arXiv:2602.23179v3 Announce Type: replace Abstract: Protein sequences are abundant in repeating segments, both as exact copies and as approximate segments with mutations. These repeats are important for protein structure and function, motivating decades of algorithmic work on repeat identification. Recent work has shown that protein language models (PLMs) identify repeats, by examining their behavior in masked-token prediction. To elucidate their internal mechanisms, we investigate how PLMs detect both exact and approximate repeats. We find that the mechanism for approximate repeats functional
This research is published as protein language models become increasingly sophisticated, making their internal mechanisms a critical area of study for improving biological applications.
Understanding how protein language models identify repeats is key to enhancing their utility in drug discovery, protein engineering, and synthetic biology, accelerating R&D cycles.
This elucidates a core mechanism of PLMs, providing insights that can lead to more robust and explainable AI in biology, moving beyond black-box applications.
- · Synthetic Biology Researchers
- · Pharmaceutical Companies
- · AI-driven Drug Discovery Startups
- · Protein Engineering Firms
- · Traditional Protein Analysis Methods
- · Companies reliant on brute-force biological experimentation
Improved protein design and understanding due to more effective AI tools.
Faster development and optimization of novel proteins for therapeutics, enzymes, and materials.
Potential for designing entirely new biological systems with unprecedented functionality, leading to breakthroughs in medicine and materials science.
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Read at arXiv cs.LG