Biomedical subjects

Minghu Song

Publications and source records attributed to Minghu Song.

5 recordsLinked to original sources

PubMed2004

Three-dimensional quantitative structure-activity relationship analyses of piperidine-based CCR5 receptor antagonists.

The CCR5 chemokine receptor has recently been found to play a crucial role in the viral entry stage of HIV infection and has therefore become an attractive potential target for anti-HIV therapeutics. On the other hand, the lack of CCR5 crystal structure data has impeded the development of structure-based CCR5 antagonist design. In this paper, we compare two three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) methods: Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) on a series of piperidine-based CCR5 antagonists as an alternative approach to investigate the interaction between CCR5 antagonists and their receptor. Superimposition of antagonist structures was performed using two alignment rules: atomic/centroid rms fit and rigid body field fit techniques. The 3D QSAR models were derived from a training set of 72 compounds, and were found to have predictive capability for a set of 19 holdout test compounds. The resulting contour maps produced by the best CoMFA and CoMSIA models were used to identify the structural features relevant to biological activity in this series of compounds. Further analyses of these interaction-field contour maps also showed a high level of internal consistency.

Animals↗

PubMed2003

Prediction of the effect of mobile-phase salt type on protein retention and selectivity in anion exchange systems.

This study examines the effect of different salt types on protein retention and selectivity in anion exchange systems. Particularly, linear retention data for various proteins were obtained on two structurally different anion exchange stationary-phase materials in the presence of three salts with different counterions. The data indicated that the effects are, for the most part, nonspecific, although various specific effects could also be observed. Quantitative structure retention relationship (QSRR) models based on support vector machine feature selection and regression models were developed using the experimental chromatographic data in conjunction with various molecular descriptors computed from protein crystal structure geometries. Star plots for each descriptor used in the final model were generated to aid in interpretation. The resulting QSRR models were predictive, with cross-validated r2 values of 0.9445, 0.9676, and 0.8897 for Source 15Q and 0.9561, 0.9876, and 0.9760 for Q Sepharose resins in the presence of three different salts. The predictive power of these models was validated using a set of test proteins that were not used in the generation of these models. Interpretation of the models revealed that particular trends for proteins and salts could be captured using QSRR techniques.

Algorithms↗

PubMed2003

A mass spectrometry study of tirapazamine and its metabolites. insights into the mechanism of metabolic transformations and the characterization of reaction intermediates.

Tandem mass spectrometry methods were used to study the sites of protonation and for identification of 3-amino-1,2,4-benzotriazine 1,4-dioxide (1, tirapazamine), and its metabolites (3-amino-1,2,4-benzotriazine 1-oxide (3), 3-amino-1,2,4-benzotriazine 4-oxide (4), 3-amino-1,2,4-benzotriazine (5), and a related isomer 3-amino-1,2,4-benzotriazine 2-oxide (6). Fragmentation pathways of 3 and 5 indicated the 4-N-atom as the most likely site of protonation. Among the N-oxides studied, the 4-oxide (4) showed the highest degree of protonation at the oxygen atom. The differences in collision-induced dissociation of isomeric protonated 1-, 2- and 4-oxides allowed for their identification by LC/MS/MS. Gas phase and liquid phase protonation of tirapazamine occurred exclusively at the oxygen in the 4-position. A loss of OH radical from these ions (2(+)) resulted in ionized 3. Neutralization-reionization mass spectrometry (NR MS) experiments demonstrated the stability of the neutral analogue of protonated tirapazamine in the gas phase in the micro s time-frame. A significant portion of the neutral tirapazamine radicals (2) dissociated by loss of hydroxyl radical during the NR MS event, which indicates that previously proposed mechanisms for redox-activated DNA damage are reasonable. The activation energy for loss of hydroxyl radical from activated tirapazamine (2) was estimated to be approximately 14 kcal mol(-1). Stable neutral analogues of [3 + H](+) and [5 + H](+) ions were also generated in the course of NR MS experiments. Structures of these radicals were assigned to the molecules having an extra hydrogen atom at one of the ring N-atoms. Quantum chemical calculations of protonated 1, 3, 4 and 5 and the corresponding neutrals were performed to assist in the interpretation of experimental results and to help identify their structures.

Antineoplastic Agents↗

PubMedDate not supplied

Prediction of protein retention times in anion-exchange chromatography systems using support vector regression.

Quantitative Structure-Retention Relationship (QSRR) models are developed for the prediction of protein retention times in anion-exchange chromatography systems. Topological, subdivided surface area, and TAE (Transferable Atom Equivalent) electron-density-based descriptors are computed directly for a set of proteins using molecular connectivity patterns and crystal structure geometries. A novel algorithm based on Support Vector Machine (SVM) regression has been employed to obtain predictive QSRR models using a two-step computational strategy. In the first step, a sparse linear SVM was utilized as a feature selection procedure to remove irrelevant or redundant information. Subsequently, the selected features were used to produce an ensemble of nonlinear SVM regression models that were combined using bootstrap aggregation (bagging) techniques, where various combinations of training and validation data sets were selected from the pool of available data. A visualization scheme (star plots) was used to display the relative importance of each selected descriptor in the final set of "bagged" models. Once these predictive models have been validated, they can be used as an automated prediction tool for virtual high-throughput screening (VHTS).

Anion Exchange Resins↗

PubMedDate not supplied

Development and evaluation of an in silico model for hERG binding.

It has been recognized that drug-induced QT prolongation is related to blockage of the human ether-a-go-go-related gene (hERG) ion channel. Therefore, it is prudent to evaluate the hERG binding of active compounds in early stages of drug discovery. In silico approaches provide an economic and quick method to screen for potential hERG liability. A diverse set of 90 compounds with hERG IC(50) inhibition data was collected from literature references. Fragment-based QSAR descriptors and three different statistical methods, support vector regression, partial least squares, and random forests, were employed to construct QSAR models for hERG binding affinity. Important fragment descriptors relevant to hERG binding affinity were identified through an efficient feature selection method based on sparse linear support vector regression. The support vector regression predictive model built upon selected fragment descriptors outperforms the other two statistical methods in this study, resulting in an r(2) of 0.912 and 0.848 for the training and testing data sets, respectively. The support vector regression model was applied to predict hERG binding affinities of 20 in-house compounds belonging to three different series. The model predicted the relative binding affinity well for two out of three compound series. The hierarchical clustering and dendrogram results show that the compound series with the best prediction has much higher structural similarity and more neighbors of training compounds than the other two compound series, demonstrating the predictive scope of the model. The combination of a QSAR model and postprocessing analysis, such as clustering and visualization, provides a way to assess the confidence level of QSAR prediction results on the basis of similarity to the training set.

Cell Line↗