Systematic Analysis of Biomolecular Conformational Ensembles with PENSA

Vögele, Martin; Thomson, Neil J.; Truong, Sang; McAvity, Jasper; Zachariae, Ulrich; Dror, Ron O.

doi:10.48550/arxiv.2212.02714

Cited by 11 publications

(14 citation statements)

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“…For two distributions P i and P j , and considering a feature x f from two different ensembles i and j

where

. The Kullback-Leibler divergence, D KL , corresponds to two distributions P i and P j is of the following form

JSD values were computed using the Python ENSemble Analysis (PENSA) open-source library ( 65 ). Kernel density estimations of the JSD values were plotted to describe the JSD range and compare the systems.…”

Section: Methodsmentioning

confidence: 99%

High-fidelity, hyper-accurate, and evolved mutants rewire atomic-level communication in CRISPR-Cas9

Skeens,

Sinha,

Ahsan

et al. 2024

Sci. Adv.

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The high-fidelity (HF1), hyper-accurate (Hypa), and evolved (Evo) variants of the CRISPR-associated protein 9 (Cas9) endonuclease are critical tools to mitigate off-target effects in the application of CRISPR-Cas9 technology. The mechanisms by which mutations in recognition subdomain 3 (Rec3) mediate specificity in these variants are poorly understood. Here, solution nuclear magnetic resonance and molecular dynamics simulations establish the structural and dynamic effects of high-specificity mutations in Rec3, and how they propagate the allosteric signal of Cas9. We reveal conserved structural changes and dynamic differences at regions of Rec3 that interface with the RNA:DNA hybrid, transducing chemical signals from Rec3 to the catalytic His-Asn-His (HNH) domain. The variants remodel the communication sourcing from the Rec3 α helix 37, previously shown to sense target DNA complementarity, either directly or allosterically. This mechanism increases communication between the DNA mismatch recognition helix and the HNH active site, shedding light on the structure and dynamics underlying Cas9 specificity and providing insight for future engineering principles.

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“…For two distributions P i and P j , and considering a feature x f from two different ensembles i and j

where

. The Kullback-Leibler divergence, D KL , corresponds to two distributions P i and P j is of the following form

Section: Methodsmentioning

confidence: 99%

High-fidelity, hyper-accurate, and evolved mutants rewire atomic-level communication in CRISPR-Cas9

Skeens,

Sinha,

Ahsan

et al. 2024

Sci. Adv.

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show abstract

“…79 Uncertainty is reported as the standard error of the mean, with each independent trajectory being treated as an independent measurement. The Jensen-Shannon distance 80 and the corresponding backbone torsion angles were computed using the PENSA 81 package for Python 3 with 10 bins. In the analysis of the β 7- β 10 gap, the autocorrelation was computed in Python 3, and fit using a sum of two exponentials with the form given in Equation 1 since the data was not well fit by a single exponential (Figure S4).…”

Section: Methodsmentioning

confidence: 99%

PEG-mCherry interactions beyond classical macromolecular crowding

Haas-Neill,

Joron,

Lerner

et al. 2024

Preprint

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The dense cellular environment influences bio-macromolecular structure, dynamics, interactions and function. Despite advancements in understanding protein-crowder interactions, predicting their precise effects on protein structure and function remains challenging. Here, we elucidate the effects of PEG-induced crowding on the fluorescent protein mCherry using molecular dynamics simulations and fluorescence-based experiments. We identify and characterize specific PEG-induced structural and dynamical changes in mCherry. Importantly, we find interactions in which PEG molecules wrap around specific surface-exposed residues in a binding mode previously observed in protein crystal structures. Fluorescence correlation spectroscopy experiments capture PEG-induced changes, including aggregation, suggesting a potential role for the specific PEG-mCherry interactions identified in simulations. Additionally, mCherry fluorescence lifetimes are influenced by PEG and not by the bulkier crowder dextran, highlighting the importance of crowder-protein soft interactions. This work augments our understanding of macromolecular crowding effects on protein structure and dynamics.

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“…We used the trajectories from these MD simulations to derive starting structure and, where necessary, restraints for the subsequent FEP calculations, following a systematic approach to quantify different structural ensembles (Supporting Information Text and Figure S5). 46 We computed distances between the Cα atoms of residues in the binding pocket and conducted k-means clustering in their joint principal-component space. We calculated average positions within each cluster for all the receptor's Cα atoms to use them as restraint centers, and the RMSF for each cluster to use it as the width of the restraints.…”

Section: Methodsmentioning

confidence: 99%

Is the functional response of a receptor determined by the thermodynamics of ligand binding?

Vögele

Zhang

Kaindl

et al. 2023

Preprint

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Although strong binding to the target protein is a prerequisite, it is not enough to be an effective drug. To produce a particular functional response, drugs need to regulate the targets’ signal transduction pathways, either blocking the proteins’ functions or modulating their activities by changing the conformational equilibrium. The routinely calculated binding free energy of a compound to its target is a good predictor of affinity but may not always predict efficacy. While the time scales for the protein conformational changes are prohibitively long to be routinely modeled via physics-based simulations, thermodynamic principles suggest that binding free energies of the ligands with different receptor conformations may infer their efficacy if the functional response of the receptor is determined by thermodynamics. However, while this hypothesis was proposed in the past, it has not been thoroughly validated and is seldom used in practice for ligand efficacy prediction. We present an actionable protocol and a comprehensive validation study to show that binding thermodynamics provides indeed a strong predictor for the efficacy of a ligand. We apply the absolute-binding free energy perturbation (ABFEP) method to ligands bound to active and inactive states of eight G protein–coupled receptors (GPCRs) and a nuclear receptor. By comparing the resulting binding free energies, we can determine with a very high accuracy whether a ligand acts as an agonist or an antagonist. We find that carefully designed restraints are often necessary to efficiently model the corresponding conformational ensembles for each state and provide a procedure for setting up these restraints. Our method achieves excellent performance in classifying ligands as agonists or antagonists across the various investigated receptors, all of which are important drug targets.

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Systematic Analysis of Biomolecular Conformational Ensembles with PENSA

Cited by 11 publications

References 0 publications

High-fidelity, hyper-accurate, and evolved mutants rewire atomic-level communication in CRISPR-Cas9

High-fidelity, hyper-accurate, and evolved mutants rewire atomic-level communication in CRISPR-Cas9

PEG-mCherry interactions beyond classical macromolecular crowding

Is the functional response of a receptor determined by the thermodynamics of ligand binding?

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