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Modulating protein–protein interactions (PPIs) with small drug-like molecules targeting it exhibits great promise in modern drug discovery. G protein-coupled receptors (GPCRs) are the largest family of targeted proteins and could form dimers in living biological cells through PPIs. However, compared to drug development of the orthosteric site, there has been lack of investigations on the druggability of the PPI interface for GPCRs and its functional implication on experiments. Thus, in order to address these issues, we constructed a novel computational strategy, which involved in molecular dynamics simulation, virtual screening and protein structure network (PSN), to study one representative GPCR homodimer (CXCR4). One druggable pocket was identified in the PPI interface and one small molecule targeting it was screened, which could strengthen PPI mainly through hydrophobic interaction between the benzene rings of the PPI molecule and TM4 of the receptor. The PSN results further reveals that the PPI molecule could increase the number of the allosteric regulation pathways between the druggable pocket of the dimer interface to the orthostatic site for the subunit A but only play minor role for the other subunit B, leading to the asymmetric change in the volume of the binding pockets for the two subunits (increase for the subunit A and minor change for the subunit B). Consequently, the screening performance of the subunit A to the antagonists is enhanced while the subunit B is unchanged nearly, implying that the PPI molecule may be beneficial to enhance the drug efficacies of the antagonists. In addition, one main regulation pathway with the highest frequency was identified for the subunit A, which consists of Trp1955.34–Tyr190ECL2–Val1965.35–Gln2005.39–Asp2626.58–Cys28N-term, revealing their importance in the allosteric regulation from the PPI molecule. The observations from the work could provide valuable information for the development of the PPI drug-like molecule for GPCRs. [ABSTRACT FROM AUTHOR]

Single-nucleotide polymorphisms (SNPs) are the most frequent form of genetic variations. Non-synonymous SNPs (nsSNPs) occurring in coding region result in single amino acid substitutions that associate with human hereditary diseases. Plenty of approaches were designed for distinguishing deleterious from neutral nsSNPs based on sequence level information. Novel in this work, combinations of protein-protein interaction (PPI) network topological features were introduced in predicting disease-related nsSNPs. Based on a dataset that was compiled from Swiss-Prot, a random forest model was constructed with an average accuracy value of 80.43 % and an MCC value of 0.60 in a rigorous tenfold crossvalidation test. For an independent dataset, our model achieved an accuracy of 88.05 % and an MCC of 0.67. Compared with previous studies, our approach presented superior prediction ability. Results showed that the incorporated PPI network topological features outperform conventional features. Our further analysis indicated that disease-related proteins are topologically different from other proteins. This study suggested that nsSNPs may share some topological information of proteins and the change of topological attributes could provide clues in illustrating functional shift due to nsSNPs. [ABSTRACT FROM AUTHOR]

RNA-protein interactions play a pivotal role in various biological processes, such as mRNA processing, protein synthesis, assembly, and function of ribosome. In this work, we have introduced a computational method for predicting RNA-binding sites in proteins based on support vector machines by using a variety of features from amino acid sequence information including position-specific scoring matrix (PSSM) profiles, physicochemical properties and predicted solvent accessibility. Considering the influence of the surrounding residues of an amino acid and the dependency effect from the neighboring amino acids, a sliding window and a smoothing window are used to encode the PSSM profiles. The outer fivefold cross-validation method is evaluated on the data set of 77 RNA-binding proteins (RBP77). It achieves an overall accuracy of 88.66% with the Matthew's correlation coefficient (MCC) of 0.69. Furthermore, an independent data set of 39 RNA-binding proteins (RBP39) is employed to further evaluate the performance and achieves an overall accuracy of 82.36% with the MCC of 0.44. The result shows that our method has good generalization abilities in predicting RNA-binding sites for novel proteins. Compared with other previous methods, our method performs well on the same data set. The prediction results suggest that the used features are effective in predicting RNA-binding sites in proteins. The code and all data sets used in this article are freely available at . [ABSTRACT FROM AUTHOR]

With the rapid increase in crystal structures of protein-protein complexes deposited in the Protein Data Bank (PDB), more and more crystal contacts have been shown to have similar or even larger interface areas than biological interfaces. However, little attention has been paid to these large crystal packing contacts and their structural principles remain unknown. To address this issue, we used a comparative feature analysis to analyze the geometric and physicochemical properties of large crystal packing contacts by comparing two types of specific protein-protein interactions (PPIs), weak transient complexes and permanent homodimers. Our results show that although large crystal packing contacts have a similar interface area and contact size as permanent homodimers, they tend to be more planar, loosely packed and less hydrophobic than permanent homodimers and cannot form a central core region that is fully buried during interaction. However, the properties of large crystal packing contacts, except for the interface area and contact size, more closely resemble those of weak transient complexes. The large overlap between biological and large crystal packing contacts indicates that interface properties are not efficient indicators for classification of biological interfaces from large crystal packing contacts and finding other specific features urgently needed. [ABSTRACT FROM AUTHOR]

