Guzmán-Vega Francisco J, Arold Stefan T
Biological and Environmental Science and Engineering Division, Computational Biology Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.
Bioinform Adv. 2024 Sep 6;4(1):vbae131. doi: 10.1093/bioadv/vbae131. eCollection 2024.
The speed and accuracy of deep learning-based structure prediction algorithms make it now possible to perform in silico "pull-downs" to identify protein-protein interactions on a proteome-wide scale. However, on such a large scale, existing scoring algorithms are often insufficient to discriminate biologically relevant interactions from false positives.
Here, we introduce AlphaCRV, a Python package that helps identify correct interactors in a one-against-many AlphaFold screen by clustering, ranking, and visualizing conserved binding topologies, based on protein sequence and fold.
AlphaCRV is a Python package for Linux, freely available at https://github.com/strubelab/AlphaCRV.
基于深度学习的结构预测算法的速度和准确性使得现在有可能在计算机上进行“下拉”操作，以在全蛋白质组范围内识别蛋白质-蛋白质相互作用。然而，在如此大规模的情况下，现有的评分算法往往不足以区分生物学上相关的相互作用和假阳性。
在此，我们引入了AlphaCRV，这是一个Python软件包，它通过基于蛋白质序列和折叠对保守的结合拓扑进行聚类、排序和可视化，帮助在一对一多的AlphaFold筛选中识别正确的相互作用分子。
AlphaCRV是一个适用于Linux的Python软件包，可在https://github.com/strubelab/AlphaCRV上免费获取。
