Recent AI advances have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OneProt, a multi-modal AI for proteins that integrates structural, sequence, alignment, and binding site data. Using the ImageBind framework, OneProt aligns the latent spaces of modality encoders along protein sequences. It demonstrates strong performance in retrieval tasks and surpasses state-of-the-art methods in various downstream tasks, including metal ion binding classification, gene-ontology annotation, and enzyme function prediction. This work expands multi-modal capabilities in protein models, paving the way for applications in drug discovery, biocatalytic reaction planning, and protein engineering.

翻译：近期人工智能的进展使得多模态系统能够建模和转换多样化的信息空间。超越文本和视觉范畴，我们提出了OneProt，一种用于蛋白质的多模态人工智能模型，它整合了结构、序列、比对和结合位点数据。利用ImageBind框架，OneProt将各模态编码器的潜在空间沿蛋白质序列进行对齐。该模型在检索任务中表现出强大性能，并在多种下游任务中超越了现有最先进方法，包括金属离子结合分类、基因本体论注释和酶功能预测。这项工作拓展了蛋白质模型中的多模态能力，为药物发现、生物催化反应规划和蛋白质工程等应用铺平了道路。