Browsing by Author "Yang, JM"

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A pharmacophore-based evolutionary approach for screening estrogen receptor antagonists
(2004)
Yang, JM
;
Shen, TW
;
生物資訊及系統生物研究所
;
Institude of Bioinformatics and Systems Biology
Virtual ligand screening has emerged as a promising approach for drug development. The inaccuracy of the scoring methods is probably major weakness for virtual ligand screening. In this paper, we have developed a pharmacophore-based evolutionary approach that was applicable to virtual screening and post-docking analysis. Our tool, referred to as the Generic Evolutionary Method for molecular DOCKing (GEMDOCK), combines an evolutionary approach and a new pharmacophore-based scoring function for virtual database screening. The former integrates discrete and continuous global search strategies with local search strategies to speed up convergence. The latter integrates a simple empirical scoring function and pharmacophore perferences. We accessed the screening accuracy of our approach on estrogen receptor alpha (ERalpha) using a ligand database on which competing tools were evaluated. The accuracies of our prediction were 0.64 for the GH score and 0.91% for the false positive rate when the true positive rate was 100%. We found that our pharmacophore-based scoring function indeed is able to reduce the number of the false positives. These results suggest that GEMDOCK is robust and can be a useful tool for virtual database screening.
A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators
(2005-05-01)
Yang, JM
;
Shen, TW
;
生物科技學系
;
生物資訊及系統生物研究所
;
Department of Biological Science and Technology
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. (c) 2005 Wiley-Liss, Inc.
A robust evolutionary algorithm for global optimization
(2002)
Yang, JM
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Lin, CJ
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Kao, CY
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生物科技學系
;
生物資訊及系統生物研究所
This paper studies an evolutionary algorithm for global optimization. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing-based mutations and self-adaptive mutations. The proposed approach is experimentally analyzed by showing that its components can integrate with one another and possess good local and global properties. Following the description of implementation details, the approach is then applied to several widely used test sets, including problems from international contests on evolutionary optimization. Numerical results indicate that the new approach performs very robustly and is competitive with other well-known evolutionary algorithms.
A robust evolutionary algorithm for training neural networks
(2001)
Yang, JM
;
Kao, CY
;
生物科技學系
;
Department of Biological Science and Technology
A new evolutionary algorithm is introduced for training both feedforward and recurrent neural networks. The proposed approach, called the Family Competition Evolutionary Algorithm (FCEA), automatically achieves the balance of the solution quality and convergence speed by integrating multiple mutations, family competition and adaptive rides. We experimentally analyse the proposed approach by, showing that its components can cooperate with one another, and possess good local and global properties. Following the description of implementation details, our approach is then applied to several benchmark problems, including an artificial ant problem, parity problems and a two-spiral problem. Experimental results indicate that the new approach is able to stably solve these problems, and is very competitive with the comparative evolutionary algorithms.
An evolutionary algorithm for large traveling salesman problems
(2004-08-01)
Tsai, HK
;
Yang, JM
;
Tsai, YF
;
Kao, CY
;
生物科技學系
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 other's 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 13 509. 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.
An evolutionary approach for gene expression patterns
(2004-06-01)
Tsai, HK
;
Yang, JM
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Tsai, YF
;
Kao, CY
;
生物資訊及系統生物研究所
This study presents an evolutionary algorithm, called a heterogeneous selection genetic algorithm (HeSGA), for analyzing the patterns of gene expression on microarray data. Microarray technologies have provided the means to monitor the expression levels of a large number of genes simultaneously. Gene clustering and gene ordering are important in analyzing a large body of microarray expression data. The proposed method simultaneously solves gene clustering and gene-ordering problems by integrating global and local search mechanisms. Clustering and ordering information is used to identify functionally related genes and to infer genetic networks from immense microarray expression data. HeSGA was tested on eight test microarray datasets, ranging in size from 147 to 6221 genes. The experimental clustering and visual results indicate that HeSGA not only ordered genes smoothly but also grouped genes with similar gene expressions. Visualized results and a new scoring function that references predefined functional categories were employed to confirm the biological interpretations of results yielded using HeSGA and other methods. These results indicate that HeSGA has potential in analyzing gene expression patterns.
