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Status: Unchecked ×Keyword: protein-protein interaction prediction ×Clear all

3 claims from 2 papers

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Improved prediction of protein-protein interactions using AlphaFold2

   Bryant, Pozzati and Elofsson · Nature Communications · 2022

   The authors applied AlphaFold2 to predict heterodimeric protein complexes, and built a score from the predicted interfaces to judge model quality and to tell interacting from non-interacting protein pairs.

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   1. UncheckedAlphaFold2 with optimised sequence alignments produced acceptable-quality models (DockQ ≥ 0.23) for 63% of the heterodimeric protein complexes tested.“We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers.”
   2. Unchecked“From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy.”

2. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   Democratizing protein language models with parameter-efficient fine-tuning

   Sledzieski, Kshirsagar, Baek, Dodhia, Ferres and Berger · Proceedings of the National Academy of Sciences · 2024

   The paper applies the LoRA parameter-efficient method to protein language models for two prediction tasks, reporting performance comparable to full fine-tuning at much lower computational cost.

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   1. UncheckedParameter-efficient fine-tuning of protein language models is reported to match full fine-tuning while using less memory and far fewer trainable parameters.“We show that these approaches are competitive with traditional FT while requiring reduced memory and substantially fewer parameters.”