Coupling Supervised Molecular Dynamics (SuMD) with Entropy Estimations To Shine Light on the Stability of Multiple Binding Sites.


Journal

ACS medicinal chemistry letters
ISSN: 1948-5875
Titre abrégé: ACS Med Chem Lett
Pays: United States
ID NLM: 101521073

Informations de publication

Date de publication:
11 Apr 2019
Historique:
received: 19 10 2018
accepted: 15 02 2019
entrez: 19 4 2019
pubmed: 19 4 2019
medline: 19 4 2019
Statut: epublish

Résumé

Exploring at the molecular level, all possible ligand-protein approaching pathways and, consequently, identifying the energetically favorable binding sites is considered crucial to depict a clear picture of the whole scenario of ligand-protein binding. In fact, a ligand can recognize a protein in multiple binding sites, adopting multiple conformations in every single binding site and inducing protein modifications upon binding. In the present work, we would like to present how it is possible to couple a supervised molecular dynamics (SuMD) approach to explore, from an unbound state, the most energetically favorable recognition pathways of the ligand to its protein, with an enthalpic and entropic characterization of the most stable ligand-protein bound states, using the protein kinase CK2α as a prototype study. We identified two accessory binding pockets surrounding the ATP-binding site having a strong enthalpic contribution but a different configurational entropy contribution, suggesting that they play a different role.

Identifiants

pubmed: 30996777
doi: 10.1021/acsmedchemlett.8b00490
pmc: PMC6466520
doi:

Types de publication

Journal Article

Langues

eng

Pagination

444-449

Déclaration de conflit d'intérêts

The authors declare no competing financial interest.

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Auteurs

Shailesh Kumar Panday (SK)

School of Computational and Integrative Sciences (SCIS), Jawaharlal Nehru University, New Delhi 110067, India.
Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35122 Padova, Italy.

Mattia Sturlese (M)

Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35122 Padova, Italy.

Veronica Salmaso (V)

Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35122 Padova, Italy.

Indira Ghosh (I)

School of Computational and Integrative Sciences (SCIS), Jawaharlal Nehru University, New Delhi 110067, India.

Stefano Moro (S)

Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35122 Padova, Italy.

Classifications MeSH