Chao-Yie Yang, Ph. D.                                        contact: chaoyie@umich.edu

 General research interests:

 My background is in theoretical chemistry. I am generally interested in applying physical chemical or chemical physical methods to study issues and problems in biology. My current research focuses in three major areas. 

  1. Protein flexibility, protein-ligand/protein interaction and structural biology.

Like any chemical molecules, proteins show dynamical motions in solution with timescales ranging from femtosecond (vibrations) to millisecond (domain motions) and to microsecond (folding). In this research area, I am interested in studying the flexibility of protein, its relation to its function and potential impact on its binding with ligands or proteins.

 Related references:

          C.-Y. Yang, Zaneta Nikolovska-Coleska, Peng Li, Peter Roller  and Shaomeng Wang, Solution Conformations of Wild-type and Mutated Bak BH3 Peptides via Dynamical Conformational Sampling and Implication to their Binding to Anti-apoptotic Bcl-2 Proteins. J. Phys. Chem. B, 2004, 108, 1467 -1477.

         Chao-Yie Yang and Shaomeng Wang, Recent Advances in Design of Small-Molecule Ligands to Target Protein-Protein Interactions. Ann. Rep. Comp. Chem., accepted. 

  1. Structure-based drug design and its applications to the development of inhibitors for treating human diseases.

Utilizing the known structures of target proteins and their binding with proteins/peptides, inhibitor design can be efficient and progress will be fruitful. In this research area, I team up closely with organic chemists, biochemists and cell biologists to develop inhibitors targeting proteins responsible for human diseases, such as cancers. My current focuses are on inhibitors for Bcl-2 and IAP family of proteins.    

Related references: 

         Haiying Sun, Zaneta Nikolobska-Coleska, Chao-Yie Yang, Liang Xu, York Tomita, Krzysztof Krajewski, Peter Roller and Shaomeng Wang, Structure-Based Design, Synthesis and Evaluation of Conformationally Constrained Mimetics of the Second Mitochondria-Derived Activator of Caspase That Target the X-Linked Inhibitor of Apoptosis Protein/Caspase-9 Interaction Site. J. Med. Chem., 2004, 47, 4147-4150.

         Niklas K. U. Koehler, Chao-Yie Yang, Judith Varady, Yipin Lu, Xiong-wu Wu, Ming Liu, Daxu Yin, Margreet Bartels, Bi-ying Xu, Peter P. Roller, Ya-qiu Long, Peng Li, Michael Kattah, Marjorie L. Cohn, Kelly Moran, Eurona Tilley, John R. Richert, and Shaomeng, Structure-Based Discovery of Nonpeptidic Small Organic Compounds to Block the T Cell Response to Myelin Basic Protein. J. Med. Chem., 2004, 47, 4989-4997.

         Haiying Sun, Zaneta Nikolobska-Coleska, Chao-Yie Yang, Liang Xu, Meilan Liu, York Tomita, Hongguang Pan, Yoshiko Yoshioka, Krzysztof Krajewski, Peter Roller and Shaomeng Wang, Structure-Based Design of Potent Conformationally Constrained Smac Mimetics. J. Am. Chem. Soc., 2004, 51, 16686-16687.

         Sun, H, Nikolovska-Coleska, Z., Chen, J., Yang, C.-Y., Tomita, Y., Pan, H., Yoshioka, Y., Krajewski, K., Roller, P. P., Wang, S., Structure-Based Design, Synthesis and Biochemical testing of novel and potent Smac Peptidomimetics. Bioorg. Med. Chem. Lett., 2005, 15, 793-797.

        Ke Ding, Jianyong Chen, Min Ji, Xihan Wu, Judith Varady, Chao-Yie Yang, Yipin Lu, Jeffrey R. Deschamps, Beth Levant and Shaomeng Wang, Design, Synthesis and Evaluation of Enantiomerically Pure Substituted Hexahydropyrazinoquinolines As Potent and Highly Selective Dopamine 3 Subtype Receptor Ligands. J. Med. Chem., 2005, 48, 3171-3181. 

  1. Scoring function development.

Recognizing that protein flexibility is important for structure-based drug design, implementation of this fundamental aspect of proteins to practical drug designs starts to flourish in recent years. I have analyzed the protein flexibility changes upon ligand binding and used the B-factor of the protein atoms reported from the crystal structures to approximate the protein flexibility in a recent knowledge-based scoring function called M-Score. Different methods of incorporating protein flexibility into scoring functions development are under way. 

Related references: 

         Wang R, Fang X, Lu Y, Yang C-Y, Wang S. The PDBbind database: Methodologies and Updates. J. Med. Chem., 2005, 48, 4111-4119.

         Chao-Yie Yang, Renxiao Wang and Shaomeng Wang, A Systematic Analysis of the Effects of Small-Molecule Binding on Protein Flexibility of the Ligand-Binding Sites. J. Med. Chem, 2005, 48, 5648-5650.

         Chao-Yie Yang, Renxiao Wang and Shaomeng Wang, M-Score: A New Knowledge-based Potential Scoring Function Accounting for Protein Atom Mobility. J. Med. Chem., 2006, 49, 5903-5911.  

M-Score

M-Score is a knowledge-based potential scoring function. In the implementation, atom types of the protein backbone and side chain atoms were classified and the atom types of the ligands are represented according to Sybyl Mol2 atom types. In addition, the flexibility (mobility) of the protein atoms is determined by the crystallographic B-factors (temperature factor). Five probabilistic positions of each protein atom around its mean position are represented by a Gaussian distribution calculated using its B-factor value. Detailed discussions and evaluation can be found at the reference.

         Chao-Yie Yang, Renxiao Wang and Shaomeng Wang, M-Score: A New Knowledge-based Potential Scoring Function Accounting for Protein Atom Mobility. J. Med. Chem., 2006, 49, 5903-5911.

Here, we provide a scoring engine for interested researchers on their targets. The source code will be released in future. 

Manual of operation:

1.      Ideal format of the protein and ligand should follow those generated by Sybyl where protein is the standard PDB file format and the ligand is the standard Sybyl Mol2 file format. Sybyl Mol2 format generated by Babel can be read in correctly. However, the user should check whether the atom types of the ligand were converted correctly before upload the file for scoring.

2.      On output, we provide the total score and atom-based scores.

3.      Note that we did not perform regression on the scores with known binding data. The score does not predict the binding affinity like other regression-based scoring functions. However, the resulting scores are correlated with the relative strength of binding affinities of the ligands.

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