category
PNAS
date
Feb 9, 2026
slug
status
Published
summary
创新性地整合蛋白质语言模型与结构上下文信息，开发AI框架提升赖氨酸翻译后修饰位点预测的准确性
tags
蛋白质组学
type
Post
📄 原文题目
Mining lysine post-translational modification sites by integrating protein language model representations with structural context
🔗 原文链接
💡 AI 核心解读
创新性地整合蛋白质语言模型与结构上下文信息，开发AI框架提升赖氨酸翻译后修饰位点预测的准确性
📝 英文原版摘要
Proceedings of the National Academy of Sciences, Volume 123, Issue 7, February 2026. <br />SignificanceLysine post-translational modifications (PTMs) are crucial for regulating protein function, yet their experimental identification remains challenging. To address this, we developed an AI framework that uniquely integrates protein sequence ...
- 作者:NotionNext
- 链接:https://tangly1024.com/article/30c48bd6-1f96-81c8-8383-d6b70f13b17b
- 声明:本文采用 CC BY-NC-SA 4.0 许可协议，转载请注明出处。
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