Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo

Ross, Gregory A.; Russell, Ellery; Deng, Yu; Lü, Chao; Harder, Edward; Abel, Robert; Wang, Lingle

doi:10.1021/acs.jctc.0c00660

Cited by 81 publications

(109 citation statements)

References 54 publications

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“…For peptides, FEP can compute the RBFE between a wild-type and point mutant (ΔΔG FEP ) via an “alchemical transformation” that “mutates” the wild-type (WT) sidechain to the mutant sidechain through a series of intermediates known as λ windows ( Figure 1 ) [ 16 , 17 ]. Notably, FEP incorporates sampling of all degrees of freedom via molecular dynamics (MD) simulations to account for conformational variations in ligand–receptor interactions and permits the displacement and introduction of explicit waters during the simulation [ 18 ]. This contrasts with the widely used molecular mechanics–generalized born/surface area (MM-GB/SA) method, in which no alchemical transformation is performed, a static structure is used, and an implicit representation of the solvent is employed [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations

et al. 2021

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Nicotinic acetylcholine receptor (nAChR) subtypes are key drug targets, but it is challenging to pharmacologically differentiate between them because of their highly similar sequence identities. Furthermore, α-conotoxins (α-CTXs) are naturally selective and competitive antagonists for nAChRs and hold great potential for treating nAChR disorders. Identifying selectivity-enhancing mutations is the chief aim of most α-CTX mutagenesis studies, although doing so with traditional docking methods is difficult due to the lack of α-CTX/nAChR crystal structures. Here, we use homology modeling to predict the structures of α-CTXs bound to two nearly identical nAChR subtypes, α3β2 and α3β4, and use free-energy perturbation (FEP) to re-predict the relative potency and selectivity of α-CTX mutants at these subtypes. First, we use three available crystal structures of the nAChR homologue, acetylcholine-binding protein (AChBP), and re-predict the relative affinities of twenty point mutations made to the α-CTXs LvIA, LsIA, and GIC, with an overall root mean square error (RMSE) of 1.08 ± 0.15 kcal/mol and an R2 of 0.62, equivalent to experimental uncertainty. We then use AChBP as a template for α3β2 and α3β4 nAChR homology models bound to the α-CTX LvIA and re-predict the potencies of eleven point mutations at both subtypes, with an overall RMSE of 0.85 ± 0.08 kcal/mol and an R2 of 0.49. This is significantly better than the widely used molecular mechanics—generalized born/surface area (MM-GB/SA) method, which gives an RMSE of 1.96 ± 0.24 kcal/mol and an R2 of 0.06 on the same test set. Next, we demonstrate that FEP accurately classifies α3β2 nAChR selective LvIA mutants while MM-GB/SA does not. Finally, we use FEP to perform an exhaustive amino acid mutational scan of LvIA and predict fifty-two mutations of LvIA to have greater than 100X selectivity for the α3β2 nAChR. Our results demonstrate the FEP is well-suited to accurately predict potency- and selectivity-enhancing mutations of α-CTXs for nAChRs and to identify alternative strategies for developing selective α-CTXs.

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Section: Introductionmentioning

confidence: 99%

Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations

et al. 2021

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“…We selected the targets from two recent studies focusing on using enhanced sampling of water motions to improve the accuracy of binding free energy calculations, 30, 31 including several proteins: Protein Tyrosine Phosphatase 1B (PTP1B), Heat Shock Protein 90 (HSP90), Bruton’s Tyrosine Kinase (BTK), transcription initiation factor TFIID subunit 2 (TAF1(2)), and thrombin. In addition to being different receptors, these targets differ in binding site positions, number and occupancy of buried water sites.…”

Section: Methodsmentioning

confidence: 99%

“…Grand canonical Monte Carlo (GCMC) 34–37 shows particular promise for enhancing water sampling and facilitating binding free energy calculations. 31, 38–40, 59–62 In the grand canonical ensemble the chemical potential ( µ ) of the fluctuating species (here, water molecules), the volume and the temperature is constant. The water molecules can be inserted (transferred from) or removed (transferred to) from the system to enhance water sampling — judicious choice of the chemical potential gives an equilibrium between the simulated system and bulk water.…”

