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Int&in: A machine learning-based web server for active split site identification in inteins.

Mirko Schmitz1,2,3, Jara Ballestin Ballestin1,2,4, Junsheng Liang1,2

  • 1BIOSS and CIBSS Research Signalling Centers, University of Freiburg, Freiburg, Germany.

Protein Science : a Publication of the Protein Society
|May 8, 2024
PubMed
Summary
This summary is machine-generated.

Inteins are proteins that perform protein splicing. A new machine learning model predicts active and inactive split sites in inteins, aiding the engineering of split inteins for synthetic biology applications.

Keywords:
AesCL inteinNpu DnaEgp41‐1machine learningprotein engineeringsplit inteinssplit site prediction

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Area of Science:

  • Biochemistry
  • Molecular Biology
  • Bioinformatics

Background:

  • Inteins are protein elements that catalyze their own excision and ligation of flanking protein sequences (protein splicing).
  • Inteins exist as contiguous or split forms; split inteins require two fragments to associate for splicing.
  • Artificial splitting of inteins enables novel applications in synthetic biology and protein interaction studies.

Purpose of the Study:

  • To develop a predictive model for identifying active and inactive intein split sites.
  • To create a user-friendly web server (Int&in) for predicting intein split sites.
  • To facilitate the engineering of novel split inteins with tailored properties.

Main Methods:

  • Development of a machine learning model using logistic regression.
  • Training the model on 126 split sites from gp41-1, Npu DnaE, and CL inteins.
  • Validation using 97 split sites from scientific literature.
  • Incorporation of protein structural features and sequence conservation data.

Main Results:

  • The machine learning model achieved high accuracy in predicting active and inactive split sites (0.79 training, 0.78 testing).
  • The Int&in web server provides accurate predictions for intein split site activity.
  • The model demonstrates the utility of structural and sequence information for predicting intein behavior.

Conclusions:

  • The Int&in web server accurately predicts intein split sites, overcoming heuristic identification methods.
  • This tool will accelerate the design and engineering of split inteins for diverse applications.
  • The findings support the use of machine learning in protein engineering and synthetic biology.