → A), and P157Q (C → A)) increased protein stability. It is suggested that these three nsSNPs may enhance the evolvability of  Leptin is one of the major hormones secreted by adipocytes. It is the primary function of regulating homeostatic control of energy balance, metabolism, neuroendocrine system, and other functions through its effects on the central nervous system , 2.via the long feedback performed by neurosecretory cells, interacting with corresponding receptors in the hypothalamus , leads to repression of orexigenic peptide production and stimulation of anorexigenic factors [The short negative feedback maintains the body leptin proportion through pituitary cells, or  factors . Then, t factors . Usually factors . The red factors . In turn factors .In vitro, functional comparison of mutated proteins with their corresponding wild-type proteins associated with specific traits of animals is a common practice that enables us to identify the impact of each nsSNP in the corresponding protein [LEP gene in regulating body weight, energy balance, and feed intake traits in sheep, we retrieved all of the mutations found in sheep LEP gene deposited in the Ensembl sheep genome browser until March 2022. The objective of this study was to characterize the potential variations in the ovine LEP gene using in silico analysis.Single nucleotide polymorphisms are essential and the most common types of mutations associated with complex traits . In part protein , 11. HowLEP gene. Details of each bioinformatics tool are presented below and summarized in We used various computational tools to screen out the functional effects of the nsSNPs in the LEP) gene SNPs were collected from the Ovis aries Ensembl genome browser 106 (https://uswest.ensembl.org/Ovis_aries/Info/Index) [LEP gene were retrieved from the Ensemble database (https://ensembl.org/) [http://www.uniprot.org).Sheep leptin (o/Index) . The trabl.org/) . Then, tLEP gene.Four tools, SIFT, PredictSNP2, PROVEAN, and SNAP2, were used to predict the practical context of missense mutations in the http://sift.jcvi.org/) tool [LEP gene obtained from Ensembl were submitted as a query to SIFT for homology searching. Results were obtained as SIFT scores which were classified as damaging (0.00–0.05), potentially damaging (0.051–0.10), borderline (0.101–0.20), or tolerant (0.201–1.00) [http://loschmidt.chemi.muni.cz/predictsnp2) was used to identify the deleterious nsSNPs. PredictSNP2 is a consensus classifier for predicting the effect of amino acid substitutions based on the output of eight different amino acid-based predictors http://loschmidt.chemi.muni.cz/predictsnp [Firstly, the amino acid substitutions that affect protein structure and function lead to phenotypic changes were screened by SIFT  tool . The ide01–1.00) . Then, Pedictsnp .http://provean.jcvi.org/index.php) is used to predict the level of impact on protein structure and biological function. A protein FASTA sequence with amino acid alterations is used as the input query. It classifies nsSNPs as deleterious or neutral based on whether the final score falls below a threshold of − 2.5. Scores over this threshold are considered neutral [PROVEAN .SNAP2 predicts the functional impact of mutations . SNAP2 pΔG or dG). In contrast, the difference in folding free energy change between wild-type and mutant protein (ΔΔG or ddG) is often considered an impact factor on protein stability changes [http://predictor.nchu.edu.tw/iStable) [http://marid.bioc.cam.ac.uk/sdm2/prediction) a server [http://biosig.unimelb.edu.au/dynamut/) was used to verify the impact of mutations on protein conformation, flexibility, and stability and to visualize the protein dynamics [Protein evolution is mainly governed by protein stability . After f changes . The proiStable) . Furthera server . Finallydynamics .LEP gene was identified by ConSurf tool (http://consurf.tau.ac.il/) [Highly conserved functional regions of the protein coded by the .ac.il/) –27.LEP was predicted using the PSIPRED server available at (http://bioinf.cs.ucl.ac.uk/psipred/) [The secondary structure of sipred/) . It is bsipred/) .The 3D structures of the protein encoded by LEP were constructed using four different homology modeling tools. No crystal structure with an appropriate length of this protein was available in the protein data bank.https://swissmodel.expasy.org/), a web-based tool for the homology modeling of protein, was used to avail the three-dimensional structure of LEP [Swiss-Model workplace available at , based on the Hidden Markov method, was used to predict the homology-based three-dimensional structure of the query amino acid sequence. It combines five steps to build a model: (1) collecting homologous sequence, (2) screening of fold library, (3) loop modelling, (4) multiple template modeling by ab initio folding simulation Poing, and (5) side chain placement [Phyre-2 tool , using RaptorX-Boost and RaptorX-MSA to construct the three-dimensional structure of a protein, predicted the 3D model of the protein coded by CDKN1A. It combines a nonlinear scoring function and a probabilistic consistency algorithm to predict the model structure [RaptorX server available at  PredictSNP2 further evaluated all the 11nsSNPs, including the nine predicted as deleterious by SIFT. The PredictSNP2 is a consensus classifier tool for predicting the effect of amino acid substitutions based on the output of eight computational prediction tools. The PredictSNP2 tool prediction outcomes revealed that eight nsSNPs were predicted as deleterious Table . The SNAThe iStable Meta server tool demonstrated that the amino