Benefiting from the high-throughput sequencing technologies, many single nucleotide variants (SNVs) among individuals have been detected. SNVs in gene code regions were known to possibly disrupt protein functions. For this, many efforts were devoted to sort deleterious SNVs from benign ones. In general, features in the past studies can be categorized into codon level, peptide level and protein level. While those at peptide level were in widespread use, few works have carried out a comprehensive analysis by combining three levels information. In the present work, we incorporated both codon and protein level information with peptide level information to predict disease-related SNVs. Taking the advantage of combinatory multiple level features, our method exhibited competitive performance against seven well-known classifiers. Additionally, by incorporating selective pressure score and protein–protein interaction (PPI) information, we found that the functional important proteins were protected through a pressure-resistant mechanism during the evolution. Although critical proteins were obviously related with more deleterious SNVs, these pathogenic SNVs were tend to under higher selective pressures comparing to the benign variants. These results support the ongoing researches about relation between genotype and phenotype. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets. [ABSTRACT FROM AUTHOR]

Protein–protein interactions play fundamental roles in almost all biological processes. Determining the protein–protein binding affinity has been recognized not only as an important step but also as a challenging task for further understanding of the molecular mechanism and the modeling of the biological systems. Unlike the traditional methods like empirical scoring algorithms and molecular dynamic which are time consuming, we developed a fast and reliable machine learning method for the prediction of protein–protein binding affinity. Based on diverse protein–protein interface features calculated using commonly used available tools, 432 features were obtained to represent hydrogen bond, Van der Waals force, hydrophobic interaction, electrostatic force, interface shape and configuration and allosteric effect. Considering the limited number of the available structures and affinity-known protein complexes, in order to avoid overfitting and remove noises in the feature set, feature importance evaluation was implemented and 154 optimal features were selected, then the prediction model based on random forest (RF) was constructed. We demonstrate that the RF model yields promising results and the predictive power of our method is better than other existing methods. Using leave-one-out cross-validation, our model gives a correlation coefficient ( r ) of 0.708 on the whole benchmark dataset of 133 complexes and a high r of 0.806 on the validated set of 53 samples. When performing the same two independent datasets, our method outperforms other two methods and achieves a high r of 0.793 and 0.907 respectively. All results indicate that our method can be a useful implement in determining protein–protein binding affinity. [ABSTRACT FROM AUTHOR]

Abstract: Deciphering interactions between protein kinases (PKs) and the target substrates are fundamental for understanding the molecular mechanisms of phosphorylation. Although all PKs have been identified in eukaryotes, the sites that they phosphorylate are only partially elucidated. Experimental identification of phosphorylation sites is labor and resource intensive, so developing an effective method to computationally predict potential sites is increasingly important. Here, a novel method was proposed for the identification of kinase-specific phosphorylation sites based on domain–domain interactions (DDIs). Using difference analysis between phosphorylation sites and non-phosphorylation sites, the distinct neighbor residues around the phosphorylation sites were firstly identified in our study. The results of difference analysis by rank sum test indicate that 19, 26, 26 and 10 neighbor residues are distinctive for the phosphorylation site prediction of four major serine (S)/threonine (T) protein kinase families—CDK, CK2, PKA and PKC respectively. Then the correlation coefficients were computed to represent the interaction between PK domains and phosphorylation domains of the substrate proteins. Four random forest models (RF) were constructed to predict the potential sites, the CDK, CK2, PKA and PKC RF models yield an accuracy of 86.57%, 91.44%, 87.02% and 80.11% on the test sets respectively. Finally, the new substrate proteins in protein data bank (PDB) were extracted to verify the distinct residues around the phosphorylation sites at 3D-structural level and the results further demonstrate the reliability of our models, which indicate that our method will be a useful tool for elucidating dynamic interactions between PKs and their substrates. [Copyright &y& Elsevier]

