# Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining (Rayna Carter)

> **NIH NIH U01** · UNIVERSITY OF GEORGIA · 2023 · $9,060

## Abstract

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.

## Key facts

- **NIH application ID:** 10809931
- **Project number:** 3U01CA271376-02S1
- **Recipient organization:** UNIVERSITY OF GEORGIA
- **Principal Investigator:** Natarajan Kannan
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $9,060
- **Award type:** 3
- **Project period:** 2022-07-07 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10809931

## Citation

> US National Institutes of Health, RePORTER application 10809931, Annotating dark ion-channel functions using evolutionary features, machine learning and knowledge graph mining (Rayna Carter) (3U01CA271376-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10809931. Licensed CC0.

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