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Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity.
MedLine Citation:
PMID:  23306464     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Quantitative structure-activity relationship (QSAR), a regression modeling methodology that establishes statistical correlation between structure feature and apparent behavior for a series of congeneric molecules quantitatively, has been widely used to evaluate the activity, toxicity and property of various small-molecule compounds such as drugs, toxicants and surfactants. However, it is surprising to see that such useful technique has only very limited applications to biomacromolecules, albeit the solved 3D atom-resolution structures of proteins, nucleic acids and their complexes have accumulated rapidly in past decades. Here, we present a proof-of-concept paradigm for the modeling, prediction and interpretation of the binding affinity of 144 sequence-nonredundant, structure-available and affinity-known protein complexes (Kastritis et al. Protein Sci 20:482-491, 2011) using a biomacromolecular QSAR (BioQSAR) scheme. We demonstrate that the modeling performance and predictive power of BioQSAR are comparable to or even better than that of traditional knowledge-based strategies, mechanism-type methods and empirical scoring algorithms, while BioQSAR possesses certain additional features compared to the traditional methods, such as adaptability, interpretability, deep-validation and high-efficiency. The BioQSAR scheme could be readily modified to infer the biological behavior and functions of other biomacromolecules, if their X-ray crystal structures, NMR conformation assemblies or computationally modeled structures are available.
Authors:
Peng Zhou; Congcong Wang; Feifei Tian; Yanrong Ren; Chao Yang; Jian Huang
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-10
Journal Detail:
Title:  Journal of computer-aided molecular design     Volume:  -     ISSN:  1573-4951     ISO Abbreviation:  J. Comput. Aided Mol. Des.     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-11     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8710425     Medline TA:  J Comput Aided Mol Des     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Center of Bioinformatics (COBI), School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, 610054, China, p_zhou@uestc.edu.cn.
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