ArticleInternational journal of molecular sciences2022
PITHIA: Protein Interaction Site Prediction Using Multiple Sequence Alignments and Attention.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Explainability of Protein Deep Learning Models.International journal of molecular sciences · 2025Article
- PMSFF: Improved Protein Binding Residues Prediction through Multi-Scale Sequence-Based Feature Fusion Strategy.Biomolecules · 2024Article
- Scoring alignments by embedding vector similarity.Briefings in bioinformatics · 2024Article
- Predicting Protein Interaction Sites Using PITHIA.Methods in molecular biology (Clifton, N.J.) · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
Cellular functions are governed by proteins, and, while some proteins work independently, most work by interacting with other proteins. As a result it is crucially important to know the interaction sites that facilitate the interactions between the proteins. Since the experimental methods are costly and time consuming, it is essential to develop effective computational methods. We present PITHIA, a sequence-based deep learning model for protein interaction site prediction that exploits the combination of multiple sequence alignments and learning attention. We demonstrate that our new model clearly outperforms the state-of-the-art models on a wide range of metrics. In order to provide meaningful comparison, we update existing test datasets with new information regarding interaction site, as well as introduce an additional new testing dataset which resolves the shortcomings of the existing ones.
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