Mass spectrometry (MS)‐based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing.R2 scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, it is demonstrated that the derived models can be “transferred” across experiments and species. For instance, the model derived from human tissues gave a R2=0.51 when applied to mouse tissue data. It is concluded that protein abundances generated in label‐free MS experiments can be computationally predicted using functional annotated attributes and can be used to highlight aberrant protein abundance values.Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label‐free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average  However, due to technical limitations and restrictions in the limits of detection and dynamic range of analyses performed, complete proteomes of complex biological samples are never fully characterized and quantified.2, 3Figure As schematically shown in  These approaches try to fill intermittent missing values across MS runs using simple or sophisticated models built on a larger set of data. There are four main approaches for imputation of missing values. One is to use multidimensional spectral alignments to match MS1 peaks between runs to increase the number of identifications between runs and assign real intensity values to missing peptides and proteins. The second approach uses fixed constant single‐values, which can be a minimal value, making the assumption that a protein is missing due to low abundance. Alternatively, an average value can be used, with the assumption being that a general 1:1 ratio across runs will cause less bias. The third approach uses local similarity to find peptides or proteins with similar expression profiles within the same dataset and then use these as the basis for estimating missing values. Finally, the missing values can be reconstituted from a global model using methods such as regression, probabilistic principal component analysis, Bayesian component analysis, and data normalization techniques. These sophisticated methods can produce a more nuanced missing value imputation at a higher computational cost.To cope with missing protein values in proteomics experiments, various methods have been developed to impute the expression values.4, 5 It has been shown that there are certain classes of proteins that are more closely regulated at the transcriptional level than others. Specifically, proteins with highly variable expression such as metabolic and immune‐related proteins often correlate well, whereas slow turn‐over proteins and those in stable complexes often correlate poorly.A major shortcoming of all the current imputation methods is that they only deal with intermittently detected proteins, requiring identification and quantification of the protein in some samples to support the modeling algorithm. With the rise of proteogenomic approaches, it is becoming increasingly common to perform both proteomic MS and RNAseq analysis in the same samples. In fact, several recent large‐scale studies have compared transcript and protein expression. These studies report a wide range in the correlation of protein and transcript expression across different genes, tissues, and samples, which overall is found to be moderately positive, although exact numbers vary enormously.7, 8Proteome of eukaryotic organisms is highly complex and has dynamic range of many orders of magnitude. It is therefore not surprising that despite significant technological advances, proteomic mass spectrometry experiments continue to be limited in coverage and depth compared to genomics. Most proteomics experiments thus quantify only part of the proteome, leaving many proteins “unobserved” even when they may be present in the proteome. Here, we show that using deep learning techniques, we can leverage gene annotation and RNAseq data to extrapolate the available protein abundances to unobserved proteins in a given experiment. Previous studies have applied machine learning methods to impute partial missing values across experiments and to predict protein abundance across similar samples. The approach presented here is the first to attempt to predict unobserved proteins and thus to provide a method for increasing coverage of the proteome, which is an important development for comparative proteogenomic analyses. Additionally our method highlights aberrantly expressed proteins; these can either be due to biological perturbation or technical and annotational issues in the data. Overall, this study further integrates proteomics and genomics data to advance our understanding of their biological relationships. The teams used linear and non‐linear models to evaluate prediction of proteomes from genomics data and prior information for unobserved samples. However, to the best of our knowledge, none of the existing approaches attempt to estimate the expression of the experimentally unobserved proteins. Here, we close this gap by proposing a novel method that estimates the expression of unobserved (absent) proteins, where no expression values are recorded in any of the given proteomics datasets. Specifically, we describe a novel method leveraging deep learning (DL) to generate a predictive model based on existing measured protein expression values, RNA expression values for all genes in the same or similar samples, and available gene annotations such as Gene Ontology (GO) and UniProt keywords. We apply the DL approach beyond imputation of partially missing values or additional samples to predict all unobserved protein abundances in label‐free MS experiments. The method predicts the protein expression values with average R2scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, we show that the derived models can be “transferred” across experiments and species. Finally, we demonstrate that such predictive models can highlight aberrant expression patterns and infer abundances for ambiguous proteins more reliably.Recently, prediction of protein expression levels in samples derived from cancer patients has been the subject of an NCI‐CPTAC DREAM proteogenomics challenge.13 The22.1 NCI60 (46 cell lines and 8000 genes), and Tissue29 (29 tissues and 12 879 genes). The experiment also used a label‐free mouse tissue dataset MouseTissue3 (3 tissues and 6591 genes). All RNA and proteome datasets were downloaded from EBI's Expression Atlas, with proteomics data for Tissue13 , NCI60 (PXD005940), and Tissue29 (dataset PXD010154) measurements based on raw data from the PRIDE database. For more information on these datasets and references, see Supporting Information.The label‐free MS experiments used in this study included three human datasets: Tissue13  is used, where v is the value of the measurement on the original scale and c is a constant, common for the entire dataset. As in most other studies, it is used c=1 .Both proteomics and RNAseq abundances are represented as log transformations. To deal with zero values in RNAseq data, in the literature, typically a function Two real value matrices were used as input data to our algorithm, where rows correspond to genes/proteins, while columns correspond to samples (tissues or cell lines). The first matrix contained RNA abundance measurement values and it was assumed that every gene and every sample in this matrix had a defined value . The second matrix contained label‐free protein abundances as quantified in the respective datasets, where some elements, including entire rows, may be missing. Unobserved or missing values in the proteomics data were ignored when training the neural network. In addition, genes were annotated by context information, specifically using GO terms or UniProt keywords (KW), where each gene can have more than one term assigned and the same term can be used to annotate several genes. The goal was to extrapolate the protein abundance values to all genes in the matrix using all the available data, as shown in Figure 2.2p(g)=α(g)+β(g)×r(g), where p(g) and r(g) are the protein and RNA abundances for each gene g, and the coefficients α(g) and β(g)are computed from the information provided to the network. It should be noted that the coefficients α(g) and β(g)are specific to each gene and are derived from the supplied context information in a non‐linear way. Further details of network architecture are provided in the Supporting Information.For the prediction of protein abundance values, we used a specially developed DL network. To train the network for a particular subset of genes, the input given was the measured protein abundances, the RNA expression values, and a binary vector for each gene, describing context information (such as GO terms and/or UniProt keywords). The trained network was then used to make protein abundance predictions, which are based on the equations run. For each dataset, we performed ten runs based on different randomizations of the initial set to validate the stability of the predictions. In total 100 models were generated and applied for each experiment. We also considered the problem of the unobserved protein expression value prediction only using RNA information or excluding it from the model, that is, only using gene annotations.The gene set for each experiment was partitioned randomly into ten subsets. Over ten iterations, ten models were then built using a training set comprising 90% (9 subsets) of the data and a test set using the remaining 10% (single subset). In each iteration, we trained a DL network on all available information  for each training set. The protein values in the test set were then predicted by applying the obtained model to the annotations and RNA values of the genes excluded in the training set. By repeating the process excluding a different subset, each time we obtained a predicted abundance value for every protein in the dataset. We call each of these described experiments a Depending on the information used for the training, we considered several prediction models: RNA (only), GO, KW, RNA+GO, RNA+KW, RNA+GO+KW, and Randomised (the abbreviations indicate the information that is presented to the DL network). The Randomised model was used as a baseline comparison with the predictions, and was based on RNA and context information being randomly permuted between the genes in the dataset. The predictions were compared to the measured abundance values for proteins in the test set for which the experimental measurements were available.As a natural benchmark for comparison, we also considered linear regression (LR)‐based predictions from the RNAseq values. Not surprisingly, DL models trained using only RNA and LR model predictions were almost identical —without any context information provided to DL network, one can hardly expect better predictions than LR, and the fact that these values largely matched at least partially re‐confirms that the proposed network design is conceptually sound. Thus, we further discuss only LR results because they are a simpler and a more familiar benchmark.