Mao Shu

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.

Hypothyroidism Side Effect in Patients Treated with Sunitinib or Sorafenib: Clinical and Structural Analyses

Tyrosine kinase inhibitors (TKIs) provide more effective targeted treatments for cancer, but are subject to a variety of adverse effects, such as hypothyroidism. TKI-induced hypothyroidism is a highly complicated issue, because of not only the unrealized toxicological mechanisms, but also different incidences of individual TKI drugs. While sunitinib is suspected for causing thyroid dysfunction more often than other TKIs, sorafenib is believed to be less risky. Here we integrated clinical data and in silico drug-protein interactions to examine the pharmacological distinction between sunitinib and sorafenib. Statistical analysis on the FDA Adverse Event Reporting System (FAERS) confirmed that sunitinib is more concurrent with hypothyroidism than sorafenib, which was observed in both female and male patients. Then, we used docking method and identified 3 proteins specifically binding to sunitinib but not sorafenib, i.e., retinoid X receptor alpha, retinoic acid receptors beta and gamma. As potential off-targets of sunitinib, these proteins are well known to assemble with thyroid hormone receptors, which can explain the profound impact of sunitinib on thyroid function. Taken together, we established a strategy of integrated analysis on clinical records and drug off-targets, which can be applied to explore the molecular basis of various adverse drug reactions.

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.

Quantitative prediction of class I MHC/epitope binding affinity using QSAR modeling derived from amino acid structural information.

The activation of T cell immune responses, which relies on peptide antigens transported by TAP and bound to major histocompatibility complex (MHC) molecules, is recognized by T cell receptors (TCR). The quantitative prediction of MHC-epitope binding affinity can facilitate epitope screening and reduce cost and experimental efforts greatly. In this study, a comprehensive quantitative prediction method of binding affinity was established using quantitative structureactivity relationship (QSAR) modeling derived from amino acid physicochemical information. Firstly, the epitope was characterized by a set of amino acid physicochemical parameters. Secondly, the structural variables were optimized by the stepwise regression (STR). Finally, the robust quantitative models with were built by multiple linear regressions (MLR) for 31 MHC Class I subtypes. The normalized regression coefficients (NRCs) of QSAR model could demonstrate the mechanism of interaction of MHC, epitope, and TCR very well. The contribution of amino acid at each position of epitope, which was calculated by NRC, could determine which one was favorable for binding affinity or not. Therefore, the quantitative models established by STR-MLR could be used to guide virtual combinational design and high throughout screening of CTL epitope. Besides, they have many advantages, such as definite physiochemical indication, easier calculation and explanation, and good performances.

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.

Predicting the Activity of Antimicrobial Peptides with Amino Acid Topological Information

In this paper, VSTPV, was recruited as a novel set of structural and topological descriptors derived from principal component analysis (PCA) on 85 structural and topological variables of 166 coded and non-coded amino acids. By using partial least squares (PLS), we applied VSTPV for the study of quantitative structure-activity models (QSARs) studies on two peptide panels as 101 synthetic cationic Antimicrobial polypeptides (CAMELs), and 28 bovine lactoferricin- (17 � 31)-pentadecapeptides (LFB). The results of QSARs models were superior to that of the earlier studies, with squared correlative coefficient R2 and cross-validated Q2 of 0.783, 0.656; and 0.864, 0.793, respectively. So, VSTPV descriptors were confirmed to be competent to extract information on 85 structural variables and to relate with biological activities.

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.