Protein Language Models for Predicting Mutational Effects in Plants

• Eva Pardo-Otero ^[1] ; Verónica Bolón-Canedo ^[1] ; Rosalía Piñeiro ^[1]
  1. [1] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
• Localización: Proceedings XoveTIC 2025: Impulsando el talento científico / coord. por Manuel Lagos Rodríguez, Hilda Romero-Velo, Álvaro Leitao , Javier Pereira-Loureiro , Manuel Penedo , 2026, ISBN 978-84-9749-925-5, págs. 265-270
• Idioma: inglés
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• Resumen

  • Predicting the effect of mutations is key for estimating genetic load—the accumulation of deleterious mutations that may reduce fitness of organisms. Traditional methods rely on predefined features, such as evolutionary conservation or the predicted impact on the protein sequence and structure. Protein language models (PLMs) offer a new data-driven alternative by learning functional constraints directly from protein sequences. In this study, we apply the PLM ESM-1v for predicting mutational effects, using 12,865 functionally annotated mutations from433 plant species, a group with limited predictive tools. We also explore integrating ESM-1v embeddings as features in supervised machine learning models.