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

arXiv: https://arxiv.org/abs/2511.13685 · HTML: https://mlsanity.com/paper/2511.13685/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 60 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [Protein Representation Learning by Geometric Structure Pretraining](https://mlsanity.com/paper/2203.06125/) (2022)
- [Protein secondary structure prediction using deep convolutional neural fields](https://mlsanity.com/paper/1512.00843/) (2015)
- [Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction](https://mlsanity.com/paper/1403.1347/) (2014)
- [Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks](https://mlsanity.com/paper/1604.07176/) (2016)
- [Protein Secondary Structure Prediction with Long Short Term Memory Networks](https://mlsanity.com/paper/1412.7828/) (2014)
- [Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction](https://mlsanity.com/paper/1702.03865/) (2017)

## Abstract (arXiv)

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.