高健身路径可以连接具有低序列重叠的蛋白质
Pranav Kantroo1,2, Günter P Wagner3,4,5, Benjamin B Machta6,2
1Computational Biology and Bioinformatics Program, Yale University, New Haven, CT-06520, USA.
bioRxiv : the preprint server for biology
|November 28, 2024
概括
研究人员开发了一种人工智能算法,用于绘制蛋白质之间的进化路径. 该工具有助于通过使用ESM2蛋白语言模型生成可行的中间序列来理解蛋白质进化和同质性.
科学领域:
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
- 进化的生物信息学
背景情况:
- 蛋白质的结构和功能是由氨基酸序列决定的,是由进化力量塑造的.
- 中性网络连接局部蛋白质序列空间,但大规模的形态空间连接不清楚.
研究的目的:
- 开发一种算法,在远距离相关的蛋白质之间生成可行的进化路径.
- 使用人工智能驱动的方法探索蛋白质形态空间的大规模连接性.
主要方法:
- 利用人工智能工具进行蛋白质结构预测和功能可信度评估.
- 开发了一种新的算法,使用ESM2蛋白语言模型来评估序列适应性.
- 通过单个残留物替代,插入和删除生成中间蛋白序列.
主要成果:
- 成功地在不同的蛋白质对之间生成了可行的途径,包括具有不同结构折叠的蛋白质对.
- 记录了连接逐渐分离的蛋白质序列的路径的定性变化.
- 证明了序列插值作为同质检测代理的潜力.
结论:
- 开发的算法提供了对蛋白质进化轨迹和 morphospace.com 的洞察.
- 序列插值易度可以作为评估蛋白质同质性的定量指标.
- 这种方法提升了我们对蛋白质进化和序列结构功能关系的理解.
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