Insights Into Membrane Protein-Lipid Interactions from Free Energy Calculations

AbstractIntegral membrane proteins are regulated by specific interactions with lipids from the surrounding bilayer. The structures of protein-lipid complexes can be determined through a combination of experimental and computational approaches, but the energetic basis of these interactions is difficult to resolve. Molecular dynamic simulations provide the primary computational technique to estimate the free energies of these interactions. We demonstrate that the energetics of protein-lipid interactions may be reliably and reproducibly calculated using three simulation-based approaches: potential of mean force calculations. alchemical free energy perturbation, and well-tempered metadynamics. We employ these techniques within the framework of a coarse-grained force field, and apply them to both bacterial and mammalian membrane protein-lipid systems. We demonstrate good agreement between the different techniques, providing a robust framework for their automated implementation within a pipeline for annotation of newly determined membrane protein structures.

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Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

10.26434/chemrxiv.11812053.v1 ◽

2020 ◽

Author(s):

Maximilian Kuhn ◽

Stuart Firth-Clark ◽

Paolo Tosco ◽

Antonia S. J. S. Mey ◽

Mark Mackey ◽

...

Keyword(s):

Free Energy ◽

Protein Structures ◽

Free Energy Calculations ◽

Distinct Advantage ◽

Binding Affinities ◽

Structure Based Drug Design ◽

Energy Calculations ◽

Kendall’S Τ ◽

Experimental Values ◽

Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods.

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Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

10.26434/chemrxiv.7841270.v2 ◽

2019 ◽

Author(s):

Kyle Konze ◽

Pieter Bos ◽

Markus Dahlgren ◽

Karl Leswing ◽

Ivan Tubert-Brohman ◽

...

Keyword(s):

Free Energy ◽

Drug Discovery ◽

Active Learning ◽

Large Scale ◽

Chemical Space ◽

Population Based ◽

Free Energy Calculations ◽

Computational Technique ◽

Cyclin Dependent Kinase ◽

Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC50 < 100 nM, and four unique cores with a predicted IC50 < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

10.26434/chemrxiv.7841270 ◽

2019 ◽

Author(s):

Kyle Konze ◽

Pieter Bos ◽

Markus Dahlgren ◽

Karl Leswing ◽

Ivan Tubert-Brohman ◽

...

Keyword(s):

Free Energy ◽

Drug Discovery ◽

Active Learning ◽

Large Scale ◽

Chemical Space ◽

Population Based ◽

Free Energy Calculations ◽

Computational Technique ◽

Cyclin Dependent Kinase ◽

Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC50 < 100 nM, and four unique cores with a predicted IC50 < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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SAMPL6 Octanol-Water Partition Coefficients from Alchemical Free Energy Calculations with MBIS Atomic Charges

10.26434/chemrxiv.9924806 ◽

2019 ◽

Author(s):

Maximiliano Riquelme ◽

Esteban Vöhringer-Martinez

Keyword(s):

Free Energy ◽

Electron Density ◽

Free Energy Calculations ◽

Basis Set ◽

Energetic Cost ◽

Atomic Charges ◽

Free Energies ◽

Energy Calculations ◽

Alchemical Free Energy ◽

Alchemical Free Energy Calculations

In molecular modeling the description of the interactions between molecules forms the basis for a correct prediction of macroscopic observables. Here, we derive atomic charges from the implicitly polarized electron density of eleven molecules in the SAMPL6 challenge using the Hirshfeld-I and Minimal Basis Set Iterative Stockholder(MBIS) partitioning method. These atomic charges combined with other parameters in the GAFF force field and different water/octanol models were then used in alchemical free energy calculations to obtain hydration and solvation free energies, which after correction for the polarization cost, result in the blind prediction of the partition coefficient. From the tested partitioning methods and water models the S-MBIS atomic charges with the TIP3P water model presented the smallest deviation from the experiment. Conformational dependence of the free energies and the energetic cost associated with the polarization of the electron density are discussed.

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Faculty Opinions recommendation of Are free energy calculations useful in practice? A comparison with rapid scoring functions for the p38 MAP kinase protein system.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1002470.28315 ◽

2001 ◽

Author(s):

Brian Shoichet

Keyword(s):

Free Energy ◽

Map Kinase ◽

P38 Map Kinase ◽

Free Energy Calculations ◽

Scoring Functions ◽

Energy Calculations ◽

Protein System

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