Helping explain the sequence, structure and functions of proteins

Our Bioinformatics program studies 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 explain the relationships between the sequence, structure and function of proteins; to uncover the molecular mechanisms underlying various diseases; and to design protein and peptide inhibitors. We work at the cutting-edge of the modern biotechnology, integrating computational and experimental approaches for rational design of therapeutic small and biologic drugs.

Biologic drugs, such as proteins, are medicinal products extracted from living systems. Unlike small molecular drugs, they are more specific to the intended target and have higher success rates in clinical trials. However, unlike small molecules, biologic drugs are expensive to manufacture due to low yields.

The aim of our research is to increase protein drug production by codon optimisation and to design peptides or proteins with specific therapeutic effects. In addition to working in the area of biologic drug discovery, our group is actively developing the next-generation computational technique for small-molecule inhibitor design.

KEY PROJECTS

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 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 optimisation, 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.

Key publications

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).

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