ProtSent：蛋白质句子变换器
ProtSent: Protein Sentence Transformers
AI总结：
ProtSent通过对比学习框架改进蛋白质语言模型，提升蛋白质功能和结构表征能力，在23项下游任务中表现优异，尤其在远程同源检测和结构检索中取得显著提升。
摘要：
蛋白质语言模型（pLMs）生成的每种残基表示能够捕捉进化和结构信息，但其均值池化序列嵌入并未显式训练以反映蛋白质之间的功能、进化或结构相似性。我们提出了蛋白质句子变换器（ProtSent），一种用于将pLMs适应为通用嵌入模型的对比微调框架。ProtSent在五个蛋白质对数据集上训练：Pfam家族、结构推导的难样本、AlphaFold DB结构对、StringDB蛋白质-蛋白质相互作用以及深度突变扫描数据。我们在23项下游任务上评估，使用冻结嵌入和k近邻探针测量嵌入邻域质量。在ESM-2 150M上，ProtSent在15项任务中取得提升，远程同源检测提升105%，变体效应预测提升17%，SCOPe-40结构检索Recall@1提升19.9%。35M变体在16项任务中表现优异，远程同源检测提升40.5%，SCOPe-40结构检索Recall@1提升15.5%。对比微调重构了嵌入空间，以更好地捕捉蛋白质功能和结构，无需任何任务特定监督。我们发布了模型、公共数据、训练配方和代码。
英文摘要：
Protein language models produce rich per-residue representations, but the sequence embeddings obtained by mean-pooling them are never trained to place related proteins near one another, so the geometry that retrieval, clustering and nearest-neighbor transfer depend on is never optimized. We present ProtSent, a contrastive post-training framework that supervises a protein language model with three types of biological relation at once, Pfam family co-membership, AlphaFold DB structural clusters and STRING interactions, and with deep mutational scanning fitness ordering as an optional fourth. It needs no structure at training or inference, trains on corpora filtered at 40% sequence identity against the benchmark test sets they most resemble, and is applied to ESM-2 (35M and 150M parameters) and ESM-C (300M). Post-training makes structural family membership recoverable from the frozen embedding without a trained readout: on SCOPe-40, the adjusted Rand index of the 35M model rises from 0.054 (near chance) to 0.507, and family retrieval gains +0.185, +0.190 and +0.424 Recall@1 over the three frozen backbones, with paired bootstrap intervals excluding zero. On the CATH midnight-zone benchmark, homologous-superfamily accuracy rises from 40.7% to 56.7% at 35M and from 43.3% to 62.7% at 150M, more than the 12 points a CATH-supervised model gains over its own base. Against a maximally sensitive HMMER search, alignment keeps the best single hit at both ESM-2 scales, but the embedding leads at ranking depth (MAP 0.705 against 0.607 at 150M) and returns a ranking for every query, including those where alignment finds none. Under a trained linear probe, however, the same embeddings fall below the untuned backbone, as do structure-distilled embeddings against theirs, so the gain in neighborhood structure comes at a cost in linear-probe accuracy. Code and models: https://github.com/oriel9p/ProtSent
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
