ACCURATE PREDICTION OF BINDING AFFINITIES OF LIGANDS ACROSS THE MATRIX METALLOP

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. We request computational time and storage space needed for the development of an approach to predict binding affinities of ligands binding to any member of matrix metalloproteinase (MMP) family. MMPs exhibit high sequence identity, while having subtle differences in the binding sites. We hypothesize that the coefficients in the quantum mechanics/molecular mechanics (QM/MM) linear response (LR) models optimized for one MMP can be used with sufficient precision in the models for other MMPs. The hypothesis will be tested by developing and comparison of the QM/MM LR models for published binding data for several MMPs. The four tier approach1 for binding affinity calculation, developed in our lab, using (1) docking, (2) geometry optimization by QM/MM, (3) molecular dynamics (MD) simulation and (4) single point QM/MM energy calculations for the time-averaged structures from MD simulation was found to explain the experimental binding affinities more accurately than conventional methods, especially for metalloprotein ligands. The outcome of this project will enable us to decide whether the QM/MM LR approach can be extrapolated to other members of a particular protein family for accurate prediction of binding affinities based on subtle differences in the binding site residues. The results may allow us to apply this methodology to other protein families of multiple isoenzymes, leading to a tool for developing selective ligands to closely related proteins, with applications in drug design and similar fields.