# AFragmenter: schema-free, tuneable protein domain segmentation for AlphaFold protein structures

## TL;DR

AFragmenter is a new tool for segmenting protein domains using AlphaFold structures, allowing flexible and customizable domain identification.

## Contribution

Introduces a schema-free, tuneable method for protein domain segmentation using network analysis of AlphaFold structures.

## Key findings

- AFragmenter uses network analysis and Leiden clustering to identify structural domains in AlphaFold-predicted proteins.
- Users can adjust parameters like contrast threshold and resolution for customized domain segmentation.
- The tool is available as a Python library and command line tool with open-source licensing.

## Abstract

Protein domain segmentation is a crucial aspect of understanding protein functions and interactions, and it is vital for protein modelling exercises and evolutionary studies. Current segmentation methods often rely on predefined classification schemes, leading to inconsistencies and biases. AFragmenter provides a schema-free and tuneable approach to protein domain segmentation based on network analysis of AlphaFold-predicted structures. Utilizing Predicted Aligned Error values, AFragmenter constructs a fully connected network of protein residues and identifies distinct structural domains by using Leiden clustering. This method empowers users to adjust parameters including contrast threshold and resolution, providing control over the segmentation process.

AFragmenter is implemented in Python3 and freely available under an MIT license. It can be found as a Python library and command line tool at https://github.com/sverwimp/AFragmenter, pip, and Conda.