Diverse effects of distance cutoff and residue interval on the performance of distance-dependent atom-pair potential in protein structure prediction

BMC Bioinformatics

Research Article

Yuangen Yao¹  Quan Liu¹  Rong Gui¹  Ming Yi¹  Haiyou Deng² 

[1] Department of Physics, College of Science, Huazhong Agricultural University, 430070, Wuhan, China;Department of Physics, College of Science, Huazhong Agricultural University, 430070, Wuhan, China;Institute of Applied Physics, Huazhong Agricultural University, 430070, Wuhan, China;

关键词: Distance-dependent atom-pair potential;    Protein structure prediction;    Distance cutoff;    Residue interval;    Reference state;   

DOI  :  10.1186/s12859-017-1983-3

 received in 2017-08-10, accepted in 2017-12-04,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundAs one of the most successful knowledge-based energy functions, the distance-dependent atom-pair potential is widely used in all aspects of protein structure prediction, including conformational search, model refinement, and model assessment. During the last two decades, great efforts have been made to improve the reference state of the potential, while other factors that also strongly affect the performance of the potential have been relatively less investigated.ResultsBased on different distance cutoffs (from 5 to 22 Å) and residue intervals (from 0 to 15) as well as six different reference states, we constructed a series of distance-dependent atom-pair potentials and tested them on several groups of structural decoy sets collected from diverse sources. A comprehensive investigation has been performed to clarify the effects of distance cutoff and residue interval on the potential’s performance. Our results provide a new perspective as well as a practical guidance for optimizing distance-dependent statistical potentials.ConclusionsThe optimal distance cutoff and residue interval are highly related with the reference state that the potential is based on, the measurements of the potential’s performance, and the decoy sets that the potential is applied to. The performance of distance-dependent statistical potential can be significantly improved when the best statistical parameters for the specific application environment are adopted.

【 授权许可】

CC BY   
© The Author(s). 2017

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