The Application Of 3D Amino Acids Descriptors To The Quantitative Structure-Activity Relationship Study Of Peptides

In recent years, Quantitative structure activity relationship (QSAR), which is to investigate the quantitative relationship between the molecular structural parameters and biological activities or other relative activities, has got a wide and rapid development in Computer-aided drug design (CADD). QSAR, as an effective means in research and contriving medicines, has been widely applied in organic chemistry, pharmacy chemistry, environment chemistry, computer chemistry, pesticide, and molecular biology, etc. Structural characterization is crucial to performing QSAR studies for peptides and proteins. Major information of structure and function for peptides and proteins is contained in their amino acid sequences. Therefore, characteristics of the amino acid residues for peptides and proteins are of great significance to their QSAR study.3D descriptor in QSAR is a more accurate technique in structure identification because 3D descriptors will indicate non-bonding interactions of ligand-receptor. Finally, quantum chemistry, an important method in studying molecular structure and reaction theory, has been widely applied in QSAR, thus greatly increasing the accuracy of QSAR theory.In this dissertation, we developed a series of 3D descriptors based on the basic molecular structure character, considering common intramolecular and intermolecular non-bonding interactions, like electrostatic interaction, steric interaction, and hydrophobic interaction. Molecular structure parameterization methods and modeling methods have been investigated and applied in QSAR as simple, direct and effective molecular structure parameterization methods. At the same time, the quantitative relationships of several representative drug structures and activity/spectrum have been built. The results will provide some useful basic information for analyzing molecular spectrum, function, reaction mechanism, drug design, and efficiency of medicine exploitation.The main contents are as follows: (1) Scores Vector of Three Dimension Descriptors(SVTD), which were extracted from principal component analysis of 721 indexes of 20 natural amino acids, were applied to the QSAR study of 21 oxytocin analogues and 65 HLA-A*0201 restricted CTL epitopes. First, we used stepwise multiple regressions to pick the variables and then applied the multiple linear regression to the models. Finally, the models were tested by internal and external validations. For the samples of oxytocin analogues, the correlation coefficients(Rcum), cross-validation (Rcv) and external validation correlation coefficients (Qext) were 0.981,0.960 and 0.966, respectively; For the samples of HLA-A*0201 restricted CTL epitopes, he correlation coefficients(Rcum), cross-validation (Rcv) and external validation correlation coefficients (Qext) were 0.949,0.899 and 0.922, respectively, showing the model had favorable estimation and prediction capabilities.(2) Vector of Principal Component Scores for Weighted Holistic Invariant Molecular Index(VSW), which were extracted from principal component analysis of weighted holistic invariant molecular indexes of 20 natural amino acids, were applied to the QSAR study of 152 HLA-A*0201 restricted CTL epitopes and 101 Antimicrobial peptides. For the samples of HLA-A*0201 restricted CTL epitopes, the correlation coefficients(Rcum), cross-validation (Rcv) and external validation correlation coefficients (Qext) were 0.806,0.756 and 0.693, respectively; For the samples of Antimicrobial peptides, the correlation coefficients(Rcum), cross-validation (Rcv) and external validation correlation coefficients (Qext) were 0.869,0.834 and 0.702, respectively. Favorable stability and good prediction capability of the model indicated that VSW was applicable to the molecular structural characterization and biological activity prediction.(3) Divided Physicochemical Property Scores (DPPS), which were extracted from principal component analysis of 23 electronic properties,37 steric properties,54 hydrophobic properties and 5 hydrogen bond properties of 20 natural amino acids, were applied to the QSAR study of 58 angiotensin-converting enzyme inhibitors and 25 HLA-Cw*0102 epitopes. For the samples of ACE inhibitors, the correlation coefficients(Rcum), cross-validation (Rcv) and external validation correlation coefficients (Qext) were 0.943,0.909 and 0.916, respectively; For the samples of HLA-Cw*01 02 epitopes, the correlation coefficients(Rcum), cross-validation (Rcv) were 0.868 and 0.795, respectively. Satisfactory results showed that, data of DPPS may be a useful structural expression methodology for study on peptide QSAR due to their many advantages such as easy manipulation, plentiful structural information and high characterization competence.