α(g) and β(g)allows precomputation of these on one dataset and then testing their predictive value for the same or for homologous genes in different datasets (of the same or different species).The fact that predictions were based on gene‐specific coefficients MouseTissue3, the experiments did not include the models with KW data. However, we tested the inter‐species applicability of the computed gene‐specific coefficients α(g) and β(g)for homologous genes. These coefficients were computed from Tissue29 for the two tissues it shares with the dataset MouseTissue3 (liver and testis) using the RNA+GO+KW model, and then applied for the prediction of protein abundances for 5388 homologous genes from the MouseTissue3 dataset. For detailed description see Supporting Information.In the case of the dataset 2.3R2 score, which has been adapted almost exclusively in all the related work (and in most cases is simply derived from Pearson's correlation coefficient r by taking R2=r2). For a given dataset, RDavg2 is the average score over the whole dataset, R2[t] is the score for a specific tissue (or cell line) t. RTavg2 denotes the average score over all tissues, and RMax2 and RMin2 refer to the highest and lowest prediction accuracies for tissues within a given dataset. The exact formulas for computation of the R2 scores are provided in Supporting Information. We introduce the notions of R2, which show the contribution of gene g to the overall prediction accuracy RDavg2, R2[t](g), showing gene contribution to prediction accuracy for specific tissue R2[t], and Ravg2(g)—average contribution of gene to prediction accuracy over all tissues.We assessed the prediction accuracy using the 33.1LR and RNA only models produced almost identical results, therefore the latter is not discussed. The prediction accuracy generated for these datasets is summarized in Table Figure For the three human datasets, we tested all seven DL network‐based prediction models and compared them to the LR as a benchmark as well as to the Randomised model. As already noted in Experimental Section, the LR) abundances alone. The impact of KW and GO was roughly similar, although in most cases GO performed slightly better. However, despite the fact that RNA on its own was a poorer predictor, the GO or KW predictions improved significantly when they were used together with RNA expression values. These observations were consistent across all three human datasets. The best predictions were obtained by the RNA+KW+GO model with RDavg2 values ranging between 0.49 and 0.54 (compared to the LR range of 0.16–0.32). At an individual tissue level, the best predictions were obtained for liver. Note that in this tissue, the correlation between protein and RNA abundances were also the highest. A noticeable outlier was bone marrow in the Tissue29 dataset with R2=0.347, but for most of the tissues, t score R2[t] was close to the average value. The same was true for the NCI60 dataset, which had two cell lines as outliers: HCT116 and X7860, with R2 equal to 0.236 and 0.357, respectively. The improvement of the RNA+KW+GO model over the LR model was higher in the case of the tissue datasets than for the cell line dataset.The first notable observation is that both KW and GO gene annotations alone provided better predictions of protein abundance than RNA .The DL network can be trained on a dataset with any number of samples (tissues); however, the number of available samples has very limited impact on prediction accuracy, and even data on a single tissue give similar accuracy as the use of the whole dataset. Regarding the number of data points (proteins) for the datasets tested, it was observed that the prediction accuracy remained practically stable with less than 50% of randomly selected data points removed, and then started to drop significantly, if sizes of data sets were further reduced , obtaining standard deviation (SD) values ranging between 0.04 and 0.06 for the different prediction models and datasets, showing that the predictions are stable. The R2 values shown here represent averages over these ten test runs. For the complete data of all experimental results covering all tissues and cell lines see Supporting Information.We also assessed the stability of Randomised) clearly outperformed LR. For the best prediction model RNA+GO+KW, the ratio of gene pairs from all samples that could be predicted with the R2 score of at least 0.5 ranged from 72% (NCI60) to 77% (Tissue29), and from 51% to 59% in the case of the 0.8 score threshold. As a rough approximation, these R2 thresholds can be interpreted as correlations of 0.7 and 0.9, respectively.As one can observe in Figure Ravg2(g), similar percentages of proteins reached a 0.5 threshold, but percentages decreased for larger thresholds . Notably, similar behavior patterns persisted for all prediction models and all three human datasets.Note that for Figure R2 for the Tissue29 dataset.A possible explanation for this is that for different tissues or cell lines, the highest prediction accuracy is achieved on the different set of genes. A modest trend could be observed in the results: Better predictions were obtained for proteins with larger abundance variability between different tissues. A possible explanation is that the concentrations of such proteins are more affected by the levels of RNA expression and the accuracy of the experimental quantification. The figure also shows that better predictions were obtained for proteins with lower abundances. Overall, our results show that the abundances of proteins not measured experimentally can be predicted in silico from gene annotations and RNA expression values of the respective genes.3.2Tissue29 dataset, we examined this in two ways. First, the average correlation for each functional GO or KW category was calculated by comparing the values of each gene across the tissues between the original experimental protein abundances and the predicted values. Second, the mean absolute percentage error (MAPE) of the predicted versus experimental protein expression was calculated for all genes in each functional category. Figure Since protein abundance prediction accuracy improved with GO and KW functional annotations, we conclude that this context information plays a role in the estimation. Therefore, we explored if there are particular sub‐categories of genes that can be predicted with greater accuracy than others. Focusing on the . Figure 11 When more stringent peptide confidence measures were applied, as it was the case in the original analysis of this dataset, these proteins were removed from the dataset. The high error rate can be attributed to the fact that the predicted abundance is very low , whereas the experimental values were significantly higher and probably incorrect. Another category of proteins showing poor correlation are serum proteins, which suggests contamination by blood in some experimental tissues.We also found some evidence that some high abundance errors and poor correlation values found could in part be attributed to the accuracy of the experimental protein detection and quantification. An interesting example is the “olfaction” term (GO:0004984). These proteins  are notoriously difficult to detect and due to their well‐known tissue specificity, it is highly likely that these represent experimental false‐positive identifications rather than poor predictions.24 Whe3.3MouseTissue3, four DL models were tested. The results are summarized in Table Figure RDavg2=0.45 for the RNA+GO model in comparison to a 0.3 value obtained for the LR model. The percentages of genes predictable with scores above 0.5 and 0.8 were 70% and 50%, respectively (62% and 40% for LR). Nevertheless, predictions arising from GO annotations only gave results similar to LR, which could be explained by the sparser GO annotations available for mouse.Next, our aim was to test whether the models were transferable from human to a different species. In the case of the mouse dataset α(g) and β(g)coefficients assigned to genes by DL network trained on human data  and applied these to homologous genes in mouse tissues. Such homologues were available for 5388 of 6591 mouse genes measured in the MouseTissue3 dataset.We used the RNA+GO model trained on human data provided better predictions than the same model trained on the mouse dataset, indicating that models derived from larger sets with more comprehensive GO annotations appear to be applicable to homologous genes. No‐tissue specificity of the gene specific coefficients was observed. The best predictions were obtained from coefficients computed by the DL network without including tissue information. In principle, this is consistent with observations from related work where such coefficients were assigned directly from the measurements. However, given that we used data from only two common tissues, this does not allow us to reach reliable conclusions. Tissue‐specificity of expression of some genes has been used to derive marker sets. The challenge now is how such tissue‐related information can be extracted from available gene annotation data.Unexpectedly, we observed that the coefficient computed by the urements.26 How4 nevertheless to the best of our knowledge, existing approaches for