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Toward quantitative estimates of binding affinities for protein–ligand systems involving large inhibitor compounds: A steered molecular dynamics simulation route

Authors

  • Paolo Nicolini,

    1. Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Campus Nord B4-B5, E-08034 Barcelona, Spain
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  • Diego Frezzato,

    1. Dipartimento di Scienze Chimiche, Università di Padova, Via Marzolo 1, I-35131 Padova, Italy
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  • Cristina Gellini,

    1. Dipartimento di Chimica, Università di Firenze, Via della Lastruccia 3, I-50019 Sesto Fiorentino, Italy
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  • Marco Bizzarri,

    1. Dipartimento di Chimica, Università di Firenze, Via della Lastruccia 3, I-50019 Sesto Fiorentino, Italy
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  • Riccardo Chelli

    Corresponding author
    1. Dipartimento di Chimica, Università di Firenze, Via della Lastruccia 3, I-50019 Sesto Fiorentino, Italy
    2. European Laboratory for Non-linear Spectroscopy (LENS), Università di Firenze, Via Nello Carrara 1, I-50019 Sesto Fiorentino, Italy
    • R. Chelli European Laboratory for Non-linear Spectroscopy (LENS), Università di Firenze, Via Nello Carrara 1, I-50019 Sesto Fiorentino, Italy. E-mail: riccardo.chelli@unifi.it

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Abstract

Understanding binding mechanisms between enzymes and potential inhibitors and quantifying proteinligand affinities in terms of binding free energy is of primary importance in drug design studies. In this respect, several approaches based on molecular dynamics simulations, often combined with docking techniques, have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled proteinligand complex can be well predicted by computational methods, it is still challenging to rank with chemical accuracy a series of ligand analogues in a consistent way. In this article, we face this issue calculating relative binding free energies of a focal adhesion kinase, an important target for the development of anticancer drugs, with pyrrolopyrimidine-based ligands having different inhibitory power. To this aim, we employ steered molecular dynamics simulations combined with nonequilibrium work theorems for free energy calculations. This technique proves very powerful when a series of ligand analogues is considered, allowing one to tackle estimation of proteinligand relative binding free energies in a reasonable time. In our cases, the calculated binding affinities are comparable with those recovered from experiments by exploiting the MichaelisMenten mechanism with a competitive inhibitor.

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