Track: Full Paper Track
Keywords: ESM3, foundational PLM, sampling, co-design
TL;DR: Protein sequence and structure co-design using ESM3
Abstract: Proteins perform diverse biological functions, governed by the intricate relationship between their sequence and three-dimensional structure. While protein language models (PLMs) have demonstrated remarkable success in functional annotation and structure prediction, their potential for sequence-structure co-design remains underexplored. This limitation arises from pre-training objectives that favor masked token prediction over generative modeling. In this work, we systematically explore sampling strategies to enhance the generative capabilities of PLMs for co-design. Notably, we introduce a ranked iterative decoding with re-masking scheme, enabling PLMs to generate sequences and structures more effectively. Benchmarking ESM3 across multiple scales, we demonstrate that using PLMs effectively at sampling time for co-design tasks can outperform specialized architectures that lack comparable scaling properties. Our work advances the field of computational protein design by equipping PLMs with robust generative capabilities tailored to sequence-structure interdependence.
Attendance: Jiarui Lu
Submission Number: 29
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