LogP Prediction for Blocked Tripeptides with Amino Acids Descriptors (HMLP) by Multiple Linear Regression and Support Vector Regression
The hydrophilicity/lipophilicity of peptides are very important for rational design and drug discovery of bioactive peptides. In this study, each amino acid side chain was characterized by using three structure parameters (heuristic molecular lipophilicity potential, HMLP). The HMLP parameters, total surface area(S), Hpophilic indices (L), and hydrophilic indices (H) of amino acid side chains are derived from lipophilicity potential. Based on HMLP descriptors, prediction QSAR models of the logP were constructed for blocked tripeptides by multiple linear regression (MLR) and support vector regression (SVR). All the results showed that the logP relates to the total surface area(S) and hydrophilic indices (H), and the prediction results of SVR are better than that of MLR. The prediction results are in agreement with the experimental values, and give one advice (leave-one-parameter-out, LOPO) about evaluating the importance of parameter in SVR model. The result shows HMLP parameters (S, L, H) could preferably describe the structure features of the peptides responsible for their octanol to water partition behavior. A simple and effective method is provided for predicting the partition behavior of peptide and some insight into what structural features are related to the logP of peptides in the article.
HMLP parameters peptides logP QSAR cross validation support vector regression
Jiajian YIN
College of Life and Science, Sichuan Agricultural University Yaan, P.R.China 625014 College of Chemistry, Sichuan University Chengdu, P.R.China 610064
国际会议
成都
英文
1-5
2011-01-14（万方平台首次上网日期，不代表论文的发表时间）