Abstract
Bionoi is a new software to generate Voronoi representations of ligand-binding sites in proteins for machine learning applications. Unlike many other deep learning models in biomedicine, Bionoi utilizes off-the-shelf convolutional neural network architectures, reducing the development work without sacrificing the performance. When initially generating images of binding sites, users have the option to color the Voronoi cells based on either one of six structural, physicochemical, and evolutionary properties, or a blend of all six individual properties. Encouragingly, after inputting images generated by Bionoi into the convolutional autoencoder, the network was able to effectively learn the most salient features of binding pockets. The accuracy of the generated model is evaluated both visually and numerically through the reconstruction of binding site images from the latent feature space. The generated feature vectors capture well various properties of binding sites and thus can be applied in a multitude of machine learning projects. As a demonstration, we trained the ResNet-18 architecture from Microsoft on Bionoi images to show that it is capable to effectively classify nucleotide- and heme-binding pockets against a large dataset of control pockets binding a variety of small molecules. Bionoi is freely available to the research community at https://github.com/CSBG-LSU/BionoiNet
Key words
- Bionoi
- Machine learning
- Deep learning
- Convolutional neural network
- Voronoi diagrams
- Ligand-binding site classification
- Computer-aided drug discovery
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Acknowledgments
This work has been supported in part by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM119524, by the US National Science Foundation award CCF-1619303, the Louisiana Board of Regents contract LEQSF(2016-19)-RD-B-03, and by the Center for Computation and Technology, Louisiana State University. Portions of this research were conducted with high-performance computational resources provided by Louisiana State University (HPC@LSU, http://www.hpc.lsu.edu). The authors are grateful to Ms. Manali Singha for visualizing molecular structures.
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Feinstein, J., Shi, W., Ramanujam, J., Brylinski, M. (2021). Bionoi: A Voronoi Diagram-Based Representation of Ligand-Binding Sites in Proteins for Machine Learning Applications. In: Ballante, F. (eds) Protein-Ligand Interactions and Drug Design. Methods in Molecular Biology, vol 2266. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1209-5_17
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