PyUUL provides an interface between biological structures and deep learning algorithms
Science
Q
0206 medical engineering
Computational Biology
02 engineering and technology
Article
Deep Learning
Imaging, Three-Dimensional
Humans
Protein Structural Elements
Neural Networks, Computer
Algorithms
DOI:
10.1038/s41467-022-28327-3
Publication Date:
2022-02-18T11:24:46Z
AUTHORS (6)
ABSTRACT
AbstractStructural bioinformatics suffers from the lack of interfaces connecting biological structures and machine learning methods, making the application of modern neural network architectures impractical. This negatively affects the development of structure-based bioinformatics methods, causing a bottleneck in biological research. Here we present PyUUL (https://pyuul.readthedocs.io/), a library to translate biological structures into 3D tensors, allowing an out-of-the-box application of state-of-the-art deep learning algorithms. The library converts biological macromolecules to data structures typical of computer vision, such as voxels and point clouds, for which extensive machine learning research has been performed. Moreover, PyUUL allows an out-of-the box GPU and sparse calculation. Finally, we demonstrate how PyUUL can be used by researchers to address some typical bioinformatics problems, such as structure recognition and docking.
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CITATIONS (17)
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