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@article{2420557,
author = {Sikora, Maciej and Klimentová, Eva and Uchal, Dawid and Šrámková, Denisa and Perlinska, Agata P and Nguyen, Mai Lan and Korpacz, Marta and Malinowska, Roksana and Nowakowski, Szymon and Rubach, Pawel and Šimeček, Petr and Sulkowska, Joanna I},
article_location = {HOBOKEN},
article_number = {7},
doi = {http://dx.doi.org/10.1002/pro.4998},
keywords = {AlphaFold; deep learning; knotted proteins; protein topology; SPOUT family proteins},
language = {eng},
issn = {0961-8368},
journal = {Protein Science},
title = {Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model},
url = {https://onlinelibrary.wiley.com/doi/10.1002/pro.4998},
volume = {33},
year = {2024}
}
TY - JOUR
ID - 2420557
AU - Sikora, Maciej - Klimentová, Eva - Uchal, Dawid - Šrámková, Denisa - Perlinska, Agata P - Nguyen, Mai Lan - Korpacz, Marta - Malinowska, Roksana - Nowakowski, Szymon - Rubach, Pawel - Šimeček, Petr - Sulkowska, Joanna I
PY - 2024
TI - Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model
JF - Protein Science
VL - 33
IS - 7
SP - 1-21
EP - 1-21
PB - WILEY
SN - 09618368
KW - AlphaFold
KW - deep learning
KW - knotted proteins
KW - protein topology
KW - SPOUT family proteins
UR - https://onlinelibrary.wiley.com/doi/10.1002/pro.4998
N2 - Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles continue to be a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold (AF), this study computationally examines the entire UniProt database to create a robust dataset of knotted and unknotted proteins. Utilizing this dataset, we develop a machine learning (ML) model capable of accurately predicting the presence of knots in protein structures solely from their amino acid sequences. We tested the model's capabilities on 100 proteins whose structures had not yet been predicted by AF and found agreement with our local prediction in 92% cases. From the point of view of structural biology, we found that all potentially knotted proteins predicted by AF can be classified only into 17 families. This allows us to discover the presence of unknotted proteins in families with a highly conserved knot. We found only three new protein families: UCH, DUF4253, and DUF2254, that contain both knotted and unknotted proteins, and demonstrate that deletions within the knot core could potentially account for the observed unknotted (trivial) topology. Finally, we have shown that in the majority of knotted families (11 out of 15), the knotted topology is strictly conserved in functional proteins with very low sequence similarity. We have conclusively demonstrated that proteins AF predicts as unknotted are structurally accurate in their unknotted configurations. However, these proteins often represent nonfunctional fragments, lacking significant portions of the knot core (amino acid sequence).
ER -
SIKORA, Maciej, Eva KLIMENTOVÁ, Dawid UCHAL, Denisa ŠRÁMKOVÁ, Agata P PERLINSKA, Mai Lan NGUYEN, Marta KORPACZ, Roksana MALINOWSKA, Szymon NOWAKOWSKI, Pawel RUBACH, Petr ŠIMEČEK and Joanna I SULKOWSKA. Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model. \textit{Protein Science}. HOBOKEN: WILEY, 2024, vol.~33, No~7, p.~1-21. ISSN~0961-8368. Available from: https://dx.doi.org/10.1002/pro.4998.
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