用进化嵌入生成更优多序列比对，提升低同源蛋白折叠精度
Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings
- 基于预训练模型嵌入设计轻量级比对，平衡保守与变异
- 在低同源/孤儿蛋白上提升结构准确率（lDDT/TM-score）
- 可适配ESMFold实现近AlphaFold2精度，且推理速度不变
蛋白质结构预测依赖多序列比对（MSA），但在低同源性和孤儿蛋白上表现不佳。我们提出PLAME，一种轻量级MSA设计框架，利用预训练蛋白语言模型的进化嵌入生成更有利于下游折叠的比对。PLAME结合保守性-多样性损失，平衡保守位点的一致性与合理序列变异的覆盖度。除生成外，还开发了（i）高质量候选比对筛选策略，及（ii）互补于深度度量的序列质量指标，能预测折叠性能提升。在AlphaFold2的低同源/孤儿蛋白基准测试中，PLAME显著提升结构精度（如lDDT/TM-score），并与AlphaFold3协同时保持一致增益。消融实验验证了筛选策略的有效性，案例研究揭示了比对特性如何影响AlphaFold置信度和错误模式。最后，PLAME作为轻量适配器，使ESMFold达到接近AlphaFold2的精度，同时保持原有推理速度。因此，PLAME为缺乏强进化邻居的蛋白提供了高精度折叠的实用路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。