Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs.eRankPPI, an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRankPPI employs multiple features including interface probability estimates calculated by eFindSitePPI and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRankPPI consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %.To address this problem, we developed eRankPPI was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi. Most proteins work by interacting with other proteins to fulfill their molecular functions, therefore, quaternary assemblies are the key components of the vast majority of biological processes. Consequently, the structural characterization of protein-protein complexes provides valuable insights into protein function and association mechanisms, immensely contributing to the understanding of cellular interaction networks. The knowledge of atomic-level details of protein-protein interactions (PPIs) is required for a number of practical applications, for instance, it is critical for the design of therapeutics targeting protein interfaces , 2. X-raQuaternary structure modeling to find the best relative orientation of monomers forming a stable complex can be performed using template-based or template-free techniques. Template-based methods use the similarity to known complex structures to model the interaction between a given pair target proteins. This strategy involves superposing target proteins onto the identified templates using either global or interfacial structure alignments . For insTo address the problem of model scoring, the prediction of protein quaternary structures is often supported by a variety of experimental and computational data –30. SeveInteraction symmetry is another commonly used form of constraints to model homo-oligomeric complexes. Symmetry is a prevalent feature of the global arrangement between subunits in homo-oligomer complexes formed by two or more identical protein chains. Homo-dimers are important parts of biochemical pathways that are found to occur more frequently than by chance . ApproxiIn recent years, a two-stage ranking strategy has gained significant attention. Here, a standard protocol is first employed to rapidly scan for putative dimer conformations and to identify a subset of plausible candidates. Subsequently, an additional scoring system is used to re-rank the docked conformations in order to improve the ranking of near-native poses. These methods integrate a variety of features including sophisticated energy calculations, experimental and predicted binding site locations, statistical potentials derived from databases of complex structures, and evolutionary information , 49. ForeRankPPI, an algorithm for the selection of correct docking conformations constructed by protein docking using not only experimental monomer structures but also protein models. A scoring function implemented in eRankPPI combines in a novel way certain features such as residue-level interface probabilities estimated by eFindSitePPI [eFindSitePPI is compared to state-of-the-art scoring methods.In that regard, we developed dSitePPI , proteindSitePPI , and a ndSitePPI , most prdSitePPI –61. In cThe algorithm for the re-ranking of docking models is trained and tested on the BM1905 dataset of 1905 proteins, which was compiled previously to evaluate the accuracy of interface residue prediction . This daZDOCK  version eFindSitePPI [eFindSitePPI exploits a general tendency of the location and geometry of binding sites to be highly conserved in evolutionarily weakly related dimer proteins. It employs a collection of effective algorithms, including meta-threading by eThread [In this study, putative interfacial sites are predicted for the benchmarking receptors by dSitePPI , a recen eThread , structu eThread , and mac eThread . Each reeRankPPI developed in this study employs a series of attributes to re-rank docking conformations, including residue-level interface probabilities, protein docking contact potentials, and energy-based scores. The training and evaluation is performed separately for homo- and hetero-dimers as the modeling of homo-complex structures additionally takes account of symmetry constraints. Individual features are described below.eRankPPI incorporates interface probability estimates for the receptor protein. We use probability scores assigned to each residue in the target protein by eFindSitePPI to estimate the likelihood to be at the protein-protein interface. Interfacial residues in docking models constructed by ZDOCK are identified by iAlign [eFindSitePPI.y iAlign , which uIn addition to the interface scores, we employ a protein docking potential previously developed using a linear programming technique . In thiseRankPPI.Conformational ensembles of putative dimers are constructed by ZDOCK, as described above. The scoring function implemented in ZDOCK is a linear weighted sum of van der Waals attractive and repulsive energies, short- and long-range attractive and repulsive electrostatic energies, and desolvation. The optimal set of weight factors that maximizes the discriminatory capabilities of ZDOCK was obtained by training the scoring function on the Benchmark 1.0 set , followeA (dark gray) and B (light gray) with residues numbered as A1, A2 … A5 and B1, B2 … B5, respectively. A complex shown in Fig. A3 : B4, A4 : B3, A5 : B2, and A5 : B1; the notation Ax : By means that the residue number x in chain A is in contact with the residue number y in chain B where x ≠ y. Next, we divide residue pairs into two sets, S1 and S2, so that S1 contains pairs with x < y and S2 contains pairs with x > y. For the complex shown in Fig. S1 = {A3 : B4} and S2 = {A4 : B3, A5 : B2, A5 : B1}. Finally, the CBS score is calculated as the Jaccard index to measure the similarity between S1 and S2:The vast majority of homo-dimers form symmetric interfaces, therefore, we include the deviation from an ideal point group cyclic symmetry in the scoring function to re-rank the homo-complex models. Specifically, we developed a new metric to measure the degree of symmetry at the protein-protein interface, called the contact-based symmetry score (CBS). Figure Ax : By is considered a match for Ay : Bx. CBS ranges from 1 for perfectly symmetrical interfaces to 0 for completely asymmetrical complexes. For example, CBS scores calculated for homo-dimers shown in Fig. eRankPPI for homo-complexes, whereas four features are used for hetero-dimers.Essentially, the Jaccard index is a ratio of the intersection and the union between the two sets of interacting residue pairs, where eRankPPI is trained and cross-validated on docking ensembles generated by ZDOCK separately for the BM1755C and BM150C datasets. Specifically, we calculate the set of either five (homo-dimers) or four (hetero-dimers) attributes for statistical learning in order to rank individual conformations so that near-native structures are assigned lower ranks compared to decoy complexes. The learning procedure is supervised by an iRMSD-based ranking, where the iRMSD is a root-mean-square deviation from the experimental complex structure calculated over the Cα atoms of interface residues. Consequently, the ranking problem can be formulated as the prediction of iRMSD values from individual attributes using a regression analysis. We note that all benchmarking calculations are carried out using a two-fold cross validation protocol by randomly splitting dataset proteins to avoid memorization effects in machine learning. We tested several linear and non-linear models and found that for homo-dimers, Support Vector Regression, epsilon-SVR, with a radial basis function kernel from the LIBSVM version 3.14 [x is converted to the standard score (Z-score) as follows:σx is the corresponding standard deviation.The scoring function implemented in ion 3.14  yields tion 3.14  for hetefnat and fnon-nat have been used to assess the quality of predicted interface interactions [fnat alone may be insufficient to reliably assess the model accuracy because of possible over-predicted interface contacts, which are revealed by fnon-nat. Because, a single metric is more convenient to evaluate the accuracy of protein docking predictions, we formulated a Pairwise Contact Score (PCS). Similar to the iRMSD, pairs of residues on different chains are in contact if any of their atoms are within 10 Å from each other. PCS employs Matthew’s correlation coefficient (MCC) to evaluate the strength of a correlation between the predicted and actual classes:TP (True Positives), FN  and FP  is the number of correctly predicted, under-, and over-predicted pairwise contacts, respectively. TN (True Negatives) is the number of correctly predicted non-contacting residue pairs. Importantly, PCS considers both the accuracy and error rates, and it is less affected by the imbalanced numbers of positives (pairwise interface contacts) and negatives (non-contacting pairs). Theoretically, MCC ranges from −1 to 1, where 1 corresponds to a perfect prediction and −1 is a perfectly inverse prediction; in practice, PCS scores vary from about 0 to 1.The quality of model dimer structures is assessed using two metrics, iRMSD and a contact-based score. The iRMSD is a standard evaluation measure in CAPRI corresponding to the interface Cα-RMSD between a ligand in the predicted complex and the ligand in the experimental structure upon the superposition of the receptor structures. In iRMSD calculations, interface residues are defined as those having at least one atom within 10 Å from any atom in the other protein chain. In addition to the iRMSD, the accuracy of complex structures can be evaluated at the level of pairwise residue contacts. Previously, ractions . The forProtein docking algorithms typically construct multiple dimer models for a given pair of protein structures. Therefore, a reliable scoring function is critical to rank the predicted models so that near-native structures can be selected from a large set of decoys. In that regard, we evaluate the ranking capability using the following measures:This metric reports the percentage of docking cases for which at least one hit is ranked within the top 10 models. Hits are defined as those conformations having iRMSD below a given cutoff varying from 0 to 15 Å. In addition to the iRMSD, we also calculate the percentage of successful cases