Literature DB >> 24222511

A distance- and orientation-dependent energy function of amino acid key blocks.

Lin Chen1, Jing He.   

Abstract

Blocks are the selected portions of amino acids. They have been used effectively to represent amino acids in distinguishing the native conformation from the decoys. Although many statistical energy functions exist, most of them rely on the distances between two or more amino acids. In this study, the authors have developed a pairwise energy function "DOKB" that is both distance and orientation dependent, and it is based on the key blocks that bias the distal ends of side chains. The results suggest that both the distance and the orientation are needed to distinguish the fine details of the packing geometry. DOKB appears to perform well in recognizing native conformations when compared with six other energy functions. Highly packed clusters play important roles in stabilizing the structure. The investigation about the highly packed clusters at the residue level suggests that certain residue pairs in a low-energy region have lower probability to appear in the highly packed clusters than in the entire protein. The cluster energy term appears to significantly improve the recognition of the native conformations in ig_structal decoy set, in which more highly packed clusters are contained than in other decoy sets.
Copyright © 2013 Wiley Periodicals, Inc.

Keywords:  block; cluster; energy function; orientation; protein

Mesh:

Substances:

Year:  2014        PMID: 24222511     DOI: 10.1002/bip.22440

Source DB:  PubMed          Journal:  Biopolymers        ISSN: 0006-3525            Impact factor:   2.505


  4 in total

1.  A Histogram-based Outlier Profile for Atomic Structures Derived from Cryo-Electron Microscopy.

Authors:  Lin Chen; Jing He
Journal:  ACM BCB       Date:  2019-09

2.  An Investigation of Atomic Structures Derived from X-ray Crystallography and Cryo-Electron Microscopy Using Distal Blocks of Side-Chains.

Authors:  Lin Chen; Jing He; Salim Sazzed; Rayshawn Walker
Journal:  Molecules       Date:  2018-03-08       Impact factor: 4.411

3.  A Visualization Tool for Cryo-EM Protein Validation with an Unsupervised Machine Learning Model in Chimera Platform.

Authors:  Lin Chen; Brandon Baker; Eduardo Santos; Michell Sheep; Darius Daftarian
Journal:  Medicines (Basel)       Date:  2019-08-06

4.  Outlier Profiles of Atomic Structures Derived from X-ray Crystallography and from Cryo-Electron Microscopy.

Authors:  Lin Chen; Jing He
Journal:  Molecules       Date:  2020-03-28       Impact factor: 4.411

  4 in total

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