An evolutionary approach for molecular docking
(2003)
Yang, JM
;
生物科技學系
;
Department of Biological Science and Technology
We have developed an evolutionary approach for the flexible docking that is now an important component of a rational drug design. This automatic docking tool, referred to as the GEMDOCK (Generic Evolutionary Method for DOCKing molecules), combines both global and local search strategies search mechanisms. GEMDOCK used a simple scoring function to recognize compounds by minimizing the energy of molecular interactions. The interactive types of atoms between ligands and proteins of our linear scoring function consist only hydrogen-bonding and steric terms. GEMDOCK has been tested on a diverse dataset of 100 protein-ligand complexes from Protein Data Bank. In total 76% of these complexes, it obtained docked ligand conformations with root mean square derivations (RMSD) to the crystal ligand structures less than 2.0 Angstrom when the ligand was docked back into the binding site. Experiments shows that the scoring function is simple and efficiently discriminates between native and non-native docked conformations. This study suggests that GEMDOCK is a useful tool for molecular recognition and is a potential docking tool for protein structure variations.
An evolutionary approach with pharmacophore-based scoring functions for virtual database screening
(2004)
Yang, JM
;
Shen, TW
;
Chen, YF
;
Chiu, YY
;
生物科技學系
We have developed a new tool for virtual database screening. This tool, referred to as the Generic Evolutionary Method for molecular DOCKing (GEMDOCK), combines an evolutionary approach and a new pharmacophore-based scoring function. The former integrates discrete and continuous global search strategies with local search strategies to speed up convergence. The latter simultaneously serves as the scoring function of both molecular docking and post-docking analysis to improve the number of the true positives. We accessed the accuracy of our approach on HSV-1 thymidine kinase using a ligand database on which competing tools were evaluated. The accuracies of our predictions were 0.54 for the GH score and 1.62% for the false positive rate when the true positive rate was 100%. We found that our pharmacophore-based scoring function indeed is able to reduce the number of the false positives. These results suggest that GEMDOCK is robust and can be a useful tool for virtual database screening.
Comparative molecular binding energy analysis of HIV-1 protease inhibitors using genetic algorithm-based partial least squares method
(2004)
Chen, YC
;
Yang, JM
;
Tsai, CH
;
Kao, CY
;
生物科技學系
Consensus scoring criteria for improving enrichment in virtual screening
(2005-07-01)
Yang, JM
;
Chen, YF
;
Shen, TW
;
Kristal, BS
;
Hsu, DF
Virtual screening of molecular compound libraries is a potentially powerful and inexpensive method for the discovery of novel lead compounds for drug development. The major weakness of virtual screening-the inability to consistently identify true positives (leads)-is likely due to our incomplete understanding of the chemistry involved in ligand binding and the subsequently imprecise scoring algorithms. It has been demonstrated that combining multiple scoring functions (consensus scoring) improves the enrichment of true positives. Previous efforts at consensus scoring have largely focused on empirical results, but they have yet to provide a theoretical analysis that gives insight into real features of combinations and data fusion for virtual screening. Results: We demonstrate that combining multiple scoring functions improves the enrichment of true positives only if (a) each of the individual scoring functions has relatively high performance and (b) the individual scoring functions are distinctive. Notably, these two prediction variables are previously established criteria for the performance of data fusion approaches using either rank or score combinations. This work, thus, establishes a potential theoretical basis for the probable success of data fusion approaches to improve yields in in silico screening experiments. Furthermore, it is similarly established that the second criterion (b) can, in at least some cases, be functionally defined as the area between the rank versus score plots generated by the two (or more) algorithms. Because rank-score plots are independent of the performance of the individual scoring function, this establishes a second theoretically defined approach to determining the likely success of combining data from different predictive algorithms. This approach is, thus, useful in practical settings in the virtual screening process when the performance of at least two individual scoring functions (such as in criterion a) can be estimated as having a high likelihood of having high performance, even if no training sets are available. We provide initial validation of this theoretical approach using data from five scoring systems with two evolutionary docking algorithms on four targets, thymidine kinase, human dihydrofolate reductase, and estrogen receptors of antagonists and agonists. Our procedure is computationally efficient, able to adapt to different situations, and scalable to a large number of compounds as well as to a greater number of combinations. Results of the experiment show a fairly significant improvement (vs single algorithms) in several measures of scoring quality, specifically "goodness-of-hit" scores, false positive rates, and "enrichment". This approach (available online at http://gemdock.life. nctu.edu.tw/dock/download.php) has practical utility for cases where the basic tools are known or believed to be generally applicable, but where specific training sets are absent.
