Antimicrobial peptides (AMPs) are vital components of innate immunotherapy. Existing approaches mainly rely on either deep learning for the automatic extraction of sequence features or traditional manual amino acid features combined with machine learning. The peptide sequence contains symmetrical sequence motifs or repetitive amino acid patterns, which may be related to the function and structure of the peptide. Recently, the advent of large language models has significantly boosted the representational power of sequence pattern features. In light of this, we present a novel AMP predictor called UniproLcad, which integrates three prominent protein language models—ESM-2, ProtBert, and UniRep—to obtain a more comprehensive representation of protein features. UniproLcad utilizes deep learning networks, encompassing the bidirectional long and short memory network (Bi-LSTM) and one-dimensional convolutional neural networks (1D-CNN), while also integrating an attention mechanism to enhance its capabilities. These deep learning frameworks, coupled with pre-trained language models, efficiently extract multi-view features from antimicrobial peptide sequences and assign attention weights to them. Through ten-fold cross-validation and independent testing, UniproLcad demonstrates competitive performance in the field of antimicrobial peptide identification. This integration of diverse language models and deep learning architectures enhances the accuracy and reliability of predicting antimicrobial peptides, contributing to the advancement of computational methods in this field.

The subcellular localization of long non-coding RNA (lncRNA) provides important insights and opportunities for an in-depth understanding of cell biology, revealing disease mechanisms, drug development, and innovation in the biomedical field. Although several computational methods have been proposed to identify the subcellular localization of lncRNA, it is difficult to accurately predict the subcellular localization of lncRNA effectively with these methods. In this study, a new deep-learning predictor called PreSubLncR has been proposed for accurately predicting the subcellular localization of lncRNA. This predictor firstly used the word embedding model word2vec to encode the RNA sequences, and then combined multi-scale one-dimensional convolutional neural networks with attention and bidirectional long short-term memory networks to capture the different characteristics of various RNA sequences. This study used multiple RNA subcellular localization datasets for experimental validation, and the results showed that our method has higher accuracy and robustness compared with other state-of-the-art methods. It is expected to provide more in-depth insights into cell function research.

The subcellular localization of messenger RNAs (mRNAs) is a pivotal aspect of biomolecules, tightly linked to gene regulation and protein synthesis, and offers innovative insights into disease diagnosis and drug development in the field of biomedicine. Several computational methods have been proposed to predict the subcellular localization of mRNAs within cells. However, there remains a deficiency in the accuracy of these predictions. In this study, we propose an mRCat predictor based on the gradient boosting tree algorithm specifically to predict whether mRNAs are localized in the nucleus or in the cytoplasm. This predictor firstly uses large language models to thoroughly explore hidden information within sequences and then integrates traditional sequence features to collectively characterize mRNA gene sequences. Finally, it employs CatBoost as the base classifier for predicting the subcellular localization of mRNAs. The experimental validation on an independent test set demonstrates that mRCat obtained accuracy of 0.761, F1 score of 0.710, MCC of 0.511, and AUROC of 0.751. The results indicate that our method has higher accuracy and robustness compared to other state-of-the-art methods. It is anticipated to offer deep insights for biomolecular research.

DNA N4-methylcytosine(4mC) plays an important role in numerous biological functions and is a mechanism of particular epigenetic importance. Therefore, accurate identification of the 4mC sites in DNA sequences is necessary to understand the functional mechanism. Although some effective calculation tools have been proposed to identifying DNA 4mC sites, it is still challenging to improve identification accuracy and generalization ability. Therefore, there is a great need to build a computational tool to accurately identify the position of DNA 4mC sites. Hence, this study proposed a novel predictor XGB4mcPred, a predictor for the identification of 4mC sites trained using an extreme gradient boosting algorithm (XGBoost) and DNA sequence information. Firstly, we used the One-Hot encoding on adjacent and spaced nucleotides, dinucleotides, and trinucleotides of the original 4mC site sequences as feature vectors. Then, the importance values of the feature vectors pre-trained by the XGBoost algorithm were used as a threshold to filter redundant features, resulting in a significant improvement in the identification accuracy of the constructed XGB4mcPred predictor to identify 4mC sites. The analysis shows that there is a clear preference for nucleotide sequences between 4mC sites and non-4mC site sequences in six datasets from multiple species, and the optimized features can better distinguish 4mC sites from non-4mC sites. The experimental results of cross-validation and independent tests from six different species show that our proposed predictor XGB4mcPred significantly outperformed other state-of-the-art predictors and was improved to varying degrees compared with other state-of-the-art predictors. Additionally, the user-friendly webserver we used to developed the XGB4mcPred predictor was made freely accessible.

The subcellular localization of messenger RNA (mRNA) not only helps us to understand the localization regulation of gene expression but also helps to understand the relationship between RNA localization pattern and human disease mechanism, which has profound biological and medical significance. Several predictors have been proposed for predicting the subcellular localization of mRNA. However, there is still considerable room for improvement in their predictive performance, especially regarding multi-label prediction. This study proposes a novel multi-label predictor, DRpred, for mRNA subcellular localization prediction. This predictor first utilizes Bayesian networks to capture the dependencies among labels. Subsequently, it combines these dependencies with features extracted from mRNA sequences using Word2vec, forming the input for the predictor. Finally, it employs a neural network combining BiLSTM and an attention mechanism to capture the internal relationships of the input features for mRNA subcellular localization. The experimental validation on an independent test set demonstrated that DRpred obtained a competitive predictive performance in multi-label prediction and outperformed state-of-the-art predictors in predicting single subcellular localizations, obtaining accuracies of 82.14%, 93.02%, 80.37%, 94.00%, 90.58%, 84.53%, 82.01%, 79.71%, and 85.67% for the chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus, and ribosome, respectively. It is anticipated to offer profound insights for biological and medical research.

N4-methylcytosine (4mC) is a critical epigenetic modification that plays a pivotal role in the regulation of a multitude of biological processes, including gene expression, DNA replication, and cellular differentiation. Traditional experimental methods for detecting DNA N4-methylcytosine sites are time-consuming, labor-intensive, and costly, making them unsuitable for large-scale or high-throughput research. Computational methods for identifying DNA N4-methylcytosine sites enable the rapid and cost-effective analysis of DNA 4mC sites across entire genomes. In this study, we focus on the identification of DNA 4mC sites in the mouse genome. Although there are already some computational methods that can predict DNA 4mC sites in the mouse genome, there is still significant room for improvement in accurately predicting them due to their inability to fully capture the multifaceted characteristics of DNA sequences. To address this issue, we propose a new deep learning predictor called Mus4mCPred, which utilizes multi-view feature learning and deep hybrid networks for accurately predicting DNA 4mC sites in the mouse genome. The predictor Mus4mCPred firstly employed different encoding methods to extract the feature vectors of DNA sequences, then input these features generated by different encoding methods into various hybrid deep learning models for the learning and extraction of more sophisticated representations of these features, and finally fused the extracted multi-view features to serve as the final features for DNA 4mC site prediction in the mouse genome. Multi-view features enabled the more comprehensive capture of data characteristics, enhancing the feature representation of DNA sequences. The independent test results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews’ correlation coefficient (MCC) were 0.7688, 0.9375, 0.8531, and 0.7165, respectively. The predictor Mus4mCPred outperformed other state-of-the-art methods, achieving the accurate identification of 4mC sites in the mouse genome.