using PCS as the hit criterion where the respective cutoff changes from 1 to 0.Hit count gives the average number of hits within the top 10 docking models across the benchmarking dataset. Hits are predictions whose iRMSD is below a given cutoff ranging from 0 to 15 Å. Thus the hit count measures the overall enrichment of the top ranked models with near-native conformations.n conformations, where n changes from 1 to 1000. The acceptance criteria for correct predictions are an iRMSD of ≤2.5 Å, ≤8.5 Å and ≤9.5 Å for experimental structures, high- and moderate-quality models, respectively.The docking success rate is defined as the percentage of targets for which at least one correct model is ranked within the top eRankPPI employs a new measure, called CBS, which quantifies the deviation from an ideal cyclic symmetry using inter-residue contacts rather than purely geometrical features. First, we calculated the distribution of CBS scores across the experimental homo-dimer structures from the BM1755C dataset. Figure eRankPPI employs a different approach. First, it incorporates the deviation from an ideal symmetry as a feature to improve the ranking of near-native models within docking ensembles generated through an unrestricted conformational search. Second, eRankPPI exploits a contacts-based symmetry rather than geometric regularities, which is more suitable for complex assembly using computer-generated monomers whose tertiary structures are somewhat distorted compared to experimental structures. To our knowledge, the pairwise contact-based symmetry is a novel feature used by eRankPPI in the modeling of homo-dimers.These findings encouraged us to use the CBS as one of the features to improve the ranking of homo-dimers. As a matter of fact, the concept of symmetry is widely used to construct homo-dimer complexes. Several protein docking programs were developed to model homo-oligomer structures by performing a systematic space search exclusively for symmetric conformations, e.g., M-ZDOCK , SymmRefeRankPPI, we use interface residue prediction by eFindSitePPI that produces a continuous range of probability estimates over surface residues in target proteins rather than just a binary classification of interacting and non-interacting residues. These probability estimates are used to calculate the cumulative interface score for a given docking model, which is advantageous over the binary classification as it better tolerates a weaker signal from PPI prediction with moderate and low accuracy.The knowledge of PPI sites can be used to improve the success rate in protein docking , 36, 77.eFindSitePPI. The results for BM1755C (homo-dimers) and BM149C (hetero-dimers) are presented in Fig. eFindSitePPI for homo-complexes is notably better than that for hetero-complexes, which are underrepresented in the PDB.Since the quality of predicted binding interfaces is important for the subsequent modeling of dimer structures, we first inspect the distribution of the PPI prediction accuracy across benchmarking datasets. For each protein target, we calculate Matthew’s correlation coefficient between interface residues in the experimental complex and those predicted by eRankPPI from docking ensembles constructed by ZDOCK. Specifically, we divide each dataset based on the MCC of PPI site prediction using a cutoff of 0.3 and compare the ranking capability of eRankPPI. Figure p-values using the Wilcoxon signed-rank test, a non-parametric alternative to the paired Student’s t-test [eRankPPI with a p-value of 0.027. In contrast, a p-value of 0.121 indicates that the selection of near-native models for homo-dimers is less affected by the quality of the PPI interfaces predicted by eFindSitePPI. The main reason for the higher tolerance of inaccurately annotated interface residues for homo-dimers is the additional score, CBS, which helps eliminate the majority of asymmetric decoys.Next, we investigate the effect of the PPI prediction accuracy on the quality of dimer models selected by s t-test . At the eRankPPI, we first re-ranked the top 2000 models assembled by ZDOCK from monomers in their bound conformational state. We use iRMSD and PCS to assess the native-likeness of modeled dimer structures and analyze the results in terms of the percentage of successful cases, the hit count and the success rate. First, we evaluate the ranking capability of eRankPPI compared to ZDOCK and ZRANK against homo-dimers from the BM1755C dataset. Table eRankPPI, at least one model with an iRMSD below 2.5 Å is found within the top 10 ranked conformations for 58.1 % of the benchmarking cases. This performance represents an improvement over ZDOCK and ZRANK, which give the percentage of successful cases of 51.1 and 55.2 % respectively. We also assessed the contribution of the symmetry score to the overall success; removing the symmetry score from the scoring function yields the percentage of successful cases of 56.1 %. Moreover, using PCS with a cutoff of 0.65 as the success criterion, eRankPPI improves model ranking by 17.2 % (8.6 %) with respect to ZDOCK (ZRANK).In order to evaluate the performance of eRankPPI, ZDOCK and ZRANK is shown in Fig. eRankPPI is higher than that for ZDOCK and ZRANK over a range of iRMSD and PCS threshold values used to define correct predictions. The same holds true for the hit count and the success rate; for instance, Fig. eRankPPI yields an average number of 2.21 hits per target within the top 10 ranked predictions at an iRMSD cutoff of 5 Å, whereas the hit count for ZDOCK and ZRANK is 1.60 and 1.68, respectively. Model ranking by eRankPPI is consistently better than that by ZDOCK and ZRANK not only for the top 10 but also considering lower ranks, which can be evaluated using the success rate shown in Fig. eRankPPI more reliably identifies near-native models of homo-dimer complexes across docking ensembles.Further comparison of the overall performance of eRankPPI, ZDOCK and ZRANK for the BM155 dataset. The success rate of ZDOCK, ZRANK and eRankPPI against BM155C targets is 53.7, 67.1 and 58.4, respectively. The analysis of the quality of predicted binding interfaces on the docking accuracy presented above indicates that eRankPPI is sensitive to inaccuracies in PPI annotation for hetero-complexes. Therefore, we use a subset of 58 targets selected from BM155 whose interface residues are predicted with an MCC of ≥0.3; we refer to this dataset as BM58. Figure eRankPPI for the BM58C dataset is better than that of ZDOCK and ZRANK. For example, Table eRankPPI, ZDOCK and ZRANK is 84.4, 67.8 and 75.9 % respectively. Similar improvements are observed for the PCS used as the success criterion in Fig. eRankPPI improves the ranking by ZDOCK (ZRANK) by 13.8 % (5.2 %). We note that in contrast to homo-dimers, eRankPPI does not improve model ranking for those targets whose binding interfaces are poorly annotated, therefore, a sufficiently high accuracy of PPI residue prediction is critical for the construction of hetero-dimer structures.Next, we turn over to hetero-dimers and compare the performance of Genome-wide determination of protein interaction networks is an important step in the elucidation of cellular regulatory mechanisms , 81. AltHere, we investigate how different docking scoring strategies cope with inaccuracies in the computer-generated models of query proteins. Undoubtedly, docking using protein models represents a difficult task and the quality of the resulting dimers cannot be higher than the quality of monomer structures. An iRMSD cutoff of 2.5 Å is widely accepted as a criterion for near-native models using experimental structures. However, different threshold values need to be used to evaluate dimer structures assembled from computer-generated models in order to account for distortions in individual monomers. Therefore, we first calculated the distribution of hits with an iRMSD of 2.5 Å across the top 2000 docking models constructed by ZDOCK using experimental monomer structures. A black dashed line in Fig. eRankPPI, ZDOCK and ZRANK on the BM1755H and BM1755M datasets of homo-dimers. Table eRankPPI places at least one model with an iRMSD of ≤8.5 Å (≤9.5 Å) within the top 10 conformations for 42.7 % (42.3 %) of the high- (moderate-) quality models. This performance represents a significant improvement over both ZDOCK and ZRANK, which give the percentage of successful cases of 27.6 % (26.9 %) and 22.5 % (24.6 %), respectively. Furthermore, the overall performance of eRankPPI, ZDOCK and ZRANK for homo-dimer targets is compared in Fig. eRankPPI is closer to the estimated upper limit than for ZDOCK and ZRANK over a range of iRMSD and PCS threshold values defining correct predictions. We note that the black dashed lines in Figs. Using these iRMSD and PCS cutoffs to define accurate predictions, we evaluate the ranking capability of eRankPPI yields an average number of 1.36 and 1.35 hits per target for the BM1755H and BM1755M datasets at the iRMSD cutoffs of 8.5 and 9.5 Å, respectively. For comparison, the corresponding hit counts for ZDOCK (ZRANK) are only 0.66 (0.69) and 0.46 (0.47). Furthermore, in Fig. eRankPPI, ZDOCK and ZRANK on the BM58H and BM58M datasets of hetero-dimers. For instance, Fig. eRankPPI, ZDOCK and ZRANK for BM58H (BM58M) is 50.0 % (34.4 %), 29.31 % (27.5 %) and 34.5 % (17.2 %) respectively. This comprehensive analysis using various evaluation measures demonstrates that dimer ranking by eRankPPI is consistently better than that by ZDOCK and ZRANK not only using experimental monomer structures, but also computer-generated models.Similar performance improvements are observed for the hit count and the success rate. For instance, Fig. eRankPPI, a new scoring method for protein-protein docking that integrates predicted binding site information, protein docking potentials, energy-based scoring and a contact-based symmetry constraints (for homo-dimers). Although these attributes have been used previously in protein docking, we combined them in eRankPPI as a single, machine learning-based scoring function. The results demonstrate that eRankPPI reliably selects near-native conformations from a large number of decoys generated by ZDOCK [eRankPPI consistently outperforms the state-of-the-art algorithms, ZDOCK and ZRANK, for both homo- and hetero-complexes yielding notably higher hit counts and success rates.The identification of near-native