Literature DB >> 19646926

Exploiting three kinds of interface propensities to identify protein binding sites.

Bin Liu1, Xiaolong Wang, Lei Lin, Qiwen Dong, Xuan Wang.   

Abstract

Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. In this study, we present a building block of proteins called order profiles to use the evolutionary information of the protein sequence frequency profiles and apply this building block to produce a class of propensities called order profile interface propensities. For comparisons, we revisit the usage of residue interface propensities and binary profile interface propensities for protein binding site prediction. Each kind of propensities combined with sequence profiles and accessible surface areas are inputted into SVM. When tested on four types of complexes (hetero-permanent complexes, hetero-transient complexes, homo-permanent complexes and homo-transient complexes), experimental results show that the order profile interface propensities are better than residue interface propensities and binary profile interface propensities. Therefore, order profile is a suitable profile-level building block of the protein sequences and can be widely used in many tasks of computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the protein remote homology detection.

Mesh:

Substances:

Year:  2009        PMID: 19646926     DOI: 10.1016/j.compbiolchem.2009.07.001

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  11 in total

1.  Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis.

Authors:  Bin Liu; Junjie Chen; Xiaolong Wang
Journal:  Mol Genet Genomics       Date:  2015-04-21       Impact factor: 3.291

2.  Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.

Authors:  Ruifeng Xu; Jiyun Zhou; Hongpeng Wang; Yulan He; Xiaolong Wang; Bin Liu
Journal:  BMC Syst Biol       Date:  2015-02-06

3.  SVM based model generation for binding site prediction on helix turn helix motif type of transcription factors in eukaryotes.

Authors:  Koel Mukherjee; Ambarish Saran Vidyarthi; Dev Mani Pandey
Journal:  Bioinformation       Date:  2013-06-08

4.  Using distances between Top-n-gram and residue pairs for protein remote homology detection.

Authors:  Bin Liu; Jinghao Xu; Quan Zou; Ruifeng Xu; Xiaolong Wang; Qingcai Chen
Journal:  BMC Bioinformatics       Date:  2014-01-24       Impact factor: 3.169

5.  Protein binding site prediction by combining hidden Markov support vector machine and profile-based propensities.

Authors:  Bin Liu; Bingquan Liu; Fule Liu; Xiaolong Wang
Journal:  ScientificWorldJournal       Date:  2014-07-14

6.  Prediction of protein binding sites in protein structures using hidden Markov support vector machine.

Authors:  Bin Liu; Xiaolong Wang; Lei Lin; Buzhou Tang; Qiwen Dong; Xuan Wang
Journal:  BMC Bioinformatics       Date:  2009-11-20       Impact factor: 3.169

Review 7.  Survey of Natural Language Processing Techniques in Bioinformatics.

Authors:  Zhiqiang Zeng; Hua Shi; Yun Wu; Zhiling Hong
Journal:  Comput Math Methods Med       Date:  2015-10-07       Impact factor: 2.238

8.  Predicting the types of J-proteins using clustered amino acids.

Authors:  Pengmian Feng; Hao Lin; Wei Chen; Yongchun Zuo
Journal:  Biomed Res Int       Date:  2014-04-02       Impact factor: 3.411

9.  enDNA-Prot: identification of DNA-binding proteins by applying ensemble learning.

Authors:  Ruifeng Xu; Jiyun Zhou; Bin Liu; Lin Yao; Yulan He; Quan Zou; Xiaolong Wang
Journal:  Biomed Res Int       Date:  2014-05-26       Impact factor: 3.411

10.  Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions.

Authors:  Pengmian Feng; Ning Jiang; Nan Liu
Journal:  ScientificWorldJournal       Date:  2014-08-19
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