aiqbench analysis
This research applies graph neural networks (GNNs) to predict transmembrane protein topology directly from 3D atomic structures, bypassing the need for pre-trained weights or limited feature sets like alpha-carbons. By leveraging SchNet to process full atom-level embeddings, the study demonstrates that advanced machine learning models can effectively infer complex biological topologies without relying on conventional sequence-only approaches. While the application is in computational biology, the core contribution lies in advancing graph-based deep learning architectures for structural inference tasks.
Predicting Transmembrane Protein Topology from 3D Structure
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 topological predictions.
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 topological predictions.
Artificial Intelligence (cs.AI)
arXiv:2609.30446 [cs.AI]
(or arXiv:2609.30446v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.30446 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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