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

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

• 批准号: 10661550
• 负责人: Natarajan Kannan
• 金额: $ 47.7万
• 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/10661550
• 关键词: Acceleration; Address; Amino Acid Sequence; Artificial Intelligence; Automated Abstracting; Bayesian Method; Biological Process; Blindness; Calcium; Cardiovascular Diseases; Cell physiology; Chemicals; Classification; Communities; Cryoelectron Microscopy; Darkness; Data; Disease; Disparate; Electrophysiology (science); Epilepsy; Evolution; Family; Family member; Foundations; G-Protein-Coupled Receptors; Genes; Genome; Goals; Graph; Homeostasis; Human; Human Genome; Informatics; Ion Channel; Kidney Failure; Knowledge; Language; Light; Link; Machine Learning; Malignant Neoplasms; Maps; Methods; Mining; Mission; Molecular; Mutation; Mutation Analysis; Names; Organism; Orthologous Gene; Pathway interactions; Pattern; Phosphotransferases; Physiological; Play; Protein Family; Proteins; Proteome; Readability; Regulation; Research; Research Personnel; Resources; Role; Semantics; Source; Structural Models; Structure; Structure-Activity Relationship; Systems Biology; Testing; Training; Visualization; cell type; deep learning; deep learning model; drug discovery; experimental study; genome resource; human disease; interest; knowledge graph; knowledge integration; model organism; nervous system disorder; novel; patch clamp; protein protein interaction; structural biology; tool

Project Summary 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 这项建议的总体目标是注释研究不足的暗离子通道使用的计算和实验方法相结合。我们的工作假设是，丰富的进化数据编码的离子通道序列从不同的生物体和综合挖掘的进化数据的结构，功能，途径和表达数据将提供重要的背景下预测和注释暗通道功能的分子和细胞水平。作为对我们假设的初步测试，我们使用基于蛋白质语言的深度学习模型对2.5亿个蛋白质序列进行了训练，生成了离子通道序列的功能分类，并描绘了未充分研究的钙稳态调节剂（CALHM）家族的区别序列和结构特征。我们还建立了一个集成的知识图（KG），以机器可读格式链接不同形式的离子通道信息，并部署了KG，用于使用图形嵌入方法预测生理功能，该方法可以有效地捕获编码在大型图形中的上下文信息。我们建议在这些成功研究的基础上，实现以下两个目标。Aim 1将开发新的工具和资源，用于可视化，挖掘和注释暗通道，使用通过cryo-EM研究和基于人工智能的结构预测方法提供的进化特征和结构模型。通过进化研究预测的CALHM家族门控和寡聚化机制的独特模式将通过突变研究和电生理实验进行实验验证。Aim 2将通过语义连接多个不同的数据源，包括细胞类型特异性表达，来自模型生物的直系同源物和电生理参数，进一步开发离子通道KG。知识图嵌入方法将用于预测未充分研究的通道，疾病关联和生理功能之间的联系，预测将作为IDG资源Pharos中的文本摘要提供。拟议的研究，预计将解决独特的信息学需要的离子通道社区提供新的工具和资源映射的序列-结构-功能关系。拟议的研究还将为未充分研究的通道提供新的可验证假设，并显着提高Pharos在阐明未充分研究的可药用蛋白质组功能方面的价值。