conformations across docking ensembles remains a challenging problem in the structure-based modeling of protein-protein interactions. Docking strategies need accurate scoring functions to rank the predicted conformations. Many current approaches employ the geometric, chemical and electrostatic complementarity as well as knowledge-based interaction potentials as components of their scoring functions. In this communication, we describe by ZDOCK . MoreoveeRankPPI outperforms both ZDOCK and ZRANK not only using experimental structures, but also computer-generated models. This is an important feature of eRankPPI owing to the fact that protein models represent the most challenging targets for molecular docking.In addition to experimental target structures, we performed a series of benchmarking simulations using computer-generated models. Interestingly, ZRANK performs better than ZDOCK only against experimental target structures. The main reason for this high sensitivity to distortions in target structures is likely a strong dependence on atomic potentials, therefore, ZRANK requires high-quality structural data in order to provide accurate ranking. In contrast, eRankPPI to rank hetero-dimers shows that the optimized weights for the SVC and NBC interface scores assigned by eFindSitePPI, the protein-docking potential and the ZDOCK score are 171.9, 891.8, 122.7 and 2.2, respectively. Therefore, the predicted binding site information is a major contributor to the improvement of model ranking in protein docking. Since the success of eRankPPI depends on the accuracy of protein interface prediction, using a robust PPI prediction program is essential. Here, we use eFindSitePPI, a recently developed template-based approach that effectively exploits the tendency of the location of binding sites to be highly conserved across evolutionarily related protein dimers [eFindSitePPI uses the three-dimensional structure of a query protein, evolutionarily remotely related templates and machine learning to predict interfacial sites. It was also shown to outperform several PPI site prediction programs [eFindSitePPI tolerates structural imperfections in computer-generated models. These characteristics make eFindSitePPI a preferred PPI predictor to support dimer ranking in across-proteome docking studies using eRankPPI.The analysis of the linear regression model used by n dimers . eFindSiprograms . Also, deRankPPI. Figure st by ZDOCK, whereas eRankPPI placed it at the rank 413. In contrast, the docked interface shown in Fig. eFindSitePPI; the iRMSD of this model is 6.11 Å and it is ranked 1st and 14th by eRankPPI and ZDOCK, respectively.We conclude this study discussing several examples that illustrate the key features of eRankPPI and ZDOCK, respectively. The symmetry score implemented in eRankPPI ranges from 0 (no symmetry) to 1 (perfect symmetry); the native complex has a perfect symmetry as indicated by a CBS of 1.00. The top ranked model by ZDOCK has an iRMSD of 14.89 Å and a symmetry score of 0.00. The lack of symmetry is evident in Fig. eRankPPI placed this model at rank 806 because of the low CBS score. On the other hand, the top ranked model by eRankPPI shown in Fig. Next, we present a case study that illustrates how contact-based symmetry improves the ranking of near-native models for homo-dimers. Figure th by eRankPPI. Figure th by ZDOCK, whereas eRankPPI placed it at rank 2. MCC of PPI site prediction for this target is only 0.39, nonetheless, despite the moderate accuracy of interface residue prediction, eRankPPI ranked this nearest-native conformation much higher than ZDOCK.Finally, we discuss an example of the hetero-dimer complex between the human cyclin-dependent kinase 2 and cell cycle-regulatory protein CksHs1; the crystal complex structure is shown in Fig. eRankPPI, an algorithm for the selection of correct docking conformations constructed by rigid-body protein docking. eRankPPI features a new scoring function that integrates the predicted interface location with protein docking potentials and a contact-based symmetry score. Comprehensive benchmarking calculations show that eRankPPI has a high tolerance to structural imperfections in computer-generated protein models, therefore, it opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes.In this study, we developed www.brylinski.org/erankppi.The methods and datasets used in this study are available at


Networks of protein–protein interactions (PPIs) link all aspects of cellular biology. Dysfunction in the assembly or dynamics of PPI networks is a hallmark of human disease, and as such, there is growing interest in the discovery of small molecules that either promote or inhibit PPIs. PPIs were once considered undruggable because of their relatively large buried surface areas and difficult topologies. Despite these challenges, recent advances in chemical screening methodologies, combined with improvements in structural and computational biology have made some of these targets more tractable. In this review, we highlight developments that have opened the door to potent chemical modulators. We focus on how allostery is being used to produce surprisingly robust changes in PPIs, even for the most challenging targets. We also discuss how interfering with one PPI can propagate changes through the broader web of interactions. Through this analysis, it is becoming clear that a combination of direct and propagated effects on PPI networks is ultimately how small molecules re-shape biology. Multi-protein complexes are often assembled around a central enzyme, such as a kinase, phosphatase, protease, or nuclease. Interactions of the “core” enzyme with adaptor and scaffolding proteins often direct it to specific subcellular locations and/or regulating its enzymatic activity  tend to be more challenging, while PPIs that rely on a few, closely spaced amino acids to bind with high affinity are relatively easier to inhibit  chemical inhibitors that act on the same target can produce different cellular responses because of the way that they alter local PPI networks. Extensive work on allosteric modulation of G-protein coupled receptors (GPCRs) has provided a blueprint for how this might be accomplished in other systems  approaches that are geared toward the purposeful discovery of PPI modulators with similar mechanisms. Finally, in the third section, we speculate on which new methods and ideas might be needed to take full advantage of future opportunities. The over-arching theme is that small molecules have both proximal (or direct) effects on PPIs , while also having less well-appreciated effects on downstream interactions within PPI networks.Allostery, in which binding of a ligand at one site affects protein conformation at a distant site, enables small molecules to produce dramatic effects on protein structure and function, even at a distance . In this system, gene expression is repressed when RAR is bound to a co-repressor, while gene expression is activated when RAR recruits a co-activator  PPIs, as described for the HDAC3–SMRT complex   , surface plasmon resonance (SPR), fluorescence energy transfer (FRET), bioluminescence energy transfer (BRET), differential scanning fluorimetry (DSF), hydrogen-deuterium exchange, AlphaLisa, and NMR spectroscopy have been developed and each has its strengths . This method allows for separation of complexes from their individual components based on their size and charge. Labeling one or more of the individual protein partners enables sensitive detection of bound:free ratios   contains a dynamic protein interface, termed activation function 2 (AF2) that mediates interactions with co-activator proteins  is a technique that separates macromolecules in the gas phase, analogous to electrophoresis in solution. Measurement of ion migration, or drift time, can be used to generate information on the collisional cross section of a protein of interest, which can in turn be used to infer changes in the folded conformation of a protein upon ligand or macromolecule binding  fragments form cytosolic aggregates, and this process is regulated by molecular chaperones , which are used to covalently and site-specifically capture PPIs in their native environment (Chin and Schultz, A major hurdle to AP-MS is the difficulty in identifying partners with low affinity and/or low abundance. Several groups have developed creative solutions to this challenging problem, including crosslinking. Examples of synthetic crosslinkers include activated diesters linked by a cyclic quaternary diamine. The diamine is cleavable by collision-induced dissociation and facilitates identification of crosslinked peptides (Clifford-Nunn et al., in vivo characterization of a number of diverse, dynamic protein complexes, including the chromatin-associated mediator complex (Lambert et al., Toxoplasma gondii, among others (Chen et al., A recently reported proximity biotinylation approach, BioID is a complementary method for mapping specific PPIs within large multi-protein complexes. This strategy is particularly advantageous because it does not require that complexes be maintained across numerous purification steps and therefore has the potential to identify more transient PPIs compared to traditional AP-MS techniques (Roux et al., Multi-protein complexes are the “hubs” of the cellular PPI networks and attractive drug targets for a variety of diseases. We have illustrated in this review several “success stories” of small molecules that target PPIs and the development of new methodology for identifying new PPI modulators. Creative HTS strategies are beginning to expand the toolbox of available approaches, although there is no algorithm or “road map” for a successful screen. Rather, each campaign must be designed individually, taking into account the affinity of the interactions, the topology of the interaction surfaces, and the interplay between different components of the system. For instance, a phenotypic or gray-box screen has the best chance of success for finding small molecules that can perturb protein networks. However, these strategies require significant knowledge of the structure and function of individual PPIs within a greater protein complex, highlighting the importance of basic research in order to be able to ask the right questions.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.