protein abundance prediction focus on imputing partially missing values or as is the case with the NCI‐CPTAC DREAM proteogenomics challenge, to infer proteomes in new samples from genomics datasets. Our method is aimed at extrapolating the measured experimental values to unobserved proteins  proteomics and RNAseq datasets generated by CPTAC. These models attempt to infer protein expression samples based on the transcript abundances supplemented with protein features such as interactions and conservation attributes. Of the various approaches they examined, the best method was an ensemble approach using a random forest algorithm and achieving an R score of 0.53, which is comparable to what we achieve for the prediction of unobserved proteins.Recently, an NCI‐CPTAC DREAM proteogenomics challenge has focused on the prediction of protein expression levels across samples from cancer patients, using genomics data combined with protein annotational information.13 Var Nevertheless, we can achieve the accuracy of R2 scores between 0.46 and 0.54.As noted, our approach is aimed at extrapolating label‐free global protein expression values to the full proteome, including the experimentally unobserved proteins, and thus our results are not directly comparable with these existing methods.31 NevIn our study, we have solely applied our method to label‐free MS proteomics datasets that used MS1 intensity‐based quantification (iBAQ). This particular type of experimental protein expression data was selected because currently it is the most widely used approach for protein expression data in public databases. We have opted to use datasets analyzed using MaxQuant, as it reflects a prominent workflow in label‐free proteomics. Obviously, our predictions of the true protein abundances are only as good as the data for the experimentally measured proteins. There are a myriad of methods and tools for assessing the true protein abundance in proteomics experiments; however, there are limited ways to assess the accuracy of protein quantification on a large scale without using synthetic or purified protein standards. Although some benchmarking proteomics datasets have been generated, the complication for using them in our approach is that data are needed with proteins behaving in context of their biological annotations, and with a suitable paired RNAseq expression dataset. To the best of our knowledge, such a benchmarking dataset does not yet exist. As it is essential for our method that meaningful protein annotations and complementary RNA expression data are available, the application of our method to peptide‐level quantifications would be problematic.In theory, since the neural network is trained on each dataset independently, our method could potentially be extended to support other experimental methodologies for protein quantification such as labeled MS approaches and absolute quantification experiments. However, further testing and optimization would likely be needed in these cases, which are beyond the scope of this study.In most applications of our method, an independent model will be trained for each dataset for which values are to be predicted. However, we have examined the possibility of using a model trained on one dataset to predict values in a different experiment and demonstrated that our DL method provides a good predictive model across datasets, even across different species, such as human and mouse (exploiting gene homology mappings). In fact, training the DL on larger more comprehensive datasets and applying it to a smaller dataset can yield better accuracy in the prediction. For instance, the model trained on a larger human dataset transferred to mouse gave a better prediction of values than the model trained on a small mouse dataset. It has to be noted that the prediction accuracy varies between proteins; moreover, the particularly difficult‐to‐predict proteins depended on the biological sample. Nevertheless, the prediction models were relatively stable across datasets and a model learned from one dataset could be applied to predict unobserved proteins in another dataset.It may seem surprising that GO or KW annotations are better predictors of the unobserved protein abundances than the RNA measurements. A possible explanation for this is that gene annotation terms provide links between proteins that have related functions, are parts of the same protein complexes, or are co‐regulated and thus are present in similar samples in similar abundances. It is important to note, however, that when combined with information coming from gene annotations, RNA measurements improved the predictions accuracy significantly. This is explained by the fact that the predictive power of the RNAseq values is at its best for these categories and is significantly boosting the prediction accuracy for these proteins.We have investigated the performance of the method for different protein subsets by looking at the correlation of predicted protein values to experimental protein values across tissues, taking the average correlation for each annotational term. Additionally, we calculated the average MAPE for proteins in each subset. These subsets of proteins showed a range of accuracies between the different functional annotational terms. There appear to be two factors driving the extremes in accuracy between terms. The best correlating and most accurately predicted proteins appear to include annotational terms, such as metabolism and immune response. Such proteins are known to be strongly regulated at the transcriptional level.11 ThiFor the least accurate functional categories, there seems to be a different explanation. These terms generally reflect categories of proteins that would correlate very poorly with RNAseq values. These include tissue‐specific protein sets, potentially proteins coming from contaminations in the sample, or proteins that are difficult to detect by experimental MS approaches. Additionally, one key example we can note is olfaction‐related proteins, which are notoriously difficult to identify via MS and have very high tissue specificity. However, olfactory receptors have been identified in the datasets used here. These are highly likely to be false positives, especially since these identifications are not present in the original analyses of these experiments, where stricter peptide confidence filtering was applied. These proteins display very low accuracy between predicted and experimental values. In this sense, the predicted expression is more realistic than the observed experimental values. This demonstrates the power of our method for identifying aberrant protein expression where some confounding factors, such as false‐positive identifications, variant proteins, and protein modifications, are impacting the experimental quantification. Due to the nature of the predictive method we are applying, a protein will always be given a predicted expression value, and hence there will always be a background noise even if the protein is not biologically present in that sample. This is worth noting when the predictions are applied to truly unobserved proteins, and a minimum predicted expression threshold should therefore be imposed.To test our predictions, we excluded 10% of the proteins in each round of training and then built 100 different models for each dataset and only looked at the predictive accuracy of proteins masked from the training set. This was done to enable the investigation of the accuracy of predicted proteins unobserved in all samples, extending the proteome coverage, based on the RNAseq and the functional annotational relationships to the rest of the proteomics experimental data. However, this approach is not suitable for examining the accuracy of the imputation for partially missing proteins. Although the model can be applied for this purpose, we have not explored this application here and can only hypothesize that performance would not necessarily be better than existing approaches. To take full advantage of the deep learning methodology for imputation, further optimization and testing would also be required.The fact that a model trained using the human data predicted mouse proteins better than using the model trained on the mouse model demonstrates that the accuracy of the predictions can be improved by having a larger input dataset with more complete functional annotation. To assess the accuracies of any given model when applying the method to new datasets, a similar cross validation strategy should be implemented, leaving a fraction of the data out of each model over multiple iterations. This should provide a good indication for the overall accuracy for unobserved proteins in any particular functional category.Given that our protein abundance measurement predictions are tested by masking a subset of the proteins that actually are measured experimentally, the question as to what extent our conclusions could be reliably transferred on the truly unobserved proteins can arise. Is it possible that the truly unobserved proteins have properties different from those of the measured ones and therefore our computational predictions are not reliable? This possibility is difficult to test directly; however, the observation that our models can be transferred between datasets with different sets of proteins absent in different experiments provides indirect evidence that our predictions do apply to all proteins. We also highlight that in the assessment of the accuracy of annotations, the predicted values can actually be used to expose aberrant protein abundance values  and situations where a false identification or quantification has occurred in the experimental data.In our computational study to train DL networks, we specifically use GO or UniProt KW as contextual information; in principle, our method allows for using any context information that can be associated with genes, for instance, protein sequence, domain features, and/ or KEGG pathways, among others. Taken together, this study demonstrates that our method can be used to estimate the abundance for proteins that are unobserved in label‐free