Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer therapies. In many cases resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is the understanding of the molecular determinants of drug binding. The key physical parameters determining drug effectiveness are the binding free energy and the residence time of a ligand. Using molecular simulation we can gain insights into both of these quantities, which can inform both stratified or personal treatment regimes and drug development.
This a collaborative project, that studies a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery, using automated workflows underpinned by ensemble-based high performance computing methods running at unprecedented scales. In order to do this, we developed the High-Throughput Binding Affinity Calculator (HTBAC) which is the integration of the BAC – Binding Affinity Calculator – developed by the Centre for Computational Science at UCL, and RADICAL-Cybertools (from the RADICAL Laboratory at Rutgers University). HTBAC enables rapid, accurate, scalable and reliable free energy-based binding affinity calculations. HTBAC provides the ability to separate the “setting-up” (i.e., defining) of a free energy protocols from its “execution”. HTBAC aim to provide users with the ability to scale protocols, and thereby drug candidates, while also also enabling adaptive execution. Adaptivity enables users to change the course of execution during runtime, such as favoring interesting simulations and drug candidates while discarding those that are uninteresting. Co-PIs on the project are Peter Coveney (UCL) and Shantenu Jha (Rutgers).