Application of Artificial Intelligence-Driven Design of Function-Directed Ligands for Selective Retinoic Acid Receptor Binding
Lead Research Organisation:
University of Aberdeen
Department Name: Sch of Medicine, Medical Sci & Nutrition
Abstract
Retinoic acid (RA) ligands bind to the retinoic acid receptor (RAR) class of nuclear receptor. The shape and structure of RAR ligands though that optimally activate RARs is poorly understood and this project aims to model RARs to a degree not so far obtained to be able to design ligands that activate the receptors and understand the triggering routes for RARs for both genomic and non-genomic signalling. New approaches will be developed to these ends that will be applicable not just to RARs but to many members of the nuclear receptor class of receptors.
We are world-leaders in understanding the function of RA in the brain (1), making the discovery that several mechanisms by which RAR ligands act are crucial for their action: both genomic activity, to turn on gene transcription, and rapid non-genomic action, involving kinase activation.
In this project a radically different approach will be taken to ligand design, to modelling and to understanding binding selectivities to the different RARs. This will use a combination of molecular docking, atomistic molecular dynamics simulations and machine learning techniques, to move beyond static 2D or 3D ligand descriptors and develop complex Quantitative Structure-Activity Relationship (QSAR) models which incorporate dynamics alongside shape and chemical selectivity.
The techniques employed will include an AI approach to ligand design including the use of domain-specific technologies such as DeepChem and more generic tools such as Keras and TensorFlow. From the chemical and biological side, synthetic retinoids predicted from the above work will be prepared and applied in a variety of assays for RAR activity such as transcriptional activity, non-genomic signalling via a variety of kinases and control of protein translation using cell lines and primary neural cells. A complete understanding of the ligand binding pocket of RAR and how different ligands may be designed to trigger different molecular pathways may have future potential for design of ligands for the treatment of neurodegenerative diseases.
The project is highly collaborative and interdisciplinary, involving a large and diverse consortium of researchers at different Universities, and a number of industrial partners, allowing all branches of the fundamental science of nuclear receptors to be addressed. The student will work on employing AI and modelling, designing new RAR activator ligands in collaboration with Coveney at UCL, drug target synthesis with Whiting at Durham, and biology with McCaffery and Greig at Aberdeen. The shape and properties of the designed drugs will be correlated with their biological function.
From this project, the student will become familiar with AI based techniques (such as convolutional neural networks, random forest and support vector machines) to study and manipulate receptor proteins and understand more completely ligand activation of RAR and nuclear receptor function.
References:
1. Shearer KD, Stoney PN, Morgan PJ, McCaffery PJ. A vitamin for the brain. Trends Neurosci. 2012;35:733-41
We are world-leaders in understanding the function of RA in the brain (1), making the discovery that several mechanisms by which RAR ligands act are crucial for their action: both genomic activity, to turn on gene transcription, and rapid non-genomic action, involving kinase activation.
In this project a radically different approach will be taken to ligand design, to modelling and to understanding binding selectivities to the different RARs. This will use a combination of molecular docking, atomistic molecular dynamics simulations and machine learning techniques, to move beyond static 2D or 3D ligand descriptors and develop complex Quantitative Structure-Activity Relationship (QSAR) models which incorporate dynamics alongside shape and chemical selectivity.
The techniques employed will include an AI approach to ligand design including the use of domain-specific technologies such as DeepChem and more generic tools such as Keras and TensorFlow. From the chemical and biological side, synthetic retinoids predicted from the above work will be prepared and applied in a variety of assays for RAR activity such as transcriptional activity, non-genomic signalling via a variety of kinases and control of protein translation using cell lines and primary neural cells. A complete understanding of the ligand binding pocket of RAR and how different ligands may be designed to trigger different molecular pathways may have future potential for design of ligands for the treatment of neurodegenerative diseases.
The project is highly collaborative and interdisciplinary, involving a large and diverse consortium of researchers at different Universities, and a number of industrial partners, allowing all branches of the fundamental science of nuclear receptors to be addressed. The student will work on employing AI and modelling, designing new RAR activator ligands in collaboration with Coveney at UCL, drug target synthesis with Whiting at Durham, and biology with McCaffery and Greig at Aberdeen. The shape and properties of the designed drugs will be correlated with their biological function.
From this project, the student will become familiar with AI based techniques (such as convolutional neural networks, random forest and support vector machines) to study and manipulate receptor proteins and understand more completely ligand activation of RAR and nuclear receptor function.
References:
1. Shearer KD, Stoney PN, Morgan PJ, McCaffery PJ. A vitamin for the brain. Trends Neurosci. 2012;35:733-41
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/M010996/1 | 01/10/2015 | 31/03/2024 | |||
| 2104260 | Studentship | BB/M010996/1 | 01/10/2018 | 30/09/2022 |