Journal
COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
Volume 15, Issue 4, Pages 347-353Publisher
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/138620712799361807
Keywords
Antimicrobial peptides (AMPs); descriptor; normalized regression coefficients (NRCs); peptide design; quantitative structural activity relationship (QSAR); structural characterization
Funding
- National Natural Science Foundation of China [81171508, 60873103]
- Natural Science Foundation of China [30830090]
- Innovative New Drugs National Program [2009ZX09503-005]
- University of ChongQing [KJTD201039]
- Scientific Research Foundation of Education Committee of Chongqing [KJ100801]
- Natural Science Foundation of CQ CSTC [CSTC, 2010BB5306]
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Drug resistance to existing antibiotics poses alarming threats to global public health, which inspires heightened interests in searching for new antibiotics, including antimicrobial peptides (AMPs). Accurate prediction of antibacterial activities of AMPs may expedite novel AMP design and reduce the costs and efforts involved in laboratory screening. In the present study, a novel quantitative prediction method of AMP was established by quantitative structure-activity relationship (QSAR) modeling based on the physicochemical properties of amino acids. The indices of these physicochemical properties were used to define AMP. The structural variables were optimized by stepwise regression (STR). Three series of AMPs from the QSAR model were constructed by multiple linear regressions (MLR). These QSAR models showed good performance in reliability and predictability. The normalized regression coefficients of the QSAR model and the contribution of amino acids at each position of AMP may determine the suitableness of a particular residue at any given position. QSAR models constructed by STR-MLR should prove to be useful tools in peptide design with respect to the calculation, explanation, good and reliable performance, and definition of physiochemical properties.
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