Two-Level Protein Methylation Prediction using structure model-based features

Arabidopsis AGO1 N-terminal extension acts as an essential hub for PRMT5 interaction and post-translational modifications

Plant ARGONAUTE (AGO) proteins play pivotal roles regulating gene expression through small RNA (sRNA) -guided mechanisms. Among the 10 AGO proteins in Arabidopsis thaliana, AGO1 stands out as the main effector of post-transcriptional gene silencing. Intriguingly, a specific region of AGO1, its N-terminal extension (NTE), has garnered attention in recent studies due to its involvement in diverse regulatory functions, including subcellular localization, sRNA loading and interactions with regulatory factors. In the field of post-translational modifications (PTMs), little is known about arginine methylation in Arabidopsis AGOs. In this study, we show that NTE of AGO1 (NTEAGO1) undergoes symmetric arginine dimethylation at specific residues. Moreover, NTEAGO1 interacts with the methyltransferase PRMT5, which catalyzes its methylation. Notably, we observed that the lack of symmetric dimethylarginine has no discernible impact on AGO1's subcellular localization or miRNA loading capabilities. However, the absence of PRMT5 significantly alters the loading of a subgroup of sRNAs into AGO1 and reshapes the NTEAGO1 interactome. Importantly, our research shows that symmetric arginine dimethylation of NTEs is a common process among Arabidopsis AGOs, with AGO1, AGO2, AGO3 and AGO5 undergoing this PTM. Overall, this work deepens our understanding of PTMs in the intricate landscape of RNA-associated gene regulation.

Hybrid Bayesian Optimization-Based Graphical Discovery for Methylation Sites Prediction

Protein methylation is one of the most important reversible post-translational modifications (PTMs), playing a vital role in the regulation of gene expression. Protein methylation sites serve as biomarkers in cardiovascular and pulmonary diseases, influencing various aspects of normal cell biology and pathogenesis. Nonetheless, the majority of existing computational methods for predicting protein methylation sites (PMSP) have been constructed based on protein sequences, with few methods leveraging the topological information of proteins. To address this issue, we propose an innovative framework for predicting Methylation Sites using Graphs (GraphMethySite) that employs graph convolution network in conjunction with Bayesian Optimization (BO) to automatically discover the graphical structure surrounding a candidate site and improve the predictive accuracy. In order to extract the most optimal subgraphs associated with methylation sites, we extend GraphMethySite by coupling it with a hybrid Bayesian optimization (together named GraphMethySite +) to determine and visualize the topological relevance among amino-acid residues. We evaluated our framework on two extended protein methylation datasets, and empirical results demonstrate that it outperforms existing state-of-the-art methylation prediction methods.

Machine learning-based approaches for ubiquitination site prediction in human proteins

Protein ubiquitination is a critical post-translational modification (PTMs) involved in numerous cellular processes. Identifying ubiquitination sites (Ubi-sites) on proteins offers valuable insights into their function and regulatory mechanisms. Due to the cost- and time-consuming nature of traditional approaches for Ubi-site detection, there has been a growing interest in leveraging artificial intelligence for computer-aided Ubi-site prediction. In this study, we collected experimentally verified Ubi-sites of human proteins from the dbPTM database, then conducted comprehensive state-of-the art computational methods along with standard evaluation metrics and a proper validation strategy for Ubi-site prediction. We presented the effectiveness of our framework by comparing ten machine learning (ML) based approaches in three different categories: feature-based conventional ML methods, end-to-end sequence-based deep learning (DL) techniques, and hybrid feature-based DL models. Our results revealed that DL approaches outperformed the classical ML methods, achieving a 0.902 F1-score, 0.8198 accuracy, 0.8786 precision, and 0.9147 recall as the best performance for a DL model using both raw amino acid sequences and hand-crafted features. Interestingly, our experimental results disclosed that the performance of DL methods had a positive correlation with the length of amino acid fragments, suggesting that utilizing the entire sequence can lead to more accurate predictions in future research endeavors. Additionally, we developed a meticulously curated benchmark for Ubi-site prediction in human proteins. This benchmark serves as a valuable resource for future studies, enabling fair and accurate comparisons between different methods. Overall, our work highlights the potential of ML, particularly DL techniques, in predicting Ubi-sites and furthering our knowledge of protein regulation through ubiquitination in cells.

Biological Sequence Classification: A Review on Data and General Methods

With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website (http://lab.malab.cn/~acy/BioseqData/home.html), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research.

The CARM1 transcriptome and arginine methylproteome mediate skeletal muscle integrative biology

OBJECTIVE

Coactivator-associated arginine methyltransferase 1 (CARM1) catalyzes the methylation of arginine residues on target proteins to regulate critical processes in health and disease. A mechanistic understanding of the role(s) of CARM1 in skeletal muscle biology is only gradually emerging. The purpose of this study was to elucidate the function of CARM1 in regulating the maintenance and plasticity of skeletal muscle.

METHODS

We used transcriptomic, methylproteomic, molecular, functional, and integrative physiological approaches to determine the specific impact of CARM1 in muscle homeostasis.

RESULTS

Our data defines the occurrence of arginine methylation in skeletal muscle and demonstrates that this mark occurs on par with phosphorylation and ubiquitination. CARM1 skeletal muscle-specific knockout (mKO) mice displayed altered transcriptomic and arginine methylproteomic signatures with molecular and functional outcomes confirming remodeled skeletal muscle contractile and neuromuscular junction characteristics, which presaged decreased exercise tolerance. Moreover, CARM1 regulates AMPK-PGC-1α signalling during acute conditions of activity-induced muscle plasticity.

CONCLUSIONS

This study uncovers the broad impact of CARM1 in the maintenance and remodelling of skeletal muscle biology.

Posttranslational modifications in systems biology

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.