We developed PINNACLE, a graph-based AI model for learning protein representations across cell-type contexts. These contextualized protein representations enable the integration of 3D protein structure with single-cell genomic-based representations to enhance protein–protein interaction prediction, analysis of drug effects across cell-type contexts, and prediction of therapeutic targets in a cell type-specific manner..

我们开发了PINNACLE，这是一种基于图形的AI模型，用于跨细胞类型上下文学习蛋白质表示。这些上下文化的蛋白质表示使3D蛋白质结构与基于单细胞基因组的表示相结合，以增强蛋白质-蛋白质相互作用预测，跨细胞类型背景的药物效应分析以及以细胞类型特异性方式预测治疗靶点。。

Access through your institution

通过您的机构访问

Buy or subscribe

购买或订阅

This is a preview of subscription content, access via your institution

这是订阅内容的预览，可通过您的机构访问

Access options

访问选项

Access through your institution

通过您的机构访问

Access through your institution

通过您的机构访问

Change institution

变革机构

Buy or subscribe

购买或订阅

Access Nature and 54 other Nature Portfolio journalsGet Nature+, our best-value online-access subscription24,99 € / 30 dayscancel any timeLearn moreSubscription info for Chinese customersWe have a dedicated website for our Chinese customers. Please go to naturechina.com to subscribe to this journal.Go to naturechina.comBuy this articlePurchase on Springer LinkInstant access to full article PDFBuy nowPrices may be subject to local taxes which are calculated during checkout.

Access Nature和54篇其他Nature Portfolio journalsGet Nature+，我们最有价值的在线订阅24,99欧元/30天，随时为中国客户获取更多订阅信息我们为中国客户提供了一个专门的网站。请访问naturechina.com订阅本期刊。访问naturechina.comBuy本文在Springer link上购买即时访问完整文章PDFBuy now价格可能需要缴纳结帐时计算的地方税。

Additional access options:

其他访问选项：

Log in

登录

Learn about institutional subscriptions

了解机构订阅

Read our FAQs

阅读我们的常见问题

Contact customer support

联系客户支持

Fig. 1: PINNACLE, a model for learning contextual protein representations.

图1:PINNACLE，学习上下文蛋白质表示的模型。

ReferencesPan, J. et al. Sparse dictionary learning recovers pleiotropy from human cell fitness screens. Cell Syst. 13, 286–303 (2022). This paper describes a framework to disentangle gene functions that vary across cell contexts defined by fitness screens.Article

参考Span，J。等人。稀疏字典学习从人类细胞适应性筛选中恢复多效性。细胞系统。13286-303（2022）。本文描述了一个框架，用于解开由适应性筛选定义的细胞环境中不同的基因功能。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023). This paper introduces a deep learning model using sequence and structural contexts for pathogenicity prediction of missense variants.Article

Cheng，J。等人。使用AlphaMissense进行精确的蛋白质组错义变异效应预测。科学381，eadg7492（2023）。本文介绍了一种使用序列和结构上下文进行错义变体致病性预测的深度学习模型。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). This paper describes the AlphaFold model for 3D structure protein folding prediction, which incorporates physical and biological priors into protein modeling.Article

Jumper，J.等人。使用AlphaFold进行高度准确的蛋白质结构预测。自然596583-589（2021）。本文描述了用于3D结构蛋白质折叠预测的AlphaFold模型，该模型将物理和生物学先验结合到蛋白质建模中。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

CZI Single-Cell Biology Program et al. CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data. Preprint at https://doi.org/10.1101/2023.10.30.563174 (2023). This paper describes a data platform with curated and interoperable single-cell datasets across healthy and disease states.Sheridan, C.

CZI单细胞生物学计划等。CZ CELLxGENE Discover：用于聚合数据的可扩展探索，分析和建模的单细胞数据平台。预印于https://doi.org/10.1101/2023.10.30.563174（2023年）。本文描述了一个数据平台，该平台具有跨健康和疾病状态的策划和可互操作的单细胞数据集。谢里登，C。

Can single-cell biology realize the promise of precision medicine? Nat. Biotechnol. 42, 159–162 (2024). This news article outlines the current state of the single-cell biology field and its potential for precision medicine.Article .

单细胞生物学能否实现精准医学的承诺？美国国家生物技术公司。42159-162（2024）。这篇新闻文章概述了单细胞生物学领域的现状及其在精准医学中的潜力。文章。

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Download referencesAdditional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Li, M. M. et al. Contextual AI models for single-cell protein biology. Nat. Methods https://doi.org/10.1038/s41592-024-02341-3 (2024)Rights and permissionsReprints and permissionsAbout this articleCite this article Contextual AI models for context-specific prediction in biology..

下载参考其他信息出版商的注释Springer Nature对于已发布地图和机构隶属关系中的管辖权主张保持中立。这是Li，M.M.等人的总结。单细胞蛋白质生物学的上下文AI模型。自然方法https://doi.org/10.1038/s41592-024-02341-3（2024）权利和许可打印和许可本文引用本文上下文AI模型进行生物学上下文特定预测。。

Nat Methods (2024). https://doi.org/10.1038/s41592-024-02342-2Download citationPublished: 22 July 2024DOI: https://doi.org/10.1038/s41592-024-02342-2Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Nat方法（2024）。https://doi.org/10.1038/s41592-024-02342-2Download引文发布日期：2024年7月22日OI：https://doi.org/10.1038/s41592-024-02342-2Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

Provided by the Springer Nature SharedIt content-sharing initiative

由Springer Nature SharedIt内容共享计划提供