BetaDL: A protein beta-sheet predictor utilizing a deep learning model and independent set solution

作     者：Dehghani, Toktam Naghibzadeh, Mahmoud Eghdami, Mandie 

作者机构：Ferdowsi Univ Mashhad Dept Comp Engn Mashhad Iran 

出 版 物：《COMPUTERS IN BIOLOGY AND MEDICINE》 (生物学与医学中的计算机)

年 卷 期：2019年第104卷

页      面：241-249页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 07[理学] 09[农学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to express their gratitude to Dr. Castrense Savojardo  Dr. Jessica Andreani  Dr. Johannes Soding  and Dr. Jinbo Xu for sharing their research results 

主　　题：Protein structure prediction Deep learning model Contact map Beta-sheet structure Graph search algorithm Independent set 

摘      要：The sequence-based prediction of beta-residue contacts and beta-sheet structures contain key information for protein structure prediction. However, the determination of beta-sheet structures poses numerous challenges due to long-range beta-residue interactions and the huge number of possible beta-sheet structures. Recently gaining attention has been the prediction of residue contacts based on deep learning models whose results have led to improvement in protein structure prediction. In addition, to reduce the computational complexity of determining beta-sheet structures, it has been suggested that this problem be transformed into graph-based solutions. Consequently, the current work proposes BetaDL, a combination of a deep learning and a graph-based beta-sheet structure predictor. BetaDL adopts deep learning models to capture beta-residue contacts and improve beta-sheet structure predictions. In addition, a graph-based approach is presented to model the beta-sheets conformational space and a new score function is introduced to evaluate beta-sheets. Furthermore, the present study demonstrates that the beta-sheet structure can be predicted within an acceptable computational time by the utilization of a heuristic maximum weight independent set solution. When compared to state-of-the-art methods, experimental results from BetaSheet916 and BetaSheet1452 datasets indicate that BetaDL improves the accuracy of beta-residue contact and beta-sheet structure prediction. Using BetaDL, beta-sheet structures are predicted with a 4% and 6% improvement in the F1-score at the residue and strand levels, respectively. BetaDL s source code and data are available at http://***/***/datasets/#BetaDL.