Application of artificial intelligence-driven design of function-directed ligands for selective retinoic acid receptor binding

项目描述（最多）500字，最多3篇参考文献。请提供博士项目的描述，包括项目信息和将提供的研究培训；请注意，本文将用于向学生宣传该项目。)视黄酸（RA）配体与视黄酸受体（RAR）类核受体结合。虽然对RAR配体的形状和结构的最佳激活RARs知之甚少，但本项目旨在对RARs进行建模，以达到迄今为止尚未获得的程度，从而能够设计激活受体的配体，并了解RARs在基因组和非基因组信号传导中的触发途径。为了达到这些目的，将开发新的方法，这些方法不仅适用于RARs，而且适用于核受体类的许多成员。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其作用至关重要：基因组活性（开启基因转录）和快速非基因组作用（涉及激酶激活）。在这个项目中，将采用一种完全不同的方法来设计配体，建模和理解对不同RARs的结合选择性。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，开发复杂的定量构效关系（QSAR）模型，将动力学、形状和化学选择性结合起来。所采用的技术将包括人工智能方法来设计配体，包括使用特定领域的技术，如DeepChem和更通用的工具，如Keras和TensorFlow。从化学和生物学方面来看，上述工作预测的合成类维生素a将被制备并应用于各种RAR活性测定，如转录活性、通过各种激酶进行的非基因组信号传导以及利用细胞系和原代神经细胞控制蛋白质翻译。全面了解RAR的配体结合袋以及如何设计不同的配体来触发不同的分子途径，可能为设计用于治疗神经退行性疾病的配体提供未来的潜力。该项目是高度协作和跨学科的，涉及来自不同大学的大型和多样化的研究人员联盟，以及许多工业合作伙伴，允许核受体基础科学的所有分支得到解决。该学生将利用人工智能和建模，与伦敦大学学院的科文尼合作设计新的RAR激活剂配体，与达勒姆大学的怀廷合作设计药物靶标合成，与阿伯丁大学的麦卡菲里和格雷格合作设计生物学。所设计药物的形状和性质将与其生物学功能相关联。通过本项目，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），研究和操纵受体蛋白，更全面地了解RAR配体激活和核受体功能。引用:1。Shearer KD, Stoney PN, Morgan PJ, McCaffery PJ。一种大脑维他命。《神经科学趋势》2012；35:733-41

Project Description (max. 500 words and up to 3 references. Please provide a description of the PhD project which includes information about the project and the research training it will provide; please note this text will be used to advertise the project to students.) 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