使用MSA增强和预训练的语言模型,改善了对不够同源蛋白质的结构相关预测
Qiaozhen Meng1, Fei Guo2, Jijun Tang3
1School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.
Briefings in bioinformatics
|June 15, 2023
概括
对孤儿蛋白质的精确蛋白质结构预测对于AlphaFold2这样的方法来说是具有挑战性的,因为同源序列的数量有限. 这项研究介绍了MSA增强和MSA免费的方法,像ESMFold这样的MSA免费方法显示出快速和准确的预测的希望.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 预测蛋白质结构对于理解蛋白质折叠和功能至关重要.
- 多重序列对齐 (MSA) 对于准确的蛋白质结构预测至关重要,以AlphaFold2 (AF2) 为例.
- 基于MSA的方法与缺乏足够同源序列的孤儿蛋白质进行斗争,阻碍了蛋白质设计和突变研究中的应用.
研究的目的:
- 评估蛋白质结构预测方法,用于具有有限或没有同源序列的蛋白质.
- 开发和评估新的策略,包括MSA增强和MSA免费的方法,以克服MSA限制.
- 为特定生物应用 (如酶工程和药物开发) 选择适当的预测工具提供指导.
主要方法:
- 构建两个基准数据集,Orphan62和Design204,用于缺乏/没有同质性的蛋白质.
- 开发MSA增强方法以提高MSA质量和MSA免费方法以绕过MSA依赖.
- 针对AlphaFold对四种没有MSA的方法 (trRosettaX-Single,TRFold,ESMFold,ProtT5) 和一种使用MSA增强的方法 (Bagging MSA) 的评估2.2.
主要成果:
- 没有MSA的方法,特别是trRosettaX-Single和ESMFold,实现了快速预测 (大约. 40s) 的性能与AF2相似,用于三级结构预测.
- 这些没有MSA的方法对短,α螺旋段和具有少量同源序列的目标表现出了特别的有效性.
- 通过MSA增强的方法 (Bagging MSA) 在同质性信息稀缺时,提高了二级结构预测的准确性.
结论:
- 没有MSA的方法为蛋白质结构预测提供了可行和高效的替代方案,特别是对于孤儿和de novo蛋白质.
- 用MSA增强的技术可以在低同质性场景中提升现有的基于MSA的模型的性能.
- 这些发现指导生物学家在选择蛋白质工程和药物开发的快速和准确的工具.
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