Quantum simulations of SARS-CoV-2 main protease M enable high-quality scoring of diverse ligands.

The COVID-19 pandemic has led to unprecedented efforts to identify drugs that can reduce its associated morbidity/mortality rate. Computational chemistry approaches hold the potential for triaging potential candidates far more quickly than their experimental counterparts. These methods have been widely used to search for small molecules that can inhibit critical proteins involved in the SARS-CoV-2 replication cycle.

Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery.

Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows.

Rigorous Free Energy Simulations in Virtual Screening.

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows.

Predicting mutations deleterious to function in beta-lactamase TEM1 using MM-GBSA.

Missense mutations can have disastrous effects on the function of a protein. And as a result, they have been implicated in numerous diseases. However, the majority of missense variants only have a nominal impact on protein function.

Physics-based enzyme design: predicting binding affinity and catalytic activity.

Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential.

Structure-based approach to the prediction of disulfide bonds in proteins.

Protein engineering remains an area of growing importance in pharmaceutical and biotechnology research. Stabilization of a folded protein conformation is a frequent goal in projects that deal with affinity optimization, enzyme design, protein construct design, and reducing the size of functional proteins. Indeed, it can be desirable to assess and improve protein stability in order to avoid liabilities such as aggregation, degradation, and immunogenic response that may arise during development.

AAK1 identified as an inhibitor of neuregulin-1/ErbB4-dependent neurotrophic factor signaling using integrative chemical genomics and proteomics.

Target identification remains challenging for the field of chemical biology. We describe an integrative chemical genomic and proteomic approach combining the use of differentially active analogs of small molecule probes with stable isotope labeling by amino acids in cell culture-mediated affinity enrichment, followed by subsequent testing of candidate targets using RNA interference-mediated gene silencing. We applied this approach to characterizing the natural product K252a and its ability to potentiate neuregulin-1 (Nrg1)/ErbB4 (v-erb-a erythroblastic leukemia viral oncogene homolog 4)-dependent neurotrophic factor signaling and neuritogenesis.

FURSMASA: a new approach to rapid scoring functions that uses a MD-averaged potential energy grid and a solvent-accessible surface area term with parameters GA fit to experimental data.

We demonstrate a new approach to the development of scoring functions through the formulation and parameterization of a new function, which can be used both for rapidly ranking the binding of ligands to proteins and for estimating relative aqueous molecular solubilities. The intent of this work is to introduce a new paradigm for creation of scoring functions, wherein we impose the following criteria upon the function: (1) simple; (2) intuitive; (3) requires no postparameterization tweaking; (4) can be applied (without reparameterization) to multiple target systems; and (5) can be rapidly evaluated for any potential ligand. Following these criteria, a new function, FURSMASA (function for rapid scoring using an MD-averaged grid and the accessible surface area) has been developed.

Evaluating the molecular mechanics poisson-boltzmann surface area free energy method using a congeneric series of ligands to p38 MAP kinase.

The recently described molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method for calculating free energies is applied to a congeneric series of 16 ligands to p38 MAP kinase whose binding constants span approximately 2 orders of magnitude. These compounds have previously been used to test and compare other free energy calculation methods, including thermodynamic integration (TI), OWFEG, ChemScore, PLPScore, and Dock Energy Score. We find that the MM-PBSA performs relatively poorly for this set of ligands, yielding results much inferior to those from TI or OWFEG, inferior to Dock Energy Score, and not appreciably better than ChemScore or PLPScore but at an appreciably larger computational cost than any of these other methods.