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

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 (Kennady Boyd)

基本信息

批准号：
10809950
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
项目类别：
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/10809950
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

项目摘要

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（未更改）这个提议的总体目标是使用一个计算和实验方法的结合。我们的假设是从不同的基因序列中编码在离子通道序列中的大量进化数据生物体与结构、功能、途径等进化数据的综合挖掘和表达数据将为预测和注释黑暗提供重要的背景通道在分子和细胞水平上发挥作用。作为我们的初步测试，假设，我们已经产生了离子通道序列的功能分类，基于2.5亿个蛋白质序列训练的蛋白质语言深度学习模型并在此基础上，对研究区的构造特征和识别序列进行了划分，钙稳态调节剂（CALHM）家族。我们还建立了一个综合的知识图（KG）连接机器中不同形式的离子通道信息可读格式并部署KG使用图表预测生理功能嵌入方法，有效地捕获大规模编码的上下文信息图表。我们建议在这些成功研究的基础上，目标。Aim1将开发新的工具和资源，用于可视化，挖掘和注释暗通道利用演化特征和结构模型，冷冻电镜研究和人工智能结构预测方法。独特的 CALHM家族门控和寡聚化机制的模式预测通过进化研究将通过突变研究进行实验验证，电生理学实验Aim2将进一步开发离子通道KG，在语义上链接多个不同的数据源表达、来自模式生物的直系同源物和电生理学参数。知识图嵌入方法将用于预测研究不足的渠道，疾病协会和生理功能，预测将在IDG资源Pharos中以文本摘要的形式提供。的预计拟议的研究将解决离子通道的独特信息学需求通过提供新的工具和资源来绘制序列结构，功能关系。拟议的研究还将提供新的可检验的假设在未充分研究的渠道，并显着提高灯塔的价值，未充分研究的可药用蛋白质组的功能。