Predicting protein–protein interaction sites using modified support vector machine

Guo, Hong; Liu, Bingjing; Cai, Duan; Lu, Tun

doi:10.1007/s13042-015-0450-6

Cited by 36 publications

(14 citation statements)

References 6 publications

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“…Previous studies showed that support vector machine (SVM) and its improved methods can predict effectively protein interaction sites [9][10][11][12][13][14]. Computational algorithms such as random forests, KNN, and Naive Bayes Classifier have been also applied to the prediction of PPIs [15][16][17][18]. Wang et al proposed a new method for predicting protein interaction sites in hetero-complexes using a radial basis function neural network (RBFNN) set model, which uses only evolutionary conservation information and spatial sequence profile of proteins, and achieved a good predictive result [19].…”

Section: Introductionmentioning

confidence: 99%

Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm

Deng

Zhang

Wang

et al. 2020

IJMS

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The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of protein interactions have been confirmed by experimental techniques, which greatly affects the predictive capability of computational methods. In this work, two imbalanced data processing strategies based on XGBoost algorithm were proposed to re-balance the original dataset from inherent relationship between positive and negative samples for the prediction of protein-protein interaction sites. Herein, a feature extraction method was applied to represent the protein interaction sites based on evolutionary conservatism of proteins, and the influence of overlapping regions of positive and negative samples was considered in prediction performance. Our method showed good prediction performance, such as prediction accuracy of 0.807 and MCC of 0.614, on an original dataset with 10,455 surface residues but only 2297 interface residues. Experimental results demonstrated the effectiveness of our XGBoost-based method.

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Section: Introductionmentioning

confidence: 99%

Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm

Deng

Zhang

Wang

et al. 2020

IJMS

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“…XGBPRH employs XGBoost as the classifier to determine the hot spots in protein–RNA interfaces with the six optimal features. In order to demonstrate the effectiveness of XGBoost, we used support vector machines (SVMs) [64], random forest (RF), and gradient tree boosting (GTB) to build different models and compared them with XGBPRH. Comparisons were performed with 10-fold cross validation over 50 trials according to the six optimal features.…”

Section: Resultsmentioning

confidence: 99%

XGBPRH: Prediction of Binding Hot Spots at Protein–RNA Interfaces Utilizing Extreme Gradient Boosting

Deng

Sui

Zhang

2019

Genes

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Hot spot residues at protein–RNA complexes are vitally important for investigating the underlying molecular recognition mechanism. Accurately identifying protein–RNA binding hot spots is critical for drug designing and protein engineering. Although some progress has been made by utilizing various available features and a series of machine learning approaches, these methods are still in the infant stage. In this paper, we present a new computational method named XGBPRH, which is based on an eXtreme Gradient Boosting (XGBoost) algorithm and can effectively predict hot spot residues in protein–RNA interfaces utilizing an optimal set of properties. Firstly, we download 47 protein–RNA complexes and calculate a total of 156 sequence, structure, exposure, and network features. Next, we adopt a two-step feature selection algorithm to extract a combination of 6 optimal features from the combination of these 156 features. Compared with the state-of-the-art approaches, XGBPRH achieves better performances with an area under the ROC curve (AUC) score of 0.817 and an F1-score of 0.802 on the independent test set. Meanwhile, we also apply XGBPRH to two case studies. The results demonstrate that the method can effectively identify novel energy hotspots.

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“… 50 , and machine learning based approach of Guo et al . 51 . The non-redundant database of PPIIs (NRDB) is created to facilitate the developments of such prediction tools.…”

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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Predicting protein–protein interaction sites using modified support vector machine

Cited by 36 publications

References 6 publications

Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm

Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm

XGBPRH: Prediction of Binding Hot Spots at Protein–RNA Interfaces Utilizing Extreme Gradient Boosting

PPInS: a repository of protein-protein interaction sitesbase

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