Biomedical subjects

Jiaju Zhou

Publications and source records attributed to Jiaju Zhou.

11 recordsLinked to original sources

PubMed2005

SARS-CoV protease inhibitors design using virtual screening method from natural products libraries.

Two natural products databases, the marine natural products database (MNPD) and the traditional Chinese medicines database (TCMD), were used to find novel structures of potent SARS-CoV protease inhibitors through virtual screening. Before the procedure, the databases were filtered by Lipinski's ROF and Xu's extension rules. The results were analyzed by statistic methods to eliminate the bias in target-based database screening toward higher molecular weight compounds for enhancing the hit rate. Eighteen lead compounds were recommended by the screening procedure. They were useful for experimental scientists in prioritizing drug candidates and studying the interaction mechanism. The binding mechanism was also analyzed between the best screening compound and the SARS protein.

Algorithms↗

PubMed2005

Construction of a virtual combinatorial library using SMILES strings to discover potential structure-diverse PPAR modulators.

Based on the structural characters of PPAR modulators, a virtual combinatorial library containing 1226,625 compounds was constructed using SMILES strings. Selected ADME filters were employed to compel compounds having poor drug-like properties from this library. This library was converted to sdf and mol2 files by CONCORD 4.0, and was then docked to PPARgamma by DOCK 4.0 to identify new chemical entities that may be potential drug leads against type 2 diabetes and other metabolic diseases. The method to construct virtual combinatorial library using SMILES strings was further visualized by Visual Basic.net that can facilitate the needs of generating other type virtual combinatorial libraries.

Combinatorial Chemistry Techniques↗

PubMed2004

Estimating protein-ligand binding free energy: atomic solvation parameters for partition coefficient and solvation free energy calculation.

Solvation energy calculation is one of the main difficulties for the estimation of protein-ligand binding free energy and the correct scoring in docking studies. We have developed a new solvation energy estimation method for protein-ligand binding based on atomic solvation parameter (ASP), which has been shown to improve the power of protein-ligand binding free energy predictions. The ASP set, designed to handle both proteins and organic compounds and derived from experimental n-octanol/water partition coefficient (log P) data, contains 100 atom types (united model that treats hydrogen atoms implicitly) or 119 atom types (all-atom model that treats hydrogen atoms explicitly). By using this unified ASP set, an algorithm was developed for solvation energy calculation and was further integrated into a score function for predicting protein-ligand binding affinity. The score function reproduced the absolute binding free energies of a test set of 50 protein-ligand complexes with a standard error of 8.31 kJ/mol. As a byproduct, a conformation-dependent log P calculation algorithm named ASPLOGP was also implemented. The predictive results of ASPLOGP for a test set of 138 compounds were r = 0.968, s = 0.344 for the all-atom model and r = 0.962, s = 0.367 for the united model, which were better than previous conformation-dependent approaches and comparable to fragmental and atom-based methods. ASPLOGP also gave good predictive results for small peptides. The score function based on the ASP model can be applied widely in protein-ligand interaction studies and structure-based drug design.

Databases, Protein↗

PubMed2004

Design, synthesis, and evaluation of a new class of noncyclic 1,3-dicarbonyl compounds as PPARalpha selective activators.

Lipid accumulation in nonadipose tissues is increasingly linked to the development of type 2 diabetes in obese individuals. We report here the design, synthesis, and evaluation of a series of novel PPARalpha selective activators containing 1,3-dicarbonyl moieties. Structure-activity relationship studies led to the identification of PPARalpha selective activators (compounds 10, 14, 17, 18, and 21) with stronger potency and efficacy to activate PPARalpha over PPARgamma and PPARdelta. Experiments in vivo showed that compounds 10, 14, and 17 had blood glucose lowering effect in diabetic db/db mouse model after two weeks oral dosing. The data strongly support further testing of these lead compounds in other relevant disease animal models to evaluate their potential therapeutic benefits.

Adipose Tissue↗

PubMed2004

3D QSAR studies on peroxisome proliferator-activated receptor gamma agonists using CoMFA and CoMSIA.

The peroxisome proliferator-activated receptors (PPARs) have increasingly become attractive targets for developing novel anti-type 2 diabetic drugs. We employed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to study three-dimensional quantitative structure-activity relationship (3D QSAR) based on existing agonists of PPARgamma (including five thiazolidinediones and 74 tyrosine-based compounds). Predictive 3D QSAR models with conventional r2 and cross-validated coefficient (q2) values up to 0.974 and 0.642 for CoMFA and 0.979 and 0.686 for COMSIA were established using the SYBYL package. These models were validated by a test set containing 18 compounds. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding pockets of PPARgamma, which demonstrates that the 3D QSAR models built here are very useful in predicting activities of novel compounds for activating PPARgamma.

Computer Simulation↗

PubMed2004

Quantitative structure-activity relationship study of histone deacetylase inhibitors.

