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

项目摘要(未更改) 这项提案的总体目标是使用 计算方法和实验方法的结合。我们的工作假设是 从不同的离子通道序列中编码的丰富的进化数据 生物与结构、功能、路径进化数据的综合挖掘 表情数据将为预测和注释黑暗提供重要的上下文 通道在分子和细胞水平上发挥作用。作为对我们的初步测试 假设，我们已经使用以下方法生成了离子通道序列的功能分类 基于蛋白质语言的2.5亿个蛋白质序列的深度学习模型 并圈定了未研究的特征序列和结构特征 钙稳态调节剂(CALHM)家族。我们还建立了一个完整的 连接机器中各种形式的离子通道信息的知识图(KG) 可读格式并部署了KG，用于使用图表预测生理功能 一种高效捕获大量编码的上下文信息的嵌入方法 图表。我们建议在这些成功研究的基础上，完成以下两项工作 目标。Aim1将开发用于可视化、挖掘和注释的新工具和资源 使用进化特征和结构模型的暗通道 低温电磁研究和基于人工智能的结构预测方法。独一无二的 预测CALHM家族的门控方式和齐聚机理 进化研究将通过突变研究和 电生理学实验。AIM2将通过以下方式进一步开发离子通道KG 语义上链接多个不同的数据源，包括特定于单元格的 表达，来自模式生物的同源基因和电生理学参数。 将采用知识图嵌入的方法来预测 未被充分研究的经络、疾病关联和生理功能 预测将以文本摘要的形式在IDG资源航标中提供。这个 拟议的研究有望解决离子通道的独特信息学需求 社区通过提供新的工具和资源来绘制序列结构- 函数关系。拟议的研究还将提供新的可检验的假设。 浅论航道研究不足显著提升航标灯的照明价值 未被充分研究的可用药蛋白质组的功能。

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.