ArticleBriefings in bioinformatics2026
FireProtASR 2.0: evolution-guided Design of Protein Ancestors and Successors with phylogenetics and machine learning.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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12 authors.
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Abstract
Evolution-guided protein design remains one of the most effective strategies for engineering proteins with enhanced stability, activity, or specificity. To make these approaches more accessible, we previously developed FireProtASR-a fully automated pipeline for ancestral sequence reconstruction (ASR). Here, we present FireProtASR 2.0, a significantly enhanced version that extends the design space beyond ancestral inference by integrating a successor sequence predictor (SSP) and a generative model based on variational autoencoders (VAEs). These new modules enable both 'prospective' and 'retrospective' evolutionary design strategies. The SSP module predicts likely future mutations based on site-wise evolutionary trends, and the method was previously validated through in silico benchmarks, demonstrating improvements in thermostability and activity. The VAE module captures global evolutionary constraints in a low-dimensional latent space, from which novel functional ancestral-like variants can be sampled. The VAE-based design strategy was previously validated experimentally on the haloalkane dehalogenase family, yielding variants with enhanced thermostability while maintaining catalytic activity. Both these modules are newly available in FireProtASR in a fully automated pipeline, guiding the users via an interactive graphical user interface. With expanded functionality, modernized user interface, and a more robust backend, FireProtASR 2.0 provides a comprehensive, accessible, and fully automated platform for evolutionary-based protein engineering (https://loschmidt.chemi.muni.cz/fireprotasr/).
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