Delaunay-based nonlocal interactions are sufficient and accurate in protein fold recognition

被引:8
|
作者
Mirzaie, Mehdi [1 ,2 ]
Sadeghi, Mehdi [3 ]
机构
[1] Shahid Beheshti Univ Med Sci, Fac Paramed Sci, Dept Basic Sci, Tehran, Iran
[2] Inst Res Fundamental Sci IPM, Sch Comp Sci, Dept Bioinformat, Tehran, Iran
[3] Natl Inst Genet Engn & Biotechnol, Dept Bioinformat, Tehran, Iran
关键词
GRAINED FORCE-FIELD; STRUCTURE PREDICTION; MEAN FORCE; TERTIARY STRUCTURES; CONTACT ENERGIES; POTENTIALS; RESIDUE; DISCRIMINATION; TESSELLATION; SIMULATIONS;
D O I
10.1002/prot.24407
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
This study is aimed at showing that considering only nonlocal interactions (interactions of two atoms with a sequence separation larger than five amino acids) extracted using Delaunay tessellation is sufficient and accurate for protein fold recognition. An atomic knowledge-based potential was extracted based on a Delaunay tessellation with 167 atom types from a sample of the native structures and the normalized energy was calculated for only nonlocal interactions in each structure. The performance of this method was tested on several decoy sets and compared to a method considering all interactions extracted by Delaunay tessellation and three other popular scoring functions. Features such as the contents of different types of interactions and atoms with the highest number of interactions were also studied. The results suggest that considering only nonlocal interactions in a Delaunay tessellation of protein structure is a discrete structure catching deep properties of the three-dimensional protein data. Proteins 2014; 82:415-423. © 2013 Wiley Periodicals, Inc.
引用
收藏
页码:415 / 423
页数:9
相关论文
共 50 条
  • [31] TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool
    Dániel Kozma
    Gábor E. Tusnády
    BMC Bioinformatics, 16
  • [32] A privacy-preserving approach for cloud-based protein fold recognition
    Unal, Ali Burak
    Pfeifer, Nico
    Akgun, Mete
    PATTERNS, 2024, 5 (09):
  • [33] Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition
    Wei, Leyi
    Zou, Quan
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2016, 17 (12)
  • [34] MLDH-Fold: Protein fold recognition based on multi-view low-rank modeling
    Yan, Ke
    Wen, Jie
    Xu, Yong
    Liu, Bin
    NEUROCOMPUTING, 2021, 421 : 127 - 139
  • [35] Recent Trends in Machine Learning-based Protein Fold Recognition Methods
    Mehta, Apurva
    Mazumdar, Himanshu
    BIOINTERFACE RESEARCH IN APPLIED CHEMISTRY, 2021, 11 (04): : 11233 - 11243
  • [36] MLDH-Fold: Protein fold recognition based on multi-view low-rank modeling
    Yan K.
    Wen J.
    Xu Y.
    Liu B.
    Neurocomputing, 2021, 421 : 127 - 139
  • [37] A Protein Block Based Fold Recognition Method for the Annotation of Twilight Zone Sequences
    Suresh, V.
    Ganesan, K.
    Parthasarathy, S.
    PROTEIN AND PEPTIDE LETTERS, 2013, 20 (03): : 249 - 254
  • [38] Protein fold recognition and functional assignment based on physical chemical principles.
    Honig, B
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 1998, 216 : U635 - U635
  • [39] Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information
    Kuldip K Paliwal
    Alok Sharma
    James Lyons
    Abdollah Dehzangi
    BMC Bioinformatics, 15
  • [40] Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information
    Paliwal, Kuldip K.
    Sharma, Alok
    Lyons, James
    Dehzangi, Abdollah
    BMC BIOINFORMATICS, 2014, 15