MS experiments and to reveal instances where measurements may not be reliable. This provides greater coverage of pathways and protein networks for improved downstream analysis. Additionally, it can highlight unobserved or aberrantly expressed proteins with biological relevance that can be targeted in further experiments.5Tissue13 from E‐PROT‐1 (coming from PRIDE datasets PXD000561 and PXD002967) and E‐MTAB‐2836 (RNA expression); NCI60 from E‐PROT‐25 (PRIDE dataset PXD005940) and E‐MTAB‐2770 (RNA expression); Tissue29 from E‐PROT‐29 (PRIDE dataset PXD010154) and E‐MTAB‐2836 (RNA expression); MouseTissue3 from E‐PROT‐13 and E‐GEOD‐43721 (RNA expression). Pre‐processed datasets and the developed software are available at GitHub: https://github.com/IMCS-Bioinformatics/DLNetworkForProteinAbundancePrediction. More details about datasets and software are provided in Supporting Information.The datasets used for the study are obtained from the following Expression Atlas datasets: The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Protein science has moved from a focus on individual molecules to an integrated perspective in which proteins emerge as dynamic players with multiple functions, rather than monofunctional specialists. Annotation of the full functional repertoire of proteins has impacted the fields of biochemistry and genetics, and will continue to influence basic and applied science questions – from the genotype-to-phenotype problem, to our understanding of human pathologies and drug design. In this review, we address the phenomena of pleiotropy, multidomain proteins, promiscuity, and protein moonlighting, providing examples of multitasking biomolecules that underlie specific mechanisms of human disease. In doing so, we place in context different types of multifunctional proteins, highlighting useful attributes for their systematic definition and classification in future research directions. At the time when George Beadle and Edward Tatum proposed the one gene–one enzyme paradigm , the keyProteins with multiple functions can influence the evolution of organisms, as they contribute to complexity and robustness. For instance, in early forms of life and in organisms undergoing genome reduction, multifunctionality may increase the functional repertoire of a limited set of genes . In addiOther genetic mechanisms that are also known to contribute to the overall functional diversity of living systems are not herein addressed, such as alternative promoters or splicing in transcription, overlapping ORFs, alternative UTRs in translation, or post-translational modifications . Here, wTo date, several non-mutually exclusive terms have been used to describe recurrent phenomena in which proteins perform two or more functions, notably pleiotropy, multidomain proteins, promiscuity, and moonlighting. Although the terms discussed here are widely used in the literature, there is an underlying lack of consensus in their usage, which may consequently hinder our understanding of the extent and biological significance of each of the different forms of protein multifunctionality. For instance, a single enzyme can be pleiotropic, multidomain, and show promiscuous and moonlighting activities .The definition of multifunctionality fundamentally relies on the definition of protein function itself. Protein function is the potential activity with a given physiological role that a polypeptide with a single primary sequence can partake by interacting with other biomolecules or chemical compounds. Based on Gene Ontology (GO) annotations, protein multifunctionality may be described at different overlapping levels . The GO Diverse stimuli may trigger action of different functions of a multifunctional protein -oxidoreductase: while one domain binds the cofactor, the other couples with the substrate, in such a way that the two domains catalyze a single reaction in the aromatic amino-acid biosynthesis pathway . The role of GRK2 on the regulation of cardiac receptors is the result of successful coordination among the three protein domains that compose the enzyme: a regulator of G-protein signaling homology domain, a protein-kinase domain, and a pleckstrin-homology domain , 2005. WProtein “promiscuity” describes a wide variety of related phenomena, all of which describe multiple catalytic activities or binding to multiple molecules , 2017. TE. coli or human Ras proteins have been described as promiscuous hub proteins given their many protein-protein interactions. While thioredoxin interactions take place in the same protein interface, Ras interactions occur in different binding sites , a glycoprotein with broad substrate specificity capable of hydrolyzing organophosphates, lactones, and aromatic esters . PON1 is isozyme . The alt disease .Dysfunction of catalytic-promiscuous enzymes can also influence Alzheimer’s disease and cancer incidence. The omega class gluthatione s-transferases GSTO1 and GSTO2 can carry out two different chemical reactions in the same active site: gluthatione-dependent thiol transfer and dehydroascorbate reduction . The funProtein “moonlighting” was first defined as the phenomenon whereby a polypeptide performs two or more molecular functions that are not the result of gene fusion, splice variants, or multiple proteolytic fragments . “Gene sThe molecular activities of a moonlighting protein are usually described as unrelated to each other and independently of the primary sequence . Indeed,Moonlighting proteins with different molecular and functional characteristics have been described in a wide variety of organisms and the number of examples has increased in the last few years to almost 700 carefully curated moonlighting proteins . Even thComputational strategies have been developed to characterize moonlighting proteins and to identify novel candidates for further experimental confirmation. Alignment algorithms evaluating similarity to other proteins, functional domains, or motifs have been tested to predict the nature of alternative functions within a protein . ProteinAs the number of known moonlighting proteins has increased, efforts have been made to understand how these molecules evolve and to guide in looking for more such multifunctional biomolecules. One of the first observations was that moonlighting proteins are enriched in conserved, highly expressed, proteins; however, this could also represent a bias where highly expressed and conserved proteins are also more likely of being studied . In addiDiseases and pathologies related to moonlighting phenomena are being constantly described . It has The essential enzyme glucose-6-phosphate isomerase (GPI) is an example of a moonlighting protein that has been linked to anemia and neurological pathologies due to its multifuncional nature. The dimeric form of GPI catalyzes the reversible conversion of fructose-6-phosphate to glucose-6-phosphate in glycolysis , while tPost-translational modifications involved in the modulation of moonlighting activities have been associated to the development of human disease. Phosphorylation, acetylation, and ubiquitination – among other protein modifications – affect the cellular localization and the binding affinity of proteins to other molecules . For exaDisease can also be triggered by a protein acquiring a novel activity due to a structural perturbation , in which case the new function is known as “neomorphic moonlighting function” . For exaStaphylococcus aureus, Helicobacter pylori, Haemophilus influenza and Neisseria meningitides, among others (Moonlighting proteins are not relevant to disease only due to their malfunction in humans, but also due to their role in pathogenesis and interaction with a human host . Intraceg others .Huge amounts of data are being produced from experimental and computational analysis in functional genomics and systems biology, making it urgent to unify definitions of protein multifunctionality in a more systematic manner. Recent advances in large-scale genetic and genetic-interaction screens , proteomIn this context, we note that certain features are good descriptors of the types of protein multifunctionality. Structural similarity, biological relatedness, structural autonomy, and physiological relevance of the different functions of a single protein will certainly influence the evolutionary scenarios and outcomes involved in the gain and loss of multifunctionality. For example, the lesser the structural autonomy between the activities in a protein, one would expect evolution of more similar molecular functions or the appearance of stronger evolutionary trade-offs. Duplication and divergence of the gene copies of multifunctional proteins with evolutionary trade-offs could result in single monofunctional proteins . UnrelatPleiotropy, multi-domain proteins, promiscuity, and moonlighting are different biological phenomena related to protein multifunctionality. The description of different recurrent phenomena has been – and will certainly continue to be – useful to the understanding of protein multifunctionality. However, as often seen in biology, huge diversity in the mechanisms and related phenomena will most likely exist. Moreover, the terms and limits we assign to each phenomenon are also prone to evolve, thus developing a natural lack of consensus. Therefore, it is possible that an alternative, straightforward, annotation of protein multifunctionality may enable future endeavors to comprehend the diversity, extent, and significance of protein multifunctionality in a more systematic manner.AE-C, EC-B, LN-G, and AD wrote the manuscript, read, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Streptomyces coelicolor is a gram-positive soil bacterium which is well known for the production of several antibiotics used in various biotechnological applications. But numerous proteins from its genome are considered hypothetical proteins. Therefore, the present study aimed to reveal the functions of a hypothetical protein from the genome of S. coelicolor. Several bioinformatics tools were employed to predict the structure and function of this protein. Sequence similarity was