Jinn-Moon Yang

GEMDOCK: A generic evolutionary method for molecular docking

We have developed an evolutionary approach for flexible ligand docking. This approval, GEMDOCK, uses a Generic Evolutionary Method for molecular DOCKing and an empirical scoring function. The former combines both discrete and continuous global search strategies with local search strategies to speed up convergence, whereas the latter results in rapid recognition of potential ligands. GEMDOCK was tested on a diverse data set of 100 protein–ligand complexes from the Protein Data Bank. In 79% of these complexes, the docked lowest energy ligand structures had root‐mean‐square derivations (RMSDs) below 2.0 Å with respect to the corresponding crystal structures. The success rate increased to 85% if the structure water molecules were retained. We evaluated GEMDOCK on two cross‐docking experiments in which each ligand of a protein ensemble was docked into each protein of the ensemble. Seventy‐six percent of the docked structures had RMSDs below 2.0 Å when the ligands were docked into foreign structures. We analyzed and validated GEMDOCK with respect to various search spaces and scoring functions, and found that if the scoring function was perfect, then the predicted accuracy was also essentially perfect. This study suggests that GEMDOCK is a useful tool for molecular recognition and may be used to systematically evaluate and thus improve scoring functions. Proteins 2004.

(PS)2: protein structure prediction server.

Protein structure prediction provides valuable insights into function, and comparative modeling is one of the most reliable methods to predict 3D structures directly from amino acid sequences. However, critical problems arise during the selection of the correct templates and the alignment of query sequences therewith. We have developed an automatic protein structure prediction server, (PS)2, which uses an effective consensus strategy both in template selection, which combines PSI-BLAST and IMPALA, and target–template alignment integrating PSI-BLAST, IMPALA and T-Coffee. (PS)2 was evaluated for 47 comparative modeling targets in CASP6 (Critical Assessment of Techniques for Protein Structure Prediction). For the benchmark dataset, the predictive performance of (PS)2, based on the mean GTD_TS score, was superior to 10 other automatic servers. Our method is based solely on the consensus sequence and thus is considerably faster than other methods that rely on the additional structural consensus of templates. Our results show that (PS)2, coupled with suitable consensus strategies and a new similarity score, can significantly improve structure prediction. Our approach should be useful in structure prediction and modeling. The (PS)2 is available through the website at .

An evolutionary algorithm for large traveling salesman problems

This work proposes an evolutionary algorithm, called the heterogeneous selection evolutionary algorithm (HeSEA), for solving large traveling salesman problems (TSP). The strengths and limitations of numerous well-known genetic operators are first analyzed, along with local search methods for TSPs from their solution qualities and mechanisms for preserving and adding edges. Based on this analysis, a new approach, HeSEA is proposed which integrates edge assembly crossover (EAX) and Lin-Kernighan (LK) local search, through family competition and heterogeneous pairing selection. This study demonstrates experimentally that EAX and LK can compensate for each others disadvantages. Family competition and heterogeneous pairing selections are used to maintain the diversity of the population, which is especially useful for evolutionary algorithms in solving large TSPs. The proposed method was evaluated on 16 well-known TSPs in which the numbers of cities range from 318 to 13509. Experimental results indicate that HeSEA performs well and is very competitive with other approaches. The proposed method can determine the optimum path when the number of cities is under 10,000 and the mean solution quality is within 0.0074% above the optimum for each test problem. These findings imply that the proposed method can find tours robustly with a fixed small population and a limited family competition length in reasonable time, when used to solve large TSPs.

iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis

BackgroundPharmacological interactions are useful for understanding ligand binding mechanisms of a therapeutic target. These interactions are often inferred from a set of active compounds that were acquired experimentally. Moreover, most docking programs loosely coupled the stages (binding-site and ligand preparations, virtual screening, and post-screening analysis) of structure-based virtual screening (VS). An integrated VS environment, which provides the friendly interface to seamlessly combine these VS stages and to identify the pharmacological interactions directly from screening compounds, is valuable for drug discovery.ResultsWe developed an easy-to-use graphic environment, i GEMDOCK, integrating VS stages (from preparations to post-screening analysis). For post-screening analysis, i GEMDOCK provides biological insights by deriving the pharmacological interactions from screening compounds without relying on the experimental data of active compounds. The pharmacological interactions represent conserved interacting residues, which often form binding pockets with specific physico-chemical properties, to play the essential functions of a target protein. Our experimental results show that the pharmacological interactions derived by i GEMDOCK are often hot spots involving in the biological functions. In addition, i GEMDOCK provides the visualizations of the protein-compound interaction profiles and the hierarchical clustering dendrogram of the compounds for post-screening analysis.ConclusionsWe have developed i GEMDOCK to facilitate steps from preparations of target proteins and ligand libraries toward post-screening analysis. i GEMDOCK is especially useful for post-screening analysis and inferring pharmacological interactions from screening compounds. We believe that i GEMDOCK is useful for understanding the ligand binding mechanisms and discovering lead compounds. i GEMDOCK is available at http://gemdock.life.nctu.edu.tw/dock/igemdock.php.

