# StabLyzeGraph: High‐throughput screening of combinatorial mutations using graph neural networks

## TL;DR

StabLyzeGraph is a new tool that uses advanced computing to quickly find combinations of mutations that stabilize proteins, speeding up biotech and medical applications.

## Contribution

StabLyzeGraph introduces a graph neural network framework for high-throughput screening of stabilizing protein mutations, integrating diverse data types.

## Key findings

- StabLyzeGraph demonstrated strong predictive performance across 23 diverse datasets.
- The model can classify beneficial combinations of mutants based on structural impact rather than mutation frequency.
- The framework includes a Benchmarking module and a Screening module for efficient mutational analysis.

## Abstract

Engineering protein stability is a powerful strategy across biotechnology and medicine, supporting a broad range of applications such as atomic structure determination, discovery of therapeutic molecules, biomanufacturing, diagnostic reagents, industrial biocatalysis, etc. However, achieving rapid and significant improvements has been historically challenging due to the vast mutational space and the complex interplay of sequence, structure, and function. Indeed, traditional experimental and computational methods often struggle to predict the impact of multiple mutations and effectively integrate diverse data types. To address these limitations, we developed StabLyzeGraph, a novel computational framework powered by Graph Neural Networks (GNNs) for protein mutational analysis and classification of stabilizing mutations. StabLyzeGraph represents proteins as graphs, integrating amino acid physicochemical properties, evolutionary conservation scores, and mapped three‐dimensional structural information. The framework consists of a Benchmarking module to evaluate performance, and a Screening module to identify and rank impactful mutations. Benchmarking across 23 diverse datasets demonstrated strong predictive performance, highlighting the GNN's ability to leverage integrated features. Mutational analysis enables the generation and probability scoring of single‐ and multi‐site mutants, demonstrating the model's capacity to classify beneficial combinations of mutants based on learned structural impact rather than mere mutation frequency. StabLyzeGraph also features a user‐friendly Graphical User Interface and demonstrates reasonable computational efficiency and scalability for exploring mutational landscapes. This tool provides a robust and versatile approach to accelerate the efficient discovery of stabilizing mutations with tailored properties and represents a step forward in rational protein design, poised to accelerate the creation of novel biologics with enhanced performance. StabLyzeGraph is freely available on GitHub (https://github.com/cosconatilab/StabLyzeGraph) as an open‐source tool.