Consensus scoring criteria in structure-based virtual screening
(2005)
Yang, JM
;
Hsu, DF
;
生物資訊及系統生物研究所
;
Institude of Bioinformatics and Systems Biology
In this paper, we demonstrated that combining multiple scoring functions improves enrichment of true positives only if (a) each of the individual scoring functions has relatively high performance, and (b) the individual scoring functions are distinctive. The major weakness, the inability to consistently identify true positives (leads), of Virtual screening is likely due to the imprecise scoring algorithms. It has been demonstrated that consensus scoring improves enrichment of true positives, but they are yet to provide theoretical analysis that gives insight into real features of combinations and data fusion for virtual screening. This work thus establishes a potential theoretical basis for the probable success of data fusion approaches to improve yields in silico screening experiments. We provide initial validation of this theoretical approach using data from five scoring systems with two evolutionary docking algorithms on four targets, thymidine kinase, human dibydrofolate reductase, and estrogen receptors of antagonists and agonists. Results of the experiment show a fairly significant improvement (vs. single algorithms) in several measures of scoring quality, specifically "goodness-of-hit" scores, false positive rate, and "enrichment".
Development and evaluation of a generic evolutionary method for protein-ligand docking
(2004-04-30)
Yang, JM
;
生物科技學系
;
生物資訊及系統生物研究所
;
Department of Biological Science and Technology
;
Institude of Bioinformatics and Systems Biology
We have developed a generic evolutionary method with an empirical scoring function for the protein-ligand docking, which is a problem of paramount importance in structure-based drug design. This approach, referred to as the GEMDOCK (Generic Evolutionary Method for molecular DOCKing), combines both continuous and discrete search mechanisms. We tested our approach on seven protein-ligand complexes, and the docked lowest energy structures have root-mean-square derivations ranging from 0.32 to 0.99 Angstrom with respect to the corresponding crystal ligand structures. In addition, we evaluated GEMDOCK on crossdocking experiments, in which some complexes with an identical protein used for docking all crystallized ligands of these complexes. GEMDOCK yielded 98% docked structures with RMSD below 2.0 Angstrom when the ligands were docked into foreign protein structures. We have reported the validation and analysis of our approach on various search spaces and scoring functions. Experimental results show that our approach is robust, and the empirical scoring function is simple and fast to recognize compounds. We found that if GEMDOCK used the RMSD scoring function, then the prediction accuracy was 100% and the docked structures had RMSD below 0.1 Angstrom for each test system. These results suggest that GEMDOCK is a useful tool, and may systematically improve the forms and parameters of a scoring function, which is one of major bottlenecks for molecular recognition. (C) 2004 Wiley Periodicals, Inc.
