AI3SD Video: Protein-Ligand Structure Prediction for GPCR Drug Design
AI3SD Video: Protein-Ligand Structure Prediction for GPCR Drug Design
From GPCR Structure Prediction to Structural GPCR-Ligand Interaction Prediction
– The conserved TM helical fold of G Protein-Coupled Receptors (GPCRs) and progress in GPCR structural biology continues to provide homology modeling templates for protein structure prediction.
– Novel structures of GPCR-ligand complexes solved at Sosei Heptares and elsewhere continue to reveal a diversity of protein-ligand binding sites and binding modes that are challenging to predict.
Appreciating the Devil’s in the Details of Structure-Based GPCR Drug Design
– Novel structural insights into the GPCRome can be complemented by pharmacological, biophysical, and computational studies and data to identify and predict structural determinants of ligand-receptor binding and selectivity.
– Orthogonal physics-based (Molecular Dynamics, e.g. Free Energy Perturbation FEP+, WaterMap from Schrödinger) and empirical (e.g. GRID and WaterFLAP from Molecular Discovery) structure-based drug design methods to target lipophilic hotspots and modulate water networks across GPCR families.
Chemogenomic View to Navigate Structural GPCR-Ligand Interaction Space
– Integrated GPCR-ligand chemogenomics views that combine structural, pharmacological, and chemical data allow the exploration of receptor-ligand interaction space for structure-based GPCR drug design.
AI, AI3SD Event, Artifical Intelligence, Machine Intelligence, Machine Learning, ML, Proteins
De Graff, Chris
4e24f89a-4b2f-47fc-861f-6c63bbb05f4e
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
16 June 2021
De Graff, Chris
4e24f89a-4b2f-47fc-861f-6c63bbb05f4e
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
De Graff, Chris
(2021)
AI3SD Video: Protein-Ligand Structure Prediction for GPCR Drug Design.
Kanza, Samantha, Frey, Jeremy G. and Niranjan, Mahesan
(eds.)
AI 4 Proteins Seminar Series 2021.
14 Apr - 17 Jun 2021.
(doi:10.5258/SOTON/P0112).
Record type:
Conference or Workshop Item
(Other)
Abstract
From GPCR Structure Prediction to Structural GPCR-Ligand Interaction Prediction
– The conserved TM helical fold of G Protein-Coupled Receptors (GPCRs) and progress in GPCR structural biology continues to provide homology modeling templates for protein structure prediction.
– Novel structures of GPCR-ligand complexes solved at Sosei Heptares and elsewhere continue to reveal a diversity of protein-ligand binding sites and binding modes that are challenging to predict.
Appreciating the Devil’s in the Details of Structure-Based GPCR Drug Design
– Novel structural insights into the GPCRome can be complemented by pharmacological, biophysical, and computational studies and data to identify and predict structural determinants of ligand-receptor binding and selectivity.
– Orthogonal physics-based (Molecular Dynamics, e.g. Free Energy Perturbation FEP+, WaterMap from Schrödinger) and empirical (e.g. GRID and WaterFLAP from Molecular Discovery) structure-based drug design methods to target lipophilic hotspots and modulate water networks across GPCR families.
Chemogenomic View to Navigate Structural GPCR-Ligand Interaction Space
– Integrated GPCR-ligand chemogenomics views that combine structural, pharmacological, and chemical data allow the exploration of receptor-ligand interaction space for structure-based GPCR drug design.
Video
AI4Proteins-Seminar-Series-ChrisDeGraaf-160621
- Version of Record
More information
Published date: 16 June 2021
Additional Information:
Dr. Chris de Graaf is Head of Computational Chemistry at Sosei Heptares, an international biopharmaceutical group focused on the design and development of new medicines originating from its proprietary GPCR-targeted StaR® technology and Structure-Based Drug Design platform capabilities (www.soseiheptares.com). In this role Chris is leading the development and application of structural cheminformatics and computer-assisted drug design approaches across the GPCRome to help Sosei Heptares advance a broad and deep pipeline of partnered and in-house drug candidates in multiple therapeutic areas including neurology, immuno-oncology, gastroenterology, inflammation and rare/specialty diseases.
Venue - Dates:
AI 4 Proteins Seminar Series 2021, 2021-04-14 - 2021-06-17
Keywords:
AI, AI3SD Event, Artifical Intelligence, Machine Intelligence, Machine Learning, ML, Proteins
Identifiers
Local EPrints ID: 450197
URI: http://eprints.soton.ac.uk/id/eprint/450197
PURE UUID: 422bcea5-e530-4c7b-9cd0-b5db2d2e3ef9
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Date deposited: 15 Jul 2021 16:40
Last modified: 17 Mar 2024 03:51
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Contributors
Author:
Chris De Graff
Editor:
Mahesan Niranjan
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