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 该提案的总体目标是使用计算和实验方法的组合来注释研究的深色离子通道。我们的工作假设是，来自不同生物体的离子通道序列中编码的大量进化数据以及与结构，功能，途径和表达数据的进化数据的整合挖掘将为预测和注释分子和细胞水平上的黑暗通道功能提供重要背景。作为对我们假设的初步检验，我们使用基于蛋白质语言的深度学习模型对离子通道序列进行了功能分类，该模型对2.5亿蛋白质序列进行了训练，并描述了研究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知识图（kg），该图形（kg）链接了机器可读格式的不同形式的离子通道信息，并使用图形嵌入方法进行了kg来预测生理功能，从而有效地捕获了大图中的上下文信息。我们建议以这些成功的研究为基础，以实现以下两个目标。 AIM1将开发新的工具和资源，以使用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可视化，采矿和注释黑暗通道。通过进化研究预测的CALHM家族门控和寡聚机制的独特模式将通过突变研究和电生理实验实验验证。 AIM2将通过将多个不同数据源连接到包括细胞类型的特定表达，模型生物的直系同源物和电生理学参数（包括细胞类型的表达），进一步开发离子通道Kg。知识图嵌入方法将采用用于预测研究不足的渠道，疾病关联和生理功能之间的联系，并且将作为IDG资源Pharos中的文本摘要提供预测。预计拟议的研究将通过提供新的工具和资源来映射序列结构功能关系，以满足离子渠道社区的独特信息学需求。拟议的研究还将在研究的通道上提供新的可检验的假设，并显着提高Pharos在照亮研究研究的可药蛋白质组功能方面的价值。