Anticipating protein evolution with successor sequence predictor
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Article in Periodical |
| Magazine / Source | Journal of Cheminformatics |
| MU Faculty or unit | |
| Citation | |
| web | https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-00971-z |
| Doi | http://dx.doi.org/10.1186/s13321-025-00971-z |
| Keywords | Protein design; Activity; Adaptation; Evolution; Thermostability; Solubility; Evolutionary trajectory |
| Attached files | |
| Description | The quest to predict and understand protein evolution has been hindered by limitations on both the theoretical and the experimental fronts. Most existing theoretical models of evolution are descriptive, rather than predictive, leaving the final modifications in the hands of researchers. Existing experimental techniques to help probe the evolutionary sequence space of proteins, such as directed evolution, are resource-intensive and require specialised skills. We present the successor sequence predictor (SSP) as an innovative solution. Successor sequence predictor is an in silico protein design method that mimics laboratory-based protein evolution by reconstructing a protein's evolutionary history and suggesting future amino acid substitutions based on trends observed in that history through carefully selected physicochemical descriptors. This approach enhances specialised proteins by predicting mutations that improve desired properties, such as thermostability, activity, and solubility. Successor Sequence Predictor can thus be used as a general protein engineering tool to develop practically useful proteins. The code of the Successor Sequence Predictor is provided at https://github.com/loschmidt/successor-sequence-predictor, and the design of mutations will be also possible via an easy-to-use web server https://loschmidt.chemi.muni.cz/fireprotasr/. |
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