ProLLM: 用于蛋白质-蛋白质相互作用预测的蛋白质思维链增强大语言模型
ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction
AI总结：
针对现有PPI预测方法忽略间接非物理连接的局限，提出融合蛋白质思维链ProCoT的ProLLM框架，借助LLM和信号通路推理机制，结合嵌入替换与指令微调，在基准数据集上实现了预测准确率和泛化性的显著提升。
摘要：
蛋白质-蛋白质相互作用（PPI）预测对于理解生物学功能和疾病至关重要。以往用于PPI预测的机器学习方法主要关注直接的物理相互作用，忽略了通过中间蛋白形成的非物理连接的更广泛背景，从而限制了其有效性。大语言模型（LLM）的出现为应对这一复杂生物学挑战提供了新机遇。通过将结构化数据转化为自然语言提示，我们可以将蛋白质之间的关系映射到文本中。这种方法使LLM能够识别蛋白质之间的间接连接，追踪从上游到下游的路径。\n因此，我们提出了一个名为ProLLM的新型框架，这是首次将定制化LLM用于PPI预测。具体而言，我们提出了蛋白质思维链（ProCoT），它将信号通路的生物学机制复制为自然语言提示。ProCoT将信号通路视为一个蛋白质推理过程：从上游蛋白质起始，经过若干中间蛋白质，将生物信号传递给下游蛋白质。由此，我们可以利用ProCoT预测上游蛋白质与下游蛋白质之间的相互作用。ProLLM的训练采用ProCoT格式，增强了模型对复杂生物学问题的理解。\n除了ProCoT之外，本文还对自然语言提示中蛋白质位点的嵌入替换、以及蛋白质知识数据集上的指令微调进行了探索。我们通过在基准数据集上的严格验证证明了ProLLM的有效性，结果显示其在预测准确率和泛化性方面均较现有方法有显著提升。代码可在以下网址获取：https://github.com/MingyuJ666/ProLLM。
英文摘要：
The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions, ignoring the broader context of nonphysical connections through intermediate proteins, thus limiting their effectiveness. The emergence of Large Language Models (LLMs) provides a new opportunity for addressing this complex biological challenge. By transforming structured data into natural language prompts, we can map the relationships between proteins into texts. This approach allows LLMs to identify indirect connections between proteins, tracing the path from upstream to downstream. Therefore, we propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time. Specifically, we propose Protein Chain of Thought (ProCoT), which replicates the biological mechanism of signaling pathways as natural language prompts. ProCoT considers a signaling pathway as a protein reasoning process, which starts from upstream proteins and passes through several intermediate proteins to transmit biological signals to downstream proteins. Thus, we can use ProCoT to predict the interaction between upstream proteins and downstream proteins. The training of ProLLM employs the ProCoT format, which enhances the model's understanding of complex biological problems. In addition to ProCoT, this paper also contributes to the exploration of embedding replacement of protein sites in natural language prompts, and instruction fine-tuning in protein knowledge datasets. We demonstrate the efficacy of ProLLM through rigorous validation against benchmark datasets, showing significant improvement over existing methods in terms of prediction accuracy and generalizability. The code is available at: https://github.com/MingyuJ666/ProLLM.
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。
