Exploring Novel Directions in Free Energy Calculations

Armacost, Kira A.; Riniker, Sereina; Cournia, Zoe

doi:10.1021/acs.jcim.0c01266

Cited by 12 publications

(13 citation statements)

References 31 publications

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“…While previous review papers have shown how MD simulation has helped elucidate the role the lipid membrane plays in substrate and thus drug selection for membrane proteins [28][29][30][31][32], drug membrane interactions [33][34][35][36][37][38][39][40][41], drug delivery [3,[42][43][44][45], antimicrobial peptides [46][47][48][49][50][51][52][53][54], and methodologies [28,[55][56][57][58][59][60], this is the first review paper, that we are aware of, focusing on the entirety of the use of MD simulation to incorporate the role played by interactions with lipid membranes in drug design. This can be seen, in turn, as a case study of the potential for MD simulation to expand the paradigm of drug design to all aspects of the broader biophysical environment within which drug action occurs.…”

Section: Introductionmentioning

confidence: 95%

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

2021

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We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard “lock and key” paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

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Section: Introductionmentioning

confidence: 95%

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

2021

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“…We estimated potency using alchemical free energy calculations [24][25][26] , an accurate physical modeling technique that has hitherto not been deployed in a high throughput setup due to its prohibitive computational cost. We employed Folding@Home 27 ---a worldwide distributed computing network where hundreds of thousands of volunteers around the world contributed computing power to create the world's first exascale computing resource 28 ---to compute the free energy of binding of all 20,000+ crowdsourced submissions using the Open Force Field Initiative "Parsley" small molecule force fields 29 and nonequilibrium switching with the open source perses alchemical free energy toolkit 30 based on the GPU-accelerated OpenMM framework 31 , culminating in over 1 ms of simulation time 28 .…”

Section: Machine Learning and Free Energy Perturbation Enabled Rapid Optimization Cyclesmentioning

confidence: 99%

Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitors

Consortium¹,

Chodera

Lee

et al. 2021

Preprint

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The COVID-19 pandemic is a stark reminder that a barren global antiviral pipeline has grave humanitarian consequences. Future pandemics could be prevented by accessible, easily deployable broad-spectrum oral antivirals and open knowledge bases that derisk and accelerate novel antiviral discovery and development. Here, we report the results of the COVID Moonshot, a fully open-science structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a novel chemical scaffold that is differentiated to current clinical candidates in terms of toxicity and pharmacokinetics liabilities, and developed it into orally-bioavailable inhibitors with clinical potential. Our approach leverages crowdsourcing, high throughput structural biology, machine learning, and exascale molecular simulations. In the process, we generated a detailed map of the structural plasticity of the main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. In a first for a structure-based drug discovery campaign, all compound designs (>18,000 designs), crystallographic data (>500 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.

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“…Figure15. Free energy difference for decoupling the HSP90 ligand in the binding site in the presence of the bubbleligand.…”

mentioning

confidence: 99%

Challenges Encountered Applying Equilibrium and Nonequilibrium Binding Free Energy Calculations

et al. 2021

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Binding free energy calculations have become increasingly valuable to drive decision making in drug discovery projects. However, among other issues, inadequate sampling can reduce accuracy, limiting the value of the technique.In this paper we apply absolute binding free energy calculations to ligands binding to T4 lysozyme L99A and HSP90 using equilibrium and non-equilibrium approaches. We highlight sampling problems encountered in these systems, such as slow side chain rearrangements and slow changes of water placement upon ligand binding. These same types of challenges are likely to show up in other protein-ligand systems as well and we propose some strategies to diagnose and test for such problems in alchemical free energy calculations. We also explore similarities and differences in how the equilibrium and the non-equilibrium approaches handle these problems. Our results show the large amount of work still to be done to make free energy calculations robust and reliable and provide insight for future research in this area. File list (2)download file view on ChemRxiv NEQ_binding.pdf (30.14 MiB) download file view on ChemRxiv SI.pdf (8.25 MiB)

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Exploring Novel Directions in Free Energy Calculations

Cited by 12 publications

References 31 publications

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitors

Challenges Encountered Applying Equilibrium and Nonequilibrium Binding Free Energy Calculations

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