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COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization

文献类型：期刊论文

作者Reza, Md Selim1,2; Zhang, Huiling1,2; Hossain, Md Tofazzal1,2; Jin, Langxi3; Feng, Shengzhong2; Wei, Yanjie1,2
刊名MEMBRANES
出版日期2021-07-01
卷号11期号:7页码:21
关键词protein residue-residue contact contact prediction mixed integer linear programming machine learning protein sequence
DOI10.3390/membranes11070503
英文摘要Protein contact prediction helps reconstruct the tertiary structure that greatly determines a protein's function; therefore, contact prediction from the sequence is an important problem. Recently there has been exciting progress on this problem, but many of the existing methods are still low quality of prediction accuracy. In this paper, we present a new mixed integer linear programming (MILP)-based consensus method: a Consensus scheme based On a Mixed integer linear opTimization method for prOtein contact Prediction (COMTOP). The MILP-based consensus method combines the strengths of seven selected protein contact prediction methods, including CCMpred, EVfold, DeepCov, NNcon, PconsC4, plmDCA, and PSICOV, by optimizing the number of correctly predicted contacts and achieving a better prediction accuracy. The proposed hybrid protein residue-residue contact prediction scheme was tested in four independent test sets. For 239 highly non-redundant proteins, the method showed a prediction accuracy of 59.68%, 70.79%, 78.86%, 89.04%, 94.51%, and 97.35% for top-5L, top-3L, top-2L, top-L, top-L/2, and top-L/5 contacts, respectively. When tested on the CASP13 and CASP14 test sets, the proposed method obtained accuracies of 75.91% and 77.49% for top-L/5 predictions, respectively. COMTOP was further tested on 57 non-redundant alpha-helical transmembrane proteins and achieved prediction accuracies of 64.34% and 73.91% for top-L/2 and top-L/5 predictions, respectively. For all test datasets, the improvement of COMTOP in accuracy over the seven individual methods increased with the increasing number of predicted contacts. For example, COMTOP performed much better for large number of contact predictions (such as top-5L and top-3L) than for small number of contact predictions such as top-L/2 and top-L/5. The results and analysis demonstrate that COMTOP can significantly improve the performance of the individual methods; therefore, COMTOP is more robust against different types of test sets. COMTOP also showed better/comparable predictions when compared with the state-of-the-art predictors.
资助项目National Key Research and Development Program of China[2018YFB0204403] ; Strategic Priority CAS Project[XDB38000000] ; National Science Foundation of China[U1813203] ; Shenzhen Basic Research Fund[JCYJ20200109114818703] ; Shenzhen Basic Research Fund[RCYX2020071411473419] ; Shenzhen Basic Research Fund[JSGG20201102163800001] ; CAS Key Lab[2011DP173015] ; Youth Innovation Promotion Association, CAS ; Outstanding Youth Innovation Fund (CAS-SIAT)
WOS研究方向Biochemistry & Molecular Biology ; Chemistry ; Engineering ; Materials Science ; Polymer Science
语种英语
WOS记录号WOS:000676905300001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/17443]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wei, Yanjie
作者单位1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Ctr High Performance Comp, Joint Engn Res Ctr Hlth Big Data Intelligent Anal, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
3.Harbin Univ Sci & Technol, Sch Comp Sci & Technol, Dept Comp Sci & Technol, 52 Xuefu Rd, Harbin 150080, Peoples R China
推荐引用方式
GB/T 7714		 Reza, Md Selim,Zhang, Huiling,Hossain, Md Tofazzal,et al. COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization[J]. MEMBRANES,2021,11(7):21.
APA Reza, Md Selim,Zhang, Huiling,Hossain, Md Tofazzal,Jin, Langxi,Feng, Shengzhong,&Wei, Yanjie.(2021).COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization.MEMBRANES,11(7),21.
MLA Reza, Md Selim,et al."COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization".MEMBRANES 11.7(2021):21.

入库方式： OAI收割

来源：计算技术研究所

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