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Predicción de la epistasis a través de proteínas mediante lógica estructural

Michelle Tang¹, Gareth A Cromie¹, Anowarul Kabir²

¹Pacific Northwest Research Institute, Seattle, WA 98122.

Proceedings of the National Academy of Sciences of the United States of America

|January 16, 2026

Resumen

Este resumen es generado por máquina.

La complementación intragénica, una forma de epistasis, restaura la función proteica a partir de variantes emparejadas de pérdida de función. Un modelo de aprendizaje automático predice con precisión este fenómeno, ayudando a la medicina de precisión al comprender los efectos de la variación genética.

Palabras clave:

epistasis aprendizaje automático efectos de variantes

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Área de la Ciencia:

Genetics and Molecular Biology
Computational Biology
Biochemistry

Sus antecedentes:

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.

Objetivo del estudio:

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.

Principales métodos:

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.

Principales resultados:

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.

Conclusiones:

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.