TY - JOUR
T1 - AlphaCRV
T2 - a pipeline for identifying accurate binder topologies in mass-modeling with AlphaFold
AU - Guzmán-Vega, Francisco J.
AU - Arold, Stefan T.
N1 - Publisher Copyright:
© 2024 The Author(s).
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85204363212&partnerID=8YFLogxK
U2 - 10.1093/bioadv/vbae131
DO - 10.1093/bioadv/vbae131
M3 - Article
C2 - 39286602
AN - SCOPUS:85204363212
SN - 2635-0041
VL - 4
JO - Bioinformatics Advances
JF - Bioinformatics Advances
IS - 1
M1 - vbae131
ER -