标题： InstructPLM-mu：ESM2在蛋白质突变预测中胜过ESM3的1小时微调 显示英文标题

标题： InstructPLM-mu: 1-Hour Fine-Tuning of ESM2 Beats ESM3 in Protein Mutation Predictions

摘要： 多模态蛋白质语言模型在突变效应预测上表现强劲，但从头开始训练此类模型需要大量的计算资源。 在本文中，我们提出了一种称为InstructPLM-mu的微调框架，并尝试回答一个问题：\textit{多模态微调预训练的仅序列蛋白质语言模型能否达到端到端训练模型的性能？}令人惊讶的是，我们的实验表明，使用结构输入对ESM2进行微调可以达到与ESM3相当的性能。 为了理解这是如何实现的，我们系统地比较了三种不同的特征融合设计和微调方案。 我们的结果表明，融合方法和调优策略都会显著影响最终准确性，这表明微调过程并不简单。 我们希望这项工作能为将结构注入预训练蛋白质语言模型提供实际指导，并激发对更好融合机制和微调协议的进一步研究。 显示英文摘要

摘要： Multimodal protein language models deliver strong performance on mutation-effect prediction, but training such models from scratch demands substantial computational resources. In this paper, we propose a fine-tuning framework called InstructPLM-mu and try to answer a question: \textit{Can multimodal fine-tuning of a pretrained, sequence-only protein language model match the performance of models trained end-to-end? } Surprisingly, our experiments show that fine-tuning ESM2 with structural inputs can reach performance comparable to ESM3. To understand how this is achieved, we systematically compare three different feature-fusion designs and fine-tuning recipes. Our results reveal that both the fusion method and the tuning strategy strongly affect final accuracy, indicating that the fine-tuning process is not trivial. We hope this work offers practical guidance for injecting structure into pretrained protein language models and motivates further research on better fusion mechanisms and fine-tuning protocols.