Residence Time Prediction of Type 1 and 2 Kinase Inhibitors from Unbinding Simulations

Braka, A.; Garnier, Norbert; Bonnet, Pascal; Aci-Sèche, Samia

doi:10.1021/acs.jcim.9b00497

Cited by 23 publications

(24 citation statements)

References 38 publications

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“…From the loading plot, it can be deduced that larger molecular volume contributes to longer residence time, while stronger hydrophilic interactions contribute to faster dissociation (Figure 7d). The result proposed in this study is consistent with previous researches, which suggested that type I kinases inhibitors bind to the ATP binding site and usually smaller and faster, while compounds of type II are generally large since occupy additional transient sub-pocket [36,37].…”

Section: Volsurfsupporting

confidence: 92%

“…After variable selection and PLS modeling, an optimal PLS model with two variables was obtained, of which R 2 , Q 2 and R 2 val for k off are 0.821, 0.818 and 0.821, respectively (Table 4). As showed in Table 5, it can be seen that the prediction performances of the VolSurf model is superior to that of the models based on position-restrained MD [15] and biased MD simulations [36]. Furthermore, the results of 1000-repeated PLS modeling and 500-times Y-random permutations test demonstrates that the high-quality PLS model is not caused by accident ( Figure 6).…”

Section: P38 Mitogen-activated Protein Kinasementioning

confidence: 89%

See 1 more Smart Citation

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

Huang

Chen

Mei

et al. 2020

IJMS

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Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the koff values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.

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Section: Volsurfsupporting

confidence: 92%

Section: P38 Mitogen-activated Protein Kinasementioning

confidence: 89%

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

Huang

Chen

Mei

et al. 2020

IJMS

View full text Add to dashboard Cite

show abstract

“…The barrier heights from this potential of mean force for each ligand were then used to predict the dissociation rate, and a high correlation of 0.86 with the experimental dissociation rate was observed. However, this method was relatively expensive computationally, requiring a total of 4.5 µs for each ligand [43].…”

Section: Introductionmentioning

confidence: 99%

Qualitative Prediction of Ligand Dissociation Kinetics from Focal Adhesion Kinase Using Steered Molecular Dynamics

Spiriti

Wong

2021

Life

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Most early-stage drug discovery projects focus on equilibrium binding affinity to the target alongside selectivity and other pharmaceutical properties. Since many approved drugs have nonequilibrium binding characteristics, there has been increasing interest in optimizing binding kinetics early in the drug discovery process. As focal adhesion kinase (FAK) is an important drug target, we examine whether steered molecular dynamics (SMD) can be useful for identifying drug candidates with the desired drug-binding kinetics. In simulating the dissociation of 14 ligands from FAK, we find an empirical power–law relationship between the simulated time needed for ligand unbinding and the experimental rate constant for dissociation, with a strong correlation depending on the SMD force used. To improve predictions, we further develop regression models connecting experimental dissociation rate with various structural and energetic quantities derived from the simulations. These models can be used to predict dissociation rates from FAK for related compounds.

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“…A method combining steered molecular dynamics (SMD) and computation of free energies of dissociation obtained R 2 te of 0.88 for a dataset comprised of eight inhibitors. 72 In another study, a model obtained using PLS regression, a training set of 14 inhibitors, a test set of 6 inhibitors and protein-inhibitor interaction fingerprints (IFPs) from simulations of inhibitor dissociation had R 2 te of 0.56 and R 2 tr of 0.72. 73 Negative binding energy integrals obtained from ligand dissociation using local-scaled MD achieved R 2 te of 0.64 in the prediction of k of f values for 41 complexes with different kinases, including 12 complexes with p38 MAP kinase.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

Nunes-Alves¹,

Ormersbach²,

Wade³

2021

Preprint

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<div>There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each protein-ligand complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of protein-</div><div>ligand complexes to predict binding parameters. We introduce the option to use multiple structures to describe each ligand-protein complex into COMBINE analysis and</div><div>apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (k<sub>off</sub>) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression and allowed the computation of k<sub>off</sub> values. The QSKR model obtained using single, energy minimized crystal structures for each ligand-protein complex had a higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, the incorporation of protein-ligand flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, like interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures, showing that COMBINE analysis is a promising method to design leads with improved kinetic rates for flexible proteins.</div>

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Residence Time Prediction of Type 1 and 2 Kinase Inhibitors from Unbinding Simulations

Abstract: Supporting Information. Biological data of the inhibitors, details on computational method, computing time, Movie of unbinding process of B96 from one replicate, Tables S1-S2, and Figures S1-S6. This material is available free of charge via the Internet at http://pubs.acs.org.

Cited by 23 publications

References 38 publications

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

In Silico Prediction of the Dissociation Rate Constants of Small Chemical Ligands by 3D-Grid-Based VolSurf Method

Qualitative Prediction of Ligand Dissociation Kinetics from Focal Adhesion Kinase Using Steered Molecular Dynamics

Prediction of Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

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