基于大模型HelixProtX的任意模态到任意模态蛋白质生成：统一序列、结构与描述
Unifying Sequences, Structures, and Descriptions for Any-to-Any Protein Generation with the Large Multimodal Model HelixProtX
AI总结：
本研究提出基于大多模态模型的HelixProtX系统，支持蛋白质序列、结构、文本描述间的任意模态转换，在多项任务中准确率优于现有最优模型，为蛋白质研究开辟新路径。
摘要：
蛋白质是生物系统的基本组成部分，可通过多种模态表示，包括序列、结构和文本描述。尽管深度学习和科学大语言模型（LLMs）在蛋白质研究中取得了进展，但当前方法主要集中在有限的专用任务上——通常是从一种蛋白质模态预测另一种。这些方法限制了对多模态蛋白质数据的理解和生成。相比之下，大多模态模型已展现出生成文本、图像、视频等任意到任意内容的潜在能力，丰富了各领域的用户交互。将这些多模态模型技术融入蛋白质研究具有重大前景，有望改变蛋白质的研究方式。为此，我们提出HelixProtX，这是一个基于大多模态模型构建的系统，旨在通过支持任意到任意的蛋白质模态生成，为蛋白质研究提供全面解决方案。与现有方法不同，它支持将任何输入蛋白质模态转换为任何所需的蛋白质模态。实验结果证实了HelixProtX的先进能力，不仅能从氨基酸序列生成功能描述，还能执行从文本描述设计蛋白质序列和结构等关键任务。初步研究结果表明，HelixProtX在一系列蛋白质相关任务中始终能达到更高的准确率，优于现有的最优模型。通过将多模态大模型融入蛋白质研究，HelixProtX为理解蛋白质生物学开辟了新途径，有望加速科学发现。
英文摘要：
Proteins are fundamental components of biological systems and can be represented through various modalities, including sequences, structures, and textual descriptions. Despite the advances in deep learning and scientific large language models (LLMs) for protein research, current methodologies predominantly focus on limited specialized tasks -- often predicting one protein modality from another. These approaches restrict the understanding and generation of multimodal protein data. In contrast, large multimodal models have demonstrated potential capabilities in generating any-to-any content like text, images, and videos, thus enriching user interactions across various domains. Integrating these multimodal model technologies into protein research offers significant promise by potentially transforming how proteins are studied. To this end, we introduce HelixProtX, a system built upon the large multimodal model, aiming to offer a comprehensive solution to protein research by supporting any-to-any protein modality generation. Unlike existing methods, it allows for the transformation of any input protein modality into any desired protein modality. The experimental results affirm the advanced capabilities of HelixProtX, not only in generating functional descriptions from amino acid sequences but also in executing critical tasks such as designing protein sequences and structures from textual descriptions. Preliminary findings indicate that HelixProtX consistently achieves superior accuracy across a range of protein-related tasks, outperforming existing state-of-the-art models. By integrating multimodal large models into protein research, HelixProtX opens new avenues for understanding protein biology, thereby promising to accelerate scientific discovery.
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。
