This work involves a combined research and education initiative aimed at the development of structure-based models of biological function, which can be used as research tools in biochemistry and cell biology labs as well as in structural and biophysical labs. The research component involves the development and experimental validation of an ensemble-based description of molecular recognition. This computational model, which will provide a quantitative link between structure and energy, will be tested and refined by comparison to the experimentally determined thermodynamic parameters for the binding of the Src-homology-3 (SH3) domain SEM5 with numerous variants of its putative peptide ligand, Sos. The resultant ensemble-based view should not only provide the structural and thermodynamic origins of the affinity, it should allow access to the determinants of specificity as well. As the model is intended to be used as a research tool for deriving hypothesis, interpreting results, and directing research efforts, all elements of this algorithm will be coded into Mathematica notebooks which can be distributed upon request to suitably equipped labs both within UTMB and outside of the University. The education component involves developing a Mathematica-based curriculum in the Department of Human Biological Chemistry and Genetics (HBC&G). In addition to the co-development of a Mathematica -based syllabus for three courses, the education plan centers around introductory courses for students and faculty, wherein students (and faculty) are taught various strategies for incorporating computational approaches in their own research.
2. Non-technical
Recent advances in computer technology, coupled to the near exponential growth of biological databases have spawned the rapidly growing field of bioinformatics. From within this emerging field, numerous computational efforts have focused on integrating different types of data in tackling complex biological problems. Indeed, such is the case in this work, where research efforts are centered on integrating the structural (e.g. Protein Data Bank) and thermodynamic databases to derive models for molecular recognition, allosterism, and signal transduction. The task at hand in this new computational era in biology is not only to develop the methods themselves, but to provide a medium through which these methods can be disseminated among groups, and to provide access to strategies for incorporating these methods in a diverse array of research specialties. The goal of this combined research and education initiative is to promote the use of these computational approaches by developing useful methods, implementing a convenient and accessible medium of interaction (i.e. Mathematica ), and providing students and faculty with training in both the methods and the medium.
