重新连接蛋白质序列和结构生成模型,以增强蛋白质稳定性预测
1School of Computational Science and Engineering, Georgia Institute of Technology.
bioRxiv : the preprint server for biology
|March 3, 2025
概括
新的深度学习框架SPURS通过整合序列和结构模型,准确预测突变导致的蛋白质稳定性变化. 这促进了蛋白质工程和疾病的理解.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 在生物信息学中的机器学习.
背景情况:
- 从氨基酸替代中预测蛋白质热稳定性变化对于疾病研究和蛋白质工程至关重要.
- 现有的蛋白质生成模型显示出希望,但在预测蛋白质功能 (如稳定性) 方面存在局限性.
- 这些模型提高蛋白质稳定性预测的潜力在很大程度上仍未被探索.
研究的目的:
- 推出SPURS,这是一个新的深度学习框架,用于预测蛋白质的热稳定性.
- 整合蛋白质语言模型 (ESM) 和反向折叠模型 (ProteinMPNN) 以提高稳定性预测.
- 评估SPURS在蛋白质稳定性和功能分析中的性能和多功能性.
主要方法:
- SPURS集成ESM和ProteinMPNN,使用神经网络模块将序列和结构信息结合起来.
- 一个重新连接策略通过结合结构先验来增强序列表示学习.
- 该框架以大规模的热稳定性数据集进行训练,用于监督预测突变效应.
主要成果:
- 在12个基准数据集中,SPURS在准确性,速度,可扩展性和通用性方面始终超过了最先进的方法.
- 该框架在与蛋白质语言模型相结合时,以无监督的方式准确识别蛋白质功能位点.
- SPURS通过作为稳定性先验来改善低N蛋白健身预测模型.
结论:
- SPURS是一个强大的工具,用于推进蛋白质稳定性预测和机器学习引导的蛋白质工程.
- 该框架集成序列和结构数据的能力提供了显著的优势.
- SPURS展示了多功能性,提高了稳定性预测和功能性网站识别.
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