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Variational Autoencoder for Generation of Antimicrobial Peptides.

ABSTRACT: Over millennia, natural evolution has allowed for the emergence of countless biomolecules with highly specific roles within natural systems. As seen with peptides and proteins, often evolution produces molecules with a similar function but with variable amino acid composition and structure but diverging from a common ancestor, which can limit sequence diversity. Using antimicrobial peptides as a model biomolecule, we train a generative deep learning algorithm on a database of known antimicrobial peptides to generate novel peptide sequences with antimicrobial activity. Using a variational autoencoder, we are able to generate a latent space plot that can be surveyed for peptides with known properties and interpolated across a predictive vector between two defined points to identify novel peptides that show dose-responsive antimicrobial activity. These proof-of-concept studies demonstrate the potential for artificial intelligence-directed methods to generate new antimicrobial peptides and motivate their potential application toward peptide and protein design without the need for exhaustive screening of sequence libraries.

SUBMITTER: Dean SN

PROVIDER: S-EPMC7450509 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

Variational Autoencoder for Generation of Antimicrobial Peptides.

Dean Scott N SN Walper Scott A SA

ACS omega 20200810 33

Over millennia, natural evolution has allowed for the emergence of countless biomolecules with highly specific roles within natural systems. As seen with peptides and proteins, often evolution produces molecules with a similar function but with variable amino acid composition and structure but diverging from a common ancestor, which can limit sequence diversity. Using antimicrobial peptides as a model biomolecule, we train a generative deep learning algorithm on a database of known antimicrobial ...[more]

PMID: 32875208

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Project description:BackgroundMultiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively modest number of tumor profiles are clinically annotated for response to chemotherapy. The paucity of labeled examples and the high dimension of the feature data limit performance for predicting therapeutic response using fully-supervised classification methods. Recently, multiple studies have established the utility of a deep neural network approach, the variational autoencoder (VAE), for generating meaningful latent features from original data. Here, we report the first study of a semi-supervised approach using VAE-encoded tumor transcriptome features and regularized gradient boosted decision trees (XGBoost) to predict chemotherapy drug response for five cancer types: colon, pancreatic, bladder, breast, and sarcoma.ResultsWe found: (1) VAE-encoding of the tumor transcriptome preserves the cancer type identity of the tumor, suggesting preservation of biologically relevant information; and (2) as a feature-set for supervised classification to predict response-to-chemotherapy, the unsupervised VAE encoding of the tumor's gene expression profile leads to better area under the receiver operating characteristic curve and area under the precision-recall curve classification performance than the original gene expression profile or the PCA principal components or the ICA components of the gene expression profile, in four out of five cancer types that we tested.ConclusionsGiven high-dimensional "omics" data, the VAE is a powerful tool for obtaining a nonlinear low-dimensional embedding; it yields features that retain biological patterns that distinguish between different types of cancer and that enable more accurate tumor transcriptome-based prediction of response to chemotherapy than would be possible using the original data or their principal components.

Dataset Information

Variational Autoencoder for Generation of Antimicrobial Peptides.

Publications

Variational Autoencoder for Generation of Antimicrobial Peptides.

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