ADIPOR1) gene mutations with vulnerability to several human metabolic diseases, there is lack of computational analysis on the functional and structural impacts of single nucleotide polymorphisms (SNPs) of the human ADIPOR1 at protein level. Therefore, sequence- and structure-based computational tools were employed in this study to functionally and structurally characterize the coding nsSNPs of ADIPOR1 gene listed in the dbSNP database. Our in silico analysis by SIFT, nsSNPAnalyzer, PolyPhen-2, Fathmm, I-Mutant 2.0, SNPs&GO, PhD-SNP, PANTHER, and SNPeffect tools identified the nsSNPs with distorting functional impacts, namely, rs765425383 (A348G), rs752071352 (H341Y), rs759555652 (R324L), rs200326086 (L224F), and rs766267373 (L143P) from 74 nsSNPs of ADIPOR1 gene. Finally the aforementioned five deleterious nsSNPs were introduced using Swiss-PDB Viewer package within the X-ray crystal structure of ADIPOR1 protein, and changes in free energy for these mutations were computed. Although increased free energy was observed for all the mutants, the nsSNP H341Y caused the highest energy increase amongst all. RMSD and TM scores predicted that mutants were structurally similar to wild type protein. Our analyses suggested that the aforementioned variants especially H341Y could directly or indirectly destabilize the amino acid interactions and hydrogen bonding networks of ADIPOR1.Despite the reported association of adiponectin receptor 1 ( Decrα) , 14. The ADIPOR1 and ADIPOR2 genes are located at chromosomal locations 1p36.13-q41 and 12p13.31, respectively [ ADIPOR1 gene is found principally in skeletal muscles but may be presented ubiquitously also, while the expression of ADIPOR2 is the most abundant in liver [ ADIPOR1 gene can hamper the physiological functions exerted by the ADIPOR1 protein. A recent comprehensive investigation on a European-Australian population has established the association of genetic variation in adiponectin receptors with type 2 diabetes [ ADIPOR1 gene have been found to exert significant effects on the risk of prostate cancer (rs12733285) [ in vivo studies describing the association of SNPs in the ADIPOR1 gene with metabolic disorders [To employ proper functions, adiponectin binds to a number of receptors. Different studies have identified two receptors named adiponectin receptor-1 (ADIPOR1) and adiponectin receptor-2 (ADIPOR2) (those are homologous to G protein-coupled receptors) as well as one receptor similar to the cadherin family , 16. In ectively . The expin liver . Among tin liver . Accordidiabetes , 20. Mor2733285) , insulin2733285) , and eve2733285) . Althougisorders , 25, com in silico studies have effectively filtrated functional SNPs out of large pool of diseases sensitive SNPs of BRCA1, ATM [ PON1 [ ADIPOR1 gene mutations in human diseases, the computational analysis of nsSNPs is still unveiled.In current years, computational tools are being widely used to characterize the impacts of deleterious nsSNPs in candidate genes by utilizing the information obtained from physicochemical properties of polypeptides , 27, conCA1, ATM , and PONM [ PON1  genes ba ADIPOR1 were collected for in silico analysis. By utilizing these data, we employed different publicly available bioinformatics tools and databases for a comprehensive analysis of nsSNPs in ADIPOR1 gene. We also calculated the free energy changes for mutants and wild type ADIPOR1 protein in order to evaluate their stability. This study might be helpful for further investigation in order to discover new therapeutic drugs related to adiponectin receptor 1 associated diseases.In this study, the clinical variants of ADIPOR1 gene were obtained from web-based data sources such as Online Mendelian Inheritance in Man  and the National Center for Biological Information (http://www.ncbi.nlm.nih.gov/). The information about SNPs of ADIPOR1 gene of Homo sapiens was collected from the dbSNP-NCBI (http://www.ncbi.nlm.nih.gov/SNP/) [ ADIPOR1 gene was obtained from UniProtKB database (http://www.uniprot.org/uniprot/).The data on the humanov/SNP/)  for furthttp://sift.jcvi.org/) predicts the deleterious and tolerated SNPs in order to characterize the consequences of amino acid substitutions on phenotypic and functional changes of protein molecules. By using sequence homology based method, SIFT assumes that significant positions in a protein sequence have been conserved throughout evolution and, therefore, substitutions at these positions may affect protein function. The identification numbers (rsIDs) of each nsSNP of ADIPOR1 gene were submitted as an input to SIFT server for homology searching. SIFT calculates the SIFT score or tolerance index (TI) score for each nsSNP. The SIFT value ≤ 0.05 indicates the deleterious effect of nonsynonymous variants on protein function [SIFT  server was used to predict whether a nsSNP of ADIPOR1 protein affects its phenotypic effect. The input options for nsSNPAnalyzer are protein sequences in FASTA format and detailed information on amino acid substitutions. This server usually uses information contained in the multiple sequence alignment and the 3D structure in order to make a prediction. The prediction of this tool is based on a machine learning method known as Random Forest. The results of this server depict whether an nsSNP is associated with disease or neutral [nsSNPAnalyzer  server was used. The prediction done by SNAP2 is based on a learning device method known as neural network. In order to make a prediction, SNAP2 utilizes the information of automatically created multiple sequence alignment and also some structural features such as predicted secondary structure and solvent accessibility. FASTA format of protein sequences is only the input option for SNAP2. The output of this server consists of prediction , score , and expected accuracy [To find the functional effects of nsSNP, SNAP2 (accuracy .http://genetics.bwh.harvard.edu/pph2/) is an advanced version of the PolyPhen tool that was used to find out the possible effect of an amino acid substitution on the structure and function of ADIPOR1 protein. UniProtKB accession number/FASTA sequence and details of amino acid substitutions are required for the input options of PolyPhen-2 server. This tool calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimation of corresponding false positive and true positive rate. A mutation is estimated qualitatively as probably damaging (probabilistic score >0.85), possibly damaging (probabilistic score >0.15), and benign (remaining) with specificity and sensitivity values [PolyPhen-2  is a support vector machine (SVM) based classifier [SNPs&GO  not only predicts the potentially deleterious nature of protein variants but also the skill of annotating the molecular and phenotypic consequences of these mutations [Fathmm (utations . This sehttp://snpeffect.switchlab.org/) utilizes sequence- and structure-based bioinformatics tools in order to make prediction of molecular phenotypic impacts of nsSNP on ADIPOR1 gene. This server mainly integrates three different tools such as TANGO, WALTZ, and LIMBO and also uses FoldX server to find out a decision whether the mutation is stabilizing or destabilizing the structure of native proteins. TANGO algorithm identifies the aggregation prone regions in a protein sequence by calculating the hydrophobicity and beta-sheet forming propensity. WALTZ algorithm predicts amyloid forming regions in protein sequences with accuracy and specificity, while LIMBO algorithm predicts a chaperone binding site for the Hsp70 chaperones. The input options are usually composed of FASTA sequence/PDB ID/PDB file/UniProt ID and details of nsSNP [The SNPeffect database 4.0 (of nsSNP .http://folding.biofold.org/i-mutant/i-mutant2.0.html) is a support vector machine- (SVM-) based tool which was used to predict the protein stability changes upon nsSNPs. In this study, sequence of protein, temperature (25°C), pH (7), and details of nsSNPs were used as input parameters to this server. The output is a free energy change value (ΔΔG) of protein after and before mutation. Positive ΔΔG value concludes that the protein being mutated is of higher stability and vice versa is also true [I-Mutant 2.0 (lso true .http://consurf.tau.ac.il/) is a web-based tool that automatically analyzes evolutionary conservation of amino acid substitutions in protein by using an empirical Bayesian inference. This server is composed of combining two self-governing servers (ConSeq and ConSurf). After providing the FASTA sequence of ADIPOR1 protein to ConSurf tool, the conserved regions were predicted with conservation grades color-coded onto its surface that can finally be pictured online using the Protein Explorer engine [ConSurf (r engine .http://www.cbs.dtu.dk/services/NetSurfP/) server in order to predict its secondary structure, surface, and solvent accessibility of amino acids [The active site of a protein in its three-dimensional conformation can be traced by surface and solvent accessibility region of amino acids of that protein. The FASTA sequence of ADIPOR1 protein was submitted to NetSurfP   has an Ihttp://ftsite.bu.edu/) predicts ligands or small molecule binding sites of proteins based on experimental evidence with 94% accuracy [Detection of ligand binding sites on unbound proteins is essential to elucidate the protein structure-function relation and for protein engineering. FTSite (accuracy . The inphttp://www.genome.jp/kegg/) PATHWAY and LIGAND to make prediction of the functional networking of ADIPOR1 protein.Protein-protein interaction networks are important to investigate the functions of the interactions of a particular protein with other proteins at cellular level. Online database resource Search Tool for the Retrieval of Interacting Genes (STRING) was applied to identify the interactions of ADIPOR1 protein with other corresponding proteins . This se ADIPOR1 gene (Gene ID: 51094). A total of 138 SNPs were found in the exonic region, among them 62 (44.93%) were synonymous, 74 (53.62%) nonsynonymous and missense, 1 (0.72%) nonsynonymous and nonsense, and 1 (0.72%) frame-shift mutations. However, only nonsynonymous SNPs were selected from coding region for this computational analysis.The dbSNP-NCBI database was searched for retrieving the SNPs in the human ADIPOR1 gene function. This computational study was accomplished and authenticated using different in silico tools, namely, SIFT, nsSNPAnalyzer, SNAP2, PolyPhen-2, SNPs&GO, Fathmm, SNPeffect, and I-Mutant 2.0.The searching of functionally significant nsSNPs was done by predicting those which substitute the amino acids that are critical forhttp://dx.doi.org/10.1155/2016/9142190).SIFT tools filtrated that a total of 13 variants (17.568%) were damaging (score of 0.00–0.04) and the remaining 61 variants (82.432%) became tolerated (score of 0.08–0.55). It was noted that, among 13 variants, 2 nsSNPs  were predicted as damaging with low confidence. Therefore, SIFT suggested that these 11 nsSNPs might disrupt both the protein function and structure. The detailed results are provided in supporting information  predicted that a total of 27 nsSNPs (36.486%) might be disease causal. In contrast, 47 nsSNPs (63.513%) have no effect on protein function and, hence, are considered as neutral. In addition, the results from SNAP2 server (Table S2) indicated 19 variants (25.675%) as significant and the remaining nsSNPs (74.324%) as neutral. Among the three computational tools, the highest number of significant nsSNP (27 variants) was detected by the nsSNPAnalyzer. The results obtained from SIFT, nsSNPAnalyzer, and SNAP2 concluded that the 7 nsSNPs with rsIDs of rs764078304, rs765425383, rs752071352, rs759555652, rs764912508, rs200326086, and rs766267373 are found as significant among three servers and thereby the result has one step refined and validated .The results (Table S3) obtained from PolyPhen-2 server indicated that 12 (16.216%) out of 74 nsSNPs were predicted as probably damaging  and 12  nsSNPs were ranked as possibly damaging (score of 0.531–0.874) as well. Meanwhile, 50 (67.567%) nsSNPs were also classified as benign (score of 0.411–0.000). The classification of SNPs on the basis of PolyPhen-2 scores permits us to assess the potential quantitative effect of SNPs on wild type protein. Moreover, 7 nsSNPs , predicted as damaging by SIFT, are also found as damaging using PolyPhen-2. This result gives clear indication that there is a strong correlation exists between evolutionary based approaches SIFT and the structural based approach PolyPhen-2 tools. ADIPOR1 nsSNPs in order to add another layer of refinement