Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining (Rayna Carter)

使用进化特征、机器学习和知识图挖掘注释暗离子通道函数 (Rayna Carter)

• 批准号: 10809931
• 负责人: Natarajan Kannan
• 金额: $ 0.91万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-07 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10809931
• 关键词: Acceleration; Address; Amino Acid Sequence; Artificial Intelligence; Automated Abstracting; Blindness; Calcium; Cardiovascular Diseases; Cell physiology; Classification; Communities; Cryoelectron Microscopy; Darkness; Data; Disease; Disparate; Electrophysiology (science); Epilepsy; Family; Family member; Genome; Goals; Graph; Homeostasis; Informatics; Ion Channel; Kidney Failure; Language; Link; Machine Learning; Malignant Neoplasms; Maps; Methods; Mining; Mission; Molecular; Mutation; Names; Organism; Orthologous Gene; Pathway interactions; Physiological; Proteins; Proteome; Readability; Resources; Semantics; Source; Structural Models; Structure; Structure-Activity Relationship; Testing; Training; Visualization; cell type; deep learning model; drug discovery; experimental study; genome resource; human disease; knowledge graph; knowledge integration; model organism; nervous system disorder; tool

Project Summary (unchanged) The overall goal of this proposal is to annotate understudied dark ion channels using a combination of computational and experimental approaches. Our working hypothesis is that the wealth of evolutionary data encoded in ion-channel sequences from diverse organisms and integrative mining of evolutionary data with structure, function, pathway and expression data will provide important context for predicting and annotating dark channel functions at the molecular and cellular level. As a preliminary test of our hypothesis, we have generated a functional classification of ion channel sequences using a protein language based deep learning model trained on 250 million protein sequences and have delineated the distinguishing sequence and structural features of understudied Calcium Homeostasis Modulator (CALHM) family. We have also built an integrated Knowledge Graph (KG) linking diverse forms of ion channel information in machine readable format and deployed the KG for predicting physiological functions using a graph embedding approach that efficiently captures contextual information encoded in large graphs. We propose to build on these successful studies to accomplish the following two aims. Aim1 will develop new tools and resources for visualizing, mining and annotating dark channels using evolutionary features and structural models made available through cryo-EM studies and artificial intelligence based structure prediction methods. The unique modes of CALHM family gating and oligomerization mechanisms predicted through evolutionary studies will be experimentally validated through mutational studies and electrophysiology experiments. Aim2 will further develop the ion channel KG by semantically linking multiple disparate sources of data including cell-type specific expression, orthologs from model organisms and electrophysiology parameters. Knowledge graph embedding approaches will be employed to predict links between understudied channels, disease associations and physiological functions and the predictions will be made available as text summaries in the IDG resource Pharos. The proposed studies are expected to address the unique informatics needs of the ion channel community by providing new tools and resources for mapping sequence-structure- function relationships. The proposed studies will also provide new testable hypotheses on understudied channels and significantly enhance the value of Pharos in illuminating the functions of the understudied druggable proteome.

项目摘要（不变） 该建议的总体目标是使用A注释研究的黑暗离子通道 计算方法和实验方法的组合。我们的工作假设是 从多种多样的离子通道序列中编码的大量进化数据 具有结构，功能，途径的进化数据的生物和综合挖掘 表达数据将为预测和注释黑暗提供重要背景 通道在分子和细胞水平上的功能。作为我们的初步考验 假设，我们已经使用 基于蛋白质语言的深度学习模型，接受了2.5亿个蛋白质序列的培训 并描述了研究不足的区别序列和结构特征 钙稳态调节剂（CALHM）家族。我们还建立了一个集成的 知识图（kg）链接机器中不同形式的离子渠道信息 可读格式并部署了kg，用于使用图表预测生理功能 嵌入方法有效地捕获大型编码的上下文信息 图。我们建议以这些成功的研究为基础，以完成以下两个 目标。 AIM1将开发新的工具和资源来可视化，采矿和注释 使用进化特征和结构模型可用的黑暗通道 基于冷冻EM研究和基于人工智能的结构预测方法。独特 通过 进化研究将通过突变研究和 电生理实验。 AIM2将通过 语义上链接多个不同数据源，包括细胞类型的特定数据 表达，模型生物和电生理学参数的直系同源物。 知识图嵌入方法将用于预测 研究的渠道，疾病关联和生理功能以及 预测将作为IDG资源Pharos中的文本摘要提供。这 预计拟议的研究将满足离子渠道的独特信息学需求 通过提供新工具和资源来映射序列结构 - 功能关系。拟议的研究还将提供新的可检验的假设 在研究的渠道上，并显着提高了Pharos在照明中的价值 细小的可药蛋白质组的功能。