Abstract: Hot spots residues in protein–protein interface play crucial roles in protein binding. In the present study, complex network method was applied to uncover influence of neighboring residues on hot spots and then several network and microenvironment features were designed to describe the diversity of environment of hot spots. After feature analysis by permutation importance in Random Forest (RF), an optimal 58-dimensional feature set including ten network and microenvironment features was selected and then applied to construct a Support Vector Machine (SVM) prediction model for hot spots. A satisfactory accuracy (ACC) value of 79.0% and a Mathew's correlation coefficient (MCC) value of 0.470 were obtained for independent test set. The novel network features and microenvironment features were proved to be promising in discovering hot spots in interfaces. A further microenvironment analysis was also performed. Amino acid residues directly contacting with hot spots in residue–residue interaction network exhibit significant importance for the microenvironment of hot spots. Amino acid alanine (A), aspartic acid (D), glycine (G), histidine (H), isoleucine (I), asparagine (N), serine (S) and tyrosine (Y) are more likely to occur in the vicinity of hot spots than in the vicinity of non-hot spots. These amino acid residues probably cluster together to construct a proper microenvironment for hot spots. [Copyright &y& Elsevier]

Abstract: Protein kinases mediate many important cellular and molecular processes by phosphorylating specific substrates. Therefore, it is of great significance to properly detect the functions of these kinases and their down-stream substrates. However, only 35% of human kinases have known substrates, but protein–protein interaction (PPI) data are available for 85% of them. So it is probable that we can reveal potential kinase–substrate pairs using these PPIs. In this paper, we compiled five unbiased interaction datasets of four major serine/threonine (S/T) protein kinase families—CDK, CK2, PKA, PKC and an important tyrosine (Y) kinase family—SRC. Based on these datasets, prediction models for identifying kinase-specific interactions were developed by random forest (RF). Seven physicochemical properties of amino acids and auto covariance (AC) transform were used for numerical representation of protein sequences. Then permutation importance analysis was used for feature optimization and 102 optimal features were selected from all the 420 variables. The model gives an area under the receiver operating curve (AUC) values of 0.8785 in classifying phosphorylational and non-phosphorylational interactions of kinases and their substrates. For predicting kinase family-specific phosphorylational interactions, when performing on the independent datasets, the method yields the prediction accuracy of over 90% for CDK, CK2, PKA, PKC and SRC. Finally tolerance test and shuffling experiment were done to further verify the reliability of the models. All results demonstrate a useful method for identifying new PPIs of different kinase families. [Copyright &y& Elsevier]

Abstract: In proteins, the number of interacting pairs is usually much smaller than the number of non-interacting ones. So the imbalanced data problem will arise in the field of protein–protein interactions (PPIs) prediction. In this article, we introduce two ensemble methods to solve the imbalanced data problem. These ensemble methods combine the based-cluster under-sampling technique and the fusion classifiers. And then we evaluate the ensemble methods using a dataset from Database of Interacting Proteins (DIP) with 10-fold cross validation. All the prediction models achieve area under the receiver operating characteristic curve (AUC) value about 95%. Our results show that the ensemble classifiers are quite effective in predicting PPIs; we also gain some valuable conclusions on the performance of ensemble methods for PPIs in imbalanced data. The prediction software and all dataset employed in the work can be obtained for free at http://cic.scu.edu.cn/bioinformatics/Ensemble_PPIs/index.html. [Copyright &y& Elsevier]

Biomolecules in organisms play various functions, which are largely achieved by linking with multiple interacting partners. The comprehensive study about these links could facilitate our systematic understand on biology progress. Recently, RNA-RNA interaction analysis provides a sight in disease study by revealing the RNA regulatory mechanisms. However, many studies have focused on specific types of RNAs, and the few have been reported from the perspective of the genome wide RNA-RNA interactions. In this study, we constructed an RNA-RNA interaction network by extensively collecting reported RNA-RNA interactions and then compared it with the protein-protein interaction and the gene co-expression networks. Significant diversities were observed between these networks, involving the composition, the topological features and the pattern of the expression correlation between RNAs. When applied the RNA-RNA interaction network into the breast cancer, 15 miRNAs were identified as hub nodes and verified related to breast cancer. The topological properties of mRNAs in the network associated with their expression pattern and breast cancer correlation. The differentially expressed lncRNAs tend to interact with the differentially expressed mRNAs, indicating their importance in gene regulatory. These results suggest that RNA-RNA interaction network could be anticipated as a complement to the well-studied protein-protein interaction and co-expression network methods. • A genome wide RNA-RNA interaction (RRI) network is proposed for the transcriptomics large-scale data analysis. • Significant diversities are observed between the RRI, protein-protein interaction (PPI), and gene co-expression networks. • The RRI network could be anticipated as a complement to the well-studied PPI and co-expression network methods. [ABSTRACT FROM AUTHOR]