Title: “FTMap utilization and development for the study of druggable binding sites”
Sandor Vajda, PhD – BME (Advisor, Chair)
James Galagan, PhD – BME
Dima Kozakov, PhD – BME
Adrian Whitty, PhD – Chemistry
Drug development relies on computational strategies to circumvent tedious experimental methods. First, identification and characterization of binding sites is critical for selection of a druggable target. The FTMap server identifies binding hot spots with in silico solvent mapping; results show excellent agreement with experimentally determined hot spots and they are obtained at a much faster rate and lower cost. Here, we expand previous characterization of traditional binding sites with FTMap to proteins that benefit from beyond rule-of-five drugs and clinical candidates. Once a protein target has been identified for drug development, a multitude of fragments are experimentally tested to identify “hits” that bind the target of interest. Development towards a drug begins with hit-to-lead and lead optimization methods that rely heavily on accurate and efficient computational methods as the multitude of structural characterization experiments and binding affinity assays needed to cover thousands of potential compounds is unfeasible. Molecular dynamics simulations are typically used to predict the drug-binding site orientation and affinity because they permit flexible movement of the protein and drug candidate, however, these simulations are indeterminate, computationally expensive, and timely. We propose to develop an alternative, competitive, FTMap-based method that can model different conformations of a binding site and rank drug candidates according to their relative binding affinities. Finally, FTMap developments can also be used outside the realm of drug discovery, in the study of metabolomics, to predict and structurally model novel protein-metabolite interactions.