IDLDDG: predicting protein stability changes from missense mutations in DNA-binding proteins using integrated deep learning features.

ABSTRACT: To understand disease mechanisms and advance therapies, accurately predicting how missense mutations alter protein-DNA binding affinity is critical. Many existing models neglect the unique characteristics of missense mutations in both double-stranded DNA-binding proteins (DSBs) and single-stranded DNA-binding proteins (SSBs). To address this issue, we constructed a comprehensive dataset from diverse sources. By leveraging sequence-based embeddings from pretrained protein language models including ESM2, ProtTrans, and ESM1v, we developed iDLDDG, a deep learning framework that integrates multi-scale structural and evolutionary information via a multi-channel architecture. To balance residue-wise information density against entropy, our entropy-based algorithm determined 181 residues as optim

SUBMITTER: Yu X 

PROVIDER: S-EPMC12903960 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature