Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings
Abstract
Protein structure prediction often hinges on multiple sequence alignments (MSAs), which underperform on low-homology and orphan proteins. We introduce PLAME, a lightweight MSA design framework that leverages evolutionary embeddings from pretrained protein language models to generate MSAs that better support downstream folding. PLAME couples these embeddings with a conservation--diversity loss that balances agreement on conserved positions with coverage of plausible sequence variation. Beyond generation, we develop (i) an MSA selection strategy to filter high-quality candidates and (ii) a sequence-quality metric that is complementary to depth-based measures and predictive of folding gains. On AlphaFold2 low-homology/orphan benchmarks, PLAME delivers state-of-the-art improvements in structure accuracy (e.g., lDDT/TM-score), with consistent gains when paired with AlphaFold3. Ablations isolate the benefits of the selection strategy, and case studies elucidate how MSA characteristics shape AlphaFold confidence and error modes. Finally, we show PLAME functions as a lightweight adapter, enabling ESMFold to approach AlphaFold2-level accuracy while retaining ESMFold-like inference speed. PLAME thus provides a practical path to high-quality folding for proteins lacking strong evolutionary neighbors.
Cite
@article{arxiv.2507.07032,
title = {Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings},
author = {Hanqun Cao and Xinyi Zhou and Zijun Gao and Chenyu Wang and Xin Gao and Zhi Zhang and Cesar de la Fuente-Nunez and Chunbin Gu and Ge Liu and Pheng-Ann Heng},
journal= {arXiv preprint arXiv:2507.07032},
year = {2025}
}