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Literature DB >> 18537737

Predicting key long-range interaction sites by B-factors.

Peng Chen¹, Kyungsook Han, Xueling Li, De-Shuang Huang.

Abstract

In this paper, we adopted the bounded support vector machine to locate the key long-range interaction sites by the use of predicted local lowest B-factors. As a result, the key long-range interaction residues can be located based on information of local lowest B-factor sites.

Mesh：

Substances：
Proteins

Year: 2008 PMID： 18537737 DOI： 10.2174/092986608784567573

Source DB: PubMed Journal: Protein Pept Lett ISSN： 0929-8665 Impact factor: 1.890

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Cited

6 in total

1. Prediction of inter-residue contact clusters from hydrophobic cores.

Authors: Peng Chen; Chunmei Liu; Legand Burge; Mohammad Mahmood; William Southerland; Clay Gloster
Journal: Int J Data Min Bioinform Date: 2008-12-11 Impact factor: 0.667

2. Evaluation of residue-residue contact predictions in CASP9.

Authors: Bohdan Monastyrskyy; Krzysztof Fidelis; Anna Tramontano; Andriy Kryshtafovych
Journal: Proteins Date: 2011-09-17

3. Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers.

Authors: Peng Chen; Jinyan Li
Journal: BMC Struct Biol Date: 2010-05-17

Predicting key long-range interaction sites by B-factors.

1. Prediction of inter-residue contact clusters from hydrophobic cores.

2. Evaluation of residue-residue contact predictions in CASP9.

3. Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers.

4. Evaluation of residue-residue contact prediction in CASP10.

5. Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning.

6. Semi-supervised prediction of protein interaction sites from unlabeled sample information.