Yuanqiang Wang

New descriptors of amino acids and their application to peptide QSAR study

A new set of descriptors was derived from a matrix of three structural variables of the natural amino acid, including van der Waals volume, net charge index and hydrophobic parameter of side residues. They were selected from many properties of amino acid residues, which have been validated being the key factors to influence the interaction between peptides and its protein receptor. They were then applied to structure characterization and QSAR analysis on bitter tasting di-peptide, agiotensin-converting enzyme inhibitor and bactericidal peptides by using multiple linear regression (MLR) method. The leave one out cross validation values (Q(2)) were 0.921, 0.943 and 0.773. The multiple correlation coefficients (R(2)) were 0.948, 0.970 and 0.926, the root mean square (RMS) error for estimated error were 0.165, 0.154 and 0.41, respectively for bitter tasting di-peptide, angiotensin-converting enzyme inhibitor and bactericidal peptides. 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, the new set of descriptors is a useful structure characterization method for peptide QSAR analysis, which has multiple advantages, such as definite physical and chemical meaning, easy to get, and good structural characterization ability.

QSAR study on angiotensin-converting enzyme inhibitor oligopeptides based on a novel set of sequence information descriptors

A novel set of descriptors G-scale was derived from 457 physicochemical properties of the natural amino acids. The descriptors were then applied to study on quantitative structure-activity relationships (QSARs) of nine peptide datasets of angiotensin-converting enzyme inhibitor (ACE-inhibitor) oligopeptides (between dipeptides and decapeptides) by using partial least square (PLS) regression. The multiple correlation coefficients (R2) and leave one out cross validation values (Q2) of PLS models are better than or close to the results of references. The results show that the descriptors proposed here may be a useful structural expression method, and they may be hopefully used in biological activity study of ACE-inhibitor oligopeptides.

Predicting the Activity of ACE Inhibitory Peptides with a Novel Mode of Pseudo Amino Acid Composition

In this study, physicochemical scale (P-scale), was recruited as a novel set of physicochemical descriptors derived from component analysis on four short of physicochemical properties variables (hydrophobic, electronic, steric and hydrogen bond contribution) of 20 coded amino acids, By using partial least squares (PLS), we applied P-scale for the study of quantitative structure activity relationship models (QASRs) on three angiotensin I converting enzyme (ACE) inhibitory peptides datasets (58 dipeptides, 55 tripeptides, and 50 tetrapeptides).The results of QSARs were superior to that of the earlier studies, with correlation coefficient (r(2)) and cross-validated(q(2)) equal to 0.902, 0.86; 0.985, 0.951 and 0.872, 0.77, respectively. By analysis, hydrophobic and steric properties of ACE-inhibitory peptide sequences play important roles in their bioactivities, and novel peptide sequence could be designed based on these properties of the amino acid residues. These results showed that P-scale descriptors can well represent the peptide sequence. Furthermore, the robust models show that P-scale descriptors can be further expanded for polypeptides and can serve as a useful quantitative tool for the rational drug design and discovery.

Combined Pharmacophore Modeling, 3D-QSAR, Homology Modeling and Docking Studies on CYP11B1 Inhibitors

