SBIR-STTR Award

It is estimated that 40 to 50% of known enzymes can be characterized as metalloenzymes,while currently only 7% of FDA-approved drugs in the United States target this class of protein. This is despitethe fact that there are many dozens of already identified metalloenzyme targets involved in virtually everytherapeutic area, including anti-inflammatory, antibiotics, antivirals, anticancer drugs, and more. This is in largepart because the already very difficult drug design requirement to maintain/increase the potency of an initialligand (drug-like molecule) while improving/maintaining its target selectivity and pharmacokinetic properties,is made even harder by the complicated and often non-intuitive nature of metal-ligand and metal-proteininteractions. Accurate molecular modeling predictions of metalloenzyme-ligand binding affinities, then, wouldbe highly impactful in pharmaceutical industry drug research and development programs, because they wouldallow R&D scientists to carry out computational experiments drastically reducing the number of expensive andtime-consuming bench experiments required to overcome the difficult metalloenzyme inhibitor designchallenges they face. However, currently available molecular modeling approaches are unable to makepredictions reliable enough to do this. Docking and scoring methods are able to determine, in many cases, thepose of inhibitors in metalloenzyme active sites, but they cannot correctly rank candidate inhibitors in order ofbinding affinity as they lack the required detail in their energy models. Recently, free energy-based methods haveadvanced to the point of providing reliable binding affinity predictions for many non-metal protein-ligand seriesand can, therefore, help speed ligand discovery efforts for these systems. They cannot provide good bindingaffinities for metalloenzyme-ligand systems though, because to-date they are all entirely based on classicalforcefields, which fundamentally limits the accuracy of their descriptions of metal-ligand and metal-proteininteractions. This is due, in part, to lack of inclusion of important polarization and charge transfer effects, but itis also because the complex electronic structure, which metals often exhibit, is intrinsically quantum mechanical.This fast-track SBIR proposal will address this by developing a new and unique molecular modeling softwaretool called Mzyme-QM-VM2, which will provide reliably accurate binding free energies for metalloenzyme-inhibitor complexes by a novel combination of statistical mechanics and highly scalable quantum chemistrymethods. This software will be based on mining minima free energy calculation methodology and will bedeveloped as an extension of VeraChem's VM2 free energy software platform.

Public Health Relevance Statement:
Project narrative Metalloenzymes have been identified as drug targets in virtually every therapeutic area, including anti-inflammatory, antibiotics, antivirals, and anticancer, but the complicated nature of metal interactions with potential drug molecules makes the already difficult drug development process even harder for this class of targets. This project aims to develop a software tool that can accurately predict the binding affinities of metalloenzymes and potential drug molecules, and therefore help research scientists more quickly discover drug candidates suitable for preclinical testing and beyond. Currently available molecular modeling methods are unable to make accurate enough predictions to help scientists with the design of metalloenzyme-targeting drugs; therefore, the proposed project will provide a new capability with significant impact on the development of treatments for human disease.

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