Biomedical subjects

Keng-Chang Tsai

Publications and source records attributed to Keng-Chang Tsai.

11 recordsLinked to original sources

PubMed2006

Structure-based drug design and structural biology study of novel nonpeptide inhibitors of severe acute respiratory syndrome coronavirus main protease.

Severe acute respiratory syndrome coronavirus (SARS-CoV) main protease (M(pro)), a protein required for the maturation of SARS-CoV, is vital for its life cycle, making it an attractive target for structure-based drug design of anti-SARS drugs. The structure-based virtual screening of a chemical database containing 58,855 compounds followed by the testing of potential compounds for SARS-CoV M(pro) inhibition leads to two hit compounds. The core structures of these two hits, defined by the docking study, are used for further analogue search. Twenty-one analogues derived from these two hits exhibited IC50 values below 50 microM, with the most potent one showing 0.3 microM. Furthermore, the complex structures of two potent inhibitors with SARS-CoV M(pro) were solved by X-ray crystallography. They bind to the protein in a distinct manner compared to all published SARS-CoV M(pro) complex structures. They inhibit SARS-CoV M(pro) activity via intensive H-bond network and hydrophobic interactions, without the formation of a covalent bond. Interestingly, the most potent inhibitor induces protein conformational changes, and the inhibition mechanisms, particularly the disruption of catalytic dyad (His41 and Cys145), are elaborated.

Binding Sites↗

PubMed2006

Discovery of a novel family of SARS-CoV protease inhibitors by virtual screening and 3D-QSAR studies.

The severe acute respiratory syndrome-associated coronavirus (SARS-CoV) 3C-like protease (3CL(pro) or M(pro)) is an attractive target for the development of anti-SARS drugs because of its crucial role in the viral life cycle. In this study, a compound database was screened by the structure-based virtual screening approach to identify initial hits as inhibitors of SARS-CoV 3CL(pro). Out of the 59,363 compounds docked, 93 were selected for the inhibition assay, and 21 showed inhibition against SARS-CoV 3CL(pro) (IC(50) <or= 30 microM), with three of them having common substructures. Furthermore, a search for analogues with common substructure in the Maybridge, ChemBridge, and SPECS_SC databases led to the identification of another 25 compounds that exhibited inhibition against SARS-CoV 3CL(pro) (IC(50) = 3-1,000 microM). These compounds, 28 in total, were subjected to 3D-QSAR studies to elucidate the pharmacophore of SARS-CoV 3CL(pro).

Binding Sites↗

PubMed2005

Characterization of binding site of closed-state KCNQ1 potassium channel by homology modeling, molecular docking, and pharmacophore identification.

This investigation was performed to assess the importance of interaction in the binding of blockers to KCNQ1 potassium using molecular modeling. This work could be considered made up by three main steps: (1) the construction of closed-state structure of KCNQ1 through homology modeling; (2) the automated docking of three blockers: IKS-142, L-735821, and BMS-IKS, using DOCK program; (3) the generation and validation of pharmacophore for KCNQ1 ligands using Catalyst/HypoGen. The obtained results highlight the hydrophobic or aromatic residues involved in S6 transmembrane domain and the base of the pore helix of KCNQ1, confirming the mutagenesis data and pharmacophore model, and giving new suggestions for the rational design of novel KCNQ1 ligands.

Amino Acid Sequence↗

PubMed2005

Pharmacophore identification of alpha(1A)-adrenoceptor antagonists.

A chemical feature based pharmacophore model was developed for alpha(1A)-adrenoceptor antagonists by HypoGen module implemented in catalyst software package. The best scoring pharmacophore hypothesis, Hypo1, consisted of four important chemical features (one positive ion, one hydrogen-bond donor, one aromatic ring, and one hydrophobic group). The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.

Adrenergic alpha-1 Receptor Antagonists↗

PubMed2004

The pharmacophore hypotheses of I(Kr) potassium channel blockers: novel class III antiarrhythmic agents.

Predictive pharmacophore models were developed for a large series of I(Kr) potassium channel blockers as class III antiarrhythmic agents using HypoGen in Catalyst software. The pharmacophore hypotheses were generated using a training set consisting of 34 compounds carefully selected from documents. Their biological data, expressed as IC(50), spanned from 1.5 nM to 2.8 mM with 7 orders difference. The most predictive hypothesis (Hypo1), consisting of four features (one positive ionizable feature, two aromatic rings and one hydrophobic group), had a best correlation coefficient of 0.825, a lowest rms deviation of 1.612, and a highest cost difference (null cost-total cost) of 77.552, which represents a true correlation and a good predictivity. The hypothesis Hypo1 was then validated by a test set consisting of 21 compounds and by a cross-validation of 95% confidence level with randomizing the data using CatScramble program. Accordingly, our model has strong predictivity to identify structural diverse I(Kr) potassium channel blockers with desired biological activity by virtual screening

Anti-Arrhythmia Agents↗

PubMed2004

3D-QSAR studies on PU3 analogues by comparative molecular field analysis.

