LC-SEPLM：用于仅序列蛋白质表示学习的远程接触监督适应
LC-SEPLM: long-range contact-supervised adaptation for sequence-only protein representation learning
AI总结：
研究针对蛋白质语言模型未明确学习三维残基接触的问题，提出LC-SEPLM，通过LoRA和远程残基对接触监督适配ESM2，经训练后在多项蛋白质任务上优于ESM2，证明该方法能有效引入结构信息并保留序列推理。
AI中文摘要：
蛋白质语言模型学习可转移的序列表示。然而，由于它们主要沿氨基酸序列对上下文依赖性建模，其训练目标未明确约束模型学习折叠后形成的三维残基接触。本文引入LC-SEPLM，它通过LoRA和远程残基对接触监督来适配ESM2，同时保留仅序列的下游推理。特定对查询使用跨注意力提取与远程空间接触相关的全局序列上下文。在50万个AlphaFold Swiss-Prot蛋白质上训练LC-SEPLM，下游评估中它在所有八项蛋白质水平任务上优于ESM2，在远程同源识别中提升显著，在官方ESM-S EC基准测试中也表现出色。这些结果支持了残基对接触监督是在保留仅序列推理的同时将结构信息引入蛋白质序列表示的有效途径。
英文摘要：
Protein language models learn transferable sequence representations. However, because they primarily model contextual dependencies along amino-acid sequences, their training objectives do not explicitly constrain the model to learn three-dimensional residue contacts formed after folding . Here, we introduce LC-SEPLM (Long-range Contact-supervised ESM Protein Language Model), which adapts ESM2 with LoRA and long-range residue-pair contact supervision while retaining sequence-only downstream inference. Pair-specific queries use cross-attention over the complete sequence to extract global sequence context associated with long-range spatial contacts. To expose the model to diverse structural information, we trained LC-SEPLM on 500,000 AlphaFold Swiss-Prot proteins. In downstream evaluation, LC-SEPLM improved all eight protein-level tasks relative to ESM2. The largest gain occurred in remote-homology recognition, where macro-F1 increased from 0.6122 to 0.6769 (+0.0647, or 6.47 percentage points). On the official ESM-S EC benchmark, LC-SEPLM also outperformed ESM-S with a maximum absolute gain of 0.1771. These results support residue-pair contact supervision as a bounded route for introducing structural information into protein sequence representations while preserving sequence-only inference.