in nsSNPs characterization. The predicted results by these servers are shown in Tables S4 and S5.We performed PhD-SNP, PANTHER, SNPs&GO, and Fathmm analyses of humanThe predictions gained form PhD-SNP server offer the fact that 29 nsSNPs cause disease with probability score greater than 0.5 and the remaining nsSNPs are marked as neutral. The number of disease causing variants has been decreased in case of the prediction of PANTHER and SNPs&GO tools. The disease causing variants predicted by PANTHER and SNPs&GO are 15 and 13 nsSNPs, respectively. The results from PANTHER server showed that 17 nsSNPs remain unclassified.From Fathmm, the nsSNPs in amino acids positions 4 to 122 in human ADIPOR1 protein are found to be damaging with score of −3.97 to −4.20.The efficacy of functional SNP prediction can be increased more reliably by integrating the results of SVM based approaches. By combining the predictions of SIFT, nsSNPAnalyzer, SNAP2, PolyPhen-2, PhD-SNP, PANTHER, SNPs&GO, and Fathmm, five nsSNPs  are found to br more deleterious and disease associated  and metabolic diseases such as diabetes, obesity, and insulin resistance have been reported [ ADIPOR1 gene together with the computational method to predict their possible functional impact may help in the analysis of ADIPOR1 gene variants and establish their effects on protein functional characteristics. Specifically, this approach permits the estimation of the probability of amino acid changes which can be detrimental for ADIPOR1 protein functions.The associations between polymorphisms ofreported , 22, 23. ADIPOR1 gene with the help of different bioinformatics tools. The variants were predicted to be similar to wild type ADIPOR1 protein structurally. However, decreased stability of mutant proteins has been observed with classical molecular dynamics study compared to wild type. Among the potential five nsSNPs, H341Y mutant has been found to cause considerable changes in amyloid forming propensity and aggregation tendency of ADIPOR1 protein. Additionally, there might be chance to disrupt the zinc coordination domain which is responsible for adiponectin stimulated MPK phosphorylation and UCP2 upregulation. The deleterious mutations of ADIPOR1 should be further investigated to establish their roles in the pathogenesis of related diseases.In this comprehensive computational study, we have identified five deleterious mutations  among the coding region ofADIPOR1 gene analyzed by SIFT, SNAP2, nsSNPAnalyzer, PolyPhen-2, PhD-SNP, PANTHER, SNPs&GO, and Fathmm.Table S1 to S5 depict results of nonsynonymous SNPs of the human


ProtPhylo infers functional associations by comparing protein phylogenetic profiles (co-occurrence patterns of orthology relationships) for more than 9.7 million non-redundant protein sequences from all three domains of life. Users can query any of 2048 fully sequenced organisms, including 1678 bacteria, 255 eukaryotes and 115 archaea. In addition, they can tailor ProtPhylo to a particular kind of biological question by choosing among four main orthology inference methods based either on pair-wise sequence comparisons  or clustering of orthologous proteins across multiple species (OrthoMCL and eggNOG). Next, ProtPhylo ranks phylogenetic neighbors of query proteins or phenotypic properties using the Hamming distance as a measure of similarity between pairs of phylogenetic profiles. Candidate hits can be easily and flexibly prioritized by complementary clues on subcellular localization, known protein–protein interactions, membrane spanning regions and protein domains. The resulting protein list can be quickly exported into a csv text file for further analyses. ProtPhylo is freely available at  Advances in sequencing technologies and genome annotation tools continuously increase the repertoire of protein-coding genes in numerous organisms. The number of sequenced genomes is growing exponentially, with over 10 000 prokaryotic and eukaryotic species sequenced to date . One of In silico-based predictions of functionally linked proteins often allow inferring a function for uncharacterized components via 'guilt-by-association' with known components , a web-based tool for prediction of protein-to-protein and phenotype-to-protein functional associations based on phylogenetic profiling. ProtPhylo achieves flexibility and state-of-the-art taxonomic and functional coverage by generating phylogenetic profiles for 9.7 million non-redundant protein sequences across 2048 organisms and by implementing four independent orthology detection algorithms. In addition, it provides an integrated framework for fast, easy and flexible prioritization of phylogenetic neighbors based on widely used tools for prediction of subcellular localization  database  and filtggNOGv.4 . BrieflyE-value < 10−5). Instead, OrthoMCL and eggNOGv.4 extend the sequence similarity search over multiple proteomes at once to generate groups of orthologous proteins. Both, OrthoMCL and eggNOGv.4 algorithms, derive orthologous groups based on BRH  as their first step but apply different clustering techniques to assemble protein groups from multiple species, Markov Cluster (MCL)   and triaer (MCL) , respecter (MCL) .n entries, where n corresponds to the size of the species set and the entry indicates the presence (1) or absence (0) of an ortholog or a similar phenotype across species.Next, for all 9.7 million proteins we generated phylogenetic profiles across 2048 organisms using the abovementioned orthology inference methods. Here, the phylogenetic profile of a protein or a phenotype is represented by a binary string with MitoProt II Score) that a protein has a mitochondrial-targeting sequence. Prediction of mitochondrial localization for Homo sapiens and Mus musculus is also retrieved from MitoCarta , for example ion uptake, thermogenesis, or multicellularity, or a protein of interest (Protein Phylogenetic Profiling) and are therefore likely to be functionally associated. Phenotype Phylogenetic Profiling can only be applied when users have a priori knowledge of the presence and/or the absence of the phenotype across any of the 2048 organisms in ProtPhylo (Species WITH and/or Species WITHOUT the Phenotype of Interest). In both search options, users can select among four main orthology methods , as well as five additional OrthoMCL settings (OBH is set as the default orthology method). ProtPhylo uses all 2048 organisms to generate a protein phylogenetic profile, while the set of species used to generate a phenotype phylogenetic profile can be defined by the user.Users can search for proteins, within a query organism, that co-evolve with either a phenotype (Source Protein ID). When selecting OrthoMCL or eggNOGv.4 as orthology detection methods, users can also retrieve the list of orthologs for each ranked protein (Orthologs). For OBH and BRH, the retrieval of orthologs is available for the following query organisms: H. sapiens, M. musculus, S. cerevisiae, C. elegans, D. melanogaster and D. rerio. The output of a Protein Phylogenetic Profiling search includes two additional calculations: the HD Percentile and the Reciprocal HD Percentile. The former refers to the percentage of proteins within the query proteome that have equal or lower HD than the phylogenetic neighbor HD, while the latter is calculated when clicking the magnifier icon and reflects the HD percentile of the user-defined query protein if the phylogenetic neighbor is used as a query. By default, ProtPhylo reports a protein list with less than or equal to the fifth percentile. The output list can be further prioritized directly in ProtPhylo based on five complementary filtering criteria: cut-off HD values and percentile; combined (And) or stand-alone (Or) evidence of subcellular localization; presence (>0) or absence (=0) of transmembrane helices; presence of conserved Pfam domains (by Pfam ID or name); keywords ; confidence score for functional associations predicted by STRING (STRING score).ProtPhylo compares the query phylogenetic profiles (protein or phenotype) to the profiles of all other proteins within the query organism and ranks proteins based on their similarity scores, from the smallest (closest phylogenetic neighbor) to the largest. Here, we use the Hamming distance (HD) to quantify the similarity between pairs of phylogenetic profiles . This coy STRING , , localize to the plasma membrane  but not to the mitochondria (MitoCarta) and have at least one transmembrane domain. As a result, ProtPhylo identifies 16 proteins matching the selected criteria, with Orai1 being the top phylogenetic neighbor of Stim1 (HD = 5).As an illustrative example of ium pool . Calciumium pool ,38, it fium pool –41. HereProtPhylo aims to provide a fast, flexible and user-friendly tool to assist biologists seeking functional clues for a protein or a phenotype of interest. Several features distinguish ProtPhylo from other web servers ,30,42–43


Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved.This paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO) and multi-label elastic net (mEN), for large-scale predictions of protein subcellular localization. Both algorithms can yield sparse and interpretable solutions. By using the one-vs-rest strategy, mLASSO and mEN identified 87 and 429 out of more than 8,000 GO terms, respectively, which play essential roles in determining subcellular localization. More interestingly, many of the GO terms selected by mEN are from the biological process and molecular function categories, suggesting that the GO terms of these categories also play vital roles in the prediction. With these essential GO terms, not only where a protein locates can be decided, but also why it resides there can be revealed.http://bioinfo.eie.polyu.edu.hk/SpaPredictorServer/.Experimental results show that the output of both mEN and mLASSO are interpretable and they perform significantly better than existing state-of-the-art predictors. Moreover, mEN selects more features and performs better than mLASSO on a stringent human benchmark dataset. For readers’ convenience, an online server called SpaPredictor for both mLASSO and mEN is available at  To tackle tremendous numbers of newly discovered protein sequences generated by large-scale sequencing projects, efficient computational methods are required for fast and accurate prediction of protein subcellular localization (PSCL).Within living organisms, proteins need to locate in the right subcellular compartments to perform various biological functions. Mislocalized human proteins are liable to cause numerous human diseases, such as kidney stone , Bartter2 terms T is the LASSO vector to be optimized, t>0 is a parameter controlling the shrinkage level applied to β, ε0 is a bias,12 and xi,j is the j-th element of xi. Equation subject to λ>0 is a penalized parameter controlling the degree of regularization. Equation where L1+L2)-style regularization on Eq. EN is to impose an  are trained, one for each class. The labels transformed labels T is the weight vector to be optimized and i-th training protein. Note that respectively, where i-th query protein are given by: To predict the subcellular locations of datasets containing both single-label and multi-label proteins, a decision scheme for multi-label LASSO (EN) classifiers should be used. Unlike the single-label problem where each protein has one predicted label only, a multi-label protein should have more than one predicted labels. In this paper, we used the decision scheme described in mGOASVM . In thisFor ease of presentation, we refer to the two proposed predictors as mLASSO and mEN, respectively.1http://www.proteinatlas.org/2http://www.geneontology.org3http://prosite.expasy.org/4http://www.ebi.ac.uk/GOA5http://www.uniprot.org/6 Locative proteins are defined as follows. If a protein exists in two different subcellular locations, it will be counted as two locative proteins; if a protein coexists in three locations, then it will be counted as three locative proteins; and so forth.7 In our case, N=3106 for the human dataset.8 Specific weights 9 Hum-mPLoc 2.0 [OLA is provided in [PLoc 2.0  performsvided in . Therefo10 Note that because the number of novel reviewed human proteins that were added to Swiss-Prot after 2014 is too small to constitute a meaningful test set, we used some representative novel proteins to test mEN instead.11 The scores and weights for the essential GO terms for all of the 14 subcellular locations can be seen by inputing the query protein sequence to our SpaPredictor web-server.12 For ease of presentation, we omitted the bias in equations thereafter.