Finding regularity in various types of secondary protein structures
(2003-11-01)
Chu, YW
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Yang, JM
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生物科技學系
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資訊工程學系
;
Department of Biological Science and Technology
Protein function is structurally determined, but analyzing the structure of a protein is both difficult and time-consuming. The authors look at the natural instincts of protein secondary structures, which used to be analyzed in terms of their statistical relationship with a single amino acid. Several schemas are offered for identifying regular patterns among various types of secondary protein structures. The schemas employ genetic algorithms based on a steady-state strategy. Two disjunctive data sets were used to verify fitness. The paper concludes with some illustrations of significant schemas produced as part of this study, with brief explanations of their significance.
Fine-grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple-parameter sets
(2003-03-01)
Yu, CS
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Wang, JY
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Yang, JM
;
Lyu, PC
;
Lin, CJ
In the coarse-grained fold assignment of major protein classes, such as all-alpha, all-beta, alpha + beta, alpha/beta proteins, one can easily achieve high prediction accuracy from primary amino acid sequences. However, the fine-grained assignment of folds, such as those defined in the Structural Classification of Proteins (SCOP) database, presents a challenge due to the larger amount of folds available. Recent study yielded reasonable prediction accuracy of 56.0% on an independent set of 27 most populated folds. In this communication, we apply the support vector machine (SVM) method, using a combination of protein descriptors based on the properties derived from the composition of n-peptide and jury voting, to the fine-grained fold prediction, and are able to achieve an overall prediction accuracy of 69.6% on the same independent set-significantly higher than the previous results. On 10-fold cross-validation, we obtained a prediction accuracy of 65.3%. Our results show that SVM coupled with suitable global sequence-coding schemes can significantly improve the fine-grained fold prediction. Our approach should be useful in structure prediction and modeling. (C) 2003 Wiley-Liss, Inc.
GEM: A Gaussian evolutionary method for predicting protein side-chain conformations
(2002-08-01)
Yang, JM
;
Tsai, CH
;
Hwang, MJ
;
Tsai, HK
;
Hwang, JK
We have developed an evolutionary approach to predicting protein side-chain conformations. This approach, referred to as the Gaussian Evolutionary Method (GEM), combines both discrete and continuous global search mechanisms. The former helps speed up convergence by reducing the size of rotamer space, whereas the latter, integrating decreasing-based Gaussian mutations and self-adaptive Gaussian mutations, continuously adapts dihedrals to optimal conformations. We tested our approach on 38 proteins ranging in size from 46 to 325 residues and showed that the results were comparable to those using other methods. The average accuracies of our predictions were 80% for chi(1), 66% for chi(1+2), and 1.36 Angstrom for the root mean square deviation of side-chain positions. We found that if our scoring function was perfect, the prediction accuracy was also essentially perfect. However, perfect prediction could not be achieved if only a discrete search mechanism was applied. These results suggest that GEM is robust and can be used to examine the factors limiting the accuracy of protein side-chain prediction methods. Furthermore, it can be used to systematically evaluate and thus improve scoring functions.
GEMDOCK: A generic evolutionary method for molecular docking
(2004-05-01)
Yang, JM
;
Chen, CC
;
生物科技學系
;
Department of Biological Science and Technology
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 Angstrom 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 Angstrom 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. (C) 2004 Wiley-Liss, Inc.
GEMPLS: A new QSAR method combining generic evolutionary method and partial least squares
(2005)
Chen, YC
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Yang, JM
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Tsai, CH
;
Kao, CY
;
生物科技學系
We have proposed a new method for quantitative structure-activity relationship (QSAR) analysis. This tool, termed GEMPLS, combines a genetic evolutionary method with partial least squares (PLS). We designed a new genetic operator and used Mahalanobis distance to improve predicted accuracy and speed up a solution for QSAR. The number of latent variables (lv) was encoded into the chromosome of GA, instead of scanning the best lv for PLS. We applied GEMPLS on a comparative binding energy (COMBINE) analysis system of 48 inhibitors of the HIV-1 protease. Using GEMPLS, the cross-validated correlation coefficient (q(2)) is 0.9053 and external SDEP (SDEPex) is 0.61. The results indicate that GEMPLS is very comparative to GAPLS and GEMPLS is faster than GAPLS for this data set. GEMPLS yielded the QSAR models, in which selected residues are consistent with some experimental evidences.
GEMSCORE: A new empirical energy function for protein folding
(2005)
Chiu, YY
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Hwang, JK
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Yang, JM
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生物科技學系
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生物資訊及系統生物研究所
We have developed a new energy function, termed CEMSCORE, for the protein structure prediction, which is an emergent problem in the field of computational structural biology. The GEMSCORE combines knowledge-based and physics-based energy functions. Instead of hundreds and thousands parameters used in many physics-based energy functions, we optimized nine weights of energy terms in the GEMSCORE by using a generic evolutionary method. These nine energy terms are the electrostatic, the der Waals, the hydrogen-bonding potential, and six terms for solvation potentials. The GEMSCORE has been evaluated on six decoy sets, including 96 proteins with more 70,000 structures. The result indicates that our method is able to successfully identify 74 native proteins from these 96 proteins. Our GEMSCORE is fast and simple to discriminate between native and nonnative structures from thousands of protein structure candidates in these decoy sets. We believe that the GEMSCORE is robust and should be a useful energy function for the protein structure prediction.
Heterogeneous selection genetic algorithms for traveling salesman problems
(2003-06-01)
Tsai, HK
;
Yang, JM
;
Tsai, YF
;
Kao, CY
;
生物科技學系
This paper proposes a genetic algorithm, called the heterogeneous selection genetic algorithm (HSGA), integrating local and global strategies via family competition and edge similarity, for the traveling salesman problem (TSP). Local strategies include neighbor-join mutation and family competition, and global strategies consist of heterogeneous pairing selection and edge assembly crossover. Based on the mechanisms of preserving and adding edges, the search behaviors of neighbor-join mutation and edge assembly crossover are studied. The proposed method has been implemented and applied to 17 well-known TSPs whose numbers of cities range from 101 to 13,509. Experimental results indicate that this approach, although somewhat slower, performs very robustly and is very competitive with other approaches in the best surveys. This approach is able to find the optimum, and the average solution quality is within 0.00048 above the optima of each test problem.
Modeling the binding and inhibition mechanism of nucleotides and sulfotransferase using molecular docking
(2003-06-01)
Lin, ES
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Yang, JM
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Yang, YS
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生物科技學系
;
生物資訊及系統生物研究所
Phenol sulfotransferase (PST) catalyzes sulfuryl group transfer from adenosine 3'-phosphate 5'-phosphosulfate (PAPS) to a variety of nucleophilic acceptors in biological systems. Physiological sulfation by PAPS results in the production of adenosine 3',5'-diphosphate (PAP). PAP may become a strong inhibitor or cofactor for the physiological or the transfer reactions, respectively. Several nucleotides other than, PAP also serve as substrates, inhibitors or cofactors of PST catalyzed reactions. We are interested in the effects of these nucleotides on the PST catalyzed reactions and their possible physiological significance in biology. Several nucleotide inhibitors (such as adenosine 5'-diphosphate and adenosine 5'-triphosphate) of sulfotransferase in physiological reactions have been reported in the literature. However, our data suggests that they are not inhibitors for the transfer reaction or the reverse physiological reaction catalyzed by PST. The aim of this work is to show that the molecular docking analysis can be successfully used to underline the inhibition mechanism of these nucleotides. First, the selected compounds were subjected to a detailed docking analysis, by means of GEMDOCK, a program able to reveal the most likely binding mode for each ligand. By comparing these results with binding sites and binding compounds of the nucleotides with known X-ray structures, it is possible to highlight the site specificity and the inhibition mechanism of the select compounds. The results obtained by the above algorithm were further confirmed experimentally. In this paper, we show the effect of a variety of nucleotides on the activity of sulfotransferase in different docking conditions.