arXiv:2509.24895v2 Announce Type: replace Abstract: While protein language models (PLMs) are one of the most promising avenues of research for future de novo protein design, the way in which they transform sequences to hidden representations, as well as the information encoded in such representations is yet to be fully understood. Several works have attempted to propose interpretability tools for PLMs, but they have focused on understanding how individual sequences are transformed by such models. Therefore, the way in which PLMs transform the whole space of sequences along with their relations
The paper is published as research into protein language models, a nascent but rapidly developing field, matures and seeks deeper theoretical understanding.
Understanding the representations within protein language models is crucial for advancing de novo protein design, which has significant implications for new therapeutics, materials, and other biotechnologies.
This paper deepens the theoretical understanding of how protein language models work, potentially accelerating their effective application and improving design capabilities.
- · Synthetic biology researchers
- · Pharmaceutical companies
- · Biotechnology startups
Improved understanding leads to more efficient and accurate protein design using AI.
Accelerated development of novel proteins with tailored functions for medical and industrial applications.
The emergence of new, AI-driven biomanufacturing processes and therapeutic modalities.
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