Result Details
Anticipating protein evolution with successor sequence predictor
Kohout Pavel, Ing.
Musil Miloš, Ing., Ph.D., DIFS (FIT)
Rosinská Monika, Ing.
Damborský Jiří, prof. Mgr., Dr., UMEL (FEEC)
Mazurenko Stanislav, Ph.D.
Bednář David, FIT (FIT)
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 fnal modifcations 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/freprotasr/.
Protein design, Activity, Adaptation, Evolution, Thermostability, Solubility,
Evolutionary trajectory
@article{BUT197679,
author="Rayyan {Khan} and Pavel {Kohout} and Miloš {Musil} and Monika {Rosinská} and Jiří {Damborský} and Stanislav {Mazurenko} and David {Bednář}",
title="Anticipating protein evolution with successor sequence predictor",
journal="Journal of Cheminformatics",
year="2025",
volume="17",
number="34",
pages="1--12",
doi="10.1186/s13321-025-00971-z",
url="https://pmc.ncbi.nlm.nih.gov/articles/PMC11927200/"
}