Quantitative and interface-aware prediction of peptide–protein interactions by VITAL
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TL;DR - VITAL is a dual-channel deep learning framework for quantitatively predicting peptide–protein interactions. It jointly models sequence and structural context to estimate binding affinity and identify interaction interfaces.
- Co-learns complementary sequence and structural representations.
- Predicts peptide–protein interactions quantitatively rather than only classifying binding.
- Maps likely binding interfaces between peptides and proteins.
- Could support computational studies of molecular recognition and peptide-based discovery.
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Quantitative and interface-aware prediction of peptide–protein interactions by VITAL
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TL;DR - VITAL is a dual-channel deep learning framework for quantitatively predicting peptide–protein interactions. It jointly models sequence and structural context to estimate binding affinity and identify interaction interfaces.
- Co-learns complementary sequence and structural representations.
- Predicts peptide–protein interactions quantitatively rather than only classifying binding.
- Maps likely binding interfaces between peptides and proteins.
- Could support computational studies of molecular recognition and peptide-based discovery.