Fatgraph Models of Proteins

TL;DR

This paper presents a novel fatgraph-based model for proteins that captures their intrinsic geometry, enabling improved structural classification and property prediction through efficient computational methods.

Contribution

The paper introduces a new fatgraph model for proteins that extends traditional graphical representations and facilitates fast computational analysis.

Findings

Enhanced protein classification accuracy

Effective prediction of geometric properties

Efficient computational implementation

Abstract

We introduce a new model of proteins, which extends and enhances the traditional graphical representation by associating a combinatorial object called a fatgraph to any protein based upon its intrinsic geometry. Fatgraphs can easily be stored and manipulated as triples of permutations, and these methods are therefore amenable to fast computer implementation. Applications include the refinement of structural protein classifications and the prediction of geometric and other properties of proteins from their chemical structures.