Biomedical subjects

Shaomeng Wang

Publications and source records attributed to Shaomeng Wang.

59 records · Page 4Linked to original sources

PubMed2002

Structural basis of RasGRP binding to high-affinity PKC ligands.

The Ras guanyl releasing protein RasGRP belongs to the CDC25 class of guanyl nucleotide exchange factors that regulate Ras-related GTPases. These GTPases serve as switches for the propagation and divergence of signaling pathways. One interesting feature of RasGRP is the presence of a C-terminal C1 domain, which has high homology to the PKC C1 domain and binds to diacylglycerol (DAG) and phorbol esters. RasGRP thus represents a novel, non-kinase phorbol ester receptor. In this paper, we investigate the binding of indolactam(V) (ILV), 7-(n-octyl)-ILV, 8-(1-decynyl)benzolactam(V) (benzolactam), and 7-methoxy-8-(1-decynyl)benzolactam(V) (methoxylated benzolactam) to RasGRP through both experimental binding assays and molecular modeling studies. The binding affinities of these lactams to RasGRP are within the nanomolar range. Homology modeling was used to model the structure of the RasGRP C1 domain (C1-RasGRP), which was subsequently used to model the structures of C1-RasGRP in complex with these ligands and phorbol 13-acetate using a computational docking method. The structural model of C1-RasGRP exhibits a folding pattern that is nearly identical to that of C1b-PKCdelta and is comprised of three antiparallel-strand beta-sheets capped against a C-terminal alpha-helix. Two loops A and B comprising residues 8-12 and 21-27 form a binding pocket that has some positive charge character. The ligands phorbol 13-acetate, benzolactam, and ILV are recognized by C1-RasGRP through a number of hydrogen bonds with loops A and B. In the models of C1-RasGRP in complex with phorbol 13-acetate, benzolactam, and ILV, common hydrogen bonds are formed with two residues Thr12 and Leu21, whereas other hydrogen bond interactions are unique for each ligand. Furthermore, our modeling results suggest that the shallower insertion of ligands into the binding pocket of C1-RasGRP compared to C1b-PKCdelta may be due to the presence of Phe rather than Leu at position 20 in C1-RasGRP. Taken together, our experimental and modeling studies provide us with a better understanding of the structural basis of the binding of PKC ligands to the novel phorbol ester receptor RasGRP.

Amino Acid Sequence↗

PubMed2002

Further development and validation of empirical scoring functions for structure-based binding affinity prediction.

New empirical scoring functions have been developed to estimate the binding affinity of a given protein-ligand complex with known three-dimensional structure. These scoring functions include terms accounting for van der Waals interaction, hydrogen bonding, deformation penalty, and hydrophobic effect. A special feature is that three different algorithms have been implemented to calculate the hydrophobic effect term, which results in three parallel scoring functions. All three scoring functions are calibrated through multivariate regression analysis of a set of 200 protein-ligand complexes and they reproduce the binding free energies of the entire training set with standard deviations of 2.2 kcal/mol, 2.1 kcal/mol, and 2.0 kcal/mol, respectively. These three scoring functions are further combined into a consensus scoring function, X-CSCORE. When tested on an independent set of 30 protein-ligand complexes. X-CSCORE is able to predict their binding free energies with a standard deviation of 2.2 kcal/mol. The potential application of X-CSCORE to molecular docking is also investigated. Our results show that this consensus scoring function improves the docking accuracy considerably when compared to the conventional force field computation used for molecular docking.

Binding Sites↗

PubMedDate not supplied

A web-based 3D-database pharmacophore searching tool for drug discovery.

Three-Dimensional (3D) structural database pharmacophore searching has become a very effective approach for discovery of novel lead compounds in drug discovery. Although several commercial programs are available, these commercial programs are primarily used as a stand alone and require a local database. In recent years, the Internet has become the main medium of choice for multiuser application program distribution. Herein, we describe our development of a Web-based 3D-database pharmacophore-searching tool based on the server-client Web architecture. Both rigid and conformationally flexible searching methods are implemented. Our results show that for a typical three-center rigid pharmacophore search, the run time for searching 50 000 compounds is less than three minutes, and for four-center pharmacophore searching, the run time is less than 10 minutes on a desktop computer. For a flexible 3D-pharmacophore search, the run time for searching 50 000 compounds generally takes between one and several hours. The search results are comparable to those obtained using a commercial program. We expect that this Web-based tool will be very useful for scientists who are interested in 3D-database pharmacophore searching via the Internet.

Database Management Systems↗

PubMedDate not supplied

Web-based tools for mining the NCI databases for anticancer drug discovery.

In this paper, we describe the development of a set of integrated Web-based tools for mining the National Cancer Institute's (NCI) anticancer databases for anticancer drug discovery. For data mining, three different correlation algorithms were implemented, which included the commonly used Pearson's correlation algorithm available from the NCI's COMPARE program, the Spearman's and Kendall's correlation algorithms. In addition, we implemented the p-value test to evaluate the significance of the correlation results. These Web-based data mining tools allow robust analysis of the correlation between the in vitro anticancer activity of the drugs in the NCI anticancer database, the protein levels and mRNA levels of molecular targets (genes) in the NCI 60 human cancer cell lines for identification of potential lead compounds for a specific molecular target and for study of the molecular mechanism action of a drug. Examples were provided to identify PKC ligands using a lead compound and to identify potential ErbB-2 inhibitors using the mRNA levels of ErbB-2 in the NCI 60 tumor cell lines.

Antineoplastic Agents↗

PubMedDate not supplied

An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes.

Fourteen popular scoring functions, i.e., X-Score, DrugScore, five scoring functions in the Sybyl software (D-Score, PMF-Score, G-Score, ChemScore, and F-Score), four scoring functions in the Cerius2 software (LigScore, PLP, PMF, and LUDI), two scoring functions in the GOLD program (GoldScore and ChemScore), and HINT, were tested on the refined set of the PDBbind database, a set of 800 diverse protein-ligand complexes with high-resolution crystal structures and experimentally determined Ki or Kd values. The focus of our study was to assess the ability of these scoring functions to predict binding affinities based on the experimentally determined high-resolution crystal structures of proteins in complex with their ligands. The quantitative correlation between the binding scores produced by each scoring function and the known binding constants of the 800 complexes was computed. X-Score, DrugScore, Sybyl::ChemScore, and Cerius2::PLP provided better correlations than the other scoring functions with standard deviations of 1.8-2.0 log units. These four scoring functions were also found to be robust enough to carry out computation directly on unaltered crystal structures. To examine how well scoring functions predict the binding affinities for ligands bound to the same target protein, the performance of these 14 scoring functions were evaluated on three subsets of protein-ligand complexes from the test set: HIV-1 protease complexes (82 entries), trypsin complexes (45 entries), and carbonic anhydrase II complexes (40 entries). Although the results for the HIV-1 protease subset are less than desirable, several scoring functions are able to satisfactorily predict the binding affinities for the trypsin and the carbonic anhydrase II subsets with standard deviation as low as 1.0 log unit (corresponding to 1.3-1.4 kcal/mol at room temperature). Our results demonstrate the strengths as well as the weaknesses of current scoring functions for binding affinity prediction.

Algorithms↗