Publication Details

Anticipating protein evolution with successor sequence predictor

KHAN Rayyan T., KOHOUT Pavel, MUSIL Miloš, ROSINSKÁ Monika, DAMBORSKÝ Jiří, MAZURENKO Stanislav and BEDNÁŘ David. Anticipating protein evolution with successor sequence predictor. Journal of Cheminformatics, vol. 17, no. 34, 2025, pp. 1-12. ISSN 1758-2946. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11927200/

Czech title

Předvídání evoluce proteinů pomocí prediktoru následníků

Type

journal article

Language

english

Authors

Khan Rayyan T., M.Sc. (LL)
Kohout Pavel, Ing. (FIT BUT)
Musil Miloš, Ing., Ph.D. (DIFS FIT BUT)
Rosinská Monika, Ing. (FIT BUT)
Damborský Jiří, prof. Mgr., Dr. (SCI MUNI)
Mazurenko Stanislav, Ph.D. (LL)
Bednář David, Mgr. (LL)

URL

https://pmc.ncbi.nlm.nih.gov/articles/PMC11927200/

Keywords

Protein design, Activity, Adaptation, Evolution, Thermostability, Solubility, Evolutionary trajectory

Abstract

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/.

Published

2025

Pages

1-12

Journal

Journal of Cheminformatics, vol. 17, no. 34, ISSN 1758-2946

Publisher

BioMed Central

DOI

10.1186/s13321-025-00971-z

BibTeX

@ARTICLE{FITPUB13489,
   author = "T. Rayyan Khan and Pavel Kohout and Milo\v{s} Musil and Monika Rosinsk\'{a} and Ji\v{r}\'{i} Damborsk\'{y} and Stanislav Mazurenko and David Bedn\'{a}\v{r}",
   title = "Anticipating protein evolution with successor sequence predictor",
   pages = "1--12",
   journal = "Journal of Cheminformatics",
   volume = 17,
   number = 34,
   year = 2025,
   ISSN = "1758-2946",
   doi = "10.1186/s13321-025-00971-z",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/13489"
}