Validation of Free Energy Methods in AMBER

Tsai, Hsu‐Chun; Tao, Yujun; T, Lee; Merz, Kenneth M.; York, Darrin M.

doi:10.1021/acs.jcim.0c00285

“…As is the case for all phenomena studied through MD simulation, the precise values for the parameters of the potential set used are of critical importance, thus the testing and validation of the model are crucial [153][154][155][156][157][158][159][160][161][162]. Partition coefficients of the molecule (log K) provide an important test of the potential set that can be directly correlated to experimental measurements.…”

Section: Location and Orientation Of Drug Molecules In The Lipid Bilayermentioning

confidence: 99%

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

Róg

¹

,

Girych

²

,

Bunker

³

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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“…61,[66][67][68][69] Further, all non-bonded interactions, including 1-4 terms, between the TS and (TC+I) regions should be scaled. The pre-AMBER20 behavior (gti_add_sc=0) is not theoretically correct 70 and has been fixed, although the option has been kept to enable testing and comparisons with earlier versions but should not be used for production work.…”

Section: T S/(t C+i)mentioning

confidence: 99%

Robust, Efficient and Automated Methods for Accurate Prediction of Protein-Ligand Binding Affinities in AMBER Drug Discovery Boost

T

¹

,

Tsai²,

Ganguly³

et al. 2021

Preprint

Self Cite

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Recent concurrent advances in methodology development, computer hardware and simulation software has transformed our ability to make practical, quantitative predictions of relative ligand binding affinities to guide rational drug design. In the past, these calculations have been hampered by the lack of affordable software with highly efficient implementations of state-of-the-art methods on specialized hardware such as graphical processing units, combined with the paucity of available workflows to streamline throughput for real-world industry applications. Herein we discuss recent methodology development, GPU-accelerated implementation, and workflow creation for alchemical free energy simulation methods in the AMBER Drug Discovery Boost (AMBER-DD Boost) package available as a patch to AMBER20. Among the methodological advances are 1) new methods for the treatment of softcore potentials that overcome long standing end-point catastrophe and softcore imbalance problems and enable single-step alchemical transformations between ligands, and 2) new adaptive enhanced sampling methods in the "alchemical" (or "λ") dimension to accelerate convergence and obtain high precision ligand binding affinity predictions, 3) robust network-wide analysis methods that include cycle closure and reference constraints and restraints, and 4) practical workflows that enable streamlined calculations on large datasets to be performed. Benchmark calculations on various systems demonstrate that these tools deliver an outstanding combination of accuracy and performance, resulting in reliable high-throughput binding affinity predictions at affordable cost.<br>

show abstract

“…61,[66][67][68][69] Further, all non-bonded interactions, including 1-4 terms, between the TS and (TC+I) regions should be scaled. The pre-AMBER20 behavior (gti_add_sc=0) is not theoretically correct 70 and has been fixed, although the option has been kept to enable testing and comparisons with earlier versions but should not be used for production work.…”

Section: T S/(t C+i)mentioning

confidence: 99%

“…This flag was created in order to have compatability with earlier versions of AMBER, and was corrected in a recent validation paper. 70 A smoothstep function with a higher order will have a smoother function curve and smaller derivatives near 0 and 1 but a larger derivative in between. The zero-order (P = 0) smoothstep function is in fact simply linear with constant slope, including at the endpoints, which can lead to endpoint catastrophe problems.…”

Section: Functional Forms Of the Weight Functionsmentioning

confidence: 99%

Robust, Efficient and Automated Methods for Accurate Prediction of Protein-Ligand Binding Affinities in AMBER Drug Discovery Boost

T

¹

,

Tsai²,

Ganguly³

et al. 2021

Preprint

Self Cite

0

View full text Add to dashboard Cite

Recent concurrent advances in methodology development, computer hardware and simulation software has transformed our ability to make practical, quantitative predictions of relative ligand binding affinities to guide rational drug design. In the past, these calculations have been hampered by the lack of affordable software with highly efficient implementations of state-of-the-art methods on specialized hardware such as graphical processing units, combined with the paucity of available workflows to streamline throughput for real-world industry applications. Herein we discuss recent methodology development, GPU-accelerated implementation, and workflow creation for alchemical free energy simulation methods in the AMBER Drug Discovery Boost (AMBER-DD Boost) package available as a patch to AMBER20. Among the methodological advances are 1) new methods for the treatment of softcore potentials that overcome long standing end-point catastrophe and softcore imbalance problems and enable single-step alchemical transformations between ligands, and 2) new adaptive enhanced sampling methods in the "alchemical" (or "λ") dimension to accelerate convergence and obtain high precision ligand binding affinity predictions, 3) robust network-wide analysis methods that include cycle closure and reference constraints and restraints, and 4) practical workflows that enable streamlined calculations on large datasets to be performed. Benchmark calculations on various systems demonstrate that these tools deliver an outstanding combination of accuracy and performance, resulting in reliable high-throughput binding affinity predictions at affordable cost.<br>

show abstract

Validation of Free Energy Methods in AMBER

Cited by 26 publications

References 27 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

Robust, Efficient and Automated Methods for Accurate Prediction of Protein-Ligand Binding Affinities in AMBER Drug Discovery Boost

Robust, Efficient and Automated Methods for Accurate Prediction of Protein-Ligand Binding Affinities in AMBER Drug Discovery Boost

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