The mitochondrial cytochrome P450 enzymes inhibitor steroid 11β-hydroxylase (CYP11B1) can decrease the production of cortisol. Therefore, these inhibitors have an effect in the treatment of Cushing’s syndrome. A pharmacophore model generated by Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD) was used to align the compounds and perform comparative molecular field analysis (CoMFA) with Q2 = 0.658, R2 = 0.959. The pharmacophore model contained six hydrophobic regions and one acceptor atom, and electropositive and bulky substituents would be tolerated at the A and B sites, respectively. A three-dimensional quantitative structure-activity relationship (3D-QSAR) study based on the alignment with the atom root mean square (RMS) was applied using comparative molecular field analysis (CoMFA) with Q2 = 0.666, R2 = 0.978, and comparative molecular similarity indices analysis (CoMSIA) with Q2 = 0.721, R2 = 0.972. These results proved that all the models have good predictability of the bioactivities of inhibitors. Furthermore, the QSAR models indicated that a hydrogen bond acceptor substituent would be disfavored at the A and B groups, while hydrophobic groups would be favored at the B site. The three-dimensional (3D) model of the CYP11B1 was generated based on the crystal structure of the CYP11B2 (PDB code 4DVQ). In order to probe the ligand-binding modes, Surflex-dock was employed to dock CYP11B1 inhibitory compounds into the active site of the receptor. The docking result showed that the imidazolidine ring of CYP11B1 inhibitors form H bonds with the amino group of residue Arg155 and Arg519, which suggested that an electronegative substituent at these positions could enhance the activities of compounds. All the models generated by GALAHAD QSAR and Docking methods provide guidance about how to design novel and potential drugs for Cushing’s syndrome treatment.

3D-QSAR studies of dihydropyrazole and dihydropyrrole derivatives as inhibitors of human mitotic kinesin Eg5 based on molecular docking.

Human mitotic kinesin Eg5 plays an essential role in mitoses and is an interesting drug target against cancer. To find the correlation between Eg5 and its inhibitors, structure-based 3D-quantitative structure–activity relationship (QSAR) studies were performed on a series of dihydropyrazole and dihydropyrrole derivatives using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Based on the LigandFit docking results, predictive 3D-QSAR models were established, with cross-validated coefficient values (q2) up to 0.798 for CoMFA and 0.848 for CoMSIA, respectively. Furthermore, the CoMFA and CoMSIA models were mapped back to the binding sites of Eg5, which could provide a better understanding of vital interactions between the inhibitors and the kinase. Ligands binding in hydrophobic part of the inhibitor-binding pocket were found to be crucial for potent ligand binding and kinases selectivity. The analyses may be used to design more potent EG5 inhibitors and predict their activities prior to synthesis.

QSAR Study on MHC Class I A Alleles Based on the Novel Parameters of Amino Acids

MHC-epitope binding plays a key role in the cellular immune response. Accurate prediction of MHC-epitope binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. In this paper, 13 T descriptors， which derived from 544 physicochemical properties of the natural amino acids, were used to characterize 4 MHC class I alleles epitope peptide sequences, the optimal QSAR models were constructed by using stepwise regression combines with multiple linear regression (STR-MLR). For HLA-A*0201, HLA-A*0203, HLA-A*0206 and HLA-A*1101 alleles, the leave one out cross validation values (Q(2)(train)) were 0.581, 0.553, 0.525 and 0.588, the correlation coefficients (R(2)(train)) of training datasets were 0.607, 0.582, 0.556 and 0.606, the correlation coefficients (R(2)(test)) of test datasets were 0.533, 0.506, 0.501 and 0.502, respectively. The results showed that all models can obtain good performance for prediction and explain the mechanism of interaction between MHC and epitope. The descriptors will be useful in structure characterization and activity prediction of peptide sequences.

Discovery of vascular endothelial growth factor receptor tyrosine kinase inhibitors by quantitative structure–activity relationships, molecular dynamics simulation and free energy calculation

