Graph Neural Networks for Predicting Protein Folding Pathways

Abstract

Predicting the folding pathway of a protein – the process by which a linear chain of amino acids adopts its functional three-dimensional structure – is a central challenge in computational biology. Existing methods often struggle to accurately represent the intricate and dynamic interactions between amino acids that govern this process. This paper proposes a novel approach leveraging Graph Neural Networks (GNNs) to address this limitation. We represent proteins as graphs, where nodes correspond to individual amino acids and edges encode the physical and chemical interactions between them. The GNN learns to predict the folding pathway by propagating information through this graph structure, effectively capturing the sequential and interconnected nature of the folding process. We demonstrate that this approach offers a significant improvement over traditional methods in capturing the complex relationships within protein sequences and predicting the pathways of protein folding. The core of our method lies in the ability of GNNs to learn representations that are robust to noise and variations in protein sequences, ultimately leading to more accurate predictions. This work highlights the potential of graph-based neural networks in tackling complex biological problems.