ProSol-multi:通过氨基酸多层相关性和歧视性分布来预测蛋白质溶解度
Hina Ghafoor1,2, Muhammad Nabeel Asim2, Muhammad Ali Ibrahim1,2
1Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany.
Heliyon
|September 16, 2024
概括
ProSol-Multi 通过使用新型的 MLCDE 编码器和随机森林分类器,增强了蛋白质可溶性预测. 这种人工智能方法准确地区分可溶和不可溶的蛋白质,有助于药物开发和疾病生物标志物的发现.
科学领域:
- 生物技术是生物技术.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 蛋白质溶解性对于药物开发,重组蛋白质合成和疾病生物标志物识别至关重要.
- 预测蛋白质溶解性的实验方法往往耗时,昂贵,容易出现错误.
- 现有的基于人工智能的预测器很难完全捕捉歧视性氨基酸分布,以准确地预测溶解度.
研究的目的:
- 开发一个强大的计算预测器,准确地预测蛋白质溶解度.
- 为蛋白质序列分析引入一种新的多级相关和差别分布编码 (MLCDE) 方法.
- 与现有方法相比,改进可溶和不可溶蛋白之间的区别.
主要方法:
- 开发MLCDE编码器,将蛋白质序列转化为统计载体,捕捉氨基酸相关性和分布.
- 使用随机森林分类器与MLCDE编码器一起用于蛋白质可溶性预测.
- 在基准数据集上对MLCDE编码器对56种现有方法和ProSol-Multi预测器对20种现有预测器进行评估.
主要成果:
- 在内在评估中,MLCDE编码器生成了可溶性和不可溶性蛋白质类的非重叠集群.
- 使用MLCDE编码器的机器学习分类器在外部评估中表现优于现有的56个编码器.
- 在四个数据集中,ProSol-Multi预测器实现了卓越的性能,超过了20个现有预测器,平均准确率为3%,MCC/AU-ROC为2%.
结论:
- 由MLCDE编码器驱动的ProSol-Multi预测器在计算蛋白质溶解度预测方面取得了重大进展.
- 这种人工智能驱动的方法为选择候选蛋白质的实验方法提供了更准确,更有效的替代方案.
- ProSol-Multi网络应用程序可用于研究和药物开发中的更广泛使用.
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