AI3SD Video: Machine learning applications for macro-molecular X-ray crystallography at Diamond

Abstract

Proteins are the core machinery in any living organism. Understanding their structure means understanding their function and the mechanism with which they carry out this function. In many diseases, the structure of a protein is altered through amino acid exchange usually as a result of mutations in the encoding DNA. The changes in the structure in turn alter the functions and mechanisms in proteins. Being able to understand these changes on an atomic level, offers the opportunity to design drugs to manipulate, regulate and control these proteins with the aim to reduce or even eliminate the effects they cause on an organism. X-ray crystallography, besides cryo-EM and NMR, is one of the methods with which a protein’s structure can be revealed to atomic level. Synchrotron facilities such as Diamond have made great investments over the last decade to push for automation and high-throughput. On the other hand, the large data amounts produced as a result, require a new thinking of how to automate data analysis in turn. As a proof-of-principal work, a machine learning (ML) based decision maker has been implemented into the automated data analysis pipelines at Diamond. The aim is, to explore ML based applications for decision making in the data analysis process to change the current threshold-based, brute-force system to one that offers more flexibility. This in turn will reduce the number of executed jobs but does not diminish the success rate and makes more efficient use of the limited, shared compute resources.

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Identifiers

ORCID for Jeremy G. Frey: orcid.org/0000-0003-0842-4302

ORCID for Samantha Kanza: orcid.org/0000-0002-4831-9489

ORCID for Mahesan Niranjan: orcid.org/0000-0001-7021-140X

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