RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins

EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides

BACKGROUND

The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe acute respiratory syndrome (SARS-CoV) and SARS-CoV-2. Many peptides in the host defense system have antiviral activity. How to establish a set of efficient models to identify anti-coronavirus peptides is a meaningful study.

METHODS

Given this, a new prediction model EACVP is proposed. This model uses the evolutionary scale language model (ESM-2 LM) to characterize peptide sequence information. The ESM model is a natural language processing model trained by machine learning technology. It is trained on a highly diverse and dense dataset (UR50/D 2021_04) and uses the pre-trained language model to obtain peptide sequence features with 320 dimensions. Compared with traditional feature extraction methods, the information represented by ESM-2 LM is more comprehensive and stable. Then, the features are input into the convolutional neural network (CNN), and the convolutional block attention module (CBAM) lightweight attention module is used to perform attention operations on CNN in space dimension and channel dimension. To verify the rationality of the model structure, we performed ablation experiments on the benchmark and independent test datasets. We compared the EACVP with existing methods on the independent test dataset.

RESULTS

Experimental results show that ACC, F1-score, and MCC are 3.95%, 35.65% and 0.0725 higher than the most advanced methods, respectively. At the same time, we tested EACVP on ENNAVIA-C and ENNAVIA-D data sets, and the results showed that EACVP has good migration and is a powerful tool for predicting anti-coronavirus peptides.

CONCLUSION

The results prove that this model EACVP could fully characterize the peptide information and achieve high prediction accuracy. It can be generalized to different data sets. The data and code of the article have been uploaded to https://github.- com/JYY625/EACVP.git.

RBProkCNN: Deep learning on appropriate contextual evolutionary information for RNA binding protein discovery in prokaryotes

RNA-binding proteins (RBPs) are central to key functions such as post-transcriptional regulation, mRNA stability, and adaptation to varied environmental conditions in prokaryotes. While the majority of research has concentrated on eukaryotic RBPs, recent developments underscore the crucial involvement of prokaryotic RBPs. Although computational methods have emerged in recent years to identify RBPs, they have fallen short in accurately identifying prokaryotic RBPs due to their generic nature. To bridge this gap, we introduce RBProkCNN, a novel machine learning-driven computational model meticulously designed for the accurate prediction of prokaryotic RBPs. The prediction process involves the utilization of eight shallow learning algorithms and four deep learning models, incorporating PSSM-based evolutionary features. By leveraging a convolutional neural network (CNN) and evolutionarily significant features selected through extreme gradient boosting variable importance measure, RBProkCNN achieved the highest accuracy in five-fold cross-validation, yielding 98.04% auROC and 98.19% auPRC. Furthermore, RBProkCNN demonstrated robust performance with an independent dataset, showcasing a commendable 95.77% auROC and 95.78% auPRC. Noteworthy is its superior predictive accuracy when compared to several state-of-the-art existing models. RBProkCNN is available as an online prediction tool (https://iasri-sg.icar.gov.in/rbprokcnn/), offering free access to interested users. This tool represents a substantial contribution, enriching the array of resources available for the accurate and efficient prediction of prokaryotic RBPs.

Advancing microRNA target site prediction with transformer and base-pairing patterns

MicroRNAs (miRNAs) are short non-coding RNAs involved in various cellular processes, playing a crucial role in gene regulation. Identifying miRNA targets remains a central challenge and is pivotal for elucidating the complex gene regulatory networks. Traditional computational approaches have predominantly focused on identifying miRNA targets through perfect Watson-Crick base pairings within the seed region, referred to as canonical sites. However, emerging evidence suggests that perfect seed matches are not a prerequisite for miRNA-mediated regulation, underscoring the importance of also recognizing imperfect, or non-canonical, sites. To address this challenge, we propose Mimosa, a new computational approach that employs the Transformer framework to enhance the prediction of miRNA targets. Mimosa distinguishes itself by integrating contextual, positional and base-pairing information to capture in-depth attributes, thereby improving its predictive capabilities. Its unique ability to identify non-canonical base-pairing patterns makes Mimosa a standout model, reducing the reliance on pre-selecting candidate targets. Mimosa achieves superior performance in gene-level predictions and also shows impressive performance in site-level predictions across various non-human species through extensive benchmarking tests. To facilitate research efforts in miRNA targeting, we have developed an easy-to-use web server for comprehensive end-to-end predictions, which is publicly available at http://monash.bioweb.cloud.edu.au/Mimosa.

