BioSemAF-BiLSTM: a protein sequence feature extraction framework based on semantic and evolutionary information.

ABSTRACT: S-sulfenylation is a critical post-translational modification that plays an important role in regulating protein function, redox signaling, and maintaining cellular homeostasis. Accurate identification of S-sulfenylation sites is essential for understanding its biological significance and relevance to disease. However, the exclusive detection of S-sulfenylation sites through experimental methods remains challenging, as these approaches are often time-consuming and costly. Motivated by this issue, the present work proposed a deep learning-based computational framework, named BioSemAF-BiLSTM, which integrated evolutionary and semantic features to improve the prediction performance of S-sulfenylation sites. The framework employed fastText to generate subword-based sequence embeddings that cap

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12483914 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature