A practical and efficient description of drug-target recognition and binding is highly desirable and possesses a great potential for computational chemistry and drug discovery.<br/>However, because of the deficiencies of statically depicting bimolecular complex formation, dynamical examinations are required. The experimental characterization of the dynamics of this kind of phenomena at the atomistic level is still in its infancy. Therefore, computational approaches, and especially molecular dynamics (MD), are expected to assume a major role, permitting a more informed compound identification and lessening the attrition rate in Drug Discovery. In this respect, plain MD could in principle correctly describe complex biological processes, such as protein-ligand binding, but present algorithms and computational resources still struggle with finding a good description in reasonable times and with the needed statistics, particularly for low-probability events.<br/>Here, I’ll describe a novel biased-MD based approach for simulating the binding of a ligand to a target protein. This technique is of general applicability but takes inspiration from the force that is maximally responsible for the binding of natural substrates, namely electrostatics. The method yielded very promising results over a number of real-world drug-target complexes starting from unbound, non-complementary protein conformations in a fully and explicitly solvated environment. These results, together with pros and cons of the approach will be discussed.<br/>In particular, comparison with the binding paths observed by us and other groups doing plain-MD simulations on three of these systems will be made.<br/>In the final part of the presentation I will give a brief prospect on future MD-based techniques aimed at studying the bimolecular recognition and association with limited a priori knowledge.

MD-binding: Enabling fully dynamic simulation of binding for real-world drug-target systems

254th National Meeting (2017)

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