A comparative molecular field analysis (CoMFA) of PU3 derivatives of Hsp90 (Heat shock protein 90) inhibitors has been performed to determine the factors contributing the corresponding activities. The energy minimized conformations were obtained by molecular mechanics using SYBYL package. The developed model, with r(2) value of 0.947, was verified by performing leave-one out (LOO) cross-validation, which showed q(2) value of 0.513. The calculated model not only elucidates the relationship between compound structures and biological activities but, more importantly, facilitates design of new Hsp90 inhibitors with calculated antiproliferative activity.

Antineoplastic Agents↗

PubMed2003

Homology modeling of the central catalytic domain of insertion sequence ISLC3 isolated from Lactobacillus casei ATCC 393.

The tertiary structure of the central catalytic domain of insertion sequence ISLC3 isolated from Lactobacillus casei ATCC 393 was predicted using the homology modeling approach. The novel insertion sequence was isolated by us from the template bacteriophage phiA3 of L.casei ATCC 393. The number of amino acid residues of the ISLC3 central catalytic domain was 116 and was treated as the query sequence. There were five Web-available threading methods used to find some primary structure templates for the query sequence. These primary templates were further screened using the SWISS-MODEL Protein Modeling Server and the default parameter settings therein to give six final structure templates. All of these final structure templates were the integrase (IN) protein of retroviruses. Multiple sequence alignment using these IN sequences against the query one revealed the signature DDE motif. Based on the structures of these final templates, the structure of the query sequence was constructed using the InsightII/Discover/Homology programs. A metal ion, Mg(2+), was inserted into the center of the putative catalytic pocket formed by the DDE residues of the predicted structure in the final rounds of refinement by molecular dynamics (MD) simulations. The structure with a metal ion included was designated with Mg and that without a metal ion was designated free Mg. The average exposed surface area of some hydrophobic residues of both the predicted free Mg and with Mg structures were computed and compared with those computed for the six structure templates. Whereas the predicted with Mg structure was slightly more exposed than the predicted free Mg structure, the former appeared to be more stable than the latter, as revealed by the lower conformation energy recorded for the former during the structure refinement by MD simulations. To verify further the predicted structures, the coordinates of both predicted structures were fed into the ERRAT Protein Verification Server. It was found that the quality of the predicted with Mg structure was much better than that of the free Mg structure. The validation results also indicated that regions of the predicted with Mg structure that can be rejected at the 95% confidence level were approximately 20% whereas those which can be rejected at the same level for the six structure templates were approximately 10%. The predicted with Mg structure was also docked into a short oligonucleotide representing the substrate of the ISLC3 transposase using the DOCK_4.0.2 program. It was found that both Glu140 and Asp68 residues of the DDE motif of the predicted with Mg structure were able to form hydrogen bonds with the DNA substrate, which was similar to what was observed in a docking study using the retrovirus IN 1asu and its DNA substrate.

Amino Acid Sequence↗

PubMedDate not supplied

Implementing the Fisher's discriminant ratio in a k-means clustering algorithm for feature selection and data set trimming.

The Fisher's discriminant ratio has been used as a class separability criterion and implemented in a k-means clustering algorithm for performing simultaneous feature selection and data set trimming on a set of 221 HIV-1 protease inhibitors. The total number of molecular descriptors computed for each inhibitor is 43, and they are scaled to lie between 1 and 0 before being subjected to the feature selection process. Since the purpose is to select some of the most class sensitive descriptors, several feature evaluation indices such as the Shannon entropy, the linear regression of selected descriptors on the pKi of selected inhibitors, and a stepwise variable selection program are used to filter them. While the Shannon entropy provides the information content for each descriptor computed, more class sensitive descriptors are searched by both the linear regression and stepwise variable selection procedures. The inhibitors are divided into several different numbers of classes. They are subsequently divided into five classes due to the fact that the best feature selection result is obtained by the division. Most of the good features selected are the topological descriptors, and they are correlated well with the pKi values. The outliers or the inhibitors with less class-sensitive descriptor values computed for each selected descriptor are identified and gathered by the k-means clustering algorithm. These are the trimmed inhibitors, while the remaining ones are retained or selected. We find that 44% or 98 inhibitors can be retained when the number of good descriptors selected for clustering is three. The descriptor values of these selected inhibitors are far more class sensitive than the original ones as evidenced by substantial increasing in statistical significance when they are subjected to both the SYBYL CoMFA PLS and Cerius2 PLS regression analyses.

