Large-scale binding affinity calculations on commodity compute clouds

Zasada, Stefan J.; Wright, David W.; Coveney, Peter V.

doi:10.1098/rsfs.2019.0133

Cited by 3 publications

(4 citation statements)

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“…But the ability to easily and reliably deploy components of the application on cloud and supercomputing resources is not sufficient to allow users to trivially perform complex investigations using the software. To remedy this, we have developed ufBAC [148], a Web portal interface to the BAC, which allows a user to build models of molecule-compound binding, and execute and analyse multi-replica MD simulations using the model. ufBAC enables BAC to be run via a Software as a Service model, hiding from the user the complexities of the command line tools used to build models, execute them and analyse the results.…”

Section: Distributed Computing Approachesmentioning

confidence: 99%

“…Flare [147], implemented in Cresset's structure-based drug design suite, offers a graphical user interface to automate setup, simulation and analysis of free energy calculations via interfaces to the open source packages OpenMM, Sire, LOMAP, SOMD and BioSimSpace. We have developed our own free energy workflow called the binding affinity calculator (BAC) [141,148], designed to automate the end-to-end execution of ESMACS, TIES and ABFE calculations, and to handle ensemble calculations. A relative free energy calculation usually requires a hybrid topology and coordinate files to transfer one ligand to another.…”

Section: Distributed Computing Approaches To Enhance Samplingmentioning

confidence: 99%

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Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

et al. 2020

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A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

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Section: Distributed Computing Approachesmentioning

confidence: 99%

Section: Distributed Computing Approaches To Enhance Samplingmentioning

confidence: 99%

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

et al. 2020

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“…( Loeffler et al, 2018 ) validate alchemical simulation results from different software packages in the calculation of hydration free energies and determine that the tested packages (AMBER, CHARMM, GROMACS, and SOMD) produce consistent free energies. The scale of alchemical simulations is growing dramatically by harnessing cloud computing ( Zasada et al, 2020 ). The report of massive-scale simulation of 301 HIV-1 integrase inhibitors on the IBM World Community Grid ( Xia et al, 2019 ) highlights how the availability of distributed computing is enabling high-throughput FEP screening.…”

Section: Free Energy Calculation Approachesmentioning

confidence: 99%

Recent Developments in Free Energy Calculations for Drug Discovery

King

Aitchison

et al. 2021

Front. Mol. Biosci.

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The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

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“…To achieve the highest rates of throughput, it is necessary to have access to the latest supercomputers whose architectures today, being based on a vast proliferation of nodes, within which are present large numbers of cores and accelerators (mainly general-purpose graphics processing units), facilitate ensemble-based simulations of very large numbers of ligand–protein simulations. Zasada et al [9] describe ways in which such free energy calculations can be off-loaded and run with considerable facility on modern computational clouds by exploiting concepts such as virtual machines and containerization to facilitate secure, rapid and reliable deployment. Indeed, their high-throughput binding affinity calculator can be used to run such calculations as hybrid workloads across combinations of distributed heterogeneous resources from the ultra-high-end of high-performance computing to elastic compute clouds.…”

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confidence: 99%