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CPPAtools: a computation toolset for chimera protein prediction and analysis
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DOI：10.1186/s12859-026-06598-7.png)
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Abstract
En 中文
Genetic or non-genetic frameshifting can increase the production of erroneous proteins, with far-reaching impacts on cellular phenotypes, including cancer. While the presence and impact of genetic alterations cannot be foreseen and are often defined clinically following phenotypic alterations, non-genetic errors may be predictable. Recent reports have uncovered non-genetic chimera proteins resulting from ribosomal frameshifting induced by environmental factors, such as the immune response, DNA damage, metabolic stress, and genetic alterations (e.g., tRNA modification). However, we currently lack computational tools to predict such chimera proteins and their potential impact on cellular processes. Here, we present CPPAtools (Chimera Protein Prediction and Analysis tools), a computational toolbox for predicting and comparing large numbers of chimera proteins in a differential manner relative to their canonical wild-type counterparts. Protein differential analysis can be categorized into four distinct groups: chemical properties, secondary structures, domain prediction, and protein structure prediction. We demonstrate the toolbox’s capabilities for identifying chimera proteins by analyzing several case studies. We further focused on the tryptophan codon due to the prominent role of tryptophan in anti-tumor immune response and predicted all possible chimera proteins in a large-scale analysis. We conclude that CPPAtools can be used to analyse and predict, on a large scale and in a comparative manner, the potential impact of chimera proteins. The toolbox is openly accessible (https://github.com/pkorner218/CPPAtools and https://sourceforge.net/projects/cppatools/ ) and it offers new ways to analyse the impact of multiple genetic and non-genetic protein mutations on the proteome, significantly advancing our available methods.
Keywords:
Chimera proteins
Frameshifting
Protein prediction
Non-canonical protein prediction