PIT-GCL: Protein Interaction using Topological Graph Contrastive Learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.
arXiv:2608.29207v1 Announce Type: new Abstract: Protein structure modeling rests on a single computational primitive: the interaction between what a residue is (sequence content) and where it sits (t...
arXiv:2606. 02629v1 Announce Type: cross Abstract: Protein-protein interactions (PPIs) are essential for many biological processes.
arXiv:2604. 06336v2 Announce Type: replace-cross Abstract: Fragment-level representations provide a natural way to capture recurring molecular substructures and reuse their learned representations across molecules.
MT-ProtBERT is a multi‑task extension of ProtBERT designed for classifying intrinsically disordered proteins (IDPs) in low‑data settings. It combines Dynamic Window Masking, a Multi‑Scale 1D Convolutional classifier, and auxiliary biochemistry‑informed objectives to jointly optimize masked language modeling and domain‑specific tasks. In experiments on phosphorylation site prediction and protein compaction prediction, MT‑ProtBERT outperforms the RNN‑based IDP model PARROT across all limited‑data tasks.
arXiv:2607. 22777v1 Announce Type: cross Abstract: Protein language models learn transferable sequence representations.