Predicting antibiotic resistance in complex protein targets using alchemical free energy methods

Mapping Intimacies ◽

10.26434/chemrxiv-2021-shfgp ◽

2021 ◽

Author(s):

Alice Brankin ◽

Philip Fowler

Keyword(s):

Free Energy ◽

Binding Free Energy ◽

Large Change ◽

Repeated Measurements ◽

Nucleotide Polymorphisms ◽

Protein Targets ◽

Charged Amino Acids ◽

Tb Treatment ◽

Complex Protein ◽

Small Change

Multi-drug resistant Mycobacterium tuberculosis requires a complex antibiotic treatment program and poses a major threat to tuberculosis (TB) treatment outcomes. Resistance is mostly conferred by chromosomal single nucleotide polymorphisms, many of which are well characterized and catalogued. However, not all mutations have been mapped and novel mutations can emerge. Methods able to quickly predict the effects of such mutations are needed to complement the existing catalogues, thereby permitting the prescription of effective treatment for patients and preventing the further spread of resistant strains. Relative binding free energy (RBFE) calculations can rapidly predict the effects of mutations, but this approach has not been tested on large, complex proteins. We use RBFE calculations to predict the effects of seven M. tuberculosis RNA polymerase mutations on rifampicin susceptibility and five M. tuberculosis DNA gyrase mutations on moxifloxacin susceptibility. These mutations encompass a range of amino acid substitutions with known effects and include large steric perturbations and charged moieties. We find that moderate numbers (n=3-15) of short RBFE calculations can predict resistance in cases where the mutation results in a large change in the binding free energy, but that the method lacks discrimination in cases with either a small change in energy or that involve charged amino acids, due to the associated large magnitude of error. We investigate how this error may be decreased by analyzing the sources of error and the distributions of repeated measurements from the different components of the RBFE calculations.

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A new entropy model for RNA: part III. Is the folding free energy landscape of RNA funnel shaped?

Journal of Nucleic Acids Investigation ◽

10.4081/jnai.2014.2652 ◽

2014 ◽

Vol 5 (1) ◽

Cited By ~ 5

Author(s):

Wayne Dawson ◽

Toshikuni Takai ◽

Nobuharu Ito ◽

Kentaro Shimizu ◽

Gota Kawai

Keyword(s):

Free Energy ◽

Degrees Of Freedom ◽

Binding Free Energy ◽

Relative Difference ◽

Rna Structures ◽

Native State ◽

Rna Sequences ◽

Entropy Model ◽

Complex Protein ◽

Folding Free Energy

The concept of a free energy (FE) landscape, in which the conformations of a polymer progressively take on the structure of the native state while spiraling down a FE surface that resembles the shape of a funnel, has long been viewed as the reason why a complex protein structure forms so rapidly compared to the number of conformations available to it. On the other hand, this landscape picture is less clear with RNA due to the multiplicity of conformations and the uncertainties in the current thermodynamics. It is therefore sometimes proposed that within the ensemble of suboptimal states of the RNA molecule, the vast majority of those states all closely resemble the native state and therefore simply overwhelm the few states that represent the global minimum FE. However, calculations of the free energy of observed structures often suggest that the most frequently observed cluster of structures are far from the minimum FE, particularly in the case of long sequences. If so, then such a FE surface is unlikely to be funnel shaped. We have been developing a version of <em>vsfold</em> that can evaluate the suboptimal structures of the FE surface (through a modified version called <em>vs_subopt</em>). Here we show that the ensemble of suboptimal structures for a number of known RNA structures can actually be both close to the minimum FE and also be the dominant observed structure when a proper Kuhn length is selected. Two state aptamers known as riboswitches can show neighboring FE states in the suboptimal structures that match the observed structures and their relative difference in FE is well within the range of the binding free energy of the metabolite. For the riboswitches and other short RNA sequences (less than 250 nt), the flow of the suboptimal structures (including pseudoknots) tended to resemble a rock rolling down a hill along the reaction coordinate axis. An important insight yielded by the cross-linking entropy (CLE) model is that the global entropy limits the size of domains. Hence, based on the CLE model, Levinthal’s paradox is overcome by the funnel shape in the FE, by a reduction in the number of degrees of freedom due to Kuhn length, and by limits on the size of the domains that can form. These concepts are also applicable to calculating transition rates between different suboptimal structures.

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Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

10.26434/chemrxiv.11364884.v2 ◽

2020 ◽

Cited By ~ 3

Author(s):

Christina Schindler ◽

Hannah Baumann ◽

Andreas Blum ◽

Dietrich Böse ◽

Hans-Peter Buchstaller ◽

...

