Hot Spot Prediction in Protein-protein Interactions by an Ensemble System

Overview

Journal BMC Syst Biol

Publisher Biomed Central

Specialty Biology

Date 2019 Jan 2

PMID 30598091

Citations 17

Authors

Quanya Liu

Peng Chen

Bing Wang

Jun Zhang

Jinyan Li

Affiliations

Soon will be listed here.

Abstract

Background: Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features.

Results: This paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance.

Conclusion: The experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction.

Availability: http://deeplearner.ahu.edu.cn/web/HotspotEL.htm .

Citing Articles

PPI-hotspot for detecting protein-protein interaction hot spots from the free protein structure.

Chen Y, Sargsyan K, Wright J, Chen Y, Huang Y, Lim C Elife. 2024; 13.

PMID: 39283314 PMC: 11405013. DOI: 10.7554/eLife.96643.

Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model.

Bao W, Liu Y, Chen B Front Microbiol. 2024; 14:1277121.

PMID: 38384719 PMC: 10879614. DOI: 10.3389/fmicb.2023.1277121.

Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network.

Zhang Y, Yao S, Chen P PLoS One. 2023; 18(9):e0290899.

PMID: 37721924 PMC: 10506709. DOI: 10.1371/journal.pone.0290899.

RF_phage virion: Classification of phage virion proteins with a random forest model.

Zhang Y, Li Z Front Genet. 2023; 13:1103783.

PMID: 36846294 PMC: 9945117. DOI: 10.3389/fgene.2022.1103783.

Glycoprotein attachment with host cell surface receptor ephrin B2 and B3 in mediating entry of nipah and hendra virus: a computational investigation.

Priyadarsinee L, Sarma H, Sastry G J Chem Sci (Bangalore). 2022; 134(4):114.

PMID: 36465097 PMC: 9685031. DOI: 10.1007/s12039-022-02110-9.

References

Liu B, Wu H, Zhang D, Wang X, Chou K . Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods. Oncotarget. 2017; 8(8):13338-13343. PMC: 5355101. DOI: 10.18632/oncotarget.14524. View

Romero R, Iglesias E, Borrajo L . A linear-RBF multikernel SVM to classify big text corpora. Biomed Res Int. 2015; 2015:878291. PMC: 4386713. DOI: 10.1155/2015/878291. View

Liu Q, Ren J, Song J, Li J . Co-Occurring Atomic Contacts for the Characterization of Protein Binding Hot Spots. PLoS One. 2015; 10(12):e0144486. PMC: 4684219. DOI: 10.1371/journal.pone.0144486. View

Marsh J, Teichmann S . Relative solvent accessible surface area predicts protein conformational changes upon binding. Structure. 2011; 19(6):859-67. PMC: 3145976. DOI: 10.1016/j.str.2011.03.010. View

Tuncbag N, Gursoy A, Keskin O . Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics. 2009; 25(12):1513-20. DOI: 10.1093/bioinformatics/btp240. View

Chen Z, Zhao P, Li F, Leier A, Marquez-Lago T, Wang Y . iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics. 2018; 34(14):2499-2502. PMC: 6658705. DOI: 10.1093/bioinformatics/bty140. View

Liu Q, Hoi S, Keong Kwoh C, Wong L, Li J . Integrating water exclusion theory into β contacts to predict binding free energy changes and binding hot spots. BMC Bioinformatics. 2014; 15:57. PMC: 3941611. DOI: 10.1186/1471-2105-15-57. View

Otaki J, Tsutsumi M, Gotoh T, Yamamoto H . Secondary structure characterization based on amino acid composition and availability in proteins. J Chem Inf Model. 2010; 50(4):690-700. DOI: 10.1021/ci900452z. View

Chen P, Li J, Wong L, Kuwahara H, Huang J, Gao X . Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences. Proteins. 2013; 81(8):1351-62. DOI: 10.1002/prot.24278. View

10.

Hu S, Chen P, Wang B, Li J . Protein binding hot spots prediction from sequence only by a new ensemble learning method. Amino Acids. 2017; 49(10):1773-1785. DOI: 10.1007/s00726-017-2474-6. View

11.

Caufield J, Wimble C, Shary S, Wuchty S, Uetz P . Bacterial protein meta-interactomes predict cross-species interactions and protein function. BMC Bioinformatics. 2017; 18(1):171. PMC: 5353844. DOI: 10.1186/s12859-017-1585-0. View

12.

Zhang S, Pan Q, Zhang H, Shao Z, Shi J . Prediction of protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and Naive Bayes Feature Fusion. Amino Acids. 2006; 30(4):461-8. DOI: 10.1007/s00726-006-0263-8. View

13.

Moal I, Fernandez-Recio J . SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models. Bioinformatics. 2012; 28(20):2600-7. DOI: 10.1093/bioinformatics/bts489. View

14.

Tuncbag N, Keskin O, Gursoy A . HotPoint: hot spot prediction server for protein interfaces. Nucleic Acids Res. 2010; 38(Web Server issue):W402-6. PMC: 2896123. DOI: 10.1093/nar/gkq323. View

15.

Wells J . Systematic mutational analyses of protein-protein interfaces. Methods Enzymol. 1991; 202:390-411. DOI: 10.1016/0076-6879(91)02020-a. View

16.

Kim D, Chivian D, Baker D . Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. 2004; 32(Web Server issue):W526-31. PMC: 441606. DOI: 10.1093/nar/gkh468. View

17.

Xia J, Yue Z, Di Y, Zhu X, Zheng C . Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features. Oncotarget. 2016; 7(14):18065-75. PMC: 4951271. DOI: 10.18632/oncotarget.7695. View

18.

Xu D, Si Y, Meroueh S . A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein-Protein Interaction Interfaces. J Chem Inf Model. 2017; 57(9):2250-2272. PMC: 6148373. DOI: 10.1021/acs.jcim.7b00181. View

19.

Chen P, Hu S, Zhang J, Gao X, Li J, Xia J . A Sequence-Based Dynamic Ensemble Learning System for Protein Ligand-Binding Site Prediction. IEEE/ACM Trans Comput Biol Bioinform. 2015; 13(5):901-912. DOI: 10.1109/TCBB.2015.2505286. View

20.

Liu B, Liu F, Wang X, Chen J, Fang L, Chou K . Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res. 2015; 43(W1):W65-71. PMC: 4489303. DOI: 10.1093/nar/gkv458. View