AlloBench:一个数据集管道,用于开发和基准对比Allosteric站点预测工具
Dibyajyoti Maity1, Baofu Qiao1
1Department of Natural Sciences, Baruch College, City University of New York, New York 10010, New York United States.
ACS omega
|May 12, 2025
概括
我们开发了AlloBench,这是一个用于创建高质量的全基数据集的管道. 目前的预测工具的准确性很低,这凸显了对研究蛋白质质的改进计算方法的需求.
科学领域:
- 生物化学和结构生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 艾洛斯对于通过远程效应体结合来调节宏分子活动至关重要.
- 现有的全性数据集已经过时,不适合进行数据密集型计算研究.
- 需要全面的,最新的资源来研究蛋白质化.
研究的目的:
- 介绍AlloBench管道,以生成高质量的生物分子数据集,包括全性和活性位点信息.
- 为了创建一个强大的数据集,适合计算和数据驱动的研究蛋白质全ostery.
- 在标准化数据集上对现有的全位预测工具进行基准测试.
主要方法:
- 从多个数据库 (AlloSteric数据库,UniProt,MCSA,PDB) 整合数据,使用AlloBench管道.
- 从2034个蛋白质结构中生成了2141个全位的数据集.
- 在100种蛋白质的子集上评估了七种全位预测工具 (APOP,PASSer,Ohm,ALLO,Allosite,STRESS,AlloPred).
主要成果:
- AlloBench 管道成功创建了一个大型,高质量的数据集,用于研究全.
- 所有评估的全位预测工具都显示精度低于60%.
- 与其他经过测试的预测工具相比,PASSer (Ensemble) 显示出更高的性能.
结论:
- 该AlloBench数据集提供了一个宝贵的资源,以推进蛋白质全ostery的研究.
- 目前的全位预测工具需要显著改进.
- AlloBench将促进更准确的预测工具的开发,并作为菌研究的一般参考.
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