Expanding Automated Multiconformer Ligand Modeling to Macrocycles and Fragments

ABSTRACT Small molecule ligands exhibit a diverse range of conformations in solution. Upon binding to a target protein, this conformational diversity is reduced. However, ligands can retain some degree of conformational flexibility even when bound to a receptor. In the Protein Data Bank (PDB), a small number of ligands have been modeled with distinct alternative conformations that are supported by macromolecular X-ray crystallography density maps. However, the vast majority of structural models are fit to a single ligand conformation, potentially ignoring the underlying conformational heterogeneity present in the sample. We previously developed qFit-ligand to sample diverse ligand conformations and to select a parsimonious ensemble consistent with the density. While this approach indicated that many ligands populate alternative conformations, limitations in our sampling procedures often resulted in non-physical conformations and could not model complex ligands like macrocycles. Here, we introduce several improvements to qFit-ligand, including integrating RDKit for stochastic conformational sampling. This new sampling method greatly enriches low energy conformations of small molecules and macrocycles. We further extended qFit-ligand to identify alternative conformations in PanDDA-modified density maps from high throughput X-ray fragment screening experiments, as well as single-particle cryo-electron microscopy (cryo-EM) density maps. The new version of qFit-ligand improves fit to electron density and reduces torsional strain relative to deposited single conformer models and our prior version of qFit-ligand. These advances enhance the analysis of residual conformational heterogeneity present in ligand-bound structures, which can provide important insights for the rational design of therapeutic agents. Competing Interest Statement J.S.F. is a consultant to, shareholder of, and receives sponsored research support from Relay Therapeutics and a consultant to Vilya Therapeutics. H.v.d.B. is an employee of Atomwise Inc, but the work in this publication does not overlap with his role there. A.R.R. is a co-founder of TheRas, Elgia Therapeutics, and Tatara Therapeutics, and receives sponsored research support from Merck, Sharp and Dohme. Footnotes This version corresponds to the response from reviewers. It includes additions for cryoEM, a wide look at strain across the PDB, and algorithmic updates for the speed of the algorithm.