searched through the available bioinformatics databases to find out the homologous protein. The secondary and tertiary structure were predicted and further validated with quality assessment tools. Furthermore, the active site and the interacting proteins were also explored with the utilization of CASTp and STRING server. The hypothetical protein showed the important biological activity having with two functional domain including POD-like_MBL-fold and rhodanese homology domain. The functional annotation exposed that the selected hypothetical protein could show the hydrolase activity. Furthermore, protein-protein interactions of selected hypothetical protein revealed several functional partners those have the significant role for the bacterial survival. At last, the current study depicts that the annotated hypothetical protein is linked with hydrolase activity which might be of great interest to the further research in bacterial genetics. Streptomyces coelicolor A3(2) is one of the best studied representatives amongst other members of the genus Streptomyces [Streptomyces coelicolor A3(2) has the potential to make such secondary metabolites, and metagenomic analysis has revealed it has tremendous quantities of significant biosynthetic gene sets [ptomyces . Like thicolor A3 is one optomyces . They haptomyces . This baptomyces . Some ofene sets ,8.Streptomyces coelicolor [S. coelicolor are necessary for some secondary metabolite production [Streptomyces coelicolor has three such genes, cloY is one of them [These characteristics have elicited biotechnological interest in this bacterium and have aroused the interest of researchers in the past few years to investigate the different proteins involved in secondary metabolites production. As an example, it is recently found that albaflavenone, germicidin A, and chalcone are produced during germination of elicolor  and the elicolor . Anotheroduction . Strepto of them . When al of them .Streptomyces coelicolor A3(2) is reported to have 8,667,507 base pair linear chromosome, containing the largest number of genes so far discovered in a bacterium [acterium . The genacterium . HoweverS. coelicolor for an improved understanding of the protein. Subcellular localization, secondary structure, and active site were predicted and protein-protein interaction was analyzed. Further, a good quality model of the SCO0618 was tried to generate using homology modeling techniques.With the advancement of in-silico analysis, it became easier to annotate function to a hypothetical protein using various bioinformatic tools. Thus, the purpose of this study was to assign structural and biological function to the hypothetical protein SCO0618 (accession No. NP_624929.1) of https://www.ncbi.nlm.nih.gov/) against nonredundant and SwissProt [The sequence information of the hypothetical protein (NP_624929.1) was retrieved from the NCBI database. The sequence was then collected as a FASTA format sequence and submitted to several prediction servers for the in-silico characterization . To get wissProt  databasewissProt .https://www.ebi.ac.uk/Tools/msa/muscle/) [http://www.clcbio.com). The phylogeny analysis was done by using the webtool Phylogeny.fr (http://phylogeny.lirmm.fr/) [Multiple sequence alignment was performed using MUSCLE server of EBI (muscle/)  and visurmm.fr/) .http://web.expasy.org/protparam/) [The physical and chemical properties including molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient, estimated half-life, total number of negatively charged residues (Asp + Glu), total number of positively charged residues (Arg + Lys), instability index, aliphatic index, and grand average of hydropathicity (GRAVY) predictions, etc. were performed by the ProtParam (tparam/)  tool of Subcellular localization was predicted by CELLO . ResultsSearch carried out at conserved domain database  , for conPSI-blast based secondary structure Prediction (PSIPRED)  and selfhttps://toolkit.tuebingen.mpg.de/tools/hhpred) [The three-dimensional structure was predicted by HHpred server (/hhpred)  of the M/hhpred) .https://servicesn.mbi.ucla.edu/PROCHECK/) [http://nihserver.mbi.ucla.edu/Verify_3D/) [https://servicesn.mbi.ucla.edu/ERRAT/) [Finally, PROCHECK (OCHECK/) , Verify3ify_3D/) , and ERR/ERRAT/)  were usehttp://sts.bioengr.uic.edu/castp/) [The active site of the protein was determined by the Computed Atlas of Surface Topography of Protein (CASTp) (/castp/)  which prThe work-flow of the study was shown in The Blastp result against non-redundant and SwissProt database showed homology with other hydrolase and sulfurtransferase proteins  and 3. MEscherichia coli (in vivo). And the molecular formula of protein was identified as C2119H3350N636O643S6.The protein consist of 461 amino acids, among the most abundant were Ala (92) followed by Val (51), Arg (42), Gly (41), Leu (40), Asp (32), Glu (30), Pro (26), Thr (21), Ser (19), His (17), Phe (11), Ile (10), Tyr (8), Trp (6), Asn (5), Gln (4), Met (4), and Cys (2). The calculated molecular weight was 48216.15 Da and theoretical pI was 5.27 indicating the protein to be negatively charged. Total number of positively charged residues (Arg + Lys) and the total number of negatively charged residues (Asp + Glu) were found to be 62 and 42, respectively. The computed instability index was 32.67 classifying the protein as stable one. Aliphatic index was 94.34 which gives an indication of proteins’ stability over a wide temperature range. The grand average of hydropathicity (GRAVY) was 0.053. Positive value of GRAVY indicates that the protein is polar. Protein half-life computed was found to be 30 h in mammalian reticulocytes (in vitro), >20 hours in yeast (in vivo), >10 h in The conserved domain search tool revealed that this hypothetical protein sequence was found to have two domains, MBL-fold metallo-hydrolase domain (accession No. cd07724) and rhodanese homology domain (RHOD) (accession No. cd00158). The result was also checked by two other domain searching tools namely InterProScan and Pfam. Pfam server predicted the Rhodanese like domain at 362–444 amino acid residues with an e-value of 2.3e-05 and Metallo-beta-lactamase superfamily domain at 16–171 amino acid residues with an e-value of 4.7e-07. InterproScan server predicted Rhodanese like domain at 249–454 amino acid residues and Metallo-beta-lactamas domain at 13–180 amino acid residues. Rhodanese like domain, lactamase-B and MreB-Mbl domains were also found by Motif server. Superfamily search revealed present of Metallo-hydrolase/oxidoreductase and rhodanese/cell cycle control phosphatase superfamily. β-Lactamases can catalyze the hydrolysis of a wide range of β-lactam antibiotics. Members of the MBL-fold metallohydrolase superfamily are mainly hydrolytic enzymes which carry out various biological functions. Both the active and inactive version of the Rhodanese domain in a variety of proteins including certain protein phosphatases, sulfide dehydrogenases, certain stress proteins and sulfuryl transferases, where they are thought to play a regulatory role in multidomain proteins . All theSubcellular localization analysis was predicted by CELLO and validated by PSORTb, SOSUIGramN, and PSLpred. The subcellular localization of the hypothetical protein was predicted to be a cytoplasmic protein . Absent The SOPMA secondary structure prediction server analysis revealed the proportions of alpha helix, beta turn, extended strand, and the random coil of protein as 31.89%, 9.11%, 18.87%, and 40.13%, respectively .Alicyclobacillus acidocaldarius protein with β-lactamase and rhodanese domains. This protein is a homo-dimer which has two chains (chain A and chain B) and the chain A was used as template to build the model. Validation of the predicted three-dimensional model was assessed by PROCHECK through Ramachandran plot analysis, where the distribution of φ and ψ angle in the model within the limits are shown (Prediction of 3D structure was done by HHPRED server. The server predicted 3D structure of the protein with 100% identity with the highest scoring template (PDB ID: 3TP9_A) . 3TP9 isre shown , Fig. 6.STRING 10.0 search was carried out for the identification of possible functional interaction network of the protein . The ideThe predicted active site of the protein found that 42 amino acids are involved in potent active site . The besS. coelicolor. So, extended in-vitro research has to be carried out to experimentally validate the possibilities shown here and to find out the proteins’ role in biotechnology.The identification of protein functions is fundamental for the understanding of biological processes. So, this study was aimed to determine the structural and biological function of SCO0618, a hypothetical protein of this bacterium through an in-silico approach. The identified protein revealed several characteristics such as cytoplasmic nature, hydrolytic enzymes containing domain presence, ‘(TIM)-barrel’ fold presence, and hydrolase activity emphasize the significance of this protein. These characters of the hypothetical protein will strengthen basic knowledge on


