Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (1): 3-15.doi: 10.1007/s11390-019-1895-y

Special Issue: Artificial Intelligence and Pattern Recognition; Emerging Areas

• Special Section of Advances in Computer Science and Technology—Current Advances in the NSFC Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao 2014-2017 (Part 1) • Previous Articles     Next Articles

Decoding the Structural Keywords in Protein Structure Universe

Wessam Elhefnawy1, Min Li2, Jian-Xin Wang2, Member, IEEE, and Yaohang Li1,*, Member, ACM, IEEE   

  1. 1 Department of Computer Science, Old Dominion University, Norfolk, VA 23452, U.S.A.;
    2 Department of Computer Science, Central South University, Changsha 410083, China
  • Received:2018-07-13 Revised:2018-12-04 Online:2019-01-05 Published:2019-01-12
  • About author:Wessam Elhefnawy is a Ph.D. candidate in the Department of Computer Science at Old Dominion University, Norfolk, Virginia. His research interest lies in computational biology, scientific computing, machine learning, artificial intelligence, image processing, and bioinformatics. Wessam received his B.Sc. degree in computer engineering from Arab Academy for Science & Technology, and Maritime Transport, Cairo, Egypt, in 2004. He received his M.Sc. degree in computer engineering from Arab Academy for Science & Technology, Cairo, Egypt, in 2011.
  • Supported by:
    This work is supported in part by the National Natural Science Foundation of China under Grant Nos. 61728211 and 61832019.

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Although the protein sequence-structure gap continues to enlarge due to the development of high-throughput sequencing tools, the protein structure universe tends to be complete without proteins with novel structural folds deposited in the protein data bank (PDB) recently. In this work, we identify a protein structural dictionary (Frag-K) composed of a set of backbone fragments ranging from 4 to 20 residues as the structural "keywords" that can effectively distinguish between major protein folds. We firstly apply randomized spectral clustering and random forest algorithms to construct representative and sensitive protein fragment libraries from a large scale of high-quality, non-homologous protein structures available in PDB. We analyze the impacts of clustering cut-offs on the performance of the fragment libraries. Then, the Frag-K fragments are employed as structural features to classify protein structures in major protein folds defined by SCOP (Structural Classification of Proteins). Our results show that a structural dictionary with ~400 4- to 20-residue Frag-K fragments is capable of classifying major SCOP folds with high accuracy.

Key words: protein fragment; fold recognition; protein structure universe;

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