Reconciling Simulated Ensembles of Apomyoglobin with Experimental Hydrogen/Deuterium Exchange Data Using Bayesian Inference and Multiensemble Markov State Models

Wan, Hongbin; Ge, Yunhui; Razavi, Asghar M.; Voelz, Vincent A.

doi:10.1021/acs.jctc.9b01240

Cited by 36 publications

(51 citation statements)

References 49 publications

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“…Accurate prediction of k o↵ is highly desirable, as the dissociation rate is recognized as an important quantity in drug e cacy and safety in drug discovery. [87][88][89] Much pioneering work has recently been done to develop improved methods for predicting dissociation rates of protein-ligand interactions, [90][91][92][93] and we are eager to contribute e↵orts towards this goal.…”

Section: Resultsmentioning

confidence: 99%

Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2

Zhang

Erdélyi

et al. 2021

J. Chem. Inf. Model.

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Understanding mechanisms of protein folding and binding is crucial to designing their molecular function.Molecular dynamics (MD) simulations and Markov State Model (MSM) approaches provide a powerful way to understand complex conformational change that occurs over long timescales. Consideration of such dynamics are important for the design of therapeutic peptidomimetic ligands, whose affinity and binding mechanism are dictated by a combination of folding and binding. To examine the role of preorganization in peptide binding to protein targets, we performed massively parallel explicit-solvent MD simulations of cyclic β-hairpin ligands designed to disrupt the MDM2-p53 interaction. MSM analysis of over 3 ms of aggregate trajectory data enabled us to build a detailed mechanistic model of coupled folding and binding of four cyclic peptides which we compare to experimental binding affinities and rates. The results show a striking relationship between the relative preorganization of each ligand in solution and its affinity for MDM2. Specifically, changes in peptide conformational populations predicted by the MSMs suggest that entropy loss upon binding is the main factor influencing affinity. The MSMs also enable detailed examination of non-native interactions which lead to misfolded states, and comparison of structural ensembles with experimental NMR measurements. In contrast to an MSM study of p53 TAD binding to MDM2, MSMs of cyclic β-hairpin binding show a conformational selection mechanism. Finally, we make progress towards predicting accurate off-rates of cyclic peptides using multiensemble Markov models (MEMMs) constructed from unbiased and biased simulated trajectories. File list (3) download file view on ChemRxiv MDM2_binding-v2.pdf (6.47 MiB) download file view on ChemRxiv MDM2_binding-SI.pdf (7.31 MiB) download file view on ChemRxiv MDM2_binding_final.pdf (14.23 MiB)

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Section: Resultsmentioning

confidence: 99%

Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2

Zhang

Erdélyi

et al. 2021

J. Chem. Inf. Model.

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“…A typical approach is to first generate a conformational ensemble for the protein of interest with molecular dynamics (MD) or Monte Carlo simulations. The simulated data must then be translated into predicted peptide-deuteration levels that can be correlated with the experimental data ( 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ). Typically, this is achieved using empirical models that predict protection factors P i from an ensemble of protein structures.…”

Section: Introductionmentioning

confidence: 99%

Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles

Bradshaw

Marinelli

Faraldo‐Gómez

et al. 2020

Biophysical Journal

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Hydrogen-deuterium exchange combined with mass spectrometry (HDX-MS) is a widely applied biophysical technique that probes the structure and dynamics of biomolecules without the need for site-directed modifications or bio-orthogonal labels. The mechanistic interpretation of HDX data, however, is often qualitative and subjective, owing to a lack of quantitative methods to rigorously translate observed deuteration levels into atomistic structural information. To help address this problem, we have developed a methodology to generate structural ensembles that faithfully reproduce HDX-MS measurements. In this approach, an ensemble of protein conformations is first generated, typically using molecular dynamics simulations. A maximum-entropy bias is then applied post hoc to the resulting ensemble such that averaged peptide-deuteration levels, as predicted by an empirical model, agree with target values within a given level of uncertainty. We evaluate this approach, referred to as HDX ensemble reweighting (HDXer), for artificial target data reflecting the two major conformational states of a binding protein. We demonstrate that the information provided by HDX-MS experiments and by the model of exchange are sufficient to recover correctly weighted structural ensembles from simulations, even when the relevant conformations are rarely observed. Degrading the information content of the target data—e.g., by reducing sequence coverage, by averaging exchange levels over longer peptide segments, or by incorporating different sources of uncertainty—reduces the structural accuracy of the reweighted ensemble but still allows for useful insights into the distinctive structural features reflected by the target data. Finally, we describe a quantitative metric to rank candidate structural ensembles according to their correspondence with target data and illustrate the use of HDXer to describe changes in the conformational ensemble of the membrane protein LeuT. In summary, HDXer is designed to facilitate objective structural interpretations of HDX-MS data and to inform experimental approaches and further developments of theoretical exchange models.

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“…There several possible explanations for this result, perhaps the most important being that the MD simulations (and subsequent MSMs) were performed in aqueous solution, whereas the NMR data was obtained for DMSO-solutions (made necessary by the low aqueous solubility of the studied peptides). That said, it is likely that the NAMFIS analysis does not agree with the MSM predictions of folded state populations (Table 1) onciled with experimental restraints using the BICePs algorithm [84][85][86] ) and NAMFIS remains an interesting topic for future research. bound and unbound states, which generated hundreds of ab initio binding events.…”

Section: Methodsmentioning

confidence: 99%

Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2

Zhang²,

Erdélyi³

et al. 2021

Preprint

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<div><div><div><p>Understanding mechanisms of protein folding and binding is crucial to designing their molecular function. Molecular dynamics (MD) simulations and Markov State Model (MSM) approaches provide a powerful way to understand complex conformational change that occurs over long timescales. Consideration of such dynamics are important for the design of therapeutic peptidomimetic ligands, whose affinity and binding mechanism are dictated by a combination of folding and binding. To examine the role of preorganization in peptide binding to protein targets, we performed massively parallel explicit-solvent MD simulations of cyclic β-hairpin ligands designed to disrupt the MDM2-p53 interaction. MSM analysis of over 3 ms of aggregate trajectory data enabled us to build a detailed mechanistic model of coupled folding and binding of four cyclic peptides which we compare to experimental binding affinities and rates. The results show a striking relationship between the relative preorganization of each ligand in solution and its affinity for MDM2. Specifically, changes in peptide conformational populations predicted by the MSMs suggest that entropy loss upon binding is the main factor influencing affinity. The MSMs also enable detailed examination of non-native interactions which lead to misfolded states, and comparison of structural ensembles with experimental NMR measurements. In contrast to an MSM study of p53 TAD binding to MDM2, MSMs of cyclic β-hairpin binding show a conformational selection mechanism. Finally, we make progress towards predicting accurate off-rates of cyclic peptides using multiensemble Markov models (MEMMs) constructed from unbiased and biased simulated trajectories.</p></div></div></div>

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Reconciling Simulated Ensembles of Apomyoglobin with Experimental Hydrogen/Deuterium Exchange Data Using Bayesian Inference and Multiensemble Markov State Models

Cited by 36 publications

References 49 publications

Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2

Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2

Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles

Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2

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