The Bioinformatics flagship program aims to understand the molecular mechanisms and development of predictive bioinformatics methods for protein folding, stability, and binding of ligands, peptides, and DNA. The long-term goal of our research is to elucidate the relations between the sequence, structure, and function of proteins, to uncover the molecular mechanisms underlying various diseases, and to design protein/peptide inhibitors.
Protein function prediction (protein-protein, protein-DNA, protein-RNA, and protein-glycan binding): Identifying the function of a protein by using protein complex structures as templates.
Protein structure prediction and refinement: Mining the protein structure database so that the structures of all protein sequences can be predicted and refined to atomic resolution.
Computational protein design and experimental validation: Designing proteins with novel function by energy optimization, followed by experimental validation.
Classification of genetic variations (disease-causing versus neutral): Using machine-learning techniques to separate human disease-causing genetic variations from neutral ones.
- H. Zhao, Y. Yang, H. Lin, X. Zhang, M. Mort, D. N. Cooper, Y. Liu and Y. Zhou, "DDIG-in: Discriminating between disease-associated and neutral non-frameshifting micro-indels", Genome Biology 14 , R43 (2013).
- Z. Li, Y. Yang, J. Zhan, L. Dai and Y. Zhou, "Energy Functions in De Novo Protein Design: Current Challenges and Future Prospects", Ann. Rev. Biophysics 42, 315-335 (2013).
- Y. Yang, E. Faraggi, H. Zhao and Y. Zhou, ''Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of the query and corresponding native properties of templates'' Bioinformatics 27, 2076-2082 (2011).
- H. Zhao, Y. Yang, and Y. Zhou, "Highly accurate and high-resolution function prediction of RNA binding proteins by fold recognition and binding affinity prediction", RNA Biology 8, 988-996 (2011).
- L. Dai and Y. Zhou, "Characterizing the existing and potential structural space of proteins by large-scale multiple loop permutations" J. Molec. Biol. 408, 585-595 (2011).
- Y. Zhou, Y. Duan, Y. Yang, E. Faraggi, H. Lei, ``Trends in template/fragment-free protein structure prediction" (Invited feature article) Theor. Chem. Accounts 128, 3-16 (2011).
- H. Zhao, Y. Yang and Y. Zhou, "Prediction of RNA binding proteins comes of age from low resolution to high resolution", Molecular Biosystems 9 , 2417-2425 (2013).
Bioinformatics interest group available within Griffith University only.