Anticipating protein evolution with successor sequence predictor

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F25%3A00082224" target="_blank" >RIV/00159816:_____/25:00082224 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14310/25:00141540

Result on the web

<a href="https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-00971-z" target="_blank" >https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-00971-z</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13321-025-00971-z" target="_blank" >10.1186/s13321-025-00971-z</a>

Result language

angličtina

Original language name

Anticipating protein evolution with successor sequence predictor

Original language 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&apos;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/. Scientific Contribution The Successor Sequence Predictor advances protein evolution prediction at the amino acid level by integrating ancestral sequence reconstruction with a novel in silico approach that models evolutionary trends through selected physicochemical descriptors. Unlike prior work, SSP can forecast future amino acid substitutions that enhance protein properties such as thermostability, activity, and solubility. This method reduces reliance on resource-intensive directed evolution techniques while providing a generalizable, predictive tool for protein engineering.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10608 - Biochemistry and molecular biology

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Cheminformatics

ISSN

1758-2946

e-ISSN

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Volume of the periodical

17

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

34

UT code for WoS article

001467954700002

EID of the result in the Scopus database

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