Vascular endothelial growth factor (VEGF), along with its receptor tyrosine kinases VEGFR-2 or kinase insert domain receptor (KDR), are targets for development of novel anticancer agents. Accurately predicting the structural characteristics of the target and chemical features of ligands can greatly reduce the cost and shorten the cycle of designing selective KDR inhibitors with desired activity. In this study, a docking strategy and three dimensional holographic vector of atomic interaction field (3D-HoVAIF) were applied in QSAR analysis of KDR inhibitors. The optimal model was constructed by using stepwise regression combined with partial least squares regression (SMR-PLS). Integrating the results of QSAR analysis, ADMET, pharmacophore modeling and a reverse screening strategy, eight derivatives were identified as potential KDR inhibitors. Then molecular dynamics (MD) simulations and free energy calculations were employed to explore the detailed binding process, so as to compare the potential binding modes of inhibitors with different activities. By analyzing the key residues in the binding site, it was found that different KDR–ligand complexes had similar binding modes. The predicted binding affinities were highly correlated with the experimental biological activity. Free energy analysis indicated that van der Waals interactions provided the major driving force for the binding process. Furthermore, key residues, such as Leu840, Val848, Ala866, Lys868, Leu889, Val899, Thr916, Phe918, Cys919, Leu1035, Cys1045, Asp1046, and Phe1047 played a vital role in forming hydrogen bonds, salt bridges, and hydrophobic interactions with the conformation of KDR. The above results will help design more efficient KDR inhibitors.

QSAR Study on Insect Neuropeptide Potencies Based on a Novel Set of Parameters of Amino Acids by Using OSC-PLS Method

The potencies of natural adipokinetic hormones and synthetic variants (short peptides) have been obtained in Locusta migratoria. This short peptides (a total of sixty-nine analogues) data was used to construct the mathematical models of the hormone potencies. The sequence variations of amino acids in both natural and artificial adipokinetic hormone analogues were characterized by using a set of descriptors proposed in our laboratory, then QSAR models were developed successfully by using orthogonal signal correction combines with partial least squares (OSC-PLS) method. The cross validation correlation coefficients (Q2) were up to 0.942. The results show that the descriptors proposed in this study will be useful in structure characterization and activity prediction of biological molecules.

Identification of potential CCR5 inhibitors through pharmacophore-based virtual screening, molecular dynamics simulation and binding free energy analysis

CC chemokine receptor 5 (CCR5), a member of G protein-coupled receptors (GPCRs), plays a vital role in inflammatory responses to infection. Alterations in the expression of CCR5 have been correlated with disease progression in many types of cancers. The idea of using CCR5 as a target for therapeutic intervention has been demonstrated to prevent disease progression. To date, only a few compounds have been reported as CCR5 inhibitors. In this study, a series of CCR5 antagonists were used to construct pharmacophore models. Then the optimal model was utilized as a 3D query to identify novel chemical entities from structural databases. After refinement by molecular docking, drug-likeness analysis, molecular dynamics simulations (MDS) and binding free energy analysis, three potential inhibitors (25, 29 and 45) were identified. MD simulations suggested that the screened compounds retained the important common binding mode known for CCR5 inhibitors (maraviroc and nifeviroc), which occupied the bottom of a pocket and stabilized the conformation of CCR5. During the binding process, van der Waals interactions provided the substantial driving force. The most favorable contributions were from Tyr37, Trp86, Tyr89, Tyr108, Phe109, Phe112, Gln194, Thr195, Ile198, Trp248, Tyr251, Leu255, Thr259, Met279, Glu283 and Met287. The above results suggest that the hybrid strategy would provide a basis for rational drug design.

Quantitative Structure–Activity Relationship Model for Prediction of Protein–Peptide Interaction Binding Affinities between Human Amphiphysin-1 SH3 Domains and Their Peptide Ligands

Protein–protein interaction plays a critical role in signal transduction and many other key biological processes. The present study evaluated four parameters selected from among 554 physiochemical variables of 20 natural amino acids listed in AAindex, namely, hydrophobicity, electronic properties, steric properties, and hydrogen-bond properties. Human amphiphysin-1 Src homology 3 (SH3) domain-binding decapeptides were the object of analysis. A quantitative structure–activity relationship model of the SH3 domain-binding peptides was constructed using multivariate linear regression. The results showed that the four parameters ably characterize the structure of SH3 domain-binding decapeptides, have definitive physicochemical properties and a low level of computational complexity, are accessible, and may be used in integrated prediction models for other protein–peptide interactions.