acid changes in R2C, L35P, D98N, N136T, R142Q, P157Q, V181L, and R196Q cause loss and decreased stability of the LEP protein. However, the other three mutations  increase the protein stability Double-checking with site-directed mutator (SDM) and DynaMut servers consistently verified the molecular consequences of 5 nsSNPs. Out of these, three nsSNPs  increased stability by decreasing the molecular flexibility of the wild-type proteins. However, the other two nsSNPs (N136T and R142Q) revealed a decrease in stability and molecular flexibility Table Furthermore, the interatomic interactions prediction outcomes between wild type (left side) and after single point mutations (right side) are presented in Figures Our ConSurf results indicated that nsSNPs at position L35P, T75M, V94I, D98N, and R142Q are in the highly conserved region, with a conservation score 9. Likewise, nsSNPs R2C, N136T, and P157Q have shown a conservation score of 8 Figure . From thThe alpha helix and beta sheet distribution and the coil are exposed by PSIPRED Figure . Among tStructure refined and energy minimized homology models of LEP by Swiss-Model, Phyre-2, ConSurf, and RaptorX servers are illustrated using computational program QMEAN  Figure . These mRamachandran plot is an x-y plot of phi/psi dihedral angles between NC-alpha and Calpha-C bonds to evaluate a protein's backbone conformation. The Ramachandran plot of the wild-type protein in the Swiss-Model showed 132 residues (93.6%) in the favored region, 8 residues in allowing region (5.7%), and one residue (0.7%) in the outer region Figure . The LEPLEP gene, located on chromosome 4, encodes 204 amino acids annotated with 11 domains and is currently associated with 543 variations [Missense mutations in genes have a profound impact on protein function. Extensive computational analysis of the phenotypic features attributed to nsSNPs may reveal the susceptible variants by interrupting the original function , 38. In riations .th rib fat and marbling score [Leptin is an endocrine hormone member of the long-chain helical cytokine family. It has multiple effects on regulating food intake, energy expenditure, body weight, and immune responses . Polymorng score . In adding score .LEP) gene [LEP gene have been recommended as predictors of relative differences among individuals for such economic traits [Numerous research studies have reported polymorphisms in the sheep leptin (EP) gene , 45 and EP) gene , growth EP) gene , 48, andEP) gene , 50. Conc traits .ADIPOR2 gene by [CSN3 gene in cattle by [In this study, following the screening of nsSNPs as deleterious or neutral upon their effect on protein function using the SIFT, predictSNP2, PROVEAN, and SNAP2, five nsSNPs T86M , D98N , N136T , R142Q , and P157Q  were predicted to have a significant impact on the protein structure, stability, and function by the majority of the tools. As described  in the h gene by , and theattle by , dbSNP-bThe sequence of a protein determines the protein's physicochemical properties, such as protein structure, protein thermodynamic stability, the ability to interact with other molecules, and catalytic capacity , 55. ThuΔ vibrational entropy energy between wild-type and mutants (ΔΔSVib ENCoM) for the 3 nsSNPs were − 0.046 kcal.mol−1.K−1, − 0.056 kcal.mol−1.K−1, and − 0.027 kcal.mol−1.K−1, respectively. Our data suggest that the vibrational entropy change of protein upon mutation of the amino acid decreases the molecular flexibility of the protein. Therefore, the three nsSNPs that increased the protein stability may increase evolvability by allowing a protein to accept a wider range of beneficial mutations while still folding to its innate structure [Further analysis of these nsSNPs on protein functionality was predicted indirectly based on the effects exerted on protein stability. First, the I-stable server predicted that all the 11 nsSNPs impacted protein stability. Then, the report from I-stable was crosschecked using SDM and DynaMut. The stability analysis verified by SDM and DynaMut servers consistently narrowed to 5 nsSNPs. Three nsSNPs  had increased protein stability, whereas the other two nsSNPs (N136T and R142Q) showed a destabilizing effect on the wild-type proteins. DynaMut predicts the molecular consequences of the mutations in-depth through changes in protein dynamics and stability from vibrational entropy change . The Dyntructure , who reptructure  explaineLEP gene may have a diverse impact on the performance of the sheep for the particular traits. The results from this study provide promising insight for future in vitro studies to specify the impact of these sorted nsSNPs on sheep performance.In contrast to the other two nsSNPs, N136T  and R142Q  were evidenced by reduced stability. LEP gene may contribute to the functional discrepancies compared to the wild LEP gene, which significantly regulates energy homeostasis, body weight control, and reproduction traits. Most of the variants identified in this study are located in the conserved domain of leptin. We also calculated the free energy changes for mutants and wild-type LEP proteins to evaluate their stability. Our data provide evidence for the functional role of the 5nsSNPs, which is helpful for further in vitro study to facilitate the development of reliable molecular markers for future practical application in sheep breeding.A simulation-based study to detect the nsSNPs using many computational programs employed in this study has revealed 5 amino acid substitutions: T86M , D98N , N136T , R142Q , and P157Q  were found to be functionally deleterious. These nsSNPs in the sheep