Keyword(s):

Free Energy ◽

Drug Discovery ◽

Large Scale ◽

Active Drug ◽

Binding Free Energy ◽

Free Energy Calculations ◽

Energy Calculation ◽

Large Scale Assessment ◽

New Public ◽

Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

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Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

10.26434/chemrxiv.12781310.v1 ◽

2020 ◽

Author(s):

E. Prabhu Raman ◽

Thomas J. Paul ◽

Ryan L. Hayes ◽

Charles L. Brooks III

Keyword(s):

Free Energy ◽

Ligand Binding ◽

Binding Affinity ◽

Binding Free Energy ◽

Computational Cost ◽

Combinatorial Libraries ◽

Free Energy Calculations ◽

Lead Optimization ◽

Efficient Estimation ◽

Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

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Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

10.26434/chemrxiv.8398055 ◽

2019 ◽

Author(s):

David Wright ◽

Fouad Husseini ◽

Shunzhou Wan ◽

Christophe Meyer ◽

Herman Van Vlijmen ◽

...

Keyword(s):

Free Energy ◽

Surface Area ◽

Binding Free Energy ◽

Normal Mode Analysis ◽

Binding Mode ◽

Accessible Surface Area ◽

Solvent Accessible Surface Area ◽

Mode Analysis ◽

Energy Calculation ◽

Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

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Association/dissociation Mechanisms of Intrinsically Disordered Region of Protein Beyond Conformational Selection and Induced Fit

10.26434/chemrxiv.9874379 ◽

2019 ◽

Author(s):

Duy Phuoc Tran ◽

Akio Kitao

Keyword(s):

Free Energy ◽

Transcriptional Activation ◽

Binding Free Energy ◽

Energy Structure ◽

Induced Fit ◽

Hydrogen Bond Formation ◽

Conformational Selection ◽

Intrinsically Disordered ◽

Intrinsically Disordered Region ◽

Dissociation Mechanisms

<p>We investigate association and dissociation mechanisms of a typical intrinsically disordered region (IDR), transcriptional activation subdomain of tumor repressor protein p53 (TAD-p53) with murine double-minute clone 2 protein (MDM2). Using the combination of cycles of association and dissociation parallel cascade molecular dynamics, multiple standard MD, and Markov state model, we are successful in obtaining the lowest free energy structure of MDM2/TAD-p53 complex as the structure very close to that in crystal without prior knowledge. This method also reproduces the experimentally measured standard binding free energy, and association and dissociation rate constants solely with the accumulated MD simulation cost of 11.675 μs, in spite of the fact that actual dissociation occurs in the order of a second. Although there exist a few complex intermediates with similar free energies, TAD-p53 first binds MDM2 as the second lowest free energy intermediate dominantly (> 90% in flux), taking a form similar to one of the intermediate structures in its monomeric state. The mechanism of this step has a feature of conformational selection. In the second step, dehydration of the interface, formation of π-π stackings of the side-chains, and main-chain relaxation/hydrogen bond formation to complete α-helix take place, showing features of induced fit. In addition, dehydration (dewetting) is a key process for the final relaxation around the complex interface. These results demonstrate a more fine-grained view of the IDR association/dissociation beyond classical views of protein conformational change upon binding.</p>

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Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation

Scientific Reports ◽

10.1038/s41598-020-80769-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Germano Heinzelmann ◽

Michael K. Gilson

Keyword(s):

Free Energy ◽

Early Stage ◽

Binding Free Energy ◽

Low Cost ◽

Free Energy Calculations ◽

Free Energies ◽

Energy Calculations ◽

Drug Candidates ◽

Binding Free Energy Calculations ◽

Binding Free Energies

AbstractAbsolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

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Binding free energy predictions in host-guest systems using Autodock4. A retrospective analysis on SAMPL6, SAMPL7 and SAMPL8 challenges

Journal of Computer-Aided Molecular Design ◽

10.1007/s10822-021-00388-4 ◽

2021 ◽

Author(s):

Lorenzo Casbarra ◽

Piero Procacci

Keyword(s):

Molecular Dynamics ◽

Free Energy ◽

Drug Discovery ◽

Binding Free Energy ◽

Cost Ratio ◽

Benefit Cost Ratio ◽

Docking Program ◽

Benefit Cost ◽

Overall Reliability ◽

Absolute Binding Free Energy

AbstractWe systematically tested the Autodock4 docking program for absolute binding free energy predictions using the host-guest systems from the recent SAMPL6, SAMPL7 and SAMPL8 challenges. We found that Autodock4 behaves surprisingly well, outperforming in many instances expensive molecular dynamics or quantum chemistry techniques, with an extremely favorable benefit-cost ratio. Some interesting features of Autodock4 predictions are revealed, yielding valuable hints on the overall reliability of docking screening campaigns in drug discovery projects.

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