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

项目总结

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.