Evaluating Autoencoder-Based Featurization and Supervised Learning for Protein Decoy Selection

Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry
Fardina Fathmiul AlamAmarda Shehu

Abstract

Rapid growth in molecular structure data is renewing interest in featurizing structure. Featurizations that retain information on biological activity are particularly sought for protein molecules, where decades of research have shown that indeed structure encodes function. Research on featurization of protein structure is active, but here we assess the promise of autoencoders. Motivated by rapid progress in neural network research, we investigate and evaluate autoencoders on yielding linear and nonlinear featurizations of protein tertiary structures. An additional reason we focus on autoencoders as the engine to obtain featurizations is the versatility of their architectures and the ease with which changes to architecture yield linear versus nonlinear features. While open-source neural network libraries, such as Keras, which we employ here, greatly facilitate constructing, training, and evaluating autoencoder architectures and conducting model search, autoencoders have not yet gained popularity in the structure biology community. Here we demonstrate their utility in a practical context. Employing autoencoder-based featurizations, we address the classic problem of decoy selection in protein structure prediction. Utilizing off-th...Continue Reading

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Citations

Feb 12, 2021·Journal of Bioinformatics and Computational Biology·Fardina Fathmiul Alam, Amarda Shehu
Nov 10, 2020··Amarda ShehuFardina Fathmiul Alam

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Methods Mentioned

BETA
PCA

Software Mentioned

Isomap
PLR
PCA
Keras
dAE
Rosetta
sklearn

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