BioExcel Webinar: High-Confidence Protein−Ligand Complex Modeling by NMR Guided Docking Enables Early Hit Optimization. (2018-05-10)

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We would like to invite you to our next BioExcel Webinar tomorrow focused on early hit optimizations using NMR guided docking. Don't miss the opportunity and register here.

Webinar Abstract
Structure-based drug design is routinely used in modern day drug discovery. However, the success of this process is very much dependent upon the ability to generate protein-ligand co-structures. While NMR and EM can be used to obtain these structures, such techniques can often be labor intensive and are limited to certain subsets of targets. X-ray crystallography is therefore routinely the method of choice for obtaining co-structures but also contains caveats in that the success rates, when working with dynamic proteins or solubility limited ligands, is often variable. The lack of structural information obtained for these scenarios makes optimization of the ligand challenging and impacts the diversity of chemical space explored, since such chemical scaffolds are generally not pursued. To address this, we have developed a robust NMR restraint guided docking protocol which uses HADDOCK to generate high-quality models of protein−ligand complexes. The use of highly methyl labeled protein facilitates the experimental determination of intermolecular distance restraints between the protein and bound small molecule, which can be used to both drive the docking process and to determine the correct conformation of the ligand in the bound state. In addition, we show that the models produced by this method are of sufficient accuracy to drive a structure-based drug campaign through the successful optimization of crystallographically intractable fragment hits into more potent binders.

Presenter: Andrew Proudfoot, Novartis

Title: High-Confidence Protein−Ligand Complex Modeling by NMR Guided Docking Enables Early Hit Optimization
Date: 10th May, 2018
Time: 14:00 BST / 15:00 CEST
Registration URL:
novartis webinar