In this paper, we propose a novel method, SeekFun, to predict protein function based on weighted mapping of domains and GO terms. Firstly, a weighted mapping of domains and GO terms is constructed according to GO annotations and domain composition of the proteins. The association strength between domain and GO term is weighted by symmetrical conditional probability. Secondly, the mapping is extended along the true paths of the terms based on GO hierarchy. Finally, the terms associated with resident domains are transferred to host protein and real annotations of the host protein are determined by association strengths. Our careful comparisons demonstrate that SeekFun outperforms the concerned methods on most occasions. SeekFun provides a flexible and effective way for protein function prediction. It benefits from the well-constructed mapping of domains and GO terms, as well as the reasonable strategy for inferring annotations of protein from those of its domains. More and more sequences of proteins are available due to the advanced sequencing technologies, but the biological roles and functions of the proteins are hardly known. As reported by , only le e-value and annotated the unannotated protein with the GO terms of its homologues. GoFigure , pr(t), can be calculated asConsistent with Critical Assessment of Functional Annotations (CAFA) experiments , the prePt(x) is the set of predictive annotations whose relevance with x is above t. S is the target set for testing. m(t) is the number of proteins which at least has one predictive GO term under given t. Similarly, the recall of method at threshold t, rc(t), can be computed byIn , Pt(x)  f-measure  gives an intuitive number for comparisons of the concerned methods. For each method, the maximal value of f-measure on the overall threshold of relevance, Fmax, is calculated asTheK(x) and P(x) are extended by adding all ancestors of their members to them before comparing.Considering the relationships between GO terms, the comparisons are guided by the true path rule. That is, the RSCP) is compared with those measured by P value (RPV) and traditional conditional probability (RdSCP). In fact, it is hard to evaluate the relevance between domain and GO term for lacking of the gold standard. To determine appropriate strategies for weighting relevance, some properties of relevance are analysed. A little random noise may make a difference between observed and real datasets and the relevance should be robust on these similar datasets. To simulate similar datasets, a series of subsets of Uniref50, SwissProt, and TrEMBL is constructed by taking nine of their ten equal-size partitions randomly at a time. The calculations of relevance by different strategies are performed on these subdatasets. The varied distributions of relevance on the different datasets may be good evidence for which strategy is more proper for weighting relevance.To illustrate the rationality of weighting strategies, the relevance weighted by symmetrical conditional probability , biological process (BP), and cellular component (CC) separately. The comparison includes two steps: constructing mapping of domains and GO terms and annotating proteins based on the mapping.For validating its impact on protein function prediction, pfam2go in this paper. Meanwhile, the mapping of Pfam domains and GO terms which is weighted by RSCP is also used for prediction, namely, Predweighted. In the comparisons, Predpfam2go and Predweighted are validated by performing the same task in the same framework on the basis of different mappings of domains and GO terms. To avoid the influence of domain coverage, the weighted mapping with RSCP just includes the domains in pfam2go when it is applied. Here, to compare the influence of the strategy RSCP and RSC, the method which is the combination of them is also used to perform the same task and marked with Predcombine. Their performances are illustrated in In our experiment, the mapping of Pfam domains and GO terms (pfam2go) is downloaded from the Gene Ontology website in May, 2013. Based on this reliable mapping, all annotations which are associated with the resident domains are assigned to the host protein. This method is named Predweighted has higher recall than Predpfam2go while the latter achieves better precision than the former. These results suggest that the Predweighted could improve the specificity of annotations but it is at the cost of precision.As displayed in combine is superior to others in general. Compared to Predpfam2go, Predcombine outperforms on both precision and recall. In contrast to Predweighted, Predcombine significantly improved the precision while it does as well as Predweighted on recall. Thus, it can be concluded that RSCP tend to select specific terms for the proteins and RSC balances this bias by propagating in the GO hierarchy. It may be the reason that Predcombine shows higher performances.It also can be found from  RSC, it is compared with traditional strategy which set the relevance of domain and terms along a true path as equal (RPE). The two strategies are applied to predict protein functions based on the mapping of domains and GO terms weighted by RSCP. Their best performances are listed in In order to validate the effectiveness of theFmax. In general, RSC may be more beneficial to protein function prediction than RPE. It may be because the resolution of RSCP is effectively promoted by different relevance between protein and each term along a true path. On the contrary, RPE considered that protein has equal relatedness to every term along the true path, which makes it harder to determine the true positive associations between terms and the host protein. Even if the threshold of RPE is 1, its precision is still lower than the other one and recall goes down. It confirms that the differences of GO terms have significant influence on their relevance with protein.As displayed, RPE gives a better recall while RSC has higher precision and To assess the efficiency of SeekFun, it is compared together with NB, DRDO, DRDO-NB, and dcGO on the three benchmark datasets. The performances of concerned methods on different dataset are shown in P value while other methods calculated it based on conditional probability. These results may indicate again that the relevance estimated by P value is not sensitive enough to determine the true positive associations between domain and GO term. In other words, RPV has low resolution for distinguishing real annotations of protein. By contrast, the conditional probability is more suitable for estimating relevance.In terms of precision, SeekFun is superior to others while NB, DRDO, and DRDO-NB follow in turn. The dcGO is significantly lower than others. As aforementioned, dcGO measured relevance between domain and GO term by P value of their combination is above predefined threshold. It may miss information covered in the potential domain combinations and domain themselves. We guess this may be the reason that these methods show lower recall of functions.As for the recall, SeekFun performs better than others while dcGO follows. It also can be found that the performances of NB, DRDO, and DRDO-NB are not as well as the other methods. Comparing the details of them, NB, DRDO, and DRDO-NB infer functions of protein from annotations of domain combinations, which enhance the precision of function prediction. However, in the process of discovering domain combinations, some slightly weak associations between domain and GO term may be neglected. The resident domains of the host protein may interplay as different combinations to perform different functions. Nevertheless, these methods judge domain combination if the members of the domain combination exist in the protein and the Overall, SeekFun has better performance than others. It can attribute to the weighted mapping of domains and GO terms and the strategy for transferring annotations of resident domains to the host proteins. The weighted mapping can reflect the relationship between domain and GO term properly. The transferring strategy takes both the differences and connections of terms into consideration, which greatly promote its capability of distinguishing real associations of domains and terms from the false ones.In this paper, SeekFun is developed for protein function prediction. Instead of using amino acid sequence of protein directly, SeekFun takes the resident domains of proteins and protein-level GOA as clues to annotate proteins. We tested the overall performance of SeekFun and the results suggest that SeekFun is superior to the concerned methods: NB, DRDO, DRDO-NB, and dcGO on precision and recall generally.RSCP) and the strategy for inferring annotations of protein from the annotations of its resident domains (RSC) are validated, respectively. The results of these experiments confirmed that both of them are effective and can promote the performance of protein function prediction. In the proposed method, RSCP tend to discover specific functions of protein but it cannot ensure the precision and RSC is used to compensate for the lack of RSCP. So the combination of them achieves high performances. The main idea of SeekFun could be used to acquire knowledge from other functional ontologies based on different domain resources easily. SeekFun will facilitate the discovery of protein functions and the insights into the biological roles of proteins.Meanwhile the effects of relevance computed by symmetrical conditional probability, (


