Metalloenzyme binding affinity prediction with VM2

Active Project Grant Awarded Last action

Award ID
R44GM150323
Recipient
Awarding agency
Obligated
$4,285,609

Award facts

Assistance listing
93.859 Biomedical Research and Research Training
Completion date 99% complete
Originally
Place of performance
Maryland, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
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 despite the fact that there are many dozens of already identified metalloenzyme targets involved in virtually every therapeutic area, including anti-inflammatory, antibiotics, antivirals, anticancer drugs, and more. This is in large part because the already very difficult drug design requirement to maintain/increase the potency of an initial ligand (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-protein interactions. Accurate molecular modeling predictions of metalloenzyme-ligand binding affinities, then, would be highly impactful in pharmaceutical industry drug research and development programs, because they would allow r & d scientists to carry out computational experiments drastically reducing the number of expensive and time-consuming bench experiments required to overcome the difficult metalloenzyme inhibitor design challenges they face. However, currently available molecular modeling approaches are unable to make predictions reliable enough to do this. Docking and scoring methods are able to determine, in many cases, the pose of inhibitors in metalloenzyme active sites, but they cannot correctly rank candidate inhibitors in order of binding affinity as they lack the required detail in their energy models. Recently, free energy-based methods have advanced to the point of providing reliable binding affinity predictions for many non-metal protein-ligand series and can, therefore, help speed ligand discovery efforts for these systems. They cannot provide good binding affinities for metalloenzyme-ligand systems though, because to-date they are all entirely based on classical forcefields, which fundamentally limits the accuracy of their descriptions of metal-ligand and metal-protein interactions. This is due, in part, to lack of inclusion of important polarization and charge transfer effects, but IT is 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 software tool 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 chemistry methods. This software will be based on mining minima free energy calculation methodology and will be developed as an extension of verachem's VM2 free energy software platform.

Transactions

Action Obligated Date
Amendment $0
Amendment $915,334
Amendment $915,334
Amendment $0
Amendment $1,341
Amendment $915,334
Amendment $915,334
Amendment $311,466
Original award $311,466

Subawards

Subrecipient Amount Awarded
Iowa State University Of Science And Technology Metalloenzymes have been identified as drug targets in virtually every therapeutic area, including anti-inflammatory, antibiotics, antivirals, and anticancer,… $136,271
Iowa State University Of Science And Technology $136,271

Quick answers

Who received award R44GM150323?

R44GM150323 was awarded to Verachem LLC by National Institute of General Medical Sciences (National Institutes of Health) on May 1, 2023.

How much is award R44GM150323 worth?

$4,285,609 has been obligated on R44GM150323.

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