BionoiNet: Ligand-binding site classification with off-the-shelf deep neural network.

07:00 EST 13th February 2020 | BioPortfolio

Summary of "BionoiNet: Ligand-binding site classification with off-the-shelf deep neural network."

Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures, but also to projects in protein evolution, protein engineering, and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods.


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This article was published in the following journal.

Name: Bioinformatics (Oxford, England)
ISSN: 1367-4811


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Medical and Biotech [MESH] Definitions

The small ribonucleoprotein component of RIBOSOMES. It contains the MESSENGER RNA binding site and two TRANSFER RNA binding sites - one for the incoming AMINO ACYL TRNA (A site) and the other (P site) for the peptidyl tRNA carrying the elongating peptide chain.

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The largest of the three prokaryotic initiation factors with a molecular size of approximately 80 kD. It functions in the transcription initiation process by promoting the binding of formylmethionine-tRNA to the P-site of the 30S ribosome and by preventing the incorrect binding of elongator tRNA to the translation initiation site.

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