Ligand-protein docking by accurate atomistic simulations

Enzymes are protein molecules which catalyze important chemical reactions in our body. To perform their action, enzymes bind to other molecules called ligands or substrates. Sometimes, enzymes must be blocked to prevent them to cause diseases, like in the case of HIV-1 protease which is involved in AIDS. A small ligand (drug) is therefore designed which binds to the enzyme blocking it. Often the mechanism by which the drug binds to the enzyme is not known.

HIV virus
Binding mechanism of HIV-1 protease

We investigated the mechanism by which a small peptide substrate binds to HIV-1 protease. To this aim, we performed long molecular dynamics simulations (1.6 microsec) using an accurate explicit solvent force field, and accelerating the exploration of 7 reaction coordinates by bias-exchange metadynamics. We observed several times the ligand approaching the enzyme and binding to it. The structure of the complex is in excellent agreement with the crystallographic evidences. From the simulation we constructed a kinetic model describing the stability of the intermediate states and the rates of interconversion among them. The computed binding free energy and association/dissociation rates are in agreement with available experiments.
It turns out that opening of the protease flaps is not required for the binding process, and that expulsion of the water molecules from the enzyme cavity is a key kinetic step. We hope that the insight we obtained on the binding pathway will help the rational design of more effective drugs.

Our present research effort is devoted to make this type of binding calculations easy to use and accessible to all users of molecular dynamics programs.

Bibliography

  • F Pietrucci, F Marinelli, P Carloni, A Laio,
    Substrate Binding Mechanism of HIV-1 Protease from Explicit-Solvent Atomistic Simulations
    JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 131, 11811 (2009)

  • F Marinelli, F Pietrucci, A Laio, S Piana,
    A Kinetic Model of Trp-Cage Folding from Multiple Biased Molecular Dynamics Simulations
    PLOS COMPUTATIONAL BIOLOGY, 5, e1000452 (2009)


    This page has been written by Fabio Pietrucci. Please also refer to his home page for further details.