AlphaCRV: a pipeline for identifying accurate binder topologies in mass-modeling with AlphaFold.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-09-06 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae131
Francisco J Guzmán-Vega, Stefan T Arold
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引用次数: 0

Abstract

Motivation: 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.

Results: 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.

Availability and implementation: AlphaCRV is a Python package for Linux, freely available at https://github.com/strubelab/AlphaCRV.

AlphaCRV:利用 AlphaFold 在质量建模中识别准确粘合剂拓扑结构的管道。
动机基于深度学习的结构预测算法的速度和准确性使得在整个蛋白质组范围内进行硅学 "下拉"(pull-downs)以识别蛋白质-蛋白质相互作用成为可能。然而,在如此大的范围内,现有的评分算法往往不足以区分生物相关相互作用和假阳性相互作用:在这里,我们介绍了 AlphaCRV,这是一个 Python 软件包,它可以根据蛋白质序列和折叠,对保守的结合拓扑进行聚类、排序和可视化,从而帮助在一对多的 AlphaFold 筛选中识别出正确的相互作用者:AlphaCRV 是一个适用于 Linux 的 Python 软件包,可从 https://github.com/strubelab/AlphaCRV 免费获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.60
自引率
0.00%
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