QSAR Study on ACE Inhibitors by Using OSC-PLS Algorithm

A set of descriptors was derived from a matrix of three structural variables of the natural amino acid, including Van Der Waal's volume, net charge index and hydrophobic parameter of side residues. They were selected from many properties of amino acid residues, which were the primary properties to influence the interaction between peptides and its protein receptor. They were then applied to structure characterization and QSAR analysis on agiotensin-converting enzyme inhibitor di-peptides by using orthogonal signal correction (OSC) method combined with partial least squares (PLS) method. The results of QSAR analysis on di-peptides obtained are as following: the cross-validation correlative coefficient (Q 2 ) was 0.817, and the correlative coefficient (R 2 ) was 0.838, and the root mean-square (RMS) was 0.410. Test sets of peptides were used to validate the quantitative model, and it was shown that all these QSAR models had good predictability for outside samples. The results showed that, in comparison with the conventional descriptors, this descriptors was a useful structure characterization parameter for peptide QSAR analysis, and also showed that the OSC-PLS method is better than the traditional PLS method.