Fragment-Based Lead Discovery (FBLD) is a mainstream strategy for the generation of new drugs. In FBLD highly sensitive biochemical and biophysical screening technologies are used to detect the low-affinity binding of low-molecular-weight compounds (the so-called fragments) to...
Fragment-Based Lead Discovery (FBLD) is a mainstream strategy for the generation of new drugs. In FBLD highly sensitive biochemical and biophysical screening technologies are used to detect the low-affinity binding of low-molecular-weight compounds (the so-called fragments) to biological targets that are involved in pathophysiological processes. Once a fragment hit is identified, the knowledge of the molecular interactions between the fragment and the target protein allows the rational generation of high-quality leads for drug development. Thus, high-resolution (e.g. X-ray crystallography) structure determination technologies are key to this approach but they can become a limiting factor for both technical and economical reasons. As a matter of fact, when the experimental characterisation of binding mode fails, the success rate of FBLD approaches drastically drops. To overcome current limitations in FLBD, here we have developed, and tested on more than hundreds of systems, a computational framework based on advanced-sampling molecular dynamics simulations to map the binding of fragments to protein surfaces on the high-throughput scale. The retrospective application of the developed tools to large datasets of fragment-protein complexes, including GPCR and kinases, find excellent agreement with experimental X-ray-based data and is now being used in a prospective manner within several collaborations established.
Fragments (small molecules with low molecular weight, <250 Da) usually interact with the target with one or two cornerstone Hydrogen bonds. The computational representation of H-bonds is therefore critical to accurately recover the thermodynamics of the binding process. Thus, in a first study (Colizzi et al. Angew. Chem. Int. Ed. Engl. 2019) we probed the capability to predict the strength of H-bonds in a strictly intramolecular context. Shortly, we found a striking quantitative agreement between the computed and the experimental values—thus corroborating the use of our approach for accurately modelling H-bond breaking and formation in fragment binding. We then focused on devising, testing and benchmarking a combination of enhanced sampling methods to accelerate the exploration of the fragment-protein configurational space. The developed methodologies are currently under examination to evaluate to which extent they can be intellectually protected and/or exploited for commercial purposes. Details on the methods devised will be provided elsewhere. Below we briefly mention the results obtained from their application. In particular, the developed approaches allow to: a) systematically identify fragments binding to protein surfaces; b) reconstruct the mechanism of binding with atomistic spatiotemporal resolution; c) characterize the molecular determinants of affinity and kinetics in fragment-protein complexes; d) provide the scientific community with the Fragmentome, a freely accessible on-line atlas of binding modes & trajectories of fragments at the surface of pharmaceutically relevant as well as unexplored proteins. The overall results of this project have been presented at local and international conferences and are being published in relevant journals. The dissemination strategy also included the organization of 2-day outreach event addressing the general public.
To date, neither experimental nor computational approaches can provide a spatiotemporal characterization of the binding of fragments to protein surfaces at affordable costs while maintaining the high throughput needed for screening campaigns. Thus, the developed approaches allow a novel state-of-the-art for modern drug discovery to be established. As a consequence, the European tradition of innovation & excellence in FBLD is enhanced toward the development of a knowledge-based European economy.
More info: http://mmb.irbbarcelona.org/webdev2/FragmentOme/.