Improving ligand binding free energy estimate through enhanced sampling algorithms
|Research Area||Bio Sciences|
|Principal Investigator(s)||Andrea Cavalli|
An accurate estimation of the protein-ligand binding free energy is of paramount importance in drug discovery, as it can provide the affinity constant of a small organic molecule (i.e., a drug candidate) towards its biological counterpart according to the following equation: dG = –RTlogK. Once the binding affinities (K) are computationally predicted, the molecules can be proposed for chemical synthesis and pharmacological evaluation, thus facilitating and enhancing the drug discovery process. Unfortunately, due to the logarithmic relationship between free energy and binding affinity, the acceptable error must be less than 1 kcal/mol in the dG estimation. Currently available docking algorithms routinely used at both academic and industrial levels are usually unable to provide such accuracy because i) the protein is often treated as a rigid body, ii) empirical scoring functions are implemented to estimate the interaction energy between ligands and proteins, and iii) the entropy is neglected or roughly parameterized. With the present project we propose to develop and exploit a novel computational protocol that combines enhanced sampling (ES) methods with molecular dynamics (MD) simulations. In brief, the main objective of the project is to devise an ES/MD-based procedure able to provide a reliable (error less than 1 kcal/mol) estimate of protein-ligand binding free energy. This tool will therefore be able to predict the affinity of small but complex organic molecules for purposely selected pharmacologically relevant target proteins. . Such an objective will certainly have a great impact for both the academic and industrial drug discovery community.