ESM2-Kcr: a novel model combining protein language model and deep learning methods for predicting lysine crotonylation sites

BLOSSOM-Kcr: A structure-informed deep learning framework for lysine crotonylation site prediction.

Lysine crotonylation (Kcr) is an important post-translational modification (PTM) involved in diverse biological processes, including chromatin regulation, protein function modulation, and cellular signaling. Although mass spectrometry-based proteomics has substantially expanded the identification of Kcr sites, experime...

PLM-ArgMe: Protein language model for arginine methylation prediction for different species.

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.

DNCLA: A Deep Learning Model for TFBS Identification Based on Structural and Conformational Properties of Nucleotides and Dinucleotides

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: a dual-branch deep learning model with attention-guided fusion for predicting lysine crotonylation sites in human non-histone proteins

Introduction Lysine crotonylation (Kcr) is extensively present in human non-histone proteins and plays a critical regulatory role in essential biological processes, including cell signaling and metabolic regulation. However, conventional wet-lab approaches for Kcr site identification are costly, time-consuming, and ill...

DBP-CanPred: a machine learning model for predicting cancer-causing mutations in DNA-binding 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...

CDELDA: A Content-Based Dual-Encoder for Cold-Start lncRNA–Disease Association Prediction