Serverless Prediction of Peptide Properties with Recurrent Neural Networks

Ansari, M.; White, A. D.

doi:10.1101/2022.05.18.492545

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bioRxiv · 10.1101/2022.05.18.492545

Serverless Prediction of Peptide Properties with Recurrent Neural Networks

Ansari, M.·White, A. D.

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Abstract

We present three deep learning sequence prediction models for hemolysis, solubility, and resistance to non-specific interactions of peptides that achieve comparable results to the state-of-the-art models. Our sequence-based solubility predictor, MahLooL, outperforms the current state-of-art methods for short peptides. These models are implemented as a static website without the use of a dedicated server or cloud computing. Web-based models like this allow for accessible and effective reproducibility. Most existing approaches rely on third-party servers typically that require upkeep and maintenance. That trend leads to a relatively longer lifetime of web-based models. These predictive models do not require servers, require no installation of dependencies, and work on across a range of devices. The models are bidirectional recurrent neural networks. This serverless prediction model is a demonstration of edge machine learning that removes the dependence on cloud providers. The code and models are accessible at https://github.com/ur-whitelab/peptide-dashboard.

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Ansari, M., White, A. D.. 2022-05-19. Serverless Prediction of Peptide Properties with Recurrent Neural Networks. https://doi.org/10.1101/2022.05.18.492545

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