Deploy state-of-the-art ML models based on your sequence and function data to generate new, more diverse variants. No specialized skill required.
The OpenProtein.AI web app provides a suite of software tools to generate novel variant libraries and predict their success over multiple functions of interest. Visualize your mutagenesis data, train machine learning models for functions of interest, define your design objectives, and build optimized variant libraries.
Streamline your research process with advanced in- app data management capabilities. OpenProtein.AI is a secure data repository for large mutagenesis datasets.
OpenProtein.AI mines natural sequence databases and learns from your experimental data to accelerate the iterative design process. Design variants with significantly enhanced activity compared to standard directed mutagenesis.
OpenProtein.AI can improve multiple properties simultaneously to reduce experimental iterations. Every subsequent round and project benefits from previous data.
Develop & deploy models based on your data to predict activity for any input sequence and map all single site substitutions to identify linchpin locations for site-saturating mutagenesis. Visualize functional predictions for all single-site substitutions and export amino acid distributions for degenerate and combinatorial variant libraries.
PoET is an autoregressive, retrieval-augmented, generative transformer protein language model.
Intuitive workflows are quick and easy to use. Results are returned in minutes and can be exported in multiple formats.
Define your evolutionary context through prompt customization. Use any sequence database with custom MSAs. Adjust diversity of the model with in-software homology level settings.
Validated on 90 different deep mutational scanning datasets
PoET can model
Performance is measured as the rank correlation between variant likelihoods and measured function. N/A is reported for models that cannot predict indels.