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

AI3SD Video: Machine learning applications for macro-molecular X-ray crystallography at Diamond
AI3SD Video: Machine learning applications for macro-molecular X-ray crystallography at Diamond
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.
AI, AI3SD Event, Artificial Intelligence, Machine Intelligence, Machine Learning, ML, Proteins
Vollmar, Melanie
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Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Vollmar, Melanie
0f2f23aa-1fb6-471f-b7c8-644c35507660
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Vollmar, Melanie (2021) AI3SD Video: Machine learning applications for macro-molecular X-ray crystallography at Diamond. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI 4 Proteins Seminar Series 2021. 14 Apr - 17 Jun 2021. (doi:10.5258/SOTON/P0091).

Record type: Conference or Workshop Item (Other)

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.

Video
AI4Proteins-Seminar-Series-MelanieVollmar-140421 (1) - Version of Record
Available under License Creative Commons Attribution.
Download (545MB)

More information

Published date: 14 April 2021
Additional Information: Melanie Vollmar is a postdoctoral researcher within the macromolecular X-ray (MX) crystallography village at Diamond Light Source, the UK’s national synchrotron. Her work focuses on using the vast amounts of user data produced at the facility to develop machine learning-based decision-making tools for application in the automated data analysis pipelines. Melanie has a background in structural biology looking at membrane proteins during her PhD at the University of Düsseldorf, Germany, and working in a high-throughput environment at the Structural Genomics Consortium (SGC) at the University of Oxford.
Venue - Dates: AI 4 Proteins Seminar Series 2021, 2021-04-14 - 2021-06-17
Keywords: AI, AI3SD Event, Artificial Intelligence, Machine Intelligence, Machine Learning, ML, Proteins

Identifiers

Local EPrints ID: 450085
URI: http://eprints.soton.ac.uk/id/eprint/450085
PURE UUID: c0618054-bef9-412b-b8b3-7a2cf2393614
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489

Catalogue record

Date deposited: 09 Jul 2021 16:33
Last modified: 10 Jul 2021 01:59

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