ProLLaMA：用于多任务蛋白质语言处理的蛋白质大语言模型
ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
AI总结：
提出多任务蛋白质语言模型ProLLaMA及进化蛋白质生成框架EPGF，通过构建大规模指令数据集与两阶段训练，实现蛋白质理解与生成能力的统一，在生成质量与理解精度上均取得显著提升。
摘要：
蛋白质语言模型（PLM）的最新进展改变了蛋白质工程，但与自然语言处理（NLP）中的对应模型不同，当前的PLM存在一个根本局限：它们要么擅长蛋白质语言理解（PLU），要么擅长蛋白质语言生成（PLG），但很少兼具两者。这种碎片化阻碍了蛋白质工程的进展。为了弥合这一差距，我们引入了ProLLaMA，这是一个由进化蛋白质生成框架（EPGF）增强的多任务蛋白质语言模型。我们构建了一个包含约1300万个样本和超过11000个超家族注释的综合指令数据集，以促进更好地建模序列-功能图谱。我们利用两阶段训练方法开发了ProLLaMA，这是一个具有蛋白质领域专业知识的多任务大语言模型。我们的EPGF通过三项创新解决了统计语言建模与生物学约束之间的不匹配：一个多维可解释评分器、分层高效解码以及概率-生物物理联合选择机制。大量实验表明，ProLLaMA在无条件蛋白质生成任务和可控蛋白质生成任务中都表现出色，与现有PLM相比，实现了更优的结构质量指标。此外，ProLLaMA在超家族预测中表现出强大的理解能力，精确匹配率达到67.1%。EPGF显著增强了生成序列的生物学可行性，生物物理评分提高4.3%和结构指标提高14.5%证明了这一点。该项目可在https://github.com/PKU-YuanGroup/ProLLaMA获取。
英文摘要：
Recent advances in Protein Language Models (PLMs) have transformed protein engineering, yet unlike their counterparts in Natural Language Processing (NLP), current PLMs exhibit a fundamental limitation: they excel in either Protein Language Understanding (PLU) or Protein Language Generation (PLG), but rarely both. This fragmentation hinders progress in protein engineering. To bridge this gap, we introduce ProLLaMA, a multitask protein language model enhanced by the Evolutionary Protein Generation Framework (EPGF). We construct a comprehensive instruction dataset containing approximately 13 million samples with over 11,000 superfamily annotations to facilitate better modeling of sequence-function landscapes. We leverage a two-stage training approach to develop ProLLaMA, a multitask LLM with protein domain expertise. Our EPGF addresses the mismatch between statistic language modeling and biological constraints through three innovations: a multi-dimensional interpretable scorer, hierarchical efficient decoding, and a probabilistic-biophysical joint selection mechanism. Extensive experiments demonstrate that ProLLaMA excels in both unconditional and controllable protein generation tasks, achieving superior structural quality metrics compared to existing PLMs. Additionally, ProLLaMA demonstrates strong understanding capabilities with a 67.1% exact match rate in superfamily prediction. EPGF significantly enhances the biological viability of generated sequences, as evidenced by improved biophysical scores (+4.3%) and structural metrics (+14.5%). The project is available at https://github.com/PKU-YuanGroup/ProLLaMA.
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。
