Naive prediction of protein backbone phi and psi dihedral angles using deep learning

Matic Broz (Author), Marko Jukič (Author), Urban Bren (Author)

Abstract

Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone φ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the φ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies.

Keywords

globoke nevronske mreže;proteinske strukture;kotne napovedi;protein structure prediction;backbone dihedral angles;deep neural network;fully connected neural network;FCNN;protein secondary structure prediction;

Data

Language:	English
Year of publishing:	2023
Typology:	1.01 - Original Scientific Article
Organization:	UM FKKT - Faculty of Chemistry and Chemical Engineering
Publisher:	MDPI
UDC:	54
COBISS:	168255235
ISSN:	1420-3049
Views:	203
Downloads:	21
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	globoke nevronske mreže;proteinske strukture;kotne napovedi;
Type (COBISS):	Article
Pages:	19 str.
Volume:	ǂVol. ǂ28
Issue:	ǂiss. ǂ20, [article no.] 7046
Chronology:	Okt. 2023
DOI:	10.3390/molecules28207046
ID:	21371964