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通过HighFold2预测含有非天然氨基酸的循环的结构

Cheng Zhu¹, Sen Cao², Tianfeng Shang³

¹College of Pharmaceutical Sciences, Zhejiang University of Technology, Chaowang Road, Gongshu District, Hangzhou 310014, China.

Briefings in bioinformatics

|May 12, 2025

概括

HighFold2准确地预测了非自然氨基酸的循环的3D结构. 这一进步有助于开发用于药物发现的新型循环疗法.

科学领域:

计算生物学计算生物学
结构生物学结构生物学
药物发现药物发现药物发现

背景情况:

有非自然氨基酸的循环对药物发现有希望.
准确的3D结构预测对于开发循环疗法至关重要.
目前的深度学习模型无法预测非天然氨基酸的结构.

研究的目的:

开发一种新的深度学习模型,HighFold2,用于准确的3D结构预测含有非天然氨基酸的循环.
扩展现有的结构预测框架,以适应非自然氨基酸的独特特性.
促进基于循环的治疗方法的发展.

主要方法:

高折2是建立在AlphaFold-Multimer框架之上.
它将刚性组定义和初始坐标扩展到非自然氨基酸.
另外一个神经网络为多尺度建模和区分非自然氨基酸的原子级特征提供了特征.
相对位置编码矩阵是基于循环化的约束来生成循环的.
能量最小化用于预测结构中的碰撞消除.

主要成果:

HighFold2准确地预测了带有非自然氨基酸的循环单体的3D结构.
该模型成功地预测了循环-蛋白质复合体的结构.
对于Cα的RMSD中位数达到1.891 Å,显示出高精度.
该模型有效地区分了各种非自然氨基酸.

结论:

HighFold2在预测含有非天然氨基酸的循环的3D结构方面取得了重大进展.
该模型的有效性为加速开发基于循环的药物铺平了道路.
高折2解决了复杂疗法结构预测中的关键差距.

关键词:

循环酸是循环酸的一种.多尺度建模的多尺度建模结构预测结构预测不自然的氨基酸非天然的氨基酸.

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