通过整合蛋白质语言模型和分子动力学模拟,从微生物群中发现天然存在的防
Ibrahim A Imam1, Trevor Morey1,2, Yuexu Jiang1,3
1Department of Chemical and Materials Engineering, University of Kentucky, Lexington, KY 40506, USA. qshao@uky.edu.
Journal of materials chemistry. B
|March 9, 2026
概括
研究人员使用机器学习和模拟发现了新的抗. 这种可扩展的方法可以从各种微生物来源中识别潜在的冷保护剂和抗结冰剂.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 材料科学 材料科学 材料科学
背景情况:
- 在生物系统和工业应用中防止冰损伤,抗酸 (AFPs) 是至关重要的.
- 由于序列变异性和实验查的局限性,发现新型AFP具有挑战性.
研究的目的:
- 开发一种可扩展的计算策略,用于识别新型抗类候选者.
- 通过分子动力学模拟来验证已识别的候选物的功能潜力.
主要方法:
- 一个整体机器学习模型,结合蛋白质语言模型和随机森林元学习者,在已知的AFP的大数据集上进行了训练.
- 经过训练的模型被应用到来自北极微生物组的大型序列库中.
- 使用结构预测和原子分子动力学 (MD) 模拟来评估候选物稳定性和结冰潜力.
主要成果:
- 合奏模型成功地从北极微生物组图书馆中识别了新的抗类候选者.
- 结构分析揭示了选定的候选人的各种形状,包括螺旋结构.
- MD模拟表明,一个候选人表现出持续的螺旋性,表面两胞性和冰结合性AFP特征的水合模式.
结论:
- 这项研究提出了一种可扩展和有效的计算方法,用于从复杂的生物来源中发现功能性防.
- 已识别的候选物扩大了已知的AFP的目录,并有望在冷保存和抗结冰技术中的应用.
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