Exploiting Free‐Energy Minima to Design Novel EphA2 Protein–Protein Antagonists: From Simulation to Experiment and Return

Russo, Simonetta; Callegari, Donatella; Incerti, Matteo; Pala, Daniele; Giorgio, Carmine; Brunetti, Jlenia; Bracci, Luisa; Vicini, Paola; Barocelli, Elisabetta; Capoferri, Luigi; Rivara, Silvia; Tognolini, Massimiliano; Mor, Marco; Lodola, Alessio

doi:10.1002/chem.201600993

Cited by 16 publications

(24 citation statements)

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“…Molecular modeling investigations performed with classical force fields have identified a likely binding mode for these inhibitors consistent with available structure–activity relationship (SAR) data, i.e., proposing a reasonable role for the terminal carboxylic group and the amino acid side-chain of the inhibitors during their docking within EphA2 [ 13 , 14 ]. However, attempts to build quantitative models correlating experimental activities to docking energies led to modest results [ 13 ], suggesting that classical methods may not be able to properly describe accommodation of amino acid conjugates of LCA within EphA2 ligand binding domain (LBD).…”

Section: Introductionmentioning

confidence: 86%

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Theoretical Model of EphA2-Ephrin A1 Inhibition

2018

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This work aims at the theoretical description of EphA2-ephrin A1 inhibition by small molecules. Recently proposed ab initio-based scoring models, comprising long-range components of interaction energy, is tested on lithocholic acid class inhibitors of this protein–protein interaction (PPI) against common empirical descriptors. We show that, although limited to compounds with similar solvation energy, the ab initio model is able to rank the set of selected inhibitors more effectively than empirical scoring functions, aiding the design of novel compounds.

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Section: Introductionmentioning

confidence: 86%

“…The most promising class is represented by lithocholic acid (LCA) and its

-amino acid conjugates [ 7 , 13 ]. It has been demonstrated by surface plasmon resonance (SPR) analysis that this class of compounds prevents ephrin-A1 binding to EphA2 by targeting a conserved region of the ligand-binding domain of EphA2 [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Theoretical Model of EphA2-Ephrin A1 Inhibition

2018

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“…In this respect, much has achieved in terms of free‐energy perturbation (FEP) calculations . We are confident that efficient and easy‐to‐prepare protocols and set‐ups for methods like steered‐MD and metadynamics will soon undergo similar improvements for prospective drug design . This will further open up these methods to computational medicinal chemistry.…”

Section: Improved Sampling Of Configuration Space Through Dynamic Docmentioning

confidence: 99%

“…From the first application of metadynamics to small molecule binding studies, we have now seen prospective applications of this method to estimate the binding affinity of compounds. These compounds have then been prioritized accordingly for synthesis and experimental tests during drug discovery projects …”

Section: Path‐based Versus Alchemical Transformations In Dynamic Dockingmentioning

confidence: 99%

Recent advances in dynamic docking for drug discovery

Vivo

Cavalli

2017

WIREs Comput Mol Sci

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Molecular docking allows the evaluation of ligand‐target complementarity. This is the crucial first step in small‐molecule drug discovery. Over the last decade, increasing computer power and more efficient molecular dynamics (MD) software have prompted the use of MD for molecular docking. The resulting dynamic docking offers major improvements by (1) taking full account of the structural flexibility of the drug‐target system and (2) allowing the computation of the free energy and kinetics associated with drug binding. Here, we examine the recent advances in dynamic docking, while also considering the challenges and limitations that this powerful approach must overcome to impact fast‐paced drug discovery. WIREs Comput Mol Sci 2017, 7:e1320. doi: 10.1002/wcms.1320 This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Mechanics Software > Molecular Modeling

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“…Metadynamics (MTD) is an enhanced sampling method that increases the probability of reaching high free-energy states by adding a Gaussian bias potential to the Hamiltonian of a state 6,7 . The MTD method has been used to analyze reaction pathways [8][9][10][11][12][13][14][15] , the conformations of additives at crystal surfaces [16][17][18][19] , crystal structures [20][21][22][23][24][25] , crystal nucleation [26][27][28][29][30][31][32][33] , drug design [34][35][36][37][38] , and the transport mechanism of an ion in a sub-nanopore 39 . The MTD method affords a free-energy landscape in a space of collective variables (CVs) for a system of interest.…”

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confidence: 99%

Pathways for the formation of ice polymorphs from water predicted by a metadynamics method

Nada

2020

Sci Rep

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pathways for the formation of ice polymorphs from water predicted by a metadynamics method Hiroki nada the mechanism of how ice crystal form has been extensively studied by many researchers but remains an open question. Molecular dynamics (MD) simulations are a useful tool for investigating the molecular-scale mechanism of crystal formation. However, the timescale of phenomena that can be analyzed by MD simulations is typically restricted to microseconds or less, which is far too short to explore ice crystal formation that occurs in real systems. in this study, a metadynamics (MtD) method was adopted to overcome this timescale limitation of MD simulations. An MD simulation combined with the MtD method, in which two discrete oxygen-oxygen radial distribution functions represented by Gaussian window functions were used as collective variables, successfully reproduced the formation of several different ice crystals when the Gaussian window functions were set at appropriate oxygenoxygen distances: cubic ice, stacking disordered ice consisting of cubic ice and hexagonal ice, highpressure ice Vii, layered ice with an ice Vii structure, and layered ice with an unknown structure. the free-energy landscape generated by the MtD method suggests that the formation of each ice crystal occurred via high-density water with a similar structure to the formed ice crystal. the present method can be used not only to study the mechanism of crystal formation but also to search for new crystals in real systems.

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Exploiting Free‐Energy Minima to Design Novel EphA2 Protein–Protein Antagonists: From Simulation to Experiment and Return

Abstract: The free-energy surface (FES) of protein-ligand binding contains information useful for drug design. Here we show how to exploit a free-energy minimum of a protein-ligand complex identified by metadynamics simulations to design a new EphA2 antagonist with improved inhibitory potency.

Cited by 16 publications

References 16 publications

Theoretical Model of EphA2-Ephrin A1 Inhibition

Theoretical Model of EphA2-Ephrin A1 Inhibition

Recent advances in dynamic docking for drug discovery

Pathways for the formation of ice polymorphs from water predicted by a metadynamics method

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