Nature Biotechnology：AI 不仅开始设计蛋白质，还开始设计蛋白质在细胞内的去向。

DeepSCan 学习细胞表面展示的规则，并生成新的膜展示模块，其中多个模块的性能优于天然强序列。

这是迈向可编程细胞工程的一步。

https://nature.com/articles/s41587-026-03144-x…

强效细胞表面展示元件的发现与设计

来源：https://www.nature.com/articles/s41587-026-03144-x?error=cookies_not_supported&code=87f0dda7-6364-42f7-bcf9-79123a73488d

数据可用性

本研究所有支持研究结果的主要数据均可在文章内以及通过 Zenodo（https://doi.org/10.5281/zenodo.19179097(ref.62 (https://www.nature.com/articles/s41587-026-03144-x#ref-CR62))）获取。源数据 (https://www.nature.com/articles/s41587-026-03144-x#Sec31)随本文一并提供。

代码可用性

蛋白质语言模型使用其官方 GitHub 代码库中提供的代码和预训练参数实现，并遵循相应说明。ESM2 代码和模型可通过 GitHub（https://github.com/facebookresearch/esm）获取。我们使用 RAPIDS cuML 库中的 XGBoost 和其他经典机器学习模型（如逻辑回归、随机森林），以利用图形处理器（GPU）加速训练。微调后的深度学习模型检查点及源代码将公开发布在 Hugging Face、Docker 和 Github（https://github.com/fangzhe3/DeepSCan）57 上 (https://www.nature.com/articles/s41587-026-03144-x#ref-CR57)。该 GitHub 代码库还介绍了使用 DeepSCan 网络服务器或 Docker 镜像的三个演示场景。Genesis Quant 8,000 模型的 Docker 镜像使用户能够利用 GPU 处理大规模序列分析。补充软件包含 DeepSCan 网络服务器应用程序（版本：app_v7.0_deployed_on_AWS）、Genesis 模型训练代码（版本：Genesis_Quant_8k_attention_pooling2_LLRD_v2）、Genesis 和 Omni 模型推理代码以及其他计算分析代码。DeepSCan 模型基于 Python（v.3.11.5）+ PyTorch（v.2.5.1，CUDA v.12.4）工作流。

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