Structural basis for SARS-CoV-2 nucleocapsid (N) protein recognition by 14-3-3 proteins

Dynamic interactions of dimeric hub proteins underlie their diverse functions and structures: A comparative analysis of 14-3-3 and LC8

Hub proteins interact with a host of client proteins and regulate multiple cellular functions. Dynamic hubs have a single binding interface for one client at a time resulting in competition among clients with the highest affinity. Dynamic dimeric hubs with two identical sites bind either two different client proteins or two chains of the same client to form homogenous complexes and could also form heterogeneous mixtures of interconverting complexes. Here, we review the interactions of the dimeric hubs 14-3-3 and LC8. 14-3-3 is a phosphoserine/threonine binding protein involved in structuring client proteins and regulating their phosphorylation. LC8 is involved in promoting the dimerization of client peptides and the rigidification of their disordered regions. Both 14-3-3 and LC8 are essential genes, with 14-3-3 playing a crucial role in apoptosis and cell cycle regulation, while LC8 is critical for the assembly of proteins involved in transport, DNA repair, and transcription. Interestingly, both protein dimers can dissociate by phosphorylation, which results in their interactome-wide changes. Their interactions are also regulated by the phosphorylation of their clients. Both form heterogeneous complexes with various functions including phase separation, signaling, and viral hijacking where they restrict the conformational heterogeneity of their dimeric clients that bind nucleic acids. This comparative analysis highlights the importance of dynamic protein-protein interactions in the diversity of functions of 14-3-3 and LC8 and how small differences in structures of interfaces explain why 14-3-3 is primarily involved in the regulation of phosphorylation states while LC8 is primarily involved in the regulation of assembly of large dynamic complexes.

Look for the Scaffold: Multifaceted Regulation of Enzyme Activity by 14-3-3 Proteins

Enzyme activity is regulated by several mechanisms, including phosphorylation. Phosphorylation is a key signal transduction process in all eukaryotic cells and is thus crucial for virtually all cellular processes. In addition to its direct effect on protein structure, phosphorylation also affects protein-protein interactions, such as binding to scaffolding 14-3-3 proteins, which selectively recognize phosphorylated motifs. These interactions then modulate the catalytic activity, cellular localisation and interactions of phosphorylated enzymes through different mechanisms. The aim of this mini-review is to highlight several examples of 14-3-3 protein-dependent mechanisms of enzyme regulation previously studied in our laboratory over the past decade. More specifically, we address here the regulation of the human enzymes ubiquitin ligase Nedd4-2, procaspase-2, calcium-calmodulin dependent kinases CaMKK1/2, and death-associated protein kinase 2 (DAPK2) and yeast neutral trehalase Nth1.

PTransIPs: Identification of Phosphorylation Sites Enhanced by Protein PLM Embeddings

Phosphorylation is pivotal in numerous fundamental cellular processes and plays a significant role in the onset and progression of various diseases. The accurate identification of these phosphorylation sites is crucial for unraveling the molecular mechanisms within cells and during viral infections, potentially leading to the discovery of novel therapeutic targets. In this study, we develop PTransIPs, a new deep learning framework for the identification of phosphorylation sites. Independent testing results demonstrate that PTransIPs outperforms existing state-of-the-art (SOTA) methods, achieving AUCs of 0.9232 and 0.9660 for the identification of phosphorylated S/T and Y sites, respectively. PTransIPs contributes from three aspects. 1) PTransIPs is the first to apply protein pre-trained language model (PLM) embeddings to this task. It utilizes ProtTrans and EMBER2 to extract sequence and structure embeddings, respectively, as additional inputs into the model, effectively addressing issues of dataset size and overfitting, thus enhancing model performance; 2) PTransIPs is based on Transformer architecture, optimized through the integration of convolutional neural networks and TIM loss function, providing practical insights for model design and training; 3) The encoding of amino acids in PTransIPs enables it to serve as a universal framework for other peptide bioactivity tasks, with its excellent performance shown in extended experiments of this paper.