Protein-protein interactions (PPIs) underlie most, if not all, cellular functions. The comprehensive mapping of these complex networks of stable and transient associations thus remains a key goal, both for systems biology-based initiatives  and for focused biological studies. Despite the significant challenges of such an undertaking, major strides have been made over the past few years. They include improvements in the computation prediction of PPIs and the literature curation of low-throughput studies of specific protein complexes, but also an increase in the deposition of high-quality data from non-biased high-throughput experimental PPI mapping strategies into publicly available databases. A range of complementary approaches are currently being used to identify protein-protein interactions (PPIs) in a large-scale, high-throughput manner . These i1. Clone validation, quality control, inclusion of positive and negative controls, and development of a quantitation algorithm (CompPASS-Plus) based on abundance, detection frequency, and reproducibility were employed to increase confidence in the resulting dataset, which was deposited into the BioGRID PPI database last year. The authors consider this to be phase 1 of their long-term effort to map interactomes for the entire human ORFeome collection and are continuing to post updates that can be freely browsed or downloaded from their website.Currently, the most popular strategy for both high- and low-throughput interactome mapping is AP-MS, in which an endogenous or tagged bait protein is depleted from cell lysates by using an affinity resin and associated proteins identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) . Two rec2 involved screening a library of 1,125 HeLa cell lines with stably incorporated N- and C-terminally tagged mouse and human bacterial artificial chromosome (BAC) transgenes under near endogenous control3 by AP-MS, as demonstrated previously in focused studies analyzing chromosome segregation4 and the function of motor proteins5. In addition to identifying more than 28,000 interactions in their large-scale screen, the authors estimated interaction stoichiometries  and measured the relative cellular abundances of interaction partners. An interesting finding was the predominance of weak  interactions in the global interactome, which may suggest that stable complexes rely on weak links to connect to each other and to transient/dynamic regulators. The interaction datasets were submitted to both the IntAct database and the IMEx consortium.The approach used by Hein and colleagues6. CORUM is currently considered the “gold standard” PPI database because it is based solely on high-confidence, experimentally verified interactions and does not accept deposition of large-scale datasets (9 (MaxQB), the Human Proteome Map10, and ProteomicsDB11.Importantly, both studies demonstrated significant overlap with the CORUM  database, a manually curated repository of more than 2,800 mammalian protein complexesatasets . Proteomatasets  such as 14 and the recently described cryptic protein binding to cloning regions or “scars” where affinity tags are linked to the gene of interest15, these large-scale studies benefit tremendously from the comparison of multiple experiments. Negative controls are largely bait-independent, and thus common contaminants are highlighted by their appearance in numerous unrelated datasets. Moving forward, the limitations of AP-MS can be further minimized by a variety of strategies, including direct affinity tagging of endogenous proteins using the powerful CRISPR/Cas9  gene editing tool17, more rigorous assessment of the quality and specificity of antibodies used to capture endogenous proteins for AP-MS18, and improvements in significance analysis software20.Although the standard caveats of AP-MS strategies still apply, namely the potential for overexpression or tag-induced artefacts and the predominance of false positives such as non-specific background proteinsin vivo, thus eliminating the need for low-stringency purification strategies to preserve their integrity. Furthermore, the high affinity of streptavidin for biotin facilitates efficient recovery of biotinylated proteins from lysates for MS analysis.Although AP-MS remains the most commonly used technique for mapping PPIs, its Achilles heel has always been the necessity to break cells open to extract complexes for analysis, which can be disruptive to the underlying PPIs and hinder identification of weak or transient associations or both. The development of complementary proximity labeling approaches that use spatially restricted enzymes to biotinylate neighboring proteins has helped to address this key issue. Complex members are labeled covalentlyEscherichia coli is capable of transferring biotin only to a substrate bearing a specific recognition sequence, the generation of a promiscuous BirA (Arg118Gly mutant) permits the biotinylation of any protein found within a 10-nm labeling radius22. As with AP-MS, identification of a protein-protein association using BioID does not imply a direct physical interaction.Two particular proximity labeling techniques, BioID and APEX, have been employed recently for the analysis of multiprotein complexes and for identification of the protein components of specific cellular compartments . BioID i23. This type of approach has also been used to identify novel c-MYC24 and HIV-1 Gag25 interacting partners, highlight force-dependent molecular interactions at cell-cell adhesions26, identify proteins localized to cell junction complexes28 and the centrosome-cilium interface29, and probe the structure of the centrosome31 and the nuclear pore complex22.BioID has enabled the identification of proteins involved in important functional complexes that were previously difficult to characterize because of the limitations of AP-MS. For example, the identification of ubiquitin ligase substrates by AP-MS is challenging and this is due in part to the weak and transient interactions observed between the ligase and its substrates. A BioID approach, however, facilitated identification of novel substrates32 or soybean33 ascorbate peroxidase that catalyzes the oxidation of biotin-phenol to biotin-phenoxyl in the presence of H2O2, resulting in the biotinylation of proteins in the neighboring region. Whereas BirA-catalyzed biotinylation is limited to Lys residues, biotin-phenoxyl radicals can covalently react with electron-rich amino acids such as Tyr, Trp, His, and Cys. They are also short-lived (<5 ms) and membrane-impermeable and have a small labeling radius (<20 nm). APEX can also catalyze diaminobenzidine precipitation to generate contrast after OsO4 fixation, which allows confirmation of localization at nanometer resolution by electron microscopy32. A second-generation APEX2 (Ala134Pro mutant) with improved efficiency was shown to function even better as both a promiscuous labeling enzyme and an EM tag34. Similar to BioID, once proximity labeling has been achieved, biotinylated proteins can be identified via stringent streptavidin purification and MS analysis. An advantage of APEX over BioID is higher temporal resolution, as labeling is achieved on a minute rather than an hour scale.APEX is a monomeric peroxidase reporter derived from pea35, theDrosophila muscle mitochondrial matrix proteome36, and the proteome of the cilium37. Although the applicability of APEX to interactome mapping out with membrane-bound organelles has not yet been demonstrated, further optimization of the enzyme and substrate could extend its utility.The APEX reporter has been used to map the proteome of the human mitochondrial intermembrane space and membrane-enclosed mitochondrial matrix39, including the development of MS-cleavable cross-linkers that fragment efficiently in the MS/MS mode to yield two major fragment ions corresponding to the component peptides (which can be subsequently identified by MS3), sensitivity can be further improved in the future by the addition of pre-fractionation steps, the use of affinity-tagged cross-linking agents or complementary chemistry , digestion with complementary proteases, and the development of dedicated software for the analysis of complex XL-MS datasets.High-quality large-scale interactome datasets have also been assembled using strategies such as XL-MS, which provides additional information about the topographical structure of protein complexes . In the 43. This approach avoids affinity purification steps and instead separates and maps protein complexes using a variety of approaches that include density gradients and size-exclusion, ion-exclusion, and hydrophobicity interaction chromatography. Given the range of separation options available, PCP-MS also offers significant scope for advancement in the future.Similarly, PCP-MS studies  also conAlthough XL-MS does identify direct protein interactions, the other approaches discussed above  can confirm only that proteins exist in the same multiprotein complex. A complementary technique that has been used for more than 20 years to detect direct PPI is the Y2H assay. In this approach, the bait and prey proteins are tagged to the DNA binding and transcriptional activation domains of a split transcription factor, and direct binding drives its reconstitution and subsequent activation of a reporter gene . Althoug45. With the long-term goal of mapping the full range of human binary PPIs, their most recent update added about 14,000 new binary interactions, bringing the current total to about 17,000. The full dataset, and future updates, can be browsed using their dedicated web interface  in yeast48 and the TAP (tandem affinity purification)-MS based screening of 1,590 putative budding yeast membrane proteins using three different mild, non-denaturing detergent purification strategies in parallel49  demonstrates that these challenges are also surmountable.Additional technical challenges, posed by their hydrophobic nature, have particularly hampered the identification of PPIs among membrane proteins . However, the success of recent large-scale initiatives such as the mapping of more than 12,000 binary interactions betweenOther challenges include the necessity to define PPIs over a wider range of biological contexts, given that some are cell cycle- or developmental stage-specific, for example, or occur only under particular physiological conditions or in response to specific post-translational modifications. An ambitious future goal is a comprehensive and quantitative high-throughput approach that combines gene-editing with live super-resolution imaging and interactome mapping to define the dynamic localization, composition, and topography of functional multiprotein complexes.AP-MS, affinity purification-mass spectrometry; CORUM, Comprehensive Resource of Mammalian protein complexes; ORF, open reading frame; PCP, protein correlation profiling; PPI, protein-protein interaction; XL-MS; cross-linking mass spectrometry; Y2H, yeast two-hybrid.


