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

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 net

SUBMITTER: Alam FF 

PROVIDER: S-EPMC7179114 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature