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FRAGSITE2:一种基于结构和片段的方法,用于虚拟连接体查

Hongyi Zhou¹, Jeffrey Skolnick¹

¹Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.

Protein science : a publication of the Protein Society

|December 15, 2023

概括

FRAGSITE2增强了用于药物发现的虚拟连接体查 (VLS),识别了针对蛋白质标的多种小分子结合剂. 这种联体同质模型 (LHM) 方法的性能优于现有的方法,有助于发现新药候选药物.

科学领域:

计算化学和化学信息学
药物的发现和开发.
生物信息学和计算生物学

背景情况:

蛋白质功能注释和药物发现依赖于识别小分子结合剂.
虚拟带查 (VLS) 是药物发现的关键早期方法.
现有的VLS方法,包括联体同质模型 (LHM) 和深度学习,在识别多种结合物的过程中存在局限性.

研究的目的:

为了引入FRAGSITE2,一个改进的VLS方法.
加强对蛋白质标的多种小分子结合剂的鉴定,特别是那些缺乏已知的结合剂的蛋白质.
为早期药物发现提供强大的计算工具.

主要方法:

开发FRAGSITE2,一个先进的LHM机器学习VLS方法.
在DUD-E和DEKOIS2.0数据集上进行基准测试,以评估与FINDSITE,DenseFS和RF-score-VS等现有方法相比的性能.
利用增强树回归,并将性能与深度学习多层感知器和预训练语言模型进行比较.

主要成果:

在DUD-E和DEKOIS2.0数据集上,FRAGSITE2显著改善了1%的丰富系数 (EF1%),特别是在缺乏已知的结合剂的目标上.
与DUD-E集上的DenseFS相比,实现了优越的ROC丰富系数和精度回忆曲线下的面积 (AUPR).
与RF-score-VS和预训练的语言模型相比,表现明显更好,在特定的比较中,EF1%的两倍.

结论:

FRAGSITE2代表了VLS的重大进步,提供了更强大的性能,并使得发现新型结合配体成为可能.
该方法对于具有有限或未知的小分子结合剂的蛋白质标特别有效.
FRAGSITE2作为学术用户的免费网络服务,为药物发现研究的更广泛应用提供了便利.

关键词:

在FINDSITE上找到.这里是 FRAGSITE 的网站.发现药物的发现.连接物同质性建模模型.机器学习是机器学习.模板链接链接模板链接虚拟连接体查虚拟连接体查

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