ACP-PDAFF: Pretrained model and dual-channel attentional feature fusion for anticancer peptides prediction

Anticancer peptides(ACPs) have attracted significant interest as a novel method of treating cancer due to their ability to selectively kill cancer cells without damaging normal cells. Many artificial intelligence-based methods have demonstrated impressive performance in predicting ACPs. Nevertheless, the limitations of existing methods in feature engineering include handcrafted features driven by prior knowledge, insufficient feature extraction, and inefficient feature fusion. In this study, we propose a model based on a pretrained model, and dual-channel attentional feature fusion(DAFF), called ACP-PDAFF. Firstly, to reduce the heavy dependence on expert knowledge-based handcrafted features, binary profile features (BPF) and physicochemical properties features(PCPF) are used as inputs to the transformer model. Secondly, aimed at learning more diverse feature informations of ACPs, a pretrained model ProtBert is utilized. Thirdly, for better fusion of different feature channels, DAFF is employed. Finally, to evaluate the performance of the model, we compare it with other methods on five benchmark datasets, including ACP-Mixed-80 dataset, Main and Alternate datasets of AntiCP 2.0, LEE and Independet dataset, and ACPred-Fuse dataset. And the accuracies obtained by ACP-PDAFF are 0.86, 0.80, 0.94, 0.97 and 0.95 on five datasets, respectively, higher than existing methods by 1% to 12%. Therefore, by learning rich feature informations and effectively fusing different feature channels, ACD-PDAFF achieves outstanding performance. Our code and the datasets are available at https://github.com/wongsing/ACP-PDAFF.

GraphNABP: Identifying nucleic acid-binding proteins with protein graphs and protein language models

The computational identification of nucleic acid-binding proteins (NABP) is of great significance for understanding the mechanisms of these biological activities and drug discovery. Although a bunch of sequence-based methods have been proposed to predict NABP and achieved promising performance, the structure information is often overlooked. On the other hand, the power of popular protein language models (pLM) has seldom been harnessed for predicting NABPs. In this study, we propose a novel framework called GraphNABP, to predict NABP by integrating sequence and predicted 3D structure information. Specifically, sequence embeddings and protein molecular graphs were first obtained from ProtT5 protein language model and predicted 3D structures, respectively. Then, graph attention (GAT) and bidirectional long short-term memory (BiLSTM) neural networks were used to enhance feature representations. Finally, a fully connected layer is used to predict NABPs. To the best of our knowledge, this is the first time to integrate AlphaFold and protein language models for the prediction of NABPs. The performances on multiple independent test sets indicate that GraphNABP outperforms other state-of-the-art methods. Our results demonstrate the effectiveness of pLM embeddings and structural information for NABP prediction. The codes and data used in this study are available at https://github.com/lixiangli01/GraphNABP.

PRONTO-TK: a user-friendly PROtein Neural neTwOrk tool-kit for accessible protein function prediction

Associating one or more Gene Ontology (GO) terms to a protein means making a statement about a particular functional characteristic of the protein. This association provides scientists with a snapshot of the biological context of the protein activity. This paper introduces PRONTO-TK, a Python-based software toolkit designed to democratize access to Neural-Network based complex protein function prediction workflows. PRONTO-TK is a user-friendly graphical interface (GUI) for empowering researchers, even those with minimal programming experience, to leverage state-of-the-art Deep Learning architectures for protein function annotation using GO terms. We demonstrate PRONTO-TK's effectiveness on a running example, by showing how its intuitive configuration allows it to easily generate complex analyses while avoiding the complexities of building such a pipeline from scratch.

Big data and deep learning for RNA biology

The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.

msBERT-Promoter: a multi-scale ensemble predictor based on BERT pre-trained model for the two-stage prediction of DNA promoters and their strengths

BACKGROUND

A promoter is a specific sequence in DNA that has transcriptional regulatory functions, playing a role in initiating gene expression. Identifying promoters and their strengths can provide valuable information related to human diseases. In recent years, computational methods have gained prominence as an effective means for identifying promoter, offering a more efficient alternative to labor-intensive biological approaches.

RESULTS

In this study, a two-stage integrated predictor called "msBERT-Promoter" is proposed for identifying promoters and predicting their strengths. The model incorporates multi-scale sequence information through a tokenization strategy and fine-tunes the DNABERT model. Soft voting is then used to fuse the multi-scale information, effectively addressing the issue of insufficient DNA sequence information extraction in traditional models. To the best of our knowledge, this is the first time an integrated approach has been used in the DNABERT model for promoter identification and strength prediction. Our model achieves accuracy rates of 96.2% for promoter identification and 79.8% for promoter strength prediction, significantly outperforming existing methods. Furthermore, through attention mechanism analysis, we demonstrate that our model can effectively combine local and global sequence information, enhancing its interpretability.

CONCLUSIONS

msBERT-Promoter provides an effective tool that successfully captures sequence-related attributes of DNA promoters and can accurately identify promoters and predict their strengths. This work paves a new path for the application of artificial intelligence in traditional biology.