Quantitative measurement of intramolecular and intermolecular interactions in protein structure is an elusive task, not easy to address experimentally. The phenomenon denoted ‘energetic coupling’ describes short- and long-range interactions between two residues in a protein system. A powerful method to identify and quantitatively characterize long-range interactions and allosteric networks in proteins or protein–ligand complexes is called double-mutant cycles analysis. In this review we describe the thermodynamic principles and basic equations that underlie the double mutant cycle methodology, its fields of application and latest employments, and caveats and pitfalls that the experimentalists must consider. In particular, we show how double mutant cycles can be a powerful tool to investigate allosteric mechanisms in protein binding reactions as well as elusive states in protein folding pathways.  A rigorous description of any physical system demands the detailed characterization of its structural elements, as well as the quantitative investigation of its energetic components. In the case of proteins, whilst an arsenal of different experimental techniques has been developed to address the structural features of these molecules ,2,3, theHow can we approach this problem? The so-called ‘double mutant cycles’ approach is a powerful method to measure the strength of molecular contacts between interacting side-chains ,6,7,8,9.In his seminal analysis of the theory of heredity , WilliamP-XY→P-Y. Analogously, the change in free energy associated with the residue Y will be equal to ΔΔGP-XY→P-X. If the mutations are independent of each other, then a double mutant of X and Y will display as: It is of critical importance to define quantitatively the extent of coupling between two interacting amino acids as well as to introduce a feasible methodology to measure it, the double mutant cycle ,6,7,8,9.XY measures therefore the energetic strength of the interaction between positions X and Y and, when its value is different from zero, it represents the interaction (or coupling) energy between the two residues. A scheme summarizing the basic principles and equations of double mutant cycles is reported in Conversely, a non-zero value of: N-N′ is in fact the sum of different components [cov; (ii) that arising from the changes in noncovalent bonds located at the site of mutation, ΔGnoncov; (iii) any additional free energy changes related to a reorganization of the protein, ΔGreorg; and (iv) changes in solvation energy, ΔGsolv. When performing folding and/or binding experiments, the ΔGcov is conserved in all states and, therefore, it cancels out in the analysis. On the other hand, ΔGnoncov is generally assumed to be the dominant contributor to ΔGN-N′. However, the contribution of the four ΔG values may differ for different states. For example, the values of ΔGreorg and ΔGsolv may be different in native and denatured states for a folding reaction, such that ΔGN-N’-ΔGD-D′ will report not only on the energetics at the mutation site ΔGnoncov. This effect will complicate the analysis of the experimental data. A similar situation may occur for bound and free states for a bimolecular interaction.When introducing an experimental technique, it is of particular importance to highlight its major advantages, as well as the caveats and pitfalls. Thus, what is the advantage to perform double mutant cycles as compared to single site mutagenesis? When mutating an amino acid side-chain into another, the change in free energy upon mutation ΔGmponents : (i) TheTo exemplify this scenario, let us consider the binding between a protein and a ligand: N, the folding stability of the protein could be affected, such that its native state is destabilized. Thus, by considering the scheme:When and if a site-directed mutation is introduced in PDK would be affected by the presence of a linked equilibrium, such that:In the latter case, binding is considered in conjunction with a large conformational change, such as the (un)folding of the protein. In this case, it is clear that the apparent reorg and ΔGsolv are dominant over the value of ΔGnoncov, and any analysis of the local effect of the mutation is prevented.Thus, if the mutation affects the stability of the native state such that a significant population of the protein is shifted towards the denatured state, the apparent binding constant is also affected; even in cases in which the mutation would not perturb the binding interface at all. In those cases, ΔGBy considering these premises, it is clear that the double mutant cycle, which relies on calculating the difference between the effects on the double mutant over the two single variants, is extremely powerful. In fact, even in cases in which the mutation results in structural shifts of the protein, as exemplified above, those found in the single mutants are usually present in the double mutant and, therefore, their energetic effects in the double mutant cycle are cancelled out ,17,18. Treorg and ΔGsolv for conservative mutations are more likely to be lower than the value of ΔGnoncov, which, as briefly discussed above, tends to simplify the analysis. Additionally, since the coupling free energy arises from the difference between the value obtained for the double mutant minus the sum of the values obtained from the single mutants, it might be difficult to calculate a reliable value of ΔΔΔGXY when the effect of one mutation is very large such that limitations in the biophysical method result in a large experimental error. For example, isothermal titration calorimetry usually works best in the range 100 nM–1 μM and mutations resulting in a 100-fold destabilization will lead to a large experimental error. Due to the complexity of site directed mutagenesis, it is important to stress that conservative mutations should be used to probe binding or folding . Values reorg and ΔGsolv as low as possible. Consequently, since mutations of large hydrophobic side chains may lead to very large ΔGreorg and ΔGsolv, these are not well suited for double mutant cycles. Thus, in analogy to the Φ value analysis in protein folding studies [Over and above the theoretical basis of this type of analysis, there are also some practical issues that demand additional consideration. In fact, it is a good advice to choose the mutations properly. Since the double mutant cycle technique is aimed at measuring the free energy of interactions between two residues, it is recommended to keep the two terms ΔG studies , it is rAfter the mutations are introduced, it is critical to measure the changes in free energy accurately. This task may be achieved by employing the standard spectroscopic methodologies that are classically used in affinity and/or stability measurements for proteins. These techniques may span from equilibrium titration with spectroscopic techniques such as absorbance, fluorescence, circular dichroism, etc., to other methods, such as calorimetry.The invention of double mutant cycles was associated with the quantification of intramolecular interactions in proteins. In their pioneering work on protein engineering Winter, Fersht, and co-workers observed that, in the case of a tyrosyl-tRNA synthetase, a variant in which a threonine residue was mutated into proline increased the affinity of the enzyme for ATP . By makiFollowing this original work, several protein systems of known structure have been subjected to double mutant cycles . In thes10− helices folding [m values, a series of double mutants were produced and characterized. Interestingly, the folding characterization of these mutants allowed mapping the presence of distinct non-native interactions in the denatured state, as well as in measuring their strength in terms of ΔΔΔG. A similar analysis of structure in the denatured state was also performed on the C-terminal domain of nucleophosmin by conducting folding and unfolding experiments on several site-directed variants at different experimental conditions [Over and above the cases in which double mutant cycles have been employed to address intramolecular interactions in known structures, it is important to note that, in some context, the approach can be used to investigate elusive states that are difficult to characterize experimentally. The KIX domain is a globular domain that is a part of a large coactivator protein called CBP . The thr helices . A detai)folding . To addr)folding . In partnditions .allo-) than those in the binding site in regulating function [Ever since the pioneering work of Kendrew and Perutz, it became clear that a deep understanding of proteins’ structure and function would depend on the rigorous description of their dynamic properties. In fact, only the identification of the R and T quaternary states of haemoglobin provided a structural framework to understand its cooperative and allosteric behavior, pinpointing the role of residues ‘other’ (from the Greek function . In thisfunction ,32. WhenThe interaction between barnase and barstar represents a paradigm example of the use of double mutant cycles to understand protein-protein binding. In this case, several site-directed mutants were analyzed providing a detailed description of the energetics of such interactions ,33. In pThe double mutant cycle approach has been subsequently employed on other protein systems, with the specific aim to quantify the interaction networks between interacting protein systems. Relevant examples include coiled-coil XGCN4-p1 , as well0 to Abu substitution was observed in both PDZ2 from PTP-BL and PDZ3 from PSD95 (Out of the examples highlighted above, it is particularly interesting to discuss some of the conclusions that have been drawn on PDZ domains ,41,42,43om PSD95 , althougThe complex nature of the functions of proteins often demands their structure to be composed of multiple domains. In these cases, it is very frequent that function and binding may depend on the interdomain architecture and dynamics, such that the individual domains in isolation display different features compared to when they are present in multidomain constructs. While this concept is widely accepted, our detailed knowledge about the effects of supertertiary structure in folding and function is still limited. In this context, double mutant cycles proved very useful. An extensive double mutant cycle analysis of a PDZ domain, both in isolation and in the context of a supramodule comprising the PDZ domain, an SH3 and a GK domain, showed that allosteric networks are highly sensitive to the supertertiary structure . In partEscherichia coli chaperonin GroEL, first introduced this approach [The double mutant cycle methodology generally relies on the design, production and characterization of protein mutant variants. Nevertheless, an interesting application of this method is an in silico analysis only. Horovitz and co-workers, who investigated the interaction between two cysteine residues, namely Cys137 and Cys518, in the approach . By perfIn a seminal study published in Science, Lockless and Ranganathan extended the approach to a larger scale . By usinA very interesting application of double mutant cycles has been recently implemented by studying the formation of protein-protein interactions via native mass spectrometry . The intDouble mutant cycles by mass spectrometry have been successfully employed to study the binding between the proteins E9 and Im2  and, morThe double mutant cycles methodology represents a very powerful technique offering the tantalizing possibility to address directly the strength of pairwise interactions in proteins. As exemplified in the case of PDZ domains, the analysis of the sign of the observed energetic coupling may be used to describe protein selectivity in conserved homologous protein-protein interaction domains. Furthermore, in conjunction with other techniques, it may represent a future tool to address ambitious tasks, such as the understanding of structure of metastable states as well as the description of aggregation phenomena. With the specific aim to help the experimentalist, we have recapitulated here the key features, along with the associated caveats and pitfalls, as well as some key examples of applications.


Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein–protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/.Structural insight of the protein–protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein–protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein–protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called  These large number of experimentally verified as well computationally predicted interactions are collected and systematically stored in various PPI databases, such as molecular interaction database (MINT)13, the Human Protein Reference Database (HPRD)14, STRING15, database of interacting proteins (DIP)16, the protein interaction database (IntAct)17, etc. These databases contain important information for thousands of interactions, which are regularly used in network based ‘omics’ data analysis. However, most of these interactions lack detailed structural information and thereby making them therapeutically non-viable targets. Under this scenario, computational approaches capable of generating reliable model of protein complexes using protein–protein docking tools can play an important role in complementing the experimental initiatives. However, as these complexes are generated using predictive approaches, objective tests and evaluation tools are required to determine their reliability.Knowledge about protein–protein interactions (PPI) is critical to understand the molecular mechanisms of biochemical processes and cellular pathways. Advent of high-throughput techniques has enabled genome-wide identification of PPIs for quite a few model organisms38. Similarly, numerous scoring functions and schemas were developed for improved prediction of protein–protein interfaces out of which only a few could be mentioned due to space restraints74. Despite all these works, still gap is prevailed between the optimally scoring solutions and the biologically active complexes78.PPI interfaces have been studied extensively to analyze and understand the critical characteristics features that provide affinity, stability and specificity of the complexes. Properties like accessible surface area (ASA) and buried surface area (BSA), interface residue conservation, hydrogen bonds, electrostatic and hydrophobic interactions play major roles in determining the nature of the protein interfaces. Knowledge of interface characteristics has been studied and was further used in identification of protein interfaces or to predict binding specificity86. Our exhaustive testing and benchmarking suggest very good performance of our SVM models in distinguishing native and non-native like interfaces for homo and hetero complexes. We also implemented this approach in validating protein interactions, which are proven via experimental findings but the three-dimensional (3D) structure of the complexes and the subsequent interface(s) are yet to be discovered. Finally, we provide a web server platform namely PCPIP to predict whether the interacting interface of a given protein–protein dimer complex significantly resembles known protein interfaces.Here, we attempt to utilize protein–protein interface properties to establish discernible differences between native-like protein complexes from the non-native ones. We fed the interface properties to a support vector machine (SVM) based classification scheme and trained models to successfully differentiate between native and non-native like complexes derived by protein–protein docking. Machine learning based techniques have been used previously to analyze and predict protein–protein interactions87, which were categorized into homo (560) and hetero (429) dimers. Exhaustive redundancy check and filter was applied to these datasets using the CD-HIT88 and BLASTp89 programs so that no protein complex (both chains) is more than 40% identical to any other complex within the homo or hetero categories, respectively (Table 989 protein–protein dimer complex structures have been derived from protein data bank (PDB)ly Table . 371 and90. Initially, this dataset contained 95 protein–protein hetero complexes (holo-complexes) and their respective monomer structures. However, these 95 complexes were screened to identify native and non-native like interfaces via fraction of conserved native contacts (FNAT) based definition (please see later for details) and we could retrieve docking decoys that passed the FNAT filtration criteria for 32 such complexes . This dataset of complex (holo) and non-complex (apo) forms of proteins were collected from a recent report from Viswanathan et al.es Table . This daes Table . Similar92 . Further details about the dataset collection are provided in supplementary information file.We have also created a negative dataset of 130 protein–protein complexes and subsequent interfaces for proteins that are not supposed to interact according to the Negatome database15 were also collected individually from the PDB database. Protein–protein interaction pairs for which individual monomer structures are available were selected randomly and were further utilized to generate probable dimer structures using protein–protein docking via the PatchDock program93. 32 such docked model complexes  were generated and further evaluated by our machine learning based protein–protein interface prediction algorithm.Protein and/or domain structures for which experimentally validated protein–protein interaction is reported in the STRING database 10.5Known protein dimer complexes were utilized to create native and non-native like interfaces. Constituent monomers of the dimer complexes were separated and docked using the PatchDock protein–protein docking software and the resultant docked solutions were screened to create native and non-native like complexes based on the following criteria. Fraction of conserved native contacts (FNAT) usually provides a fraction of the common residues at interface of docked interface with respect to that from the original complex whereas interface root mean square deviation (iRMSD) compares the actual orientation of the interface forming residues between the docked and original complexes. Hence, FNAT and iRMSD complement each other in their approach of evaluation of a predicted interface.Fraction of conserved native contacts (FNAT) is the number of native (correct) residue–residue contacts in the docked complex divided by the number of contacts in the original complex. So, FNAT reflects the overlap between the original and docked complex interfaces while a FNAT value 1.0 indicates complete overlap between the two. In our training and testing models, the original complex and the docked complexes with FNAT > 0.8 were regarded as true or native like complexes for each dimer whereas false/non-native like complexes were identified using four separate FNAT thresholds, (a) FNAT ≤ 0.25 (highly distinguishable from the native like complexes), (b) FNAT > 0.25 and ≤ 0.5, (moderately distinguishable from the native like complexes) and (c) FNAT > 0.5 and ≤ 0.8 (weakly distinguishable from the native like complexes), and (d) FNAT ≤ 0.8 (mixed), respectively.Similar to the training–testing dataset, we have used the same four different FNAT thresholds to define the non-native like complexes for the benchmarking dataset  also.Interface root mean square deviation (iRMSD) is the root mean square deviation between the residues of both chains at the interface region. Similar to the FNAT based protocol, native and non-native sets were also identified based on iRMSD where original complex along with one docked complex with iRMSD < 5 Å with respect to the original complex were regarded as native like complex for each dimer whereas non-native like complexes were identified with four different thresholds, (a) iRMSD ≥ 15 Å (highly distinguishable from the native like complexes), (b) iRMSD ≥ 10 Å and < 15 Å, (moderately distinguishable from the native like complexes) and (c) iRMSD ≥ 5 Å and < 10 Å (weakly distinguishable from the native like complexes), and (d) iRMSD ≥ 5 Å (mixed), respectively.Native and non-native like complexes Table  categori95 was used to calculate structural and chemical properties of the macromolecular interfaces such as accessible/buried surface area, free energy of dissociation, presence/absence of hydrogen bond and salt bridges, etc. Further details are provided in supplementary information file. Please check Table PISA software96 was used to build the classifier models.Support vector machine (SVM) is a supervised model used for classification by analyzing given features with associated learning algorithms. Here, we have used a radial basis kernel function (RBF) via 100-fold cross validation method where 100 times randomly selected 80% of the whole data has been used to train the SVM model and the rest 20% has been used as test data. Performance on the test models was measured using average of the hundred random trials. LibSVMFor both FNAT and iRMSD based categorizations, 100 fold randomized selection of the training (80% data) and testing (20% data) followed by SVM based classification trials were performed for each of the native and non-native complex threshold criterion. Figure We have used separate set of complexes in order to check the efficacy of our SVM models in correctly identifying native and non-native complexes. Previously described native and non-native complexes within the Apo-Holo validation set were tested against the 100 SVM models built with 80% training data. Based on the definition of native and non-native like complexes using FNAT and iRMSD categorizations, each set containing highly, moderately, weakly, and mixed distinguishable non-native like complexes from the native like complexes were tested against the corresponding 100 models of the training data sets. Average sensitivity, specificity, precision, and F1 score from the 100 runs was recorded.97 where 10 different composite scoring functions such as ZRANK, ZRANK2, ROSETTADOCK, PYDOCK, FIREDOCK, PISA score, CP_PIE, and SIPPER were applied on each complex and the corresponding scores were obtained. Individual receiver operating characteristic (ROC) plot was created by calculating the true positive rate  and false positive rate  for each scoring function.The separate datasets of Apo-Holo heterodimers using the mixed distinguishable non-native like complex thresholds [FNAT-native (FNAT > 0.8): 64 and non-native (FNAT ≤ 0.8): 320 and iRMSD-native (iRMSD < 5 Å): 134 and non-native (iRMSD ≥ 5 Å):  680] Table  were use92 were subjected to PatchDock to generate biologically non-feasible protein complexes. 