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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Aprovechamiento de modelos de lenguaje de proteínas y una función de puntuación para la caracterización y el

Oriol Gracia Carmona^1,2, Vilde Leipart^1,3, Gro V Amdam^3,4

¹Research Department of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK.

Patterns (New York, N.Y.)

|February 23, 2026

Resumen

Los modelos de lenguaje de proteínas (PLM) predicen ahora eficazmente el impacto de las variantes genéticas. Una nueva herramienta, IndeLLM, puntúa con precisión la patogenicidad de los indels utilizando solo datos de secuencia, mejorando la interpretación de las variantes.

Palabras clave:

indels interpretabilidad predictores de patogenicidad modelos de lenguaje de proteínas aprendizaje de transferencia inferencia zero-shot

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Área de la Ciencia:

Genómica
Bioinformática
Biología Computacional

Sus antecedentes:

Los modelos de lenguaje de proteínas (PLM) son herramientas potentes para predecir la patogenicidad de las variantes genéticas.
La evaluación de inserciones y deleciones (indels) dentro del marco sigue siendo un desafío debido a las longitudes alteradas de las proteínas y los datos limitados.

Objetivo del estudio:

Desarrollar un nuevo enfoque de puntuación, IndeLLM, para evaluar la patogenicidad de los indels.
Crear una herramienta fácil de usar para analizar los efectos de los indels en la secuencia y la estructura de las proteínas.

Principales métodos:

Desarrolló IndeLLM, un método de puntuación zero-shot que utiliza únicamente información de la secuencia de proteínas.
Implementó una red siamesa utilizando aprendizaje de transferencia para mejorar la predicción de la patogenicidad de los indels.
Creó un cuaderno de Google Colab conectable para facilitar el acceso.

Principales resultados:

IndeLLM logra un rendimiento comparable a los predictores existentes con recursos computacionales mínimos.
El modelo de red siamesa logró un coeficiente de correlación de Matthews de 0,77, superando a otros predictores de indels.
La herramienta facilita la visualización de los impactos de los indels en la secuencia y la estructura de las proteínas.

Conclusiones:

IndeLLM ofrece un método accesible y eficiente para evaluar la patogenicidad de los indels.
La red siamesa desarrollada mejora significativamente la predicción de los efectos de los indels.
Este trabajo mejora la utilidad de los PLM para la interpretación de variantes, particularmente para los indels.