Protein-carbohydrate and protein-protein interactions: using models to better understand and predict specific molecular recognition

摘要

Any molecular recognition event results in a change in the free energy of the system. The extent of this change is related to the association constant, such that the more negative the free energy change is, the tighter the interaction between receptor and ligand. Protein-carbohydrate interactions play a critical role in signal transduction, innate immunity, and metabolism. Modeling these interactions is somewhat complicated by the inherent flexibility of carbohydrates as well as their relatively large number of functional groups. An empirical scoring function for docking carbohydrates to proteins, specifically tailored to predict both the correct binding orientation and free energy of binding of the carbohydrate-ligand/protein-receptor complex, will be presented. This new scoring function can predict free energies of binding to within 1.1 kcal/mol residual standard error, a definite improvement over existing scoring functions that result in standard errors well over 2 kcal/mol. Application of automated docking methodology to determine carbohydrate recognition specificity of the C-type lectin, human surfactant protein D, will also be presented. In the second part of the thesis, the role of pi-stacking interactions (e.g. between Tyr side chains) in stabilizing protein folds will be discussed. A 17-residue peptide derived from the naturally occurring anti-microbial peptide tachyplesin I was investigated using NMR spectroscopy. NOE cross-peaks were observed, confirming the existence of this interaction in solution. In the final part of the thesis, a quantitative NMR investigation into the self-association behavior of the regulatory domains of several Tec family member kinases will be presented. Of particular interest, self-association within Bruton\u27s tyrosine kinase (Btk) regulatory domains occurs through the formation of an asymmetric homodimer. Together this work demonstrates the importance of rigorous biophysical characterization of biomolecular recognition events and the interdependence of computational modeling and experimentation.