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Predicting epistasis across proteins by structural logic.

Michelle Tang1, Gareth A Cromie1, Anowarul Kabir2

  • 1Pacific Northwest Research Institute, Seattle, WA 98122.

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|January 16, 2026
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Summary
This summary is machine-generated.

Intragenic complementation, a form of epistasis, restores protein function from paired loss-of-function variants. A machine learning model accurately predicts this phenomenon, aiding precision medicine by understanding genetic variation effects.

Keywords:
epistasismachine learningvariant effects

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Area of Science:

  • Genetics and Molecular Biology
  • Computational Biology
  • Biochemistry

Background:

  • Predicting phenotypic outcomes of genetic variations is crucial for precision medicine.
  • Epistatic interactions, particularly positive epistasis like intragenic complementation, complicate these predictions.
  • Intragenic complementation involves pairs of loss-of-function variants restoring protein function.

Purpose of the Study:

  • To investigate intragenic complementation in the human argininosuccinate lyase (ASL) enzyme.
  • To uncover the structural basis of intragenic complementation.
  • To develop a predictive model for intragenic complementation using machine learning.

Main Methods:

  • Utilized mutational scanning in yeast to identify intragenic complementation interactions in ASL.
  • Employed machine learning algorithms leveraging protein language model embeddings.
  • Validated the model's accuracy and generalizability to related enzymes like fumarase.

Main Results:

  • Identified thousands of intragenic complementation interactions in ASL.
  • Determined that active site assembly, not amino acid properties, drives functional restoration.
  • Achieved 99.6% prediction accuracy for intragenic complementation in ASL.
  • Demonstrated over 90% accuracy when generalizing the model to fumarase.

Conclusions:

  • Intragenic complementation has a structural basis related to active site assembly.
  • A machine learning framework can accurately predict intragenic complementation.
  • This predictive framework has potential applications for at least 4% of human proteins, advancing precision medicine.