Statisitcal Modeling Of The Property And Activity Of Peptides And Proteins

Qquantitative structure-activity relationship (QSAR) describes the quantitative molecular biology's structure-activity dependence by using a mathematical model. As peptide is not only essential for the maintenance of the material life process, but also its high activity, high selectivity and low side effect, it has become one of the hot topics of pharmaceutical research. The study of protein structure and function is one of the core of molecular biology, to solve this problem not only has very important theoretical significance, but also has very important practical significance for the development of biotechnology.(1) We build QSAR models for 168 ACE inhibitory dipeptides and 141 ACE inhibitory tripeptide,48 bitter dipeptide and 52 bitter tripeptide by the heuristic method, then analyze the stability and predictability of the model by in test of the training set and outside test of the test set.We separately build QSAR model for the 168 ACE inhibitory dipeptide and the 141 ACE inhibitory tripeptide by heuristic method, then remove the outliers, get two optimized samples with 165 ACE inhibitory dipeptides and 131 ACE inhibitory tripeptide. Respectively two optimized samples randomly is selected the training set and test set. The QSAR model for training set is superior to or similar to the results in the literature. For the dipeptides, when the N terminal residues is Gly, Ala, Leu, Val, Tyr and Asp, C terminal residues is Tyr, Pro and Trp, they have strong activity; for tripeptides, when the N terminal residues is Leu, Val, Ile and Gly, C terminal residues is Pro, Leu and Tyr time, they have strong activity. By outside test of the test set, the models have good stability and predictability. We also predict and verify the activity of ACE inhibitory peptides, the results is satisfied.Respectively two samples with 48 bitter dipeptide and 52 bitter tripeptide randomly is selected the training set and test set, then separately build QSAR model by heuristic method. The QSAR model for training set is superior to or similar to the results in the literature. We analyze the model's descriptors, indicating that the bitter peptides, which have hydrophobic amino acid sequence such as phenylalanine F, tyrosine Y and tryptophan W, have high biological activity. This is the same as that the bitter tast of bitter peptides come from the hydrophobic amino acids. In the research sample, compounds WW, IW, LW, FF, FY, FL, YPF, YYY and FFF show a higher activity. By outside test of the test set, the models have good stability and predictability.Our studies have shown that the polypeptide QSAR models, which isestablished by the heuristics method, have good fitting ability and predictive power, and also can be applied to predict more new peptides'biological activity.(2) A novel amino acid descriptor termed as principal component scores of amino acid characteristic properties (SACP) was 8 significant principal components derived from 50 chemical properties of natural amino acids by using principle component analysis approach and transformed nonlinearly via mercer kernel technique to yield a vector form-based auto-correlative function. Consequently, a novel amino acid characterization protocol was presented, i.e., kernel sequence auto-correlation function (KSACF). KSACF was then applied to perform classification study of 632 non-homologous proteins with known structures. It was indicated that KSACF's capability of modeling and forecasting for internal training set and external test set have reached or even exceeded the overall performance of the current mainstream protein structure prediction method, and it is also a good performance in characterizing primary structures for proteins and potential relationship between amino acid residues, to reliably predict the different types of protein structures. Thus, KSACF has a promising prospect in protein structure prediction and sequence analysis.