High-Throughput Crystallography: Reliable and Efficient Identification of Fragment Hits

AbstractCovalent probes can display unmatched potency, selectivity and duration of action, however, their discovery is challenging. In principle, fragments that can irreversibly bind their target can overcome the low affinity that limits reversible fragment screening. Such electrophilic fragments were considered non-selective and were rarely screened. We hypothesized that mild electrophiles might overcome the selectivity challenge, and constructed a library of 993 mildly electrophilic fragments. We characterized this library by a new high-throughput thiol-reactivity assay and screened them against ten cysteine-containing proteins. Highly reactive and promiscuous fragments were rare and could be easily eliminated. By contrast, we found selective hits for most targets. Combination with high-throughput crystallography allowed rapid progression to potent and selective probes for two enzymes, the deubiquitinase OTUB2, and the pyrophosphatase NUDT7. No inhibitors were previously known for either. This study highlights the potential of electrophile fragment screening as a practical and efficient tool for covalent ligand discovery.

Download Full-text

BioMAX, a multipurpose high-throughput crystallography beamline at MAX IV

Acta Crystallographica Section A Foundations of Crystallography ◽

10.1107/s0108767312097188 ◽

2012 ◽

Vol 68 (a1) ◽

pp. s147-s147

Author(s):

M. Thunnissen ◽

J. Unge ◽

A. Labrador ◽

D. T. Logan ◽

T. Ursby

Keyword(s):

High Throughput ◽

High Throughput Crystallography

Download Full-text

COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning

10.1101/2020.10.29.339317 ◽

2020 ◽

Cited By ~ 1

Author(s):

◽

Hagit Achdout ◽

Anthony Aimon ◽

Elad Bar-David ◽

Haim Barr ◽

...

Keyword(s):

Machine Learning ◽

High Throughput ◽

Open Science ◽

Viral Inhibition ◽

Enzymatic Assays ◽

Main Protease ◽

Covalent Inhibitors ◽

Ic50 Values ◽

High Throughput Experiments ◽

High Throughput Crystallography

AbstractHerein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.

Download Full-text

PolySNAP: a computer program for analysing high-throughput powder diffraction data

Journal of Applied Crystallography ◽

10.1107/s0021889804011173 ◽

2004 ◽

Vol 37 (4) ◽

pp. 658-664 ◽

Cited By ~ 32

Author(s):

Gordon Barr ◽

Wei Dong ◽

Christopher J. Gilmore

Keyword(s):

Computer Program ◽

Powder Diffraction ◽

High Throughput ◽

Three Dimensional ◽

Principal Component ◽

Visualization Tools ◽

Diffraction Patterns ◽

Pure Phases ◽

Related Compounds ◽

High Throughput Crystallography

In high-throughput crystallography experiments, it is possible to measure over 1000 powder diffraction patterns on a series of related compounds, often polymorphs or salts, in less than one week. The analysis of these patterns poses a difficult statistical problem. A computer program is presented that can analyse such data, automatically sort the patterns into related clusters or classes, characterize each cluster and identify any unusual samples containing, for example, unknown or unexpected polymorphs. Mixtures may be analysed quantitatively if a database of pure phases is available. A key component of the method is a set of visualization tools based on dendrograms and pie charts, as well as principal-component analysis and metric multidimensional scaling as a source of three-dimensional score plots. The procedures have been incorporated into the computer programPolySNAP, which is available commercially from Bruker-AXS.

Download Full-text