NPIF Application of Artificial Intelligence-Driven Design of Function-Directed Ligands for Selective Retinoic Acid Receptor Binding

维甲酸(RA)配体与维甲酸受体(RAR)类核受体结合。虽然RAR配体以最佳方式激活RAR，但人们对其形状和结构知之甚少，本项目旨在对RAR进行建模，以达到迄今尚未获得的程度，以便能够设计激活受体的配体，并了解基因组和非基因组信号中RAR的触发途径。为此，将开发新的方法，不仅适用于RARs，而且适用于许多核受体类别的受体成员。我们在了解RA在大脑中的功能方面处于世界领先地位(1)，发现RAR配体的几种作用机制对它们的作用至关重要：既有基因组活动，开启基因转录，也有快速的非基因组活动，涉及激酶激活。在这个项目中，将采用一种截然不同的方法来设计配体，对不同的RAR进行建模和了解结合选择性。这将结合分子对接、原子分子动力学模拟和机器学习技术，超越静态的2D或3D配体描述符，开发复杂的定量结构-活性关系(QSAR)模型，将动力学与形状和化学选择性结合在一起。所采用的技术将包括使用人工智能方法进行配体设计，包括使用领域特定的技术，如DeepChem和更通用的工具，如Kera和TensorFlow。在化学和生物学方面，上述工作预测的合成维甲酸将被制备并应用于RAR活性的各种分析，如转录活性、通过各种激酶的非基因组信号以及使用细胞系和原代神经细胞控制蛋白质翻译。完全了解RAR的配体结合口袋，以及不同的配体如何被设计成触发不同的分子通路，可能会为未来设计用于治疗神经退行性疾病的配体提供潜在的可能性。该项目是高度协作和跨学科的，涉及不同大学的大量和不同的研究人员联盟，以及一些工业合作伙伴，使核受体基础科学的所有分支都能得到解决。这名学生将致力于应用人工智能和建模，在伦敦大学学院与科文尼合作设计新的RAR激活剂配体，在达勒姆与怀廷合作进行药物靶标合成，在阿伯丁与麦卡弗里和格雷格合作进行生物学。在这个项目中，学生将熟悉基于人工智能的技术(如卷积神经网络、随机森林和支持向量机)来研究和操作受体蛋白，并更全面地了解RAR的配体激活和核受体功能。参考文献：1.Sheeller KD，Stoney PN，Morgan PJ，McCaffery PJ。一种对大脑有益的维生素。趋势神经科学。2012；35：733-41

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