Protein structure database search and evolutionary classification

As more protein structures become available and structural genomics efforts provide structural models in a genome-wide strategy, there is a growing need for fast and accurate methods for discovering homologous proteins and evolutionary classifications of newly determined structures. We have developed 3D-BLAST, in part, to address these issues. 3D-BLAST is as fast as BLAST and calculates the statistical significance (E-value) of an alignment to indicate the reliability of the prediction. Using this method, we first identified 23 states of the structural alphabet that represent pattern profiles of the backbone fragments and then used them to represent protein structure databases as structural alphabet sequence databases (SADB). Our method enhanced BLAST as a search method, using a new structural alphabet substitution matrix (SASM) to find the longest common substructures with high-scoring structured segment pairs from an SADB database. Using personal computers with Intel Pentium4 (2.8 GHz) processors, our method searched more than 10 000 protein structures in 1.3 s and achieved a good agreement with search results from detailed structure alignment methods. [3D-BLAST is available at ]

Aurintricarboxylic acid inhibits influenza virus neuraminidase

Abstract There is a continuing threat that the highly pathogenic avian influenza virus will cause future influenza pandemics. In this study, we screened a library of compounds that are biologically active and structurally diverse for inhibitory activity against influenza neuraminidase (NA). We found that aurintricarboxylic acid (ATA) is a potent inhibitor of NA activity of both group-1 and group-2 influenza viruses with IC50s (effective concentration to inhibit NA activity by 50%) values at low micromolar concentrations. ATA was equally potent in inhibiting the NA activity derived from wild-type NA and its H274Y mutant which renders NA resistance to inhibition by oseltamivir. Although ATA is structurally distinct from sialic acid, molecular modeling experiments suggested that ATA binds to NA at the enzyme’s substrate binding site. These results indicate that ATA may be a good starting material for the design of a novel class of NA inhibitors for the treatment influenza viruses.

Applying Family Competition to Evolution Strategies for Constrained Optimization

This paper applies family competition to evolution strategies to solve constrained optimization problems. The family competition of Family Competition Evolution Strategy (FCES) can be viewed as a local competition involving the children generated from the same parent, while the selection is a global competition among all of the members in the population. According to our experimental results, the self-adaptation of strategy parameters with deterministic elitist selection may trap ESs into local optima when they are applied to heavy constrained optimization problems. By controlling strategy parameters with non-self adaptive rule, FCES can reduce the computation time of self-adaptive Gaussian mutation, diminish the complexity of selection from (m+1) to (m+m), and avoid to be premature. Therefore, FCES is capable of obtaining better performance and saving the computation time. In this paper, FCES is compared with other evolutionary algorithms on various benchmark problems and the results indicate that FCES is a powerful optimization technique.

Kappa-alpha plot derived structural alphabet and BLOSUM-like substitution matrix for rapid search of protein structure database.

We present a novel protein structure database search tool, 3D-BLAST, that is useful for analyzing novel structures and can return a ranked list of alignments. This tool has the features of BLAST (for example, robust statistical basis, and effective and reliable search capabilities) and employs a kappa-alpha (κ, α) plot derived structural alphabet and a new substitution matrix. 3D-BLAST searches more than 12,000 protein structures in 1.2 s and yields good results in zones with low sequence similarity.

PhosphoPOINT: a comprehensive human kinase interactome and phospho-protein database

MOTIVATION To fully understand how a protein kinase regulates biological processes, it is imperative to first identify its substrate(s) and interacting protein(s). However, of the 518 known human serine/threonine/tyrosine kinases, 35% of these have known substrates, while 14% of the kinases have identified substrate recognition motifs. In contrast, 85% of the kinases have protein-protein interaction (PPI) datasets, raising the possibility that we might reveal potential kinase-substrate pairs from these PPIs. RESULTS PhosphoPOINT, a comprehensive human kinase interactome and phospho-protein database, is a collection of 4195 phospho-proteins with a total of 15 738 phosphorylation sites. PhosphoPOINT annotates the interactions among kinases, with their down-stream substrates and with interacting (phospho)-proteins to modulate the kinase-substrate pairs. PhosphoPOINT implements various gene expression profiles and Gene Ontology cellular component information to evaluate each kinase and their interacting (phospho)-proteins/substrates. Integration of cSNPs that cause amino acids change with the proteins with the phosphoprotein dataset reveals that 64 phosphorylation sites result in a disease phenotypes when changed; the linked phenotypes include schizophrenia and hypertension. PhosphoPOINT also provides a search function for all phospho-peptides using about 300 known kinase/phosphatase substrate/binding motifs. Altogether, PhosphoPOINT provides robust annotation for kinases, their downstream substrates and their interaction (phospho)-proteins and this should accelerate the functional characterization of kinomemediated signaling. AVAILABILITY PhosphoPOINT can be freely accessed in http://kinase. bioinformatics.tw/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

A pharmacophore‐based evolutionary approach for screening selective estrogen receptor modulators

We developed a pharmacophore‐based evolutionary approach for virtual screening. This tool, termed the Generic Evolutionary Method for molecular DOCKing (GEMDOCK), combines an evolutionary approach with a new pharmacophore‐based scoring function. The former integrates discrete and continuous global search strategies with local search strategies to expedite convergence. The latter, integrating an empirical‐based energy function and pharmacological preferences (binding‐site pharmacological interactions and ligand preferences), simultaneously serves as the scoring function for both molecular docking and postdocking analyses to improve screening accuracy. We apply pharmacological interaction preferences to select the ligands that form pharmacological interactions with target proteins, and use the ligand preferences to eliminate the ligands that violate the electrostatic or hydrophilic constraints. We assessed the accuracy of our approach using human estrogen receptor (ER) and a ligand database from the comparative studies of Bissantz et al. (J Med Chem 2000;43:4759–4767). Using GEMDOCK, the average goodness‐of‐hit (GH) score was 0.83 and the average false‐positive rate was 0.13% for ER antagonists, and the average GH score was 0.48 and the average false‐positive rate was 0.75% for ER agonists. The performance of GEMDOCK was superior to competing methods such as GOLD and DOCK. We found that our pharmacophore‐based scoring function indeed was able to reduce the number of false positives; moreover, the resulting pharmacological interactions at the binding site, as well as ligand preferences, were important to the screening accuracy of our experiments. These results suggest that GEMDOCK constitutes a robust tool for virtual database screening. Proteins 2005.