Journal Article↗

PubMedDate not supplied

A ligand-based molecular modeling study on some matrix metalloproteinase-1 inhibitors using several 3D QSAR techniques.

Some three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) for a series of 84 proline-based plus 12 structurally more diversified nonproline matrix metalloproteinase inhibitors. The structures of these inhibitors were built from a structure template extracted from the crystal structure of stromelysin. The structures built were divided into the training and test sets for both the CoMFA and CoMSIA analyses for each being composed of 60 and 24 inhibitors, respectively. The structures in the training set were aligned using some alignment rules derived from the analysis of the Ligplot program on a recent crystal structure of ligand-collagenase-1 complex. Some stepwise CoMSIA's were performed on the aligned training set on which the best CoMFA result was obtained. The best CoMSIA model was identified from the stepwise results, and the corresponding pharmacophore features were used for the construction of a pharmacophore hypothesis by the Catalyst 4.9 program. The training set was extended to include 11 structurally more diversified and nonproline inhibitors. To construct a pharmacophore hypothesis, the conformation of 60 structurally aligned proline-based inhibitors was fixed, while that of the 11 structurally more diversified nonproline inhibitors was allowed to vary during the hypothesis construction process. It was found that the predicted activities by the top hypothesis constructed for both the training and test sets were as good in statistics as those predicted by the best CoMSIA model from which the hypothesis was derived. The top hypothesis was mapped onto the structures of several highly active inhibitors selected from both the training and test sets. The goodness of mapping on each inhibitor was found to be correlated well with the activity of each inhibitor.

Ligands↗

PubMedDate not supplied

Modeling ligand-receptor interaction for some MHC class II HLA-DR4 peptide mimetic inhibitors using several molecular docking and 3D QSAR techniques.

The ligand-receptor interaction between some peptidomimetic inhibitors and a class II MHC peptide presenting molecule, the HLA-DR4 receptor, was modeled using some three-dimensional (3D) quantitative structure-activity relationship (QSAR) methods such as the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these peptidomimetic inhibitors were generated theoretically, and the conformations used in the 3D QSAR studies were defined by docking them into the known structure of HLA-DR4 receptor through the GOLD, GLIDE Rigidly, GLIDE Flexible, and Xscore programs. Some of the parameters used in these docking programs were selected by docking an X-ray ligand into the receptor and comparing the root-means-square difference (RMSD) computed between the coordinates of the X-ray and docked structure. However, the goodness of a docking result for docking a series of peptidomimetic inhibitors into the HLA-DR4 receptor was judged by comparing the Spearman's rank correlation coefficient computed between each docking result and the activity data taken from the literature. The best CoMFA and CoMSIA models were constructed using the aligned structures of the best docking result. The CoMSIA was conducted in a stepwise manner to identify some important molecular features that were further employed in a pharmacophore building process by the Catalyst program. It was found that most inhibitors of the training set were accurately predicted by the best pharmacophore model, the Hypo1 hypothesis constructed. The deviation or conflict found between the actual and predicted activities of some inhibitors of both the training and the test sets were also investigated by mapping the Hypo1 hypothesis onto the corresponding structures of the inhibitors.

Binding Sites↗

PubMedDate not supplied

Supervised feature ranking using a genetic algorithm optimized artificial neural network.

A genetic algorithm optimized artificial neural network GNW has been designed to rank features for two diversified multivariate data sets. The dimensions of these data sets are 85x24 and 62x25 for 24 or 25 molecular descriptors being computed for 85 matrix metalloproteinase-1 inhibitors or 62 hepatitis C virus NS3 protease inhibitors, respectively. Each molecular descriptor computed is treated as a feature and input into an input layer node of the artificial neural network. To optimize the artificial neural network by the genetic algorithm, each interconnected weight between input and hidden or between hidden and output layer nodes is binary encoded as a 16 bits string in a chromosome, and the chromosome is evolved by crossover and mutation operations. Each input layer node and its associated weights of the trained GNW are systematically omitted once (the self-depleted weights), and the corresponding weight adjustments due to the omission are computed to keep the overall network behavior unchanged. The primary feature ranking index defined as the sum of self-depleted weights and the corresponding weight adjustments computed is found capable of separating good from bad features for some artificial data sets of known feature rankings tested. The final feature indexes used to rank the data sets are computed as a sum of the weighted frequency of each feature being ranked in a particular rank for each data set being partitioned into numerous clusters. The two data sets are also clustered by a standard K-means method and trained by a support vector machine (SVM) for feature ranking using the computed F-scores as feature ranking index. It is found that GNW outperforms the SVM method on three artificial as well as the matrix metalloproteinase-1 inhibitor data sets studied. A clear-cut separation of good from bad features is offered by the GNW but not by the SVM method for a feature pool of known feature ranking.

Algorithms↗