TL;DR
The ESM2-Kcr model not only enhances the understanding of protein regulation but also holds great potential in identifying disease biomarkers and facilitating drug development.
BLOSSOM-Kcr is proposed, a structure-informed deep learning framework for Kcr site prediction that integrates BLOSUM62-based sequence substitution features with residue-level structural descriptors, including secondary structure, solvent accessibility, backbone geometry, and spatial neighborhood information and suggest...
PLM-ArgMe is presented that is based on a symmetry-sensitive Transformer framework using context-aware ESM-2 residue embeddings, which is mapped through a novel Bio-Symmetric Mirrored Sinusoidal Encoding strategy to address the biological symmetry hypothesis of arginine methylation.
Identifying transcription factor binding sites (TFBSs) is fundamental to understanding complex gene regulatory mechanisms and the functions of non-coding regions. Although existing methods have achieved substantial strides, capturing both local structural features and long-range spatial dependencies within DNA sequence...
DFN-Kcr is proposed, a dual-branch deep learning model explicitly designed for human non-histone Kcr site prediction that significantly outperforms state-of-the-art methods for high-throughput identification of Kcr sites in non-histone proteins.
A machine learning model is trained, DBP-CanPred, to identify driver mutations in DBPs using the sequence-derived evolutionary features, as well as structure-based features such as mutation-perturbed structural descriptors, which contributes to understanding mutation patterns in DNA-binding proteins and supports varian...
Findings suggest that CDELDA can support content-based candidate prioritization under both-cold evaluation, and provide candidates for biological follow-up, with disease-specific relevance requiring independent validation.
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