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• W4387188214 abstract "Abstract Inteins are proteins that excise themselves out of host proteins and ligate the flanking polypeptides in an auto-catalytic process called protein splicing. They are gaining momentum in synthetic biology for their ability to post-translationally modify proteins of interest. In nature, inteins are either contiguous or split, in which case the two intein fragments must first form a complex for the splicing to occur. So far, heuristic methods have been employed whenever a new split site in an intein had to be identified. To make the process of split site identification in inteins faster, easier and less costly, we developed Int&in, a web server that uses a gaussian Naïve Bayes machine learning model to predict active and inactive split sites with high accuracy. The model was trained on a data set generated by us and validated using a large diverse data set from the literature, resulting in an accuracy of 0.76. Int&in will facilitate the engineering of novel split inteins for applications in biotechnology and synthetic biology." @default.
• W4387188214 created "2023-09-30" @default.
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• W4387188214 date "2023-09-29" @default.
• W4387188214 modified "2023-10-16" @default.
• W4387188214 title "Int&in: a machine learning-based web server for split site identification in inteins" @default.
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• W4387188214 doi "https://doi.org/10.1101/2023.09.27.559783" @default.
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