Diversity of short linear interaction motifs in SARS-CoV-2 nucleocapsid protein

IMPORTANCE

Short linear motifs (SLiMs) are 3-10 amino acid long binding motifs in intrinsically disordered protein regions (IDRs) that serve as ubiquitous protein-protein interaction modules in eukaryotic cells. Through molecular mimicry, viruses hijack these sequence motifs to control host cellular processes. It is thought that the small size of SLiMs and the high mutation frequencies of viral IDRs allow rapid host adaptation. However, a salient characteristic of RNA viruses, due to high replication errors, is their obligate existence as mutant swarms. Taking advantage of the uniquely large genomic database of SARS-CoV-2, here, we analyze the role of sequence diversity in the presentation of SLiMs, focusing on the highly abundant, multi-functional nucleocapsid protein. We find that motif mimicry is a highly dynamic process that produces an abundance of motifs transiently present in subsets of mutant species. This diversity allows the virus to efficiently explore eukaryotic motifs and evolve the host-virus interface.

Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach

The worldwide appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated significant concern and posed a considerable challenge to global health. Phosphorylation is a common post-translational modification that affects many vital cellular functions and is closely associated with SARS-CoV-2 infection. Precise identification of phosphorylation sites could provide more in-depth insight into the processes underlying SARS-CoV-2 infection and help alleviate the continuing COVID-19 crisis. Currently, available computational tools for predicting these sites lack accuracy and effectiveness. In this study, we designed an innovative meta-learning model, Meta-Learning for Serine/Threonine Phosphorylation (MeL-STPhos), to precisely identify protein phosphorylation sites. We initially performed a comprehensive assessment of 29 unique sequence-derived features, establishing prediction models for each using 14 renowned machine learning methods, ranging from traditional classifiers to advanced deep learning algorithms. We then selected the most effective model for each feature by integrating the predicted values. Rigorous feature selection strategies were employed to identify the optimal base models and classifier(s) for each cell-specific dataset. To the best of our knowledge, this is the first study to report two cell-specific models and a generic model for phosphorylation site prediction by utilizing an extensive range of sequence-derived features and machine learning algorithms. Extensive cross-validation and independent testing revealed that MeL-STPhos surpasses existing state-of-the-art tools for phosphorylation site prediction. We also developed a publicly accessible platform at https://balalab-skku.org/MeL-STPhos. We believe that MeL-STPhos will serve as a valuable tool for accelerating the discovery of serine/threonine phosphorylation sites and elucidating their role in post-translational regulation.

Diversity of Short Linear Interaction Motifs in SARS-CoV-2 Nucleocapsid Protein

Molecular mimicry of short linear interaction motifs has emerged as a key mechanism for viral proteins binding host domains and hijacking host cell processes. Here, we examine the role of RNA-virus sequence diversity in the dynamics of the virus-host interface, by analyzing the uniquely vast sequence record of viable SARS-CoV-2 species with focus on the multi-functional nucleocapsid protein. We observe the abundant presentation of motifs encoding several essential host protein interactions, alongside a majority of possibly non-functional and randomly occurring motif sequences absent in subsets of viable virus species. A large number of motifs emerge ex nihilo through transient mutations relative to the ancestral consensus sequence. The observed mutational landscape implies an accessible motif space that spans at least 25% of known eukaryotic motifs. This reveals motif mimicry as a highly dynamic process with the capacity to broadly explore host motifs, allowing the virus to rapidly evolve the virus-host interface.

IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has generated tremendous concern and poses a serious threat to international public health. Phosphorylation is a common post-translational modification affecting many essential cellular processes and is inextricably linked to SARS-CoV-2 infection. Hence, accurate identification of phosphorylation sites will be helpful to understand the mechanisms of SARS-CoV-2 infection and mitigate the ongoing COVID-19 pandemic. In the present study, an attention-based bidirectional gated recurrent unit network, called IPs-GRUAtt, was proposed to identify phosphorylation sites in SARS-CoV-2-infected host cells. Comparative results demonstrated that IPs-GRUAtt surpassed both state-of-the-art machine-learning methods and existing models for identifying phosphorylation sites. Moreover, the attention mechanism made IPs-GRUAtt able to extract the key features from protein sequences. These results demonstrated that the IPs-GRUAtt is a powerful tool for identifying phosphorylation sites. For facilitating its academic use, a freely available online web server for IPs-GRUAtt is provided at http://cbcb.cdutcm.edu.cn/phosphory/.