The structural context of posttranslational modifications at a proteome-wide scale

The future is bright, the future is biotechnology

As PLOS Biology celebrates its 20th anniversary, our April issue focuses on biotechnology with articles covering different aspects of the field, from genome editing to synthetic biology. With them, we emphasize our interest in expanding our presence in biotechnology research.

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors

The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.

Recent advances in kinase signaling network profiling by mass spectrometry

Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.

SARS-CoV-2 Spike Protein Post-Translational Modification Landscape and Its Impact on Protein Structure and Function via Computational Prediction

To elucidate the role of post-translational modifications (PTMs) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein's structure and virulence, we generated a high-resolution map of 87 PTMs using liquid chromatography with tandem mass spectrometry data on the extracted spike protein from SARS-CoV-2 virions and then reconstituted its structure heterogeneity caused by PTMs. Nonetheless, Alphafold2, a high-accuracy artificial intelligence tool to perform protein structure prediction, relies solely on primary amino acid sequence, whereas the impact of PTM, which often modulates critical protein structure and function, is much ignored. To overcome this challenge, we proposed the mutagenesis approach-an in silico, site-directed amino acid substitution to mimic the influence of PTMs on protein structure due to altered physicochemical properties in the post-translationally modified amino acids-and then reconstituted the spike protein's structure from the substituted sequences by Alphafold2. For the first time, the proposed method revealed predicted protein structures resulting from PTMs, a problem that Alphafold2 has yet to address. As an example, we performed computational analyses of the interaction of the post-translationally modified spike protein with its host factors such as angiotensin-converting enzyme 2 to illuminate binding affinity. Mechanistically, this study suggested the structural analysis of post-translationally modified protein via mutagenesis and deep learning. To summarize, the reconstructed spike protein structures showed that specific PTMs can be used to modulate host factor binding, guide antibody design, and pave the way for new therapeutic targets. The code and Supplementary Materials are freely available at https://github.com/LTZHKUSTGZ/SARS-CoV-2-spike-protein-PTM.

Toward an Integrated Machine Learning Model of a Proteomics Experiment

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

Sequence Coverage Visualizer: A Web Application for Protein Sequence Coverage 3D Visualization

Protein structure defines protein function