arXiv Artificial Intelligence

Predicting Transmembrane Protein Topology from 3D Structure

2 hours ago 28.09.2026 07:00 👁 2 views
Predicting Transmembrane Protein Topology from 3D Structure

Quick summary

arXiv:2609.30446v1 Announce Type: new Abstract: 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 $\alpha$-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 top

Key takeaways

  • arXiv:2609.30446v1 Announce Type: new Abstract: 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 $\alpha$-carbons as features, we have decoded our classifier in this way, so all atom-level embeddings are used.

Why it matters

“Predicting Transmembrane Protein Topology from 3D Structure” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