基于人工智能的数据库,用于预测眼睛疾病中的蛋白质结构及其变化
Ling-Ping Cen, Tsz Kin Ng1,2,3, Jie Ji4
1Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong, North Dongxia Road (Guangxia New Town), Shantou, Guangdong 515041, China.
Database : the journal of biological databases and curation
|December 18, 2023
概括
这项研究介绍了EyeProdb,这是一个公共数据库,使用AlphaFold2和RoseTTAFold预测眼病中的蛋白质结构. 它有助于研究和开发用于治疗眼睛疾病的药物.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 基因组学就是基因组学.
背景情况:
- 眼部疾病涉及复杂的遗传和蛋白质结构变化.
- 蛋白质结构的预测建模对于了解疾病机制至关重要.
- 现有的资源缺乏针对眼睛疾病的综合性,疾病特异性蛋白质结构数据.
研究的目的:
- 建立一个公开可访问的在线数据库 (EyeProdb),用于预测与眼部疾病相关的蛋白质结构.
- 利用AlphaFold2和RoseTTAFold算法来预测眼部疾病相关基因的三级蛋白质结构.
- 验证预测结构的准确性,并证明它们在下游应用中的实用性.
主要方法:
- 收集了726个与各种眼部疾病相关的基因.
- 局部实施的AlphaFold2和RoseTTAFold算法用于蛋白质结构预测.
- 使用来自蛋白质数据库 (PDB) 的48个蛋白质结构数据集验证的算法.
- 开发了一个网站,将眼睛基因与预测的蛋白质结构联系起来.
- 使用pLDDT和TM得分评估预测准确度.
- 进行了分子动力学和对接模拟.
主要成果:
- 70.8%的验证结构实现了pLDDT > 90.
- 与PDB相比,AlphaFold2和RoseTTAFold预测的结构显示出高准确性 (TM得分分别为100%和97.9%>0.5),与PDB相比.
- 从430个眼部疾病基因中生成了1329个氨基酸序列的预测结构.
- 在75.9%的野生类型和76.1%的变体序列中观察到高置信度预测 (pLDDT>70).
- 模拟证实,高可信度预测结构表现出类似PDB的分子特征.
结论:
- 开发了EyeProdb,这是一个有价值的公共资源,用于眼部疾病蛋白质结构数据.
- 该数据库有助于对眼睛疾病进行生物学调查和基于结构的药物发现.
- 预测的蛋白质结构为疾病机制和潜在的治疗点提供了可靠的见解.
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