Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron–Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool’s availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.

In this study, we focus on a detailed bioinformatics analysis of hyBpox genes, mainly within the genomes of Sclerotiniaceae (Ascomycota, Leotiomycetes), which is a specifically evolved fungal family of necrotrophic host generalists and saprophytic or biotrophic host specialists. Members of the genus Sclerotium produce only sclerotia and no fruiting bodies or spores. Thus, their physiological role for peroxidases remains open. A representative species, S. cepivorum, is a dangerous plant pathogen causing white rot in Allium species, particularly in onions, leeks, and garlic. On a worldwide basis, the white rot caused by this soil-borne fungus is apparently the most serious threat to Allium-crop production. We have also found very similar peroxidase sequences in the related fungus S. sclerotiorum, although with minor yet important modifications in the architecture of its active centre. The presence of ScephyBpox1-specific mRNA was confirmed by transcriptomic analysis. The presence of Hybrid B peroxidase at the protein level as the sole extracellular peroxidase of this fungus was confirmed in the secretome of S. cepivorum through detailed proteomic analyses. This prompted us to systematically search for all available genes coding for Hybrid B heme peroxidases in the whole fungal family of Sclerotiniaceae. We present here a reconstruction of their molecular phylogeny and analyse the unique aspects of their conserved-sequence features and structural folds in corresponding ancestral sequences.

Catalase–peroxidases (KatGs) are unique bifunctional oxidoreductases that contain heme in their active centers allowing both the peroxidatic and catalatic reaction modes. These originally bacterial enzymes are broadly distributed among various fungi allowing them to cope with reactive oxygen species present in the environment or inside the cells. We used various biophysical, biochemical, and bioinformatics methods to investigate differences between catalase–peroxidases originating in thermophilic and mesophilic fungi from different habitats. Our results indicate that the architecture of the active center with a specific post-translational modification is highly similar in mesophilic and thermophilic KatG and also the peroxidatic acitivity with ABTS, guaiacol, and L-DOPA. However, only the thermophilic variant CthedisKatG reveals increased manganese peroxidase activity at elevated temperatures. The catalatic activity releasing molecular oxygen is comparable between CthedisKatG and mesophilic MagKatG1 over a broad temperature range. Two constructed point mutations in the active center were performed selectively blocking the formation of described post-translational modification in the active center. They exhibited a total loss of catalatic activity and changes in the peroxidatic activity. Our results indicate the capacity of bifunctional heme enzymes in the variable reactivity for potential biotech applications.

Thermostable proteins are crucial in numerous biomedical and biotechnological applications. However, naturally occurring proteins have evolved to function in mild conditions, and laboratory experiments aiming at improving protein stability have proven laborious and expensive. Computational methods overcome this issue by providing a cheap and scalable alternative. Despite significant progress, their reliability is still hindered by the availability of high-quality data. FireProtDB 2.0 (http://loschmidt.chemi.muni.cz/fireprotdb) is a large-scale database aggregating stability data from multiple sources. The second version builds upon its predecessor, retaining its original functionality while introducing a new approach to data storage and maintenance. The new scheme enables the introduction of both absolute and relative data types connected with measurements of wild-types, mutants, protein domains, and de novo designed proteins. Furthermore, while the original database was limited to single-point mutations, more complex data such as insertions, deletions, and multiple-point mutations are now available. As a result, the inclusion of large-scale mutagenesis has increased the size of the database from 16 000 to almost 5 500 000 experiments. Moreover, the updated abstract scheme is fully expandable with any new measurements and annotations without the need for any restructuring. Finally, the tracking of history together with fixed identifiers is in accordance with the FAIR principles.

Single amino acid mutations are among the most common forms of genetic variation, with effects ranging from negligible to severe disruption of protein function. Accurate quantitative evaluation of protein variants therefore represents a key challenge in medicine and protein engineering. Computational variant effect predictors (VEPs) are widely used to prioritize missense variants, yet their fair comparison is hindered by data circularity, benchmarks restricted to human clinical annotations, and limited availability of code and training data, all of which can inflate apparent performance. Here, we reviewed 18 recent and 3 older, widely adopted variant effect predictors for reproducible benchmarking and assembled a rigorously curated, leakage-controlled Benchmark dataset by integrating clinically annotated human variants from ClinVar and Humsavar as well as cross-species variants with functional annotations. The evaluation on this dataset revealed a narrow overall performance range for the benchmarked tools (AUC 0.631-0.758) and a consistent drop from clinical to functional data. Surprisingly, several tools published two decades ago demonstrated comparable performance to mid-ranked modern predictors. Building on these results, we developed a gradient-boosting consensus model, PredictSNPprot, which achieved an AUC of 0.82 on a held-out test set, outperforming the original PredictSNP meta-predictor and the best individual state-of-the-art tool by 13.6 and 5.7 percentage points, respectively. The Benchmark dataset, including the predictions from the evaluated tools, is available at https://doi.org/10.5281/zenodo.23597130 and PredictSNPprot consensus predictor is available at https://github.com/loschmidt/PredictSNPprot.