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Computer-aided drug design of cyclophilins ligands by massively parallel free energy calculations

Project DGDRUG
Research Area Bio Sciences
Principal Investigator(s) Dr Julien Michel
Institution(s)
  • FAU Erlangen-N├╝rnberg, Computer Chemistry Center, Erlangen, Germany
  • University of Edinburgh, (Institute of Structural and Molecular Biology, Edinburgh, UK
  • University of Bristol, School of Chemistry, Bristol, UK

Abstract

A crucial step in preclinical pharmaceutical research is the design of small molecules that bind with strong affinity and high specificity to a target protein implicated in a disease. In this context, inexpensive computer models are routinely used to screen databases of thousands of chemicals and identify putative small-molecule ligands. However, the methodologies used to estimate the binding affinity of a trial molecule are currently very limited in accuracy, and lack the reliability necessary to predict binding selectivity.

Atomically detailed, thermodynamic simulations of small molecules binding to a protein have the potential to deliver high accuracy predictions of binding affinity and selectivity. We have recently developed free energy calculation methodologies to reliably compute binding affinities for datasets of diverse drug-like molecules. The methods are promising as they show superior accuracy over standard techniques. We are now in the position to assess the true predictive power of our methodologies. However, because they are exceptionally computationally intensive, state-of-the art case studies have been limited to small datasets.

We will use the computing resources offered via DEISA to scale-up our software and develop workflows so we can robustly predict binding affinities for large datasets of small molecules across multiple protein targets. This will allow us to not only validate the power of our methodology to predict binding affinities, but also to demonstrate the prediction of binding selectivity. Our efforts will focus on a family of proteins called cyclophilins, which are the subject of structure-based drug design efforts in our experimental collaborator`s lab. Our simulations will be run in parallel, and will be tightly coupled with experiment. Use of DEISA resources may therefore contribute to the discovery of novel ligands targeting these proteins, potentially useful for novel drug therapies.

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