5 docked decoy complexes were collected for each query pairs following the criteria described in supplementary information file. This dataset  was used to validate the false prediction rate of the SVM classifier.Additionally, 130 protein pairs extracted from the Negatome databaseIn absence of reference complex structure, we selected top ten docking solutions according to the PatchDock docking score for each 31 protein–protein interactions enlisted within the STRING dataset, which was further subjected to interface feature generation followed by SVM prediction using the heterodimer training model.Standard performance metrics such as sensitivity, specificity, precision, F1 score, Mathew’s correlation coefficient (MCC), etc. were calculated along with the ROC statistics to measure the performance of our models in different scenario. Please see supplementary information file for more details.http://www.hpppi.iicb.res.in/pcpip/ and is developed on PHP and CGI-PERL platform.We have developed a web based server named, “PCPIP (Protein Complex Prediction by Interface Properties)” where the classification and prediction schemes were embedded within a web module. Given a protein–protein complex, the PCPIP server would be able to predict whether the interacting interface resembles significantly with known protein interfaces. The server is available via The server has two input options, single and batch mode, respectively. In the single mode option, single protein–protein complex (homo or hetero dimer) file saved in standard PDB format can be uploaded to check whether the interacting interface, if there is any, resembles the interfaces extracted from the native protein–protein complexes or not.SVM models for both homo and hetero dimer along with their interface features are kept as background search models against which the uploaded protein complex can be testified based on FNAT and iRMSD criteria.p ≤ 0.01) differences between the native and non-native like complexes, categorized either by FNAT or iRMSD criteria were compared  was used for classification with 100-fold cross validation approach. Table Our SVM models performed quite well and yielded very good performances with all categories of non-native like protein–protein complexes categorized by both FNAT and iRMSD definitions Table . Utiliza93 and native and non-native like complexes were generated using various ranges of FNAT and iRMSD based criteria. Original and all docking solutions with FNAT ≥ 0.8 were regarded as native like complexes whereas solutions with FNAT ≤ 0.25, FNAT ≥ 0.25 and ≤ 0.5, FNAT ≥ 0.50 and ≤ 0.80, and FNAT < 0.80, respectively were considered as non-native like complexes. Similarly, Original and all docking solutions with iRMSD < 5 Å were regarded as native like complexes whereas solutions with iRMSD ≥ 15 Å, iRMSD ≥ 10 Å and ≤ 15 Å, iRMSD ≥ 5 Å and ≤ 10 Å, respectively were considered as non-native like complexes. Native and non-native like complexes from this validation datasets were mixed together and the classifier was asked to differentiate the native and non-native ones correctly based on their interface features. Performances of the classifiers were measured using standard parameters. Table As mentioned before, for benchmarking and validation we have used Apo-Holo-validation dataset comprising of 32 (FNAT) and 68 (iRMSD) dimer complexes for which individual monomer structures are also available separately. Monomers were docked using the PatchDock protein–protein docking software97 where 10 different composite scoring functions were applied on each complex and the corresponding scores are obtained. ROC plots were created  more than 90% of the Negatome complexes were predicted as false by FNAT model where iRMSD model is taking 0.85 probability threshold to reach 90% accuracy Fig. .Figure 387. Docking solution with highest probability threshold score was considered as the top ranked prediction based on FNAT and iRSMD models, respectively. Distributions of probability score thresholds within the FNAT and iRMSD based top ranked prediction and the PatchDock based top solutions are plotted  is developed to predict whether the interacting interface of a given protein–protein dimer complex resembles significantly with known protein interfaces. PCPIP predicts whether submitted interface(s) is likely to be native like or not. This prediction server would be particularly useful in identifying correct docking poses out of numerous solutions that standard protein docking programs offer. Figure A web based server platform namely Protein–protein interactions (PPI) are extremely crucial for intra and inter-cellular functions and inter-molecular connectivity. Due to significant improvement in experimental techniques, large numbers of protein structures are available now. Similarly, improved high-throughput studies like yeast two-hybrid system (Y2H), mass spectrometry (MS), tandem affinity purification (TAP) have identified numerous PPI that are previously unknown. However, these efforts are expensive, significantly time consuming, and have covered only a small portion of the complete PPI networks. Hence, the need for computational techniques has been increased to augment experimentally identified PPI and provide a larger repertoire of cellular PPIs. Another daunting challenge is to physically construct and map these large numbers of PPI complexes and identify the mode of interaction. Computationally generated PPI complexes could be quite useful and may expedite the experiments that are required to validate the binding interface and critical residues for the interactions. However, these predictions are generally error prone and therefore, need to be validated very carefully. Even though various protein–protein docking programs are available, methods for systematic evaluation of the predicted PPI complexes are limited.In our effort, we tried to study the known protein–protein interface properties and utilize the knowledge of native PPI interface properties to a standard machine learning technique, support vector machine (SVM) to delineate native-like complexes from non-native like complexes. Interestingly, this simple approach turned out to be quite effective as suggested by very good performance metrics of our SVM models in distinguishing native and non-native like interfaces for homo and hetero complexes. Our exhaustive testing and benchmarking exercises using a completely non-redundant training–testing dataset and various degrees of distinguishing thresholds between native and non-native like complexes suggest a very high accuracy of the models. The categorizations of non-native instances were implemented using strict, moderate and lenient definitions to consider many intermediate docking models with partial overlap with native interfaces. Performance of the models demonstrate high efficiency of the approach in distinguishing native like complexes from non-native like complexes having high, moderate and low overlap with the actual interfaces Table .We also evaluated the performance of our method under further validation test where it was subjected to differentiate native like complexes from non-native complexes prepared from individually solved monomer structures of known complex structures. This apparently difficult dataset overrules the possibility of the monomers structures to be primed to form native complexes when subjected to protein docking approaches. In this Apo-Holo validation test, our method performs reasonably well, especially for the highly distinguished native and non-native like complexes Table . HoweverWe applied this approach in real scenario where protein interactions information is proven via experimental findings but the three-dimensional (3D) structure of the complexes and the subsequent interface(s) are yet to be discovered. We generated such complexes using 32 high-confidence STRING protein–protein interactions and identified the most likely interaction modes for 12 complexes. Such filtered structural models could be very useful for designing subsequent experiments to validate the actual mode of interaction even without attempting to solve the entire complex structures.Finally, we converted the methodology into a user-friendly, easy-to-use web server platform namely PCPIP to predict whether the interacting interface of a given protein–protein dimer complex significantly resembles known protein interfaces. We believe that this resource could be a useful tool for biologists to evaluate protein–protein docking derived results and gain helpful knowledge to design confirmatory experiments.Supplementary Information.