Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier

You, Zhu-Hong; Chen, Xing; Li, Liping; Huang, De-Shuang; Ge, Yan; Nie, Rencan; Huang, Yuan

doi:10.18632/oncotarget.15564

Cited by 36 publications

(10 citation statements)

References 32 publications

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“…The evolutionary data in the form of Position-Specific Scoring Matrix (PSSM) profile are informative and have proved useful in a number of biological classification problems [28,[33][34][35][36][37][38][39][40][41][42][43][44][45]. In this work, the PSSM profile was generated by running PSI-BLAST against the uniref50 database with the parameters j = 3 and h = 0.001.…”

Section: Position-specific Scoring Matrix Based Transformation (Pssm)mentioning

confidence: 99%

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Zhang

Xie

Wang

et al. 2018

Briefings in Bioinformatics

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As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods (random forest, support vector machines, K-nearest neighbor and logistic regression) and one recently proposed [Light Gradient Boosting Machine (LightGBM)] are trained on data from three species, namely, Escherichia coli, Mus musculus and Homo sapiens, and compared using randomized 10-fold cross-validation tests. We show that integration of the single method-based models through ensemble learning further improves the prediction performance and model robustness on the independent test. When compared to the existing state-of-the-art predictor, MaloPred, the optimal ensemble models were more accurate for all three species (AUC: 0.930, 0.923 and 0.944 for E. coli, M. musculus and H. sapiens, respectively). Using the ensemble models, we developed an accessible online predictor, kmal-sp, available at http://kmalsp.erc.monash.edu/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for PTM site prediction, expedite the discovery of new malonylation and other PTM types and facilitate hypothesis-driven experimental validation of novel malonylated substrates and malonylation sites.

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Section: Position-specific Scoring Matrix Based Transformation (Pssm)mentioning

confidence: 99%

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Zhang

Xie

Wang

et al. 2018

Briefings in Bioinformatics

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“…47 , Li et al . 48 , Huang et al . 49 , position-specific scoring matrix based approaches for identification of self-interacting proteins by An et al .…”

Section: Discussionmentioning

confidence: 99%

PPInS: a repository of protein-protein interaction sitesbase

Kumar

Mahato

Munshi

et al. 2018

Sci Rep

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Protein-Protein Interaction Sitesbase (PPInS), a high-performance database of protein-protein interacting interfaces, is presented. The atomic level information of the molecular interaction happening amongst various protein chains in protein-protein complexes (as reported in the Protein Data Bank [PDB]) together with their evolutionary information in Structural Classification of Proteins (SCOPe release 2.06), is made available in PPInS. Total 32468 PDB files representing X-ray crystallized multimeric protein-protein complexes with structural resolution better than 2.5 Å had been shortlisted to demarcate the protein-protein interaction interfaces (PPIIs). A total of 111857 PPIIs with ~32.24 million atomic contact pairs (ACPs) were generated and made available on a web server for on-site analysis and downloading purpose. All these PPIIs and protein-protein interacting patches (PPIPs) involved in them, were also analyzed in terms of a number of residues contributing in patch formation, their hydrophobic nature, amount of surface area they contributed in binding, and their homo and heterodimeric nature, to describe the diversity of information covered in PPInS. It was observed that 42.37% of total PPIPs were made up of 6–20 interacting residues, 53.08% PPIPs had interface area ≤1000 Å2 in PPII formation, 82.64% PPIPs were reported with hydrophobicity score of ≤10, and 73.26% PPIPs were homologous to each other with the sequence similarity score ranging from 75–100%. A subset “Non-Redundant Database (NRDB)” of the PPInS containing 2265 PPIIs, with over 1.8 million ACPs corresponding to the 1931 protein-protein complexes (PDBs), was also designed by removing structural redundancies at the level of SCOP superfamily (SCOP release 1.75). The web interface of the PPInS (http://www.cup.edu.in:99/ppins/home.php) offers an easy-to-navigate, intuitive and user-friendly environment, and can be accessed by providing PDB ID, SCOP superfamily ID, and protein sequence.

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“…This article is a further expansion of our previous works [21, 22]. In this work, we presented a novel in silico method for predicting interactions among proteins from protein amino acid sequences by means of Discriminative Vector Machine (DVM) model and 2-Dimensional Principal Component Analysis (2DPCA) descriptor.…”

Section: Introductionmentioning

confidence: 95%

Using discriminative vector machine model with 2DPCA to predict interactions among proteins

et al. 2019

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BackgroundThe interactions among proteins act as crucial roles in most cellular processes. Despite enormous effort put for identifying protein-protein interactions (PPIs) from a large number of organisms, existing firsthand biological experimental methods are high cost, low efficiency, and high false-positive rate. The application of in silico methods opens new doors for predicting interactions among proteins, and has been attracted a great deal of attention in the last decades.ResultsHere we present a novelty computational model with the adoption of our proposed Discriminative Vector Machine (DVM) model and a 2-Dimensional Principal Component Analysis (2DPCA) descriptor to identify candidate PPIs only based on protein sequences. To be more specific, a 2DPCA descriptor is employed to capture discriminative feature information from Position-Specific Scoring Matrix (PSSM) of amino acid sequences by the tool of PSI-BLAST. Then, a robust and powerful DVM classifier is employed to infer PPIs. When applied on both gold benchmark datasets of Yeast and H. pylori, our model obtained mean prediction accuracies as high as of 97.06 and 92.89%, respectively, which demonstrates a noticeable improvement than some state-of-the-art methods. Moreover, we constructed Support Vector Machines (SVM) based predictive model and made comparison it with our model on Human benchmark dataset. In addition, to further demonstrate the predictive reliability of our proposed method, we also carried out extensive experiments for identifying cross-species PPIs on five other species datasets.ConclusionsAll the experimental results indicate that our method is very effective for identifying potential PPIs and could serve as a practical approach to aid bioexperiment in proteomics research.

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Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier

Cited by 36 publications

References 32 publications

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework

PPInS: a repository of protein-protein interaction sitesbase

Using discriminative vector machine model with 2DPCA to predict interactions among proteins

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