Histone deacetylases (HDACs) play a critical role in gene transcription and have become a novel target for the discovery of drugs against cancer and other diseases. During the past several years there have been extensive efforts in the identification and optimization of histone deacetylase inhibitors (HDACIs) as novel anticancer drugs. Here we report a comprehensive quantitative structure-activity relationship (QSAR) study of HDACIs in the hope of identifying the structural determinants for anticancer activity. We have identified, collected, and verified the structural and biological activity data for 124 compounds from various literature sources and performed an extensive QSAR study on this comprehensive data set by using various QSAR and classification methods. A highly predictive QSAR model with R(2) of 0.76 and leave-one-out cross-validated R(2) of 0.73 was obtained. The overall rate of cross-validated correct prediction of the classification model is around 92%. The QSAR and classification models provided direct guidance to our internal programs of identifying and optimizing HDAC inhibitors. Limitations of the models were also discussed.

Animals↗

PubMedDate not supplied

A marine natural product database.

A database of marine natural products has been developed. The database contains approximately 6000 chemical compounds derived from over 10,000 marine-derived materials. For each compound, the structure, physical and chemical properties, marine source, and biological activities are given. A computer program for searching this database has also been developed and is described.

Biological Factors↗

PubMedDate not supplied

3D-QSAR and receptor modeling of tyrosine kinase inhibitors with flexible atom receptor model (FLARM).

A set of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors was investigated with the aim of developing 3D-QSAR models using the Flexible Atom Receptor Model (FLARM) method. Some 3D-QSAR models were built with high correlation coefficients, and the FLARM method predicted the biological activities of compounds in test set well. The FLARM method also gave the pseudoreceptor model, which indicates the possible interactions between the receptor and the ligand. The possible interactions include two hydrogen bonds, one hydrophobic interaction, and one sulfur-aromatic interaction, which are in accord with those in the pharmacophore model given by the scientists at Novartis. This shows that the FLARM method can bridge 3D-QSAR and receptor modeling in computer-aided drug design. Pharmacophore can be obtained according to these results, and 3D searching can then be done with databases to find the lead compound of EGFR tyrosine kinase inhibitors.

Binding Sites↗

PubMedDate not supplied

Eigenvalue analysis of peroxisome proliferator-activated receptor gamma agonists.

Eigenvalue analysis (EVA) was conducted on a series of potent agonists of peroxisome proliferator-activated receptor gamma (PPARgamma). Predictive EVA quantitative structure-activity relationship (QSAR) models were established using the SYBYL package, which had conventional r2 and cross-validated coefficient (q2) values up to 0.920 and 0.587 for the AM1 method and 0.863 and 0.586 for the PM3 method, respectively. These models were validated by a test set containing 18 compounds. The capability to predict by these two models for PPARgamma agonists, with the best predictive r2pred value of 0.614 for AM1 and 0.822 for PM3 methods, set a successful example for applying a similar approach in building QSAR models for PPARalpha and -delta that could potentially offer a new opportunity in the design of novel PPAR modulators.

Models, Chemical↗

PubMedDate not supplied

Pseudoreceptor models and 3D-QSAR for imidazobenzodiazepines at GABA A/BzR subtypes alphaxbeta3gamma2 [x = 1-3, 5, and 6] via flexible atom receptor model.

Since benzodiazepines have been used widely in the treatment of anxiety, sleeplessness, and epilepsy, the receptor sites for the benzodiazepine are of prime importance. Quantitative structure-activity relationship (QSAR) studies and receptor modeling via Flexible Atom Receptor Model (FLARM) for the binding affinities of a series of imidazobenzodiazepines at five recombinant receptor subtypes were carried out successfully. The 3D-QSAR models for all five receptor subtypes were examined by a set of test set and demonstrated their high predictability for affinities of imidazobenzodiazepines at five receptor subtypes. The pseudoreceptors yielded by FLARM were compared to the united pharmacophore/receptor model. The result shows that two hydrogen bonds and other regions in the united pharmacophore/receptor model are presented in the pseudoreceptors, which demonstrates the receptor modeling capability of FLARM. The models and pseudoreceptors can help design high affinity ligands on the GABA(A)/BZ receptor and understand the GABA(A) receptor.

Benzodiazepines↗

PubMedDate not supplied

Improving the quality of 3D-QSAR by using flexible-ligand receptor models.

To address the problems associated with molecular conformations and alignments in the 3D-QSAR studies, we have developed the Flexible Ligand - Atomic Receptor Model (FLARM) 2.0 method. The FLARM 2.0 method has three unique features as compared to other pseudoreceptor model methods: (1) the training ligands are flexibly optimized inside the receptors to achieve minimal docking energies; (2) the receptor atoms are spatially moveable in the process of genetic evolving in order to avoid improper initial receptor shapes; and (3) void receptor sites are specially favored in order to obtain open receptor models that allow large gaps. Advantages of an open model include less noise information, a smaller risk of overfitting, and ease of locating the key interaction sites. The latter two features, inherited from the previous FLARM 1.0 method, can improve the predictive ability of the 3D-QSAR models, while the first feature is newly implemented to relieve the uncertainty caused by improper conformation and alignment. Three FLARM 2.0 case studies were performed, and the results show that FLARM 2.0 models are highly predictive and robust. FLARM 2.0 pseudoreceptor models can correspond well with the pharmacophore models and/or the binding sites of the real protein receptors.

Algorithms↗