Approaching protein design with multisite <i>λ</i> dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme

Hayes, Ryan L.; Vilseck, Jonah Z.; Brooks, Charles L.

doi:10.1002/pro.3500

Cited by 32 publications

(141 citation statements)

References 94 publications

(167 reference statements)

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“…MSλD is a rigorous free-energy simulation method able to calculate changes in protein binding affinity in response to point mutations (92,93). Compared to other freeenergy simulation methods, MSλD is highly efficient, requiring an order of magnitude less computation than standard approaches without loss of precision, and it facilitates sampling of different perturbations at multiple sites in a single simulation (92,(94)(95)(96)(97). This offers the advantage of exploring non-additive effects between multiple point mutations within a protein simultaneously (95).…”

Section: Molecular Modeling Quantifies the Contributions Of Individuamentioning

confidence: 99%

“…A soft-core Lennard Jones potential was used to scale all nonbonded interactions for the alchemical substituents by λ (94). Substituents dihedral angles were scaled by λ while bonds, angles, and improper dihedral angles were not, as this was found previously to yield better sampling without causing conformations to become trapped in local energy minima (95,97). The adaptive landscape flattening (ALF) algorithm was used to identify appropriate biasing potentials for MSλD (94,95).…”

Section: Molecular Modeling Computational Detailsmentioning

confidence: 99%

“…Substituents dihedral angles were scaled by λ while bonds, angles, and improper dihedral angles were not, as this was found previously to yield better sampling without causing conformations to become trapped in local energy minima (95,97). The adaptive landscape flattening (ALF) algorithm was used to identify appropriate biasing potentials for MSλD (94,95). ALF was run in two stages, first with several 100 ps simulations, followed by additional 1 ns simulations for bias refinement.…”

Section: Molecular Modeling Computational Detailsmentioning

confidence: 99%

“…This can be attributed to the difficulty of exploring small-to-large perturbations within the hydrophobic core of Pup2. To circumvent this difficulty, the best biases from unbound and bound Pup2 were then used in one 5-ns production run followed by three sequential 25 ns production runs employing variable bias replica exchange (VB-REX), an enhanced sampling algorithm (95)…”

Section: Molecular Modeling Computational Detailsmentioning

confidence: 99%

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Mutant thermal proteome profiling for characterization of missense protein variants and their associated phenotypes within the proteome

Justice

Barron

et al. 2020

Journal of Biological Chemistry

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Temperature sensitive (TS) missense mutants have been foundational for characterization of essentialgene function. However, an unbiased approach for analysis of biochemical and biophysical changes in TSmissense mutants within the context of their functional proteomes is lacking. We applied massspectrometry (MS) based thermal proteome profiling (TPP) to investigate the proteome-wide effects ofmissense mutations in an application that we refer to as mutant Thermal Proteome Profiling (mTPP).This study characterized global impacts of temperature sensitivity-inducing missense mutations in twodifferent subunits of the 26S proteasome. The majority of alterations identified by RNA-Seq and globalproteomics were similar between the mutants, which could suggest that a similar functional disruption isoccurring in both missense variants. Results from mTPP, however, provide unique insights into themechanisms that contribute to the TS phenotype in each mutant, revealing distinct changes that were notobtained using only steady-state transcriptome and proteome analyses. Computationally, multisite λ-dynamics simulations add clear support for mTPP experimental findings. This work shows that mTPP is aprecise approach to measure changes in missense mutant containing proteomes without the requirementfor large amounts of starting material, specific antibodies against proteins of interest, and/or geneticmanipulation of the biological system. Although experiments were performed under permissiveconditions, mTPP provided insights into the underlying protein stability changes that cause dramaticcellular phenotypes observed at non-permissive temperatures. Overall, mTPP provides uniquemechanistic insights into missense mutation dysfunction and connection of genotype to phenotype in arapid, non-biased fashion.

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Section: Molecular Modeling Quantifies the Contributions Of Individuamentioning

confidence: 99%

Section: Molecular Modeling Computational Detailsmentioning

confidence: 99%

Section: Molecular Modeling Computational Detailsmentioning

confidence: 99%

Section: Molecular Modeling Computational Detailsmentioning

confidence: 99%

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Mutant thermal proteome profiling for characterization of missense protein variants and their associated phenotypes within the proteome

Justice

Barron

et al. 2020

Journal of Biological Chemistry

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“…where α=0.017, and =0.18 were previously optimized to yield good fits to free energy profiles for a variety of systems. [14][15][16] The set of constants, { }, { , }, { , }, and { , } are optimized during bias optimization. The bias optimization algorithm is iterative in nature where short MSLD simulations are run, followed by calculation of free energy landscapes.…”

Section: = + + +mentioning

confidence: 99%

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

Raman

Paul²,

Hayes³

et al. 2020

Preprint

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<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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A strategy for proline and glycine mutations to proteins with alchemical free energy calculations

Hayes

Brooks

2021

J Comput Chem

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Computation of the thermodynamic consequences of protein mutations holds great promise in protein biophysics and design. Alchemical free energy methods can give improved estimates of mutational free energies, and are already widely used in calculations of relative and absolute binding free energies in small molecule design problems. In principle, alchemical methods can address any amino acid mutation with an appropriate alchemical pathway, but identifying a strategy that produces such a path for proline and glycine mutations is an ongoing challenge. Most current strategies perturb only side chain atoms, while proline and glycine mutations also alter the backbone parameters and backbone ring topology. Some strategies also perturb backbone parameters and enable glycine mutations. This work presents a strategy that enables both proline and glycine mutations and comprises two key elements: a dual backbone with restraints and scaling of bonded terms, facilitating backbone parameter changes, and a soft bond in the proline ring, enabling ring topology changes in proline mutations. These elements also have utility for core hopping and macrocycle studies in computer‐aided drug design. This new strategy shows slight improvements over an alternative side chain perturbation strategy for a set T4 lysozyme mutations lacking proline and glycine, and yields good agreement with experiment for a set of T4 lysozyme proline and glycine mutations not previously studied. To our knowledge this is the first report comparing alchemical predictions of proline mutations with experiment. With this strategy in hand, alchemical methods now have access to the full palette of amino acid mutations.

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Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme

Cited by 32 publications

References 94 publications

Mutant thermal proteome profiling for characterization of missense protein variants and their associated phenotypes within the proteome

Mutant thermal proteome profiling for characterization of missense protein variants and their associated phenotypes within the proteome

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

A strategy for proline and glycine mutations to proteins with alchemical free energy calculations

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