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AI3SD Video: Protein-Ligand Structure Prediction for GPCR Drug Design

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
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
Available under License Creative Commons Attribution.
Download (724MB)

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
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 15 Jul 2021 16:40
Last modified: 28 Jul 2021 01:54

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