qFit-ligand reveals widespread conformational heterogeneity of drug-like molecules in X-ray electron density maps

Abstract

Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein-ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor-ligand complexes. We found that up to 29% of a dataset of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand-receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with highthroughput screening or multi-temperature crystallography could therefore augment the structurebased drug design toolbox.

An investigation of structural stability in protein-ligand complexes reveals the balance between order and disorder

“…The proportion could be larger amongst ligands that fail to crystallize. The level of residual mobility is also larger and more common than the static X-ray structures lead to think, as also concluded by a recent independent study 4 . In fact, most complexes balance order and disorder by combining a firm anchor with more relaxed peripheral interactions.…”

“…While the importance of target flexibility is well-appreciated in drug discovery 3 , the flexibility of small-molecule ligands in their bound state has attracted much less attention. Detailed analyses reveal that ligands often retain residual mobility [4][5][6] . However, changes in binding mode are more the exception than the norm 7,8 and ligand design based on rigid crystallographic geometries has been remarkably successful 9 .…”

Macromolecular room temperature crystallography

“…4 d ). Since its initial implementation, qFit has expanded from side-chain rotamers (van den Bedem et al ., 2009) to backbone heterogeneity (Keedy et al ., 2015 a ) and to ligands (van Zundert et al ., 2018). The new release of qFit 3 expands the work into single-particle cryoEM density maps (Riley et al ., 2020).…”

Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning

“…Third, several systems have crystallographic electron density maps with evidence for alternative, isoenergetic ligand poses in the binding site. Systems with potential alternative ligand poses that match the electron densities can be identified using the method qfit-ligand 35 . The fit of those alternative binding poses to the experimental electron density can be quantified using the realspace R-factor (RSR) by comparing the experimental electron density to the expected density for the alternative predicted ligand pose 34 .…”