The high-resolution refinement of docked protein-protein complexes can provide valuable structural and mechanistic insight into protein complex formation complementing experiment. Monte Carlo (MC) based approaches are frequently applied to sample putative interaction geometries of proteins including also possible conformational changes of the binding partners. In order to explore efficiency improvements of the MC sampling, several enhanced sampling techniques, including temperature or Hamiltonian replica exchange and well-tempered ensemble approaches, have been combined with the MC method and were evaluated on 20 protein complexes using unbound partner structures. The well-tempered ensemble method combined with a 2-dimensional temperature and Hamiltonian replica exchange scheme (WTE-H-REMC) was identified as the most efficient search strategy. Comparison with prolonged MC searches indicates that the WTE-H-REMC approach requires approximately 5 times fewer MC steps to identify near native docking geometries compared to conventional MC searches. Protein-protein interactions are integral to many mechanisms of cellular activities, ranging from enzyme catalysis and inhibition to signal transduction and gene regulation. Atomic-level structural knowledge is essential to understand the function of protein-protein complexes in biological processes. However, experimental structure determination of protein-protein complexes is often difficult and for many interactions the corresponding complex structures are lacking ,2. CompuDocking programs often employ a two-stage protocol ,9. FirstFor high-resolution refinement, most protocols require a starting configuration that is already fairly close to the native structure. The likely reason for this strong dependence on the starting structure is the energy landscape’s ruggedness, which frustrates the sampling and renders the global energy minimum hard to reach . AccordiFocus of this work is to improve the sampling for high-resolution docking based on the MC approach. In the MC method, random translational and rotational moves or conformational changes of the partner structures are applied on the configuration in a step-wise manner using the Metropolis criterion for acceptance of a move. Advantages of the MC method are the generation of a physically relevant canonical ensemble of docking configurations, use of arbitrary energy functions that can contain discontinuities and for the possibility to include various levels of structural flexibility. However, an exhaustive high-resolution sampling of the conformational space with the MC method can be computationally demanding. In general, the docking success of MC docking is limited by the sampling of putative complex geometries and by the accuracy of the energy function used for scoring predicted complexes.Parallel tempering or replica exchange techniques promise to overcome these challenges and have received wide-spread interest in recent years –25. The Metadynamics is another popular enhanced sampling method, in which sampling is facilitated by a history-dependent biasing potential. It is constructed as the sum of Gaussian functions deposited along the trajectory in the collective variable space ,38. ChooRosettaDock and various extensions based on advanced sampling techniques. In particular, we tested four different protocols, standard Monte Carlo (MC), Temperature Replica Exchange Monte Carlo (REMC), well-tempered ensemble temperature Replica Exchange Monte Carlo (WTE-REMC), and well-tempered ensemble two dimensional Hamiltonian Replica Exchange Monte Carlo (WTE-H-REMC). The approaches were systematically evaluated on protein-protein complexes using unbound partner structures and starting in each case from the same starting placements. Overall best performance was achieved with the WTE-H-REMC method at the same computational cost compared to the alternative protocols.In the present study we have compared the efficiency of a standard MC protocol for high resolution protein-protein docking using RosettaDock as given by the weight-set docking , and the same I_sc range of [-15.0 Rosetta score units]. For each target, the tested protocols are grouped together and the corresponding protocol is indicated in the score-axis label on the left side. The snapshot number is color-coded, with dark blue and dark red dots corresponding to decoys sampled at the beginning and towards the end of the sampling interval, respectively. Figure B. Same as Figure A in S1 File but for the docking searches up to 2x106MC steps. Figure C. Same as Figure A in S1 File but for the docking refinement simulation with 107MC steps. Figure D. Scatter plot of interaction score (I_sc) vs. ligand RMSD (L_rmsd) for the additional 10 targets with 3x105MC steps. All the panels have the same L_rmsd range of [0.30], and the same I_sc range of [-15.0]. For each target, the tested protocols are grouped together and the corresponding protocol is indicated in the score-axis label on the left side. The snapshots number is color-coded, with dark blue and dark red dots corresponding to decoys sampled at the beginning and the end, respectively. Figure E. Same as Figure D in S1 File but for docking searches up to 2x106MC steps. Figure F. Same as Figure D in S1 File but for docking searches with 107MC steps.(PDF)Click here for additional data file.


All processes that maintain and replicate a living cell involve fluctuating biological macromolecules. As computational biologists, our aim is to discern the behavior of macromolecules in a way that experimental biology is not able to achieve. No single technique—experimental or computational—can capture all the relevant scales of cellular functional behavior. In principle, computations are the tools that can integrate different kinds of experimental and computational characterizations at different resolutions to obtain a more complete description of the processes of life. Computer simulations can act as a bridge between the microscopic length and time scales, and the macroscopic world of the laboratory. They can start from a macroscopic experiment-based guess of interactions between molecules, and obtain “exact” predictions of bulk and detailed properties subject to limitations. They are able to test a theory by constructing and simulating the model, and comparing the results with experimental measurements; and they are able to provide models that experiments can test. Computations can provide leads by processing large sets of data, predicting molecular behaviors, and supplying the mechanistic underpinning that experiments alone may not be able to achieve.Macromolecules play a vital role in countless biological processes, including DNA replication, transcription, genome reorganization in development and in disease, protein synthesis, protein folding, and active transport with molecular motors. Cell signaling, a multistep pathway on length scales from nanometers to micrometers, provides another inclusive example, incorporating all of the above over time and space. Signals are relayed from the extracellular space to the nucleus through dynamic shifts of molecular ensembles. Macromolecular fluctuations underlie signal amplification; they result in a large number of activated molecules across the cell, creating multiple reactions and producing a major cellular response. Changes in fluctuations through binding second messengers can regulate catalysis, and dynamic shifts in conformational ensembles can also take place through binding to membrane lipids. Key hub proteins that govern cell behaviors are often membrane-anchored. Helped by experimental data, computations can model the components of the systems and their transient interactions to provide a useful, integrated view of the flow of information, its regulation, and its deregulation.Ultimately, we want to make direct quantitative comparisons with experimental data. We would like to reduce the amount of fitting and guesswork; but at the same time we may also be interested in phenomena of a generic nature, or in discriminating between good and bad theories. Doing this well is challenging. To understand the dynamic interplay across multiple scales, to link it to the atomic-scale physicochemical basis of the conformational behavior of single molecules and their interactions, and, ultimately, to relate it to cellular function, we need efficient and reliable methods to sample the macromolecular fluctuations and identify the biological and disease-related states and their transitions. Inspiration may come from a combination of biology and other fields that model dynamic systems.Macromolecules move, and their movements are needed for a complete picture of life. Computational biology, with concepts imported from physics and chemistry, increasingly plays a major role, which has recently been recognized by the Nobel Committee . The eneComputational methods also include those for molecular modeling and refinement of three-dimensional structures, de novo design of proteins, prediction and modeling of protein-ligand interactions and development of docking protocols, and prediction of macromolecular interactions at varying spatial resolutions and timescales. They further encompass methods of ligand screening in drug development and protein-protein docking, methods for assessing sequence-structure-function relationships and prediction of macromolecular function, protocols for molecular visualization and annotation, and geometric and topological characterization of proteins and polynucleotides. This list is still far from complete.PLOS Computational Biology presents a special collection of manuscripts focusing on methods exploring macromolecular structure and dynamics. This collection does not aim to cover all methods; it does, however, aim to provide a taste of currently available approaches and strategies toward these aims. Altogether, the collection covers a broad ground: from sampling, detection of rare events, and exposing hidden alternative backbone conformations in X-ray crystallography, to multi-scale visualization of molecular architecture using real-time ambient occlusion; from discrimination between obligatory and non-obligatory protein-protein interactions based on the dynamics of the complex to binding free energies of inhibitors, to predicting the effect of mutations on protein-protein interactions by exploiting interface profiles; from a virtual mixture approach to the study of multistate equilibrium, to identification of misfolded intermediates; from multiscale estimation of binding kinetics using a combination of Brownian dynamics, molecular dynamics, and mile-stoning, to mapping the protein fold universe. This collection underscores the breadth of computational methods in structural biology, and only some of them made it into this special PLOS Computational Biology collection.To celebrate its tenth anniversary , PLOS CoComputational structural biology has made tremendous progress over the last two decades. Computational methods were developed for protein structure prediction, macromolecular function and protein design, as well as for drug discovery. It has also undertaken computational challenges related to experimental approaches in structural biology. Along with new experimental tools, higher resolution, and the rising efficiency of experimental approaches leading to huge amounts of accumulating data, computational biology is pushing the frontiers to meet its challenges. We expect that, in the future, the focus of our methods and tools may shift and more integrative tools will be developed, along with methodological adaptation to massively larger quantities of data.PLOS Computational Biology aims to meet this challenge and place a larger focus on this important and certain-to-become-central area in the biological sciences. Clearly, many challenges remain for computational biologists in the coming years.Experimentally, the biological and chemical sciences are now attempting to push boundaries in drug discovery. We may expect that a translational direction will also prevail in computational structural biology. This, however, does not mean only direct drug discovery; for these efforts to be successful, the underlying mechanistic basis of diseases needs to be understood as well. In addition, we expect even stronger emphasis on the human microbiome and its relationship to human health. PLOS Computational Biology, along with the computational biology community and the International Society for Computational Biology (ISCB), are poised to take on this challenge. We view this collection as the first in this direction, helping the community toward this aim.