Модель DDGemb объединяет мощь языковых моделей белков и глубокого обучения для предсказания стабильности мутаций. Этот ИИ-инструмент помогает ученым быстро выявлять опасные варианты белков и проектировать новые функциональные структуры для медицины.
The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimental approaches and allow a fast screening of large datasets of variations. In this work we present DDGemb, a novel method combining protein language model embeddings and transformer architectures to predict protein 𝚫𝚫G upon both single- and multipoint variations. DDGemb has been trained on a high-quality dataset derived from literature and tested on available benchmark datasets of single- and multi-point variations. DDGemb performs at the state of the art in both single- and multi-point variations.