Section: Methodsmentioning

confidence: 99%

“…Both methods show promise in improving water sampling in molecular simulations and GCMC has shown the ability to incorporate the thermodynamics of buried water in binding free energy calculations. 31, 32, 38–40 Ben-Shalom et al 30, 41 recently studied a Monte Carlo (MC)/MD hybrid approach and found robust sampling of buried hydration sites and improved accuracy in relative binding free energy calculations. However, this approach was implemented in a different simulation engine (AMBER package 42 ) than the one used in this work (OpenMM 43 ) for MD, NCMC and GCMC.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques

et al. 2021

Preprint

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Water often plays a key role in protein structure, molecular recognition, and mediating protein-ligand interactions. Thus, free energy calculations must adequately sample water motions, which often proves challenging in typical MD simulation timescales. Thus, the accuracy of methods relying on MD simulations ends up limited by slow water sampling. Particularly, as a ligand is removed or modified, bulk water may not have time to fill or rearrange in the binding site. In this work, we focus on several molecular dynamics (MD) simulation-based methods attempting to help address water motions and occupancies: BLUES, using nonequilibrium candidate Monte Carlo (NCMC); grand, using grand canonical Monte Carlo (GCMC); and normal MD. We assess the accuracy and efficiency of these methods in sampling water motions. We selected a range of systems with varying numbers of waters in the binding site, as well as those where water occupancy is coupled to the identity or binding mode of the ligand. We analyzed water motions and occupancies using both clustering of trajectories and direct analysis of electron density maps. Our results suggest both BLUES and grand enhance water sampling relative to normal MD and grand is more robust than BLUES, but also that water sampling remains a major challenge for all of the methods tested. The lessons we learned for these methods and systems are discussed.

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“…Prospective free energy calculations were run using Schrödinger FEP+ 7 with a simulation time of 20 ns, 24 λ-windows, and grand canonical Monte Carlo (GCMC) enhanced water sampling. 30…”

Section: Filtering Stagesmentioning

confidence: 99%

AutoDesigner, a De Novo Design Algorithm for Rapidly Exploring Large Chemical Space for Lead Optimization: Application to the Design and Synthesis of D-Amino Acid Oxidase Inhibitors

Bos

Houang

Ranalli

et al. 2021

Preprint

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The lead optimization stage of a drug discovery program generally involves the design, synthesis and assaying of hundreds to thousands of compounds. The design phase is usually carried out via traditional medicinal chemistry approaches and/or structure based drug design (SBDD) when suitable structural information is available. Two of the major limitations of this approach are (1) difficulty in rapidly designing potent molecules that adhere to myriad project criteria, or the multiparameter optimization (MPO) problem, and (2) the relatively small number of molecules explored compared to the vast size of chemical space. To address these limitations we have developed AutoDesigner, a de novo design algorithm. AutoDesigner employs a cloud-native, multi-stage search algorithm to carry out successive rounds of chemical space exploration and filtering. Millions to billions of virtual molecules are explored and optimized while adhering to a customizable set of project criteria such as physicochemical properties and potency. Additionally, the algorithm only requires a single ligand with measurable affinity and a putative binding model as a starting point, making it amenable to the early stages of a SBDD project where limited data is available. To assess the effectiveness of AutoDesigner, we applied it to the design of novel inhibitors of D-amino acid oxidase (DAO), a target for the treatment of schizophrenia. AutoDesigner was able to generate and efficiently explore over 1 billion molecules to successfully address a variety of project goals. The compounds generated by AutoDesigner that were synthesized and assayed (1) simultaneously met not only physicochemical criteria, clearance and central nervous system (CNS) penetration (Kp,uu) cutoffs, but also potency thresholds; (2) fully utilize structural data to discover and explore novel interactions and a previously unexplored subpocket in the DAO active site. The reported data demonstrate that AutoDesigner can play a key role in accelerating the discovery of novel, potent chemical matter within the constraints of a given drug discovery lead optimization campaign.

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Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo

Cited by 81 publications

References 54 publications

Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations

Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations

Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques

AutoDesigner, a De Novo Design Algorithm for Rapidly Exploring Large Chemical Space for Lead Optimization: Application to the Design and Synthesis of D-Amino Acid Oxidase Inhibitors

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