Developing Bioinformatics and Computational Biology Methods for Integrative Structure-Function Analysis of CRISPR Base-Editor TilingMutagenesis Experiments

About This Grant

PROJECT SUMMARY This proposal aims to develop computational methods for the integrative protein structure-function analysis of base-editor (BE) tiling library and screening results. BE tiling mutagenesis is a high-throughput gene-editing technology to introduce transition variants (A→G and C→T) in cellular models, enabling interrogation of splice-altering, nonsense, and missense mutations. In vitro and in vivo BE experiments are increasingly employed by molecular and chemical biologists to study the mechanisms of mutations and therapeutic targets. However, the scientists face challenges to interpret the experimental readouts, accounting for the molecular effects of these genetic edits, and answer questions such as: “Are the edits unfolding the protein and destabilizing its function?” or “Which edits can recapitulate known drug-resistant mutations or overlap with clinically actionable pathogenic variants?” Currently, answering these questions often requires ad hoc scripting to integrate heterogeneous data types— mRNA-level BE libraries, protein-level mutations, and atomic-resolution structures—and manual queries across disparate databases. These challenges are further amplified when researchers perform multiple screens, such as across different editors and cell lines. Efficient computational methods are needed to harmonize, normalize, and aggregate results from multiple screens, identify a consensus set of hits, and extract meaningful biological insights. To address these gaps, our project will develop methods to link BE library data and screening results to proteins and prioritize and interpret hits in the context of protein sequence-structure-function relationships. The project is organized around two Specific Aims, each with two sub-aims. Aim 1 focuses on building scalable bioinformatics pipelines to link BE tiling data to protein sequences/structures and prioritize “hits” (i.e., sites predicted to cause molecular and functional changes). We will develop tools to map and visualize editing libraries and readouts from mRNA onto protein-level annotations (Sub-aim 1.1), and methods to identify an expanded set of “hits” by leveraging structural biology insights and aggregating results across multiple screens (Sub-aim 1.2). Aim 2 centers on applying data science and AI methods to characterize and interpret BE screening hits using protein features. We will identify key protein features enriched for impactful edits (Sub-aim 2.1) and develop AI- powered models to automate interpretation and summarization of base-editing outcomes using biologically informed reasoning (Sub-aim 2.2). For each aim, we will benchmark our methods using published datasets and expert-provided new datasets. All data and methods will be made publicly available through open-access repositories and, importantly, will be integrated into the web-based Genomics 2 Proteins portal, enabling researchers to interactively explore the data and use the methods for hypothesis generation. This work will produce reusable, generalizable, and scalable tools that advance the application of informatics and data science to accelerate data-powered biomedical discovery.