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Structure-based identification of inhibitors disrupting the CD2–CD58 interactions

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Abstract

The immune system has very intricate mechanisms of fighting against the invading infections which are accomplished by a sequential event of molecular interactions in the body. One of the crucial phenomena in this process is the recognition of T-cells by the antigen-presenting cells (APCs), which is initiated by the rapid interaction between both cell surface receptors, i.e., CD2 located on T-cells and CD58 located on APCs. Under various pathological conditions, which involve undesired immune response, inhibiting the CD2–CD58 interactions becomes a therapeutically relevant opportunity. Herein we present an extensive work to identify novel inhibiting agents of the CD2–CD58 interactions. Classical molecular dynamics (MD) simulations of the CD2–CD58 complex highlighted a series of crucial CD58 residues responsible for the interactions with CD2. Based on such results, a pharmacophore map, complementary to the CD2-binding site of CD58, was created and employed for virtual screening of ~ 300,000 available compounds. On the ~ 6000 compounds filtered from pharmacophore mapping, ADME screening leads to ~ 350 molecules. Molecular docking was then performed on these molecules, and fifteen compounds emerged with significant binding energy (< − 50 kcal/mol) for CD58. Finally, short MD simulations were performed in triplicate on each complex (i) to provide a microscopic view of the ligand binding and (ii) to rule out possibly weak binders of CD58 from the identified hits. At last, we suggest eight compounds for in vitro testing that were identified as promising hits to bind CD58 with a high binding affinity.

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Abbreviations

∆Gbind :

Binding energy

Å:

Angstrom

APCs:

Antigen-presenting cells

CD2:

Cluster of differentiation 2

DruLiTo:

Drug Likeness Tool

H-bonds:

Hydrogen bonds

IFD:

Induced fit docking

MD:

Molecular dynamics

MM/GBSA:

Molecular mechanics-generalized born surface area

PME:

Particle mesh Ewald

PDB:

RCSB/Protein Data Bank

QED:

Quantitative estimation of druglikeness

RMSD:

Root mean square deviation

SP:

Standard precision

uwQED:

Unweighted quantitative estimation of druglikeness

VMD:

Visual molecular dynamics

wQED:

Weighted quantitative estimation of druglikeness

XP:

eXtra precision

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Acknowledgements

NT thanks the Région Pays de la Loire and the Centre National de la Recherche Scientifique (CNRS) for the financial support during her post-doctoral research within the PIRAMID and MimBreg projects. ADL acknowledges the Région Pays de la Loire for financial support within the framework of PIRAMID and MiM-Breg project. This research used computational resources of CCIPL (Centre de Calcul Intensif des Pays de Loire). Funding was provided by the Wallonie-Bruxelles International WBI (PHC Tournesol DoIFAD) and the Belgian National Foundation for Scientific Research (FNRS), by the French Ministry of Foreign and European Affairs, and by the Ministry of Higher Education and Research, in the framework of the Hubert Curien partnerships (PHC Tournesol #40638PL).

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Authors and Affiliations

  1. Université de Nantes, CNRS, CEISAM UMR 6230, 44000, Nantes, France

    Neha Tripathi & Adèle D. Laurent

  2. Unit of Theoretical and Structural Physical Chemistry, Department of Chemistry, NAmur Research Institute for LIfe Sciences (NARILIS), NAmur MEdicine & Drug Innovation Center (NAMEDIC), Namur Institute of Structured Matter (NISM), University of Namur, Rue de Bruxelles 61, 5000, Namur, Belgium

    Laurence Leherte & Daniel P. Vercauteren

  3. Laboratory of Structural Biological Chemistry, University of Namur, Rue de Bruxelles 61, 5000, Namur, Belgium

    Laurence Leherte

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  1. Neha Tripathi
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Tripathi, N., Leherte, L., Vercauteren, D.P. et al. Structure-based identification of inhibitors disrupting the CD2–CD58 interactions. J Comput Aided Mol Des 35, 337–353 (2021). https://doi.org/10.1007/s10822-020-00369-z

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