Deep Learning for Elucidating Modifications to RNA-Status and Challenges Ahead

RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

NCSP-PLM: An ensemble learning framework for predicting non-classical secreted proteins based on protein language models and deep learning

Non-classical secreted proteins (NCSPs) refer to a group of proteins that are located in the extracellular environment despite the absence of signal peptides and motifs. They usually play different roles in intercellular communication. Therefore, the accurate prediction of NCSPs is a critical step to understanding in depth their associated secretion mechanisms. Since the experimental recognition of NCSPs is often costly and time-consuming, computational methods are desired. In this study, we proposed an ensemble learning framework, termed NCSP-PLM, for the identification of NCSPs by extracting feature embeddings from pre-trained protein language models (PLMs) as input to several fine-tuned deep learning models. First, we compared the performance of nine PLM embeddings by training three neural networks: Multi-layer perceptron (MLP), attention mechanism and bidirectional long short-term memory network (BiLSTM) and selected the best network model for each PLM embedding. Then, four models were excluded due to their below-average accuracies, and the remaining five models were integrated to perform the prediction of NCSPs based on the weighted voting. Finally, the 5-fold cross validation and the independent test were conducted to evaluate the performance of NCSP-PLM on the benchmark datasets. Based on the same independent dataset, the sensitivity and specificity of NCSP-PLM were 91.18% and 97.06%, respectively. Particularly, the overall accuracy of our model achieved 94.12%, which was 7~16% higher than that of the existing state-of-the-art predictors. It indicated that NCSP-PLM could serve as a useful tool for the annotation of NCSPs.

RBPLight: a computational tool for discovery of plant-specific RNA-binding proteins using light gradient boosting machine and ensemble of evolutionary features

RNA-binding proteins (RBPs) are essential for post-transcriptional gene regulation in eukaryotes, including splicing control, mRNA transport and decay. Thus, accurate identification of RBPs is important to understand gene expression and regulation of cell state. In order to detect RBPs, a number of computational models have been developed. These methods made use of datasets from several eukaryotic species, specifically from mice and humans. Although some models have been tested on Arabidopsis, these techniques fall short of correctly identifying RBPs for other plant species. Therefore, the development of a powerful computational model for identifying plant-specific RBPs is needed. In this study, we presented a novel computational model for locating RBPs in plants. Five deep learning models and ten shallow learning algorithms were utilized for prediction with 20 sequence-derived and 20 evolutionary feature sets. The highest repeated five-fold cross-validation accuracy, 91.24% AU-ROC and 91.91% AU-PRC, was achieved by light gradient boosting machine. While evaluated using an independent dataset, the developed approach achieved 94.00% AU-ROC and 94.50% AU-PRC. The proposed model achieved significantly higher accuracy for predicting plant-specific RBPs as compared to the currently available state-of-art RBP prediction models. Despite the fact that certain models have already been trained and assessed on the model organism Arabidopsis, this is the first comprehensive computer model for the discovery of plant-specific RBPs. The web server RBPLight was also developed, which is publicly accessible at https://iasri-sg.icar.gov.in/rbplight/, for the convenience of researchers to identify RBPs in plants.

Prediction of Multiple Types of RNA Modifications via Biological Language Model

It has been demonstrated that RNA modifications play essential roles in multiple biological processes. Accurate identification of RNA modifications in the transcriptome is critical for providing insights into the biological functions and mechanisms. Many tools have been developed for predicting RNA modifications at single-base resolution, which employ conventional feature engineering methods that focus on feature design and feature selection processes that require extensive biological expertise and may introduce redundant information. With the rapid development of artificial intelligence technologies, end-to-end methods are favorably received by researchers. Nevertheless, each well-trained model is only suitable for a specific RNA methylation modification type for nearly all of these approaches. In this study, we present MRM-BERT by feeding task-specific sequences into the powerful BERT (Bidirectional Encoder Representations from Transformers) model and implementing fine-tuning, which exhibits competitive performance to the state-of-the-art methods. MRM-BERT avoids repeated de novo training of the model and can predict multiple RNA modifications such as pseudouridine, m6A, m5C, and m1A in Mus musculus, Arabidopsis thaliana, and Saccharomyces cerevisiae. In addition, we analyse the attention heads to provide high attention regions for the prediction, and conduct saturated in silico mutagenesis of the input sequences to discover potential changes of RNA modifications, which can better assist researchers in their follow-up research.

Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins

The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that are poorly characterised because of their highly repetitive nature and high GC content. Some of these genes encode proteins of the PE/PPE family, which are thought to be involved in host-pathogen interactions, virulence, and disease pathogenicity. Members of this family are genetically divergent and challenging to both identify and classify using conventional computational tools. Thus, advanced in silico methods are needed to identify proteins of this family for subsequent functional annotation efficiently. In this study, we developed the first deep learning-based approach, termed Digerati, for the rapid and accurate identification of PE and PPE family proteins. Digerati was built upon a multipath parallel hybrid deep learning framework, which equips multi-layer convolutional neural networks with bidirectional, long short-term memory, equipped with a self-attention module to effectively learn the higher-order feature representations of PE/PPE proteins. Empirical studies demonstrated that Digerati achieved a significantly better performance (∼18-20%) than alignment-based approaches, including BLASTP, PHMMER, and HHsuite, in both prediction accuracy and speed. Digerati is anticipated to facilitate community-wide efforts to conduct high-throughput identification and analysis of PE/PPE family members. The webserver and source codes of Digerati are publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/Digerati/.

Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network

Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.