Boosting Prediction Performance of Protein–Protein Interaction Hot Spots by Using Structural Neighborhood Properties-Reference-Cited by-同舟云学术

Boosting Prediction Performance of Protein–Protein Interaction Hot Spots by Using Structural Neighborhood Properties

Published:2013-11 Issue:11 Volume:20 Page:878-891
ISSN:1066-5277
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Deng Lei¹²,Guan Jihong¹,Wei Xiaoming¹,Yi Yuan¹,Zhang Qiangfeng Cliff³,Zhou Shuigeng⁴

Affiliation:

1. Department of Computer Science and Technology, Tongji University, Shanghai, China.

2. School of Software, Central South University, Changsha, China.

3. Department of Biochemistry and Molecular Biophysics and Center for Computational Biology and Bioinformatics, Columbia University, New York, New York.

4. School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China.

Publisher

Mary Ann Liebert Inc

Subject

Computational Theory and Mathematics,Computational Mathematics,Genetics,Molecular Biology,Modelling and Simulation

Link

http://www.liebertpub.com/doi/pdf/10.1089/cmb.2013.0083

Reference31 articles.

1. PCRPi: Presaging Critical Residues in Protein interfaces, a new computational tool to chart hot spots in protein interfaces

2. The quickhull algorithm for convex hulls

3. Anatomy of hot spots in protein interfaces

4. Fragment-based identification of druggable ‘hot spots’ of proteins using Fourier domain correlation techniques

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