Experiences From Developing Software for Large X-Ray Crystallography-Driven Protein-Ligand Studies

Abstract

The throughput of macromolecular X-ray crystallography experiments has surged over the last decade. This remarkable gain in efficiency has been facilitated by increases in the availability of high-intensity X-ray beams, (ultra)fast detectors and high degrees of automation. These developments have in turn spurred the development of several dedicated centers for crystal-based fragment screening which enable the preparation and collection of hundreds of single-crystal diffraction datasets per day. Crystal structures of target proteins in complex with small-molecule ligands are of immense importance for structure-based drug design (SBDD) and their rapid turnover is a prerequisite for accelerated development cycles. While the experimental part of the process is well defined and has by now been established at several synchrotron sites, it is noticeable that software and algorithmic aspects have received far less attention, as well as the implications of new methodologies on established paradigms for structure determination, analysis, and visualization. We will review three key areas of development of large-scale protein-ligand studies. First, we will look into new software developments for batch data processing, followed by a discussion of the methodological changes in the analysis, modeling, refinement and deposition of structures for SBDD, and the changes in mindset that these new methods require, both on the side of depositors and users of macromolecular models. Finally, we will highlight key new developments for the presentation and analysis of the collections of structures that these experiments produce, and provide an outlook for future developments.

Fragmenstein: predicting protein-ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology

“…The hits from the XChem targets SARS-COV-2 MPro (cysteine protease) [18] and Mac1 domain of SARS-COV-2 NSP3 (macrodomain ADP-ribosylhydrolase) [19], were downloaded from Fragalysis (https://fragalysis.diamond.ac.uk/) [20] and filtered for inclusion in the DSi-Poised library [21]. The templates used were PDB:6LU7 for MPro and PDB:6WOJ for Mac1, these were energy minimised with PyRosetta with the FastRelax mover constrained by its density-map [16].…”

Crystal polymorphism in fragment-based lead discovery of ligands of the catalytic domain of UGGT, the glycoprotein folding quality control checkpoint

“…For example, the XCE FBLD X-ray data processing pipeline used at Diamond Light Source ( Krojer et al, 2017 ; Collins et al, 2018 ; Douangamath et al, 2021 ; Pearce et al, 2022 ) decides on the crystal polymorph using a comparison of point group symmetry and cell (ignoring the information encoded by the known polymorph atomic models), or it leaves polymorph attribution to manual intervention on the part of the user. The FBLD efforts at the BESSY and MAXIV synchrotrons use FragMAXapp for data processing ( Lima et al, 2021 ); at EMBL Grenoble, the CRIMS suite is a large-scale, automated fragment screening pipeline enabling evaluation of libraries of over 1,000 fragments ( Cornaciu et al, 2021 ); the IspyB system used at some synchrotrons allows for data integration and storage in alternative lattices in parallel, specifically to address the possibility of multiple polymorphs ( Monaco et al, 2013 ); the FBLD efforts at the Swiss Light Source (SLS) rely on the FFCS processing pipeline ( Kaminski et al, 2022) .…”