# Predicting Transmembrane Protein Topology from 3D Structure

arXiv: https://arxiv.org/abs/2609.30446 · HTML: https://mlsanity.com/paper/2609.30446/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 1 of 1)

- [Evaluating representation learning on the protein structure universe](https://mlsanity.com/paper/2406.13864/) (2024)

## Abstract (arXiv)

This paper presents a novel approach to infer protein topology using the state-of-the-art graph neural network (GNN), SchNet. The model is trained on the same dataset used to develop the recent DeepTMHMM model with 5-fold cross-validation. Unlike the conventional approaches based on using only the protein sequences or the α-carbons as features, we have decoded our classifier in this way, so all atom-level embeddings are used. Without applying any pre-trained weight, the final results have shown great potential that GNNs can be used for topological predictions.