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BionoiNet: ligand-binding site classification with off-the-shelf deep neural network
- Source :
- Bioinformatics
- Publication Year :
- 2020
- Publisher :
- Oxford University Press, 2020.
-
Abstract
- Motivation 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. Results We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures. Availability and implementation BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects :
- Statistics and Probability
Computer science
02 engineering and technology
Machine learning
computer.software_genre
Ligands
Biochemistry
Convolutional neural network
03 medical and health sciences
Protein structure
Voxel
0202 electrical engineering, electronic engineering, information engineering
Molecule
Nucleotide
Molecular Biology
030304 developmental biology
chemistry.chemical_classification
0303 health sciences
Binding Sites
Artificial neural network
Molecular Structure
business.industry
Ligand
Deep learning
Proteins
Protein engineering
Ligand (biochemistry)
Original Papers
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
chemistry
Drug development
020201 artificial intelligence & image processing
Artificial intelligence
Neural Networks, Computer
business
Classifier (UML)
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....25e92d62446b994059568e6a8e648803