Abstract
Fragment-based lead discovery (FBLD) is one of the most efficient methods to develop new drugs. We present here a new computational protocol called High-Throughput Supervised Molecular Dynamics (HT-SuMD), which makes it possible to automatically screen up to thousands of fragments, representing therefore a new valuable resource to prioritise fragments in FBLD campaigns. The protocol was applied to Bcl-XL, an oncological protein target involved in the regulation of apoptosis through protein–protein interactions. Initially, HT-SuMD performances were validated against a robust NMR-based screening, using the same set of 100 fragments. These independent results showed a remarkable agreement between the two methods. Then, a virtual screening on a larger library of additional 300 fragments was carried out and the best hits were validated by NMR. Remarkably, all the in silico selected fragments were confirmed as Bcl-XL binders. This represents, to date, the largest computational fragments screening entirely based on MD.
Highlights
Since its introduction over 20 years ago, the technique of Fragment-based lead discovery (FBLD) turned out to be one of the most effective methods in the development of new drugs
Confident that usually the hit rate in fragment screening for protein-protein interaction usually spans around 2-3%20, we selected 100 representative fragments from our in-house library (Library details are available in SI, Dataset-1) including a sizable number of bicyclic aromatic fragments, a scaffold that previously showed an affinity for Bcl-XL target in NMR-screenings.[14,21]
The implementation of the platform for the High-Throughput Supervised Molecular Dynamics (HT-supervised molecular dynamics (SuMD)) protocol, as reported in Figure 1, entails three main phases: (I) systems preparation and equilibration, (II) SuMD trajectories collection and (III) analysis of the sampled molecular dynamics simulations (MD) data.HT-SuMD managed the preparation of 100 simulation boxes, each containing a single fragment separated from the protein binding cleft by about 50 Å
Summary
Since its introduction over 20 years ago, the technique of Fragment-based lead discovery (FBLD) turned out to be one of the most effective methods in the development of new drugs. Several examples of docking applications to FBLD are described in the literature, its routine use still remains challenging: the majority of scoring functions are trained on mature compounds, which often renders them inadequate to distinguish true binding fragments from false positives[5,6,7] Computational protocols such as Multiple-Copy Simultaneous Search (MCSS), providing an interactive map of a protein binding site through the iterative reorientation of small functional groups, have gained an increasing greater importance in the field.[8] The integration of molecular dynamics simulations (MD) with FBLD is more appealing since it would allow a better investigation of molecular recognition, taking into account the flexibility of the protein target along time. First attempts in investigating the recognition of fragments with a weak affinity (Kd in the milli- to micromolar range) showed that SuMD was able to reproduce the final bound-state reported in experimentally solved structures with root mean square deviations (RMSD) even below 1 Å12 Most interestingly, this technique allows the ligand to dynamically explore the ligand-binding site in an extensive way. It is worth remembering that this target is not suitable for X-ray soaking fragment screening, a fact that further limits the FBLD approach.[18,19]
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