Investigating Drug–Target Residence Time in Kinases through Enhanced Sampling Simulations

Gobbo, Dorothea; Piretti, Valentina; Martino, Rita Maria Concetta Di; Tripathi, Shailesh; Giabbai, Barbara; Storici, Paola; Demitri, Nicola; Girotto, Stefania; Decherchi, Sergio; Cavalli, Andrea

doi:10.1021/acs.jctc.9b00104

Cited by 41 publications

(48 citation statements)

References 53 publications

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“…Accurate prediction of k off is highly desirable, as the dissociation rate is recognized as an important quantity in drug efficacy and safety in drug discovery. [86][87][88] Much pioneering work has recently been done to develop improved methods for predicting dissociation rates of protein-ligand interactions, [89][90][91][92] and we are eager to contribute efforts towards this goal.…”

Section: Resultsmentioning

confidence: 99%

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

Ge¹,

Zhang²,

Erdélyi³

et al. 2020

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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Section: Resultsmentioning

confidence: 99%

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

Ge¹,

Zhang²,

Erdélyi³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Accurate prediction of k off is highly desirable, as the dissociation rate is recognized as an important quantity in drug efficacy and safety in drug discovery. [88][89][90] Much pioneering work has recently been done to develop improved methods for predicting dissociation rates of protein-ligand interactions, [91][92][93][94] and we are eager to contribute efforts towards this goal.…”

Section: Binding Mechanisms Of Designed Cyclic Mdm2 Ligands a Powerful Advantage Ofmentioning

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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“…Binding pathways and the kinetics extracted from these simulations, however, suffer from large statistical uncertainty, because it is difficult to observe multiple binding and unbinding events within the available computational time, even when using specialized hardware (17, 18). In overcoming the difficulty, parallel trajectory approaches predict binding pathways and kinetics at longer timescales using short-time simulation data (19–22), while various enhanced sampling methods characterize drug-target binding, with a particular focus on unbinding kinetics, by effectively crossing high energy barriers (23–31). Currently, unbinding kinetics and their molecular determinants can be reasonably estimated and directly compared with experiments, though obtaining converged free-energy landscapes to map all of the key intermediates along the pathway remains a challenge.…”

mentioning

confidence: 99%

Encounter complexes and hidden poses of kinase-inhibitor binding on the free-energy landscape

Oshima

Kasahara

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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Modern drug discovery increasingly focuses on the drug-target binding kinetics which depend on drug (un)binding pathways. The conventional molecular dynamics simulation can observe only a few binding events even using the fastest supercomputer. Here, we develop 2D gREST/REUS simulation with enhanced flexibility of the ligand and the protein binding site. Simulation (43 μs in total) applied to an inhibitor binding to c-Src kinase covers 100 binding and unbinding events. On the statistically converged free-energy landscapes, we succeed in predicting the X-ray binding structure, including water positions. Furthermore, we characterize hidden semibound poses and transient encounter complexes on the free-energy landscapes. Regulatory residues distant from the catalytic core are responsible for the initial inhibitor uptake and regulation of subsequent bindings, which was unresolved by experiments. Stabilizing/blocking of either the semibound poses or the encounter complexes can be an effective strategy to optimize drug-target residence time.

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Investigating Drug–Target Residence Time in Kinases through Enhanced Sampling Simulations

Cited by 41 publications

References 53 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

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

Encounter complexes and hidden poses of kinase-inhibitor binding on the free-energy landscape

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