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AI3SD Video: Open Access Data: A Cornerstone for Artificial Intelligence Approaches to Protein Structure Prediction

AI3SD Video: Open Access Data: A Cornerstone for Artificial Intelligence Approaches to Protein Structure Prediction
AI3SD Video: Open Access Data: A Cornerstone for Artificial Intelligence Approaches to Protein Structure Prediction
The Protein Data Bank (PDB) was established in 1971 to archive three-dimensional (3D) structures of biological macromolecules as a public good. Fifty years later, the PDB is providing millions of data consumers around the world with open access to more than 175,000 experimentally determined structures of proteins and nucleic acids (DNA, RNA) and their complexes with one another and small-molecule ligands. PDB data users are working, teaching, and learning in fundamental biology, biomedicine, bioengineering, biotechnology, and energy sciences. They also represent the fields of agriculture, chemistry, physics and materials science, mathematics, statistics, computer science, and zoology, and even the social sciences. The enormous wealth of 3D structure data stored in the PDB has underpinned significant advances in our understanding of protein architecture, culminating in recent breakthroughs in protein structure prediction accelerated by artificial intelligence approaches and machine learning methods.
AI, AI3SD Event, Artificial Intelligence, Machine Intelligence, Machine Learning, ML, Proteins
Burley, Stephen
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Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
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Burley, Stephen
a8c7fa39-0f39-476f-ae0b-839982d19413
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Burley, Stephen (2021) AI3SD Video: Open Access Data: A Cornerstone for Artificial Intelligence Approaches to Protein Structure Prediction. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI 4 Proteins Seminar Series 2021. 14 Apr - 17 Jun 2021. (doi:10.5258/SOTON/P0107).

Record type: Conference or Workshop Item (Other)

Abstract

The Protein Data Bank (PDB) was established in 1971 to archive three-dimensional (3D) structures of biological macromolecules as a public good. Fifty years later, the PDB is providing millions of data consumers around the world with open access to more than 175,000 experimentally determined structures of proteins and nucleic acids (DNA, RNA) and their complexes with one another and small-molecule ligands. PDB data users are working, teaching, and learning in fundamental biology, biomedicine, bioengineering, biotechnology, and energy sciences. They also represent the fields of agriculture, chemistry, physics and materials science, mathematics, statistics, computer science, and zoology, and even the social sciences. The enormous wealth of 3D structure data stored in the PDB has underpinned significant advances in our understanding of protein architecture, culminating in recent breakthroughs in protein structure prediction accelerated by artificial intelligence approaches and machine learning methods.

Video
AI4Proteins-Seminar-Series-StephenBurley-170621 - Version of Record
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Published date: 17 June 2021
Additional Information: Stephen Burley is an expert in structural biology, proteomics, data science, structure/fragment-based drug discovery, and clinical medicine/oncology. Burley currently serves as University Professor and Henry Rutgers Chair, Founding Director of the Institute for Quantitative Biomedicine, and Director of the RCSB Protein Data Bank at Rutgers, The State University of New Jersey. He is also a Member of the Rutgers Cancer Institute of New Jersey, where he Co-Leads the Cancer Pharmacology Research Program. From 2008 to 2012, Burley was a Distinguished Lilly Research Scholar in Eli Lilly and Co. Prior to joining Lilly, Burley served as the Chief Scientific Officer and Senior Vice President of SGX Pharmaceuticals, Inc., a publicly traded biotechnology company that was acquired by Lilly in 2008. Until 2002, Burley was the Richard M. and Isabel P. Furlaud Professor at The Rockefeller University and an Investigator in the Howard Hughes Medical Institute. He has authored/coauthored more than 300 scholarly scientific articles in top journals including Science, Science Advances, Cell, Molecular Cell, Structure, Nature, Nature Biotechnology, Nature Chemical Biology, Nature Genetics, Nature Methods, Nature Oncogene, Nature Scientific Data, Nature Structural and Molecular Biology, Nucleic Acids Research, Proceedings of the National Academy of Sciences, Journal of the American Chemical Society, Journal of Molecular Biology, PLOS Computational Biology, and Biochemistry and Molecular Biology Education. Following undergraduate training in physics and applied mathematics, Burley received an M.D. degree from Harvard Medical School in the joint Harvard-MIT Health Sciences and Technology Program and, as a Rhodes Scholar, received a D.Phil. in Structural Biology from Oxford University. He trained in internal medicine at the Brigham and Women’s Hospital in Boston and did postdoctoral work with Gregory A. Petsko at the Massachusetts Institute of Technology and Nobel Laureate William N. Lipscomb, Jr. at Harvard University. With William J. Rutter and others at the University of California San Francisco and Rockefeller, Burley co-founded Prospect Genomics, Inc., which was acquired by SGX in 2001. He is a Fellow of the Royal Society of Canada, the New York Academy of Sciences, and the American Crystallographic Association, and recipient of a Doctor of Science (Honoris causa) from his alma mater the University of Western Ontario, from which he received a B.Sc. in Physics in 1980.
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: 450176
URI: http://eprints.soton.ac.uk/id/eprint/450176
PURE UUID: 6d5dae47-99c0-42e0-bb2e-072db6a43430
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: 14 Jul 2021 16:50
Last modified: 28 Jul 2021 01:54

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