Prodigy protein: Python package for zero-shot protein engineering using protein language models.

backgroundProtein Language Models (PLMs) are emerging as powerful tools for designing human proteins, including antibodies. These models can predict the effects of mutations in a zero-shot setting-without requiring additional fine-tuning-and suggest plausible amino acid substitutions.

resultsWe introduce Protein Diversification and Generation through Yielded Mutations (Prodigy) Protein which provides several DirectedEvolution classes that introduce amino acid substitutions in a stepwise manner. Each substitution is evaluated using one of two scoring strategies, and the most promising candidates are sampled accordingly. Users can customize the number of evolution steps, specify target regions within the protein sequence, and set score thresholds to filter out low-quality substitutions during the design process.

conclusionProtein Diversification and Generation through Yielded Mutations (Prodigy) Protein is a fast and flexible tool for in silico protein design. It introduces a consistent and efficient probabilistic framework that leverages any masked language modeling Protein Language Model (PLM) available via Hugging Face. Unlike existing tools, Prodigy Protein can integrate multiple PLMs to design protein variants-an approach not currently supported by other publicly available software.