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AI3SD Video: So you predicted a protein structure – What now?

AI3SD Video: So you predicted a protein structure – What now?
AI3SD Video: So you predicted a protein structure – What now?
Recent advances in technologies like cryoEM structure resolution and protein de novo folding prediction have resulted in a wealth of macromolecular structures that have not been resolved to the level of detail a high-resolution X-ray crystal structure could provide. Taking full advantage of these structures for rational drug design would benefit from additional validation and refinement. In this presentation, we investigate if computational refinement and structure-based modeling methods can be utilized to generate reliable complex poses. We present a solution to the induced fit docking problem for protein−ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This methodology succeeded in determining protein−ligand binding modes with a root-mean-square deviation within 2.5 Å compared to experiment in over 90% of cross-docking cases in our testing. Applications of the predicted ligand-receptor structure in free energy perturbation calculations for additional validation is demonstrated.
AI, AI3SD Event, Artificial Intelligence, Machine Intelligence, Machine Learning, ML, Proteins
Steinbrecher, Thomas
fe6dc051-9449-40c0-9824-dee779923d18
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Steinbrecher, Thomas
fe6dc051-9449-40c0-9824-dee779923d18
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Steinbrecher, Thomas (2021) AI3SD Video: So you predicted a protein structure – What now? Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI 4 Proteins Seminar Series 2021. 14 Apr - 17 Jun 2021. (doi:10.5258/SOTON/P0108).

Record type: Conference or Workshop Item (Other)

Abstract

Recent advances in technologies like cryoEM structure resolution and protein de novo folding prediction have resulted in a wealth of macromolecular structures that have not been resolved to the level of detail a high-resolution X-ray crystal structure could provide. Taking full advantage of these structures for rational drug design would benefit from additional validation and refinement. In this presentation, we investigate if computational refinement and structure-based modeling methods can be utilized to generate reliable complex poses. We present a solution to the induced fit docking problem for protein−ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This methodology succeeded in determining protein−ligand binding modes with a root-mean-square deviation within 2.5 Å compared to experiment in over 90% of cross-docking cases in our testing. Applications of the predicted ligand-receptor structure in free energy perturbation calculations for additional validation is demonstrated.

Video
AI4Proteins-Seminar-Series-ThomasSteinbrecher-160621 - Version of Record
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More information

Published date: 16 June 2021
Additional Information: Thomas Steinbrecher studied Chemistry at the University of Freiburg in Germany and earned a diploma with distinction in Physical Chemistry. He completed a Ph.D. thesis on “Computer Simulations of Protein-Ligand Interactions” in 2005. He joined the developer team of the Amber MD package as a Postdoc at the Scripps Research Institute in San Diego and Rutgers University in New Jersey. The work focus was on efficient free energy calculation methods and QM/MM simulations of charge transfer. After returning to Germany in 2008, Thomas established a junior research group at the Karlsruhe Institute of Technology, working on fast electron transfer phenomena in DNA and proteins. He joined Schrodinger in 2013 where he was responsible for the large scale application of free energy calculation methods in pharmaceutical drug design. Since 2017, he heads the Applications Science Department for Europe and supports customers in employing Schrödinger’s Drug Discovery Technology Platform for their research.
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: 450208
URI: http://eprints.soton.ac.uk/id/eprint/450208
PURE UUID: 058f7e81-6a2e-40a7-a85a-34bfdde18f85
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: 15 Jul 2021 16:43
Last modified: 28 Jul 2021 01:54

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