Protein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers

TL;DR

This paper introduces SSRGNet, a novel model combining graph neural networks and transformer-based language models to improve protein secondary structure prediction by explicitly leveraging 3D structural data.

Contribution

The study presents a new approach that integrates 3D protein graphs with relation-aware message passing transformers, enhancing structural feature extraction for secondary structure prediction.

Findings

SSRGNet outperforms baseline models on F1-score metrics.

Incorporating 3D structural data improves prediction accuracy.

The model effectively captures complex spatial relationships in proteins.

Abstract

In this study, we tackle the challenging task of predicting secondary structures from protein primary sequences, a pivotal initial stride towards predicting tertiary structures, while yielding crucial insights into protein activity, relationships, and functions. Existing methods often utilize extensive sets of unlabeled amino acid sequences. However, these approaches neither explicitly capture nor harness the accessible protein 3D structural data, which is recognized as a decisive factor in dictating protein functions. To address this, we utilize protein residue graphs and introduce various forms of sequential or structural connections to capture enhanced spatial information. We adeptly combine Graph Neural Networks (GNNs) and Language Models (LMs), specifically utilizing a pre-trained transformer-based protein language model to encode amino acid sequences and employing message-passing mechanisms like GCN and R-GCN to capture geometric characteristics of protein structures. Employing convolution within a specific node's nearby region, including relations, we stack multiple convolutional layers to efficiently learn combined insights from the protein's spatial graph, revealing intricate interconnections and dependencies in its structural arrangement. To assess our model's performance, we employed the training dataset provided by NetSurfP-2.0, which outlines secondary structure in 3-and 8-states. Extensive experiments show that our proposed model, SSRGNet surpasses the baseline on f1-scores.