BioXGEM is the Jinn-Moon Yang Laboratory Homepage in Institute of Bioinformatics, NCTU. Our research is focused on protein-ligand docking/virtual database screening, protein structure prediction, and protein-protein interaction. Our approach is to develop computational models based on experiments and public databases, and to test the models through predictions and experiments. A particularly exciting recent success with this approach was the development of the GEMDOCK^1,2 tool for the molecular recognition and structure-based virtual screening^3,4. GEMDOCK has been evaluated over 300 protein-ligand complexes and applied to identify new substrates or inhibitors for several practical applications, such as sulfotransferase⁵, ECAO (amine oxidase), and imidase. The binding-site pharmacophore (hot spots) and ligand preferences are use to substantially enhance GEMDOCK for screening large databases on interesting target proteins, such as thymidine kinase ⁴, estrogen receptor ^3,6, dihydrofolate reductase, the NS3 protease and the E protein of dengue virus. In protein structure prediction and protein classification, we have developed a side-chain rotamer library and an evolutionary method for side-chain prediction⁷, a support vector machine method (SVM) for the fine-grained fold prediction⁸. We have successfully applied evolutionary approach on DNA microarray analysis ⁹. We are currently working to apply these models to drug discovery, function genomics, and system biology.

1.      Yang, J.-M. Development and evaluation of a generic evolutionary method for protein-ligand docking. Journal of Computational Chemistry 25, 843-857 (2004).

2.      Yang, J. M. & Chen, C. C. GEMDOCK: A generic evolutionary method for molecular docking. Proteins: Structure, Function and Genetics 55 (2004).

3.      Yang, J.-M. & Shen, T.-W. A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators. Proteins: Structure, Function, and Bioinformatics 59, 205-220 (2005).

4.      Yang, J.-M., Chen, Y.-F., Shen, T.-W., Kristal, B. S. & Hsu, D. F. Consensus Scoring Criteria for Improving Enrichment in Virtual Screening. Journal of Chemical Information and Modeling Forthcoming (2005).

5.      Lin, E. S., Yang, J. M. & Yang, Y. S. Modeling the binding and inhibition mechanism of nucleotide and sulfotransferase using molecular docking. Journal of the Chinese Chemical Society 50, 655-663 (2003).

6.      Yang, J.-M. & Shen, T.-W. A pharmacophore-based evolutionary approach for screening estrogen receptor antagonists. Congress of Evolutionary Computation (CEC 2004), 1028-1035 (2004).

7.      Yang, J. M. et al. GEM: a Guassian evolutionary method for predicting protein side-chain conformations. Protein Science 11, 1897-1907 (2002).

8.      Yu, C. S. et al. Fine-grained protein fold assignment by support vector machines using generalized peptide coding schemes and jury voting from multiple parameter sets. Proteins: Structure, Function, and Bioinformatics 50, 531-536 (2003).

9.      Tsai, H. K., Yang, J. M., Tsai, Y. F. & Kao, C. F. An evolutionary approach for gene expression patterns. IEEE Transaction on Information Technology in Biomedicine 8, 69-78 (2004).