Literature DB >> 18464327

CLePAPS: fast pair alignment of protein structures based on conformational letters.

Sheng Wang1, Wei-Mou Zheng.   

Abstract

Fast, efficient, and reliable algorithms for pairwise alignment of protein structures are in ever-increasing demand for analyzing the rapidly growing data on protein structures. CLePAPS is a tool developed for this purpose. It distinguishes itself from other existing algorithms by the use of conformational letters, which are discretized states of 3D segmental structural states. A letter corresponds to a cluster of combinations of the three angles formed by Calpha pseudobonds of four contiguous residues. A substitution matrix called CLESUM is available to measure the similarity between any two such letters. CLePAPS regards an aligned fragment pair (AFP) as an ungapped string pair with a high sum of pairwise CLESUM scores. Using CLESUM scores as the similarity measure, CLePAPS searches for AFPs by simple string comparison. The transformation which best superimposes a highly similar AFP can be used to superimpose the structure pairs under comparison. A highly scored AFP which is consistent with several other AFPs determines an initial alignment. CLePAPS then joins consistent AFPs guided by their similarity scores to extend the alignment by several "zoom-in" iteration steps. A follow-up refinement produces the final alignment. CLePAPS does not implement dynamic programming. The utility of CLePAPS is tested on various protein structure pairs.

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Year:  2008        PMID: 18464327     DOI: 10.1142/s0219720008003461

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  11 in total

1.  Alignment of distantly related protein structures: algorithm, bound and implications to homology modeling.

Authors:  Sheng Wang; Jian Peng; Jinbo Xu
Journal:  Bioinformatics       Date:  2011-07-26       Impact factor: 6.937

2.  Incorporating Ab Initio energy into threading approaches for protein structure prediction.

Authors:  Mingfu Shao; Sheng Wang; Chao Wang; Xiongying Yuan; Shuai Cheng Li; Weimou Zheng; Dongbo Bu
Journal:  BMC Bioinformatics       Date:  2011-02-15       Impact factor: 3.169

3.  iPBA: a tool for protein structure comparison using sequence alignment strategies.

Authors:  Jean-Christophe Gelly; Agnel Praveen Joseph; Narayanaswamy Srinivasan; Alexandre G de Brevern
Journal:  Nucleic Acids Res       Date:  2011-05-17       Impact factor: 16.971

4.  Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Authors:  Sheng Wang; Jian Peng; Jianzhu Ma; Jinbo Xu
Journal:  Sci Rep       Date:  2016-01-11       Impact factor: 4.379

5.  Protein alignment based on higher order conditional random fields for template-based modeling.

Authors:  Juan A Morales-Cordovilla; Victoria Sanchez; Martin Ratajczak
Journal:  PLoS One       Date:  2018-06-01       Impact factor: 3.240

6.  SAFlex: A structural alphabet extension to integrate protein structural flexibility and missing data information.

Authors:  Ikram Allam; Delphine Flatters; Géraldine Caumes; Leslie Regad; Vincent Delos; Gregory Nuel; Anne-Claude Camproux
Journal:  PLoS One       Date:  2018-07-05       Impact factor: 3.240

7.  Protein structure alignment beyond spatial proximity.

Authors:  Sheng Wang; Jianzhu Ma; Jian Peng; Jinbo Xu
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

8.  DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields.

Authors:  Sheng Wang; Shunyan Weng; Jianzhu Ma; Qingming Tang
Journal:  Int J Mol Sci       Date:  2015-07-29       Impact factor: 5.923

9.  AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.

Authors:  Jianzhu Ma; Sheng Wang
Journal:  Biomed Res Int       Date:  2015-08-03       Impact factor: 3.411

10.  A local average distance descriptor for flexible protein structure comparison.

Authors:  Hsin-Wei Wang; Chia-Han Chu; Wen-Ching Wang; Tun-Wen Pai
Journal:  BMC Bioinformatics       Date:  2014-04-02       Impact factor: 3.169

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