Incorporating Ab Initio energy into threading approaches for protein structure prediction

Mingfu Shao, Sheng Wang, Chao Wang, Xiongying Yuan, Shuai C. Li, Weimou Zheng, Dongbo Bu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Background: Native structures of proteins are formed essentially due to the combining effects of local and distant (in the sense of sequence) interactions among residues. These interaction information are, explicitly or implicitly, encoded into the scoring function in protein structure prediction approaches-threading approaches usually measure an alignment in the sense that how well a sequence adopts an existing structure; while the energy functions in Ab Initio methods are designed to measure how likely a conformation is near-native. Encouraging progress has been observed in structure refinement where knowledge-based or physics-based potentials are designed to capture distant interactions. Thus, it is interesting to investigate whether distant interaction information captured by the Ab Initio energy function can be used to improve threading, especially for the weakly/distant homologous templates.Results: In this paper, we investigate the possibility to improve alignment-generating through incorporating distant interaction information into the alignment scoring function in a nontrivial approach. Specifically, the distant interaction information is introduced through employing an Ab Initio energy function to evaluate the " partial" decoy built from an alignment. Subsequently, a local search algorithm is utilized to optimize the scoring function.Experimental results demonstrate that with distant interaction items, the quality of generated alignments are improved on 68 out of 127 query-template pairs in Prosup benchmark. In addition, compared with state-to-art threading methods, our method performs better on alignment accuracy comparison.Conclusions: Incorporating Ab Initio energy functions into threading can greatly improve alignment accuracy.

Original languageEnglish (US)
Article numberS54
JournalBMC bioinformatics
Volume12
Issue numberSUPPL. 1
DOIs
StatePublished - Feb 15 2011

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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