AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
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, experimental screening remains labor-intensive, costly, and difficult to apply at proteome scale. Computational methods provide an efficient strategy for prioritizing candidate Kcr sites. However, most existing predictors mainly rely on sequence-derived representations and insufficiently exploit protein structural context. In this study, we propose BLOSSOM-Kcr, a structure-informed deep learning framework for Kcr site prediction. BLOSSOM-Kcr integrates BLOSUM62-based sequence substitution features with residue-level structural descriptors, including secondary structure, solvent accessibility, backbone geometry, and spatial neighborhood information. The fused residue-level representation is further processed by residual convolutional blocks, channel attention, bidirectional long short-term memory (BiLSTM) layers, and attention pooling to capture local motif patterns, informative feature dimensions, and contextual dependencies surrounding candidate lysine residues. Fivefold cross-validation was performed for model optimization and comparative analysis, while an independent test set was used for final evaluation against existing Kcr site predictors. On the independent test set, BLOSSOM-Kcr achieved an AUC of 0.9023, an MCC of 0.6479, and an F1-score of 0.8336, outperforming representative Kcr site predictors. These results suggest that BLOSSOM-Kcr provides an effective structure-aware framework for Kcr site prediction.
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 sequences remains a major challenge for improving prediction accuracy. In this study, we propose DNCLA, a deep learning model that synergizes multisize convolutional fusion, Bidirectional Long ShortTerm Memory (Bi-LSTM) networks, and a multi-head self-attention mechanism. At the feature extraction level, DNCLA breaks through the limitations of traditional single-sequence encoding by fusing Nucleotide Chemical Properties (NCP) with Dinucleotide Physicochemical Properties (DPCP). NCP provides a refined characterization of chemical differences between bases based on ring structures, hydrogen bond sites, and functional group properties, while DPCP introduces parameters such as local structural stability and geometric flexibility of the DNA. Subsequently, the model extracts spatial evolution from these high-dimensional features through a multi-size convolutional module; captures long-range spatial dependencies using Bi-LSTM layers; and employs a multi-head self-attention mechanism to achieve adaptive weight distribution of global features, thereby enhancing the perception of key regulatory motifs. Results from training and testing the proposed model on 165 ChIPseq datasets demonstrate that DNCLA possesses robust generalization capabilities and high predictive performance in TFBSs identification. This suggests that the incorporation of physicochemical features better elucidates the essence of interactions between transcription factors and DNA.
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-suited for large-scale profiling. Although computational prediction methods have garnered increasing attention in recent years, there remains a notable lack of efficient and specialized tools tailored specifically for Kcr site prediction in human non-histone proteins. Methods To address this gap, we propose DFN-Kcr, a dual-branch deep learning model explicitly designed for human non-histone Kcr site prediction. DFN-Kcr employs a dual-input strategy combining ProteinBERT embeddings and integer-encoded amino acid sequences, leveraging residual convolutional networks to capture local sequence motifs and a Transformer architecture to model long-range contextual dependencies. A branch-level gating attention fusion mechanism is further introduced to effectively integrate complementary features from both branches. Results Extensive experiments demonstrate that DFN-Kcr significantly outperforms state-of-the-art methods, with its sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews correlation coefficient (MCC) being 0.8244, 0.7679, 0.7961, and 0.5932, respectively. Discussion This model offers a reliable solution for high-throughput identification of Kcr sites in non-histone proteins. To facilitate community access, we have deployed a user-friendly web server at http://www.lzzzlab.top/dfnkcr/.
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 variant interpretation in cancer research.
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