James S. Wright

Biography

James S. Wright Chancellor's Professor Degrees: Ph.D. (Berkeley, CA)

Research Interest

My lab is involved in the study of biochemical reactions of importance to human health, using sophisticated computational methods.  Our most recent project, and the focal point of the last 5 years, has been the computational prediction of small molecules which are active against breast cancer.  We work closely with experimental colleagues who are responsible for the synthesis, the binding affinity to the estrogen receptors ERα and ERβ, the ability of the molecules to suppress cancer cell growth, and the general toxicology of the novel molecules.  The basic idea is that ligands (small molecules) which are specific for ERα accelerate the growth of cancer cells, whereas ligands specific to ERβ retard their growth.  Our approach has been to start with ligands known to bind strongly to the estrogen receptors, e.g. the natural  hormone estradiol which has the classic ABCD steroid structure, and then “tune” the molecules to improve the binding ratio ERβ/ERα.  By eliminating the B-ring to form A-CD structures, we have been able to create a new family  of molecules with very desirable estrogen receptor selectivity.  We have obtained several patents for this work, which has been supported by the Canadian Breast Cancer Foundation, and the Centre for Drug Research and Development at the University of British Columbia. The computational work for the breast cancer project requires a program which can “dock” a ligand into a receptor active site, and then “scoring” the various docked configurations to predict which will have optimal binding.  Besides targeting specific diseases such as breast cancer, we are also working on general methods, many developed in this lab, to improve the prediction of binding affinities.  Our primary software choice is the MOE (Molecular Operating Environment) programs developed by Chemical Computing Group in Montreal.  Our most recent work, given in Publication no. 4, describes a new and very promising methodology based on MOE, which uses an iterative procedure to systematically improve the prediction of binding affinities.  This work may have a significant effect on the computational approach to a large variety of important biochemical processes, and will be the focal point of further computational work in my lab.