Serverless Prediction of Peptide Properties with Recurrent Neural Networks

We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence-based solubility predictor, MahLooL, outperforms the current state-of-the-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 that typically require upkeep and maintenance. Our predictive models do not require servers, require no installation of dependencies, and work across a range of devices. The specific architecture is bidirectional recurrent neural networks. This serverless approach 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.

Automated summary

We propose three deep learning sequence-based prediction models for peptide properties, achieving comparable results to state-of-the-art models. Our sequence-based solubility predictor, MahLooL, outperforms current methods for short peptides. These models are implemented as a static website, providing accessible and reproducible predictions without the need for third-party servers or cloud computing.