NDLI: QSAR Study of Angiotensin I-Converting Enzyme Inhibitory Dipeptides Based on PRIN Descriptors by Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Jiajian Yin Shiping Du
Copyright Year	2010
Abstract	The support vector machine (SVM) was employed to construct dipeptides Quantitative Structure-Activity Relationship (QSAR) model. Amino acid descriptors PRIN parameter has been introduced in bioactive peptides QSAR Study in the article at firstly. Outliers of 168 Angiotensin I-Converting Enzyme (ACE) Inhibitory Dipeptides are deleted and QSAR model is constructed by support vector regression (SVR) leave-one-sample-out cross-validation (LOSOCV). The importance of each parameter or property at each position in peptides is estimated by the value of the model PRESS obtained using leave-one-parameter-out (LOPO) approach. Amino acid residues with bulky side chains as well as hydrophobic side chains were preferred for dipeptides by using leave one parameter out SVR, Which is similar to the literature. This will be provided with certain guidance meaning to design and exploit ACE Inhibitor. The results indicate that SVM can be used as an alternative powerful modeling tool for peptide QSAR studies, and give a advice (LOPO) about evaluating the importance of parameter in SVR model. Moreover, it also offered an idea about nonlinear relation between bioactive of peptides and their structural descriptors PRIN. The establishment of such methods will be a very meaning work to peptide bioactive investigation in peptide analogue drug design.
Starting Page	1
Ending Page	5
File Size	310094
Page Count	5
File Format	PDF
ISBN	9781424447121
ISSN	21517622
DOI	10.1109/ICBBE.2010.5515932
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Drugs Sequences Inhibitors Peptides Amino acids Educational institutions Biochemistry Biological information theory Principal component analysis
Content Type	Text
Resource Type	Article

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