BACKGROUND: N6-methyladenosine ([Formula: see text]) plays a crucial role in enriching RNA functional and genetic information. While deep learning has advanced [Formula: see text] site identification, current state-of-the-art methods, particularly those based on large-scale Pre-trained Language Models (PLMs), often suffer from high computational complexity and over-parameterization, limiting their scalability for genome-wide analysis. RESULTS: In this work, we propose MKE-ResNet, an ultra-lightweight end-to-end framework designed to balance predictive performance with extreme computational efficiency. MKE-ResNet integrates a deep residual network with an innovative Multi-Kernel Efficient Channel Attention (MKE-ECA) module to adaptively capture multi-scale sequence patterns. Extensive experiments on 22 datasets demonstrate that MKE-ResNet achieves competitive performance against complex SOTA models (including MST-m6A). Notably, our model exhibits exceptional stability against sequence noise perturbation and demonstrates superior generalization capability on independent test sets (leading in 8 out of 11 cases in terms of AUROC). Furthermore, interpretability analysis reveals that MKE-ResNet moves beyond the static central motif to capture dynamic sequence patterns resembling RBP binding motifs (e.g., SRSF1 and PUM2). The source code and datasets are available at https://github.com/Gxttk/MKE-Resnet. CONCLUSIONS: MKE-ResNet provides a biologically interpretable and computationally efficient solution for [Formula: see text] site identification, offering a pragmatic tool for large-scale epitranscriptome screening.

Abstract Background N6, 2'-O-dimethyladenosine (m6Am) is an abundant RNA methylation modification on vertebrate mRNAs and is present in the transcription initiation region of mRNAs. It has recently been experimentally shown to be associated with several human disorders, including obesity genes, and stomach cancer, among others. As a result, N6,2′-O-dimethyladenosine (m6Am) site will play a crucial part in the regulation of RNA if it can be correctly identified. Results This study proposes a novel deep learning-based m6Am prediction model, EMDL_m6Am, which employs one-hot encoding to expressthe feature map of the RNA sequence and recognizes m6Am sites by integrating different CNN models via stacking. Including DenseNet, Inflated Convolutional Network (DCNN) and Deep Multiscale Residual Network (MSRN), the sensitivity (Sn), specificity (Sp), accuracy (ACC), Mathews correlation coefficient (MCC) and area under the curve (AUC) of our model on the training data set reach 86.62%, 88.94%, 87.78%, 0.7590 and 0.8778, respectively, and the prediction results on the independent test set are as high as 82.25%, 79.72%, 80.98%, 0.6199, and 0.8211. Conclusions In conclusion, the experimental results demonstrated that EMDL_m6Am greatly improved the predictive performance of the m6Am sites and could provide a valuable reference for the next part of the study. The source code and experimental data are available at: https://github.com/13133989982/EMDL-m6Am .

BACKGROUND: Lysine succinylation is a newly discovered protein post-translational modifications. Predicting succinylation sites helps investigate the metabolic disease treatments. However, the biological experimental approaches are costly and inefficient, it is necessary to develop efficient computational approaches. RESULTS: In this paper, we proposed a novel predictor based on ensemble dense blocks and an attention module, called as pSuc-EDBAM, which adopted one hot encoding to derive the feature maps of protein sequences, and generated the low-level feature maps through 1-D CNN. Afterward, the ensemble dense blocks were used to capture feature information at different levels in the process of feature learning. We also introduced an attention module to evaluate the importance degrees of different features. The experimental results show that Acc reaches 74.25%, and MCC reaches 0.2927 on the testing dataset, which suggest that the pSuc-EDBAM outperforms the existing predictors. CONCLUSIONS: The experimental results of ten-fold cross-validation on the training dataset and independent test on the testing dataset showed that pSuc-EDBAM outperforms the existing succinylation site predictors and can predict potential succinylation sites effectively. The pSuc-EDBAM is feasible and obtains the credible predictive results, which may also provide valuable references for other related research. To make the convenience of the experimental scientists, a user-friendly web server has been established ( http://bioinfo.wugenqiang.top/pSuc-EDBAM/ ), by which the desired results can be easily obtained.

MOTIVATION: Sumoylation is a post-translational modification (PTM) process, in which small ubiquitin-related modifier (SUMO) is attaching by covalent bonds to substrate protein. It is critical to many different biological processes such as replicating genome, expressing gene, localizing and stabilizing proteins; unfortunately, it is also involved with many major disorders including Alzheimer's and Parkinson's diseases. Therefore, for both basic research and drug development, it is important to identify the sumoylation sites in proteins. RESULTS: To address such a problem, we developed a predictor called pSumo-CD by incorporating the sequence-coupled information into the general pseudo-amino acid composition (PseAAC) and introducing the covariance discriminant (CD) algorithm, in which a bias-adjustment term, which has the function to automatically adjust the errors caused by the bias due to the imbalance of training data, had been incorporated. Rigorous cross-validations indicated that the new predictor remarkably outperformed the existing state-of-the-art prediction method for the same purpose. AVAILABILITY AND IMPLEMENTATION: For the convenience of most experimental scientists, a user-friendly web-server for pSumo-CD has been established at http://www.jci-bioinfo.cn/pSumo-CD, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. CONTACT: [email protected], [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.