Authors: Ziyi Zhou, Liang Zhang, Yuanxi Yu, Mingchen Li, Liang Hong, Pan Tan

Published on: February 03, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.02004

Summary

• What is new: Introduction of FSFP, a training strategy that enhances protein language models’ accuracy in predicting protein fitness under data scarcity.
• Why this is important: Existing protein language models struggle with accuracy and interpretability in predicting protein fitness, and improvements require substantial labeled data.
• What the research proposes: FSFP employs meta-transfer learning, learning to rank, and parameter-efficient fine-tuning to optimize models with few labeled examples.
• Results: FSFP outperforms both unsupervised and supervised baselines in 87 datasets, showcasing effectiveness in AI-guided protein design.

Technical Details

Technological frameworks used: Meta-transfer learning, learning to rank, parameter-efficient fine-tuning

Models used: Pre-trained protein language models

Data used: Labeled single-site mutants from target proteins

Potential Impact

Biotech companies and markets focusing on protein engineering and related R&D

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