Conformational Selection and Induced Fit Mechanisms in the Binding of an Anticancer Drug to the c-Src Kinase

Morando, Maria; Saladino, Giorgio; D’Amelio, Nicola; Pucheta-Martinez, Encarna; Lovera, Silvia; Lelli, Moreno; López-Méndez, Blanca; Marenchino, Marco; Campos-Olivas, Ramón; Gervasio, Francesco Luigi

doi:10.1038/srep24439

Cited by 61 publications

(80 citation statements)

References 59 publications

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“…Many are based on atomistic molecular dynamics (MD) simulations, 18,19 often complemented by Markov State Models, e.g. combined by Brownian dynamics simulations and/or applying milestoning approaches 11,[20][21][22][23][24][25][26] . The potential of such methods in modeling kinetics, is clear, especially of ligands binding to surface pockets.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Simulation Approach to Modeling Drug–Protein Binding Kinetics

Haldar

Comitani

Saladino

et al. 2018

J. Chem. Theory Comput.

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Drug-target binding kinetics has recently emerged as a sometimes critical determinant of in vivo efficacy and toxicity. Its rational optimization to improve potency or reduce side effects of drugs is, however, extremely difficult. Molecular simulations can play a crucial role in identifying features and properties of small ligands and their protein targets affecting the binding kinetics, but significant challenges include the long time scales involved in (un)binding events and the limited accuracy of empirical atomistic force fields (lacking, e.g., changes in electronic polarization). In an effort to overcome these hurdles, we propose a method that combines state-of-the-art enhanced sampling simulations and quantum mechanics/molecular mechanics (QM/MM) calculations at the BLYP/VDZ level to compute association free energy profiles and characterize the binding kinetics in terms of structure and dynamics of the transition state ensemble. We test our combined approach on the binding of the anticancer drug Imatinib to Src kinase, a well-characterized target for cancer therapy with a complex binding mechanism involving significant conformational changes. The results indicate significant changes in polarization along the binding pathways, which affect the predicted binding kinetics. This is likely to be of widespread importance in binding of ligands to protein targets.

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Section: Introductionmentioning

confidence: 99%

A Multiscale Simulation Approach to Modeling Drug–Protein Binding Kinetics

Haldar

Comitani

Saladino

et al. 2018

J. Chem. Theory Comput.

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“…The metadynamics technique can be used to compute the binding free energy along physically meaningful pathways, predicting both the thermodynamics and kinetics of binding, and has established itself as the method of choice for free-energy prediction in complex molecular processes. 3,[13][14][15][16][17][18][19][20][21][22][23][24] However, metadynamics (and similar approaches) require the definition of one or more collective variables (CVs) that approximate the reaction coordinate, which must be chosen carefully. 21,25,26 The time needed to converge a free energy profile increases significantly when poorly suited or too many CVs are selected.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Metadynamics-Based Protocol To Model the Binding Affinity and the Transition State Ensemble of G-Protein-Coupled Receptor Ligands

Saleh

Ibrahim

Saladino

et al. 2017

J. Chem. Inf. Model.

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A generally applicable metadynamics scheme for predicting the free-energy profile of ligand binding to G-protein coupled receptors (GPCRs) is described. A common and effective collective variable (CV) has been defined using the ideally placed and highly conserved Trp6.48 as a reference point for ligand-GPCR distance measurement and the common orientation of GPCRs in the cell membrane. Using this single CV together with well-tempered multiple-walker 2 metadynamics with a funnel-like boundary allows an efficient exploration of the entire ligandbinding path from the extracellular medium to the orthosteric binding site, including vestibule and intermediate sites. The protocol can be used with X-ray structures or high-quality homology models for the receptor and is universally applicable to agonists, antagonists, partial and reverse agonists. The root mean square error (RMSE) in predicted binding free energies for 12 diverse ligands in five receptors (a total of 23 data points) is surprisingly small (less than 1 kcal mol − 1 ).The RMSEs for simulations that use receptor X-ray structures and homology models are very similar.

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“…17,18 Via diffusion, the protein and the ligand will occasionally encounter each other and depending on the precise orientations of the two molecules in this so-called encounter complex, the complex will either be productive and proceed along the reaction coordinate or nonproductive leading to the subsequent dissociation of the complex. 19,20 Subsequent to the formation of a highenergy, productive encounter complex, that is the aligned encounter complex, the two molecules will need to change conformation to adapt their surfaces either through a conformer selection process [21][22][23] or through induced fit 24,25 or both, 26,27 to optimize the final complementarity of the interacting molecules.…”

Section: P1lplmentioning

confidence: 99%

(S)Pinning down protein interactions by NMR

et al. 2017

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Protein molecules are highly diverse communication platforms and their interaction repertoire stretches from atoms over small molecules such as sugars and lipids to macromolecules. An important route to understanding molecular communication is to quantitatively describe their interactions. These types of analyses determine the amounts and proportions of individual constituents that participate in a reaction as well as their rates of reactions and their thermodynamics. Although many different methods are available, there is currently no single method able to quantitatively capture and describe all types of protein reactions, which can span orders of magnitudes in affinities, reaction rates, and lifetimes of states. As the more versatile technique, solution NMR spectroscopy offers a remarkable catalogue of methods that can be successfully applied to the quantitative as well as qualitative descriptions of protein interactions. In this review we provide an easy‐access approach to NMR for the non‐NMR specialist and describe how and when solution state NMR spectroscopy is the method of choice for addressing protein ligand interaction. We describe very briefly the theoretical background and illustrate simple protein–ligand interactions as well as typical strategies for measuring binding constants using NMR spectroscopy. Finally, this review provides examples of caveats of the method as well as the options to improve the outcome of an NMR analysis of a protein interaction reaction.

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Conformational Selection and Induced Fit Mechanisms in the Binding of an Anticancer Drug to the c-Src Kinase

Cited by 61 publications

References 59 publications

A Multiscale Simulation Approach to Modeling Drug–Protein Binding Kinetics

A Multiscale Simulation Approach to Modeling Drug–Protein Binding Kinetics

An Efficient Metadynamics-Based Protocol To Model the Binding Affinity and the Transition State Ensemble of G-Protein-Coupled Receptor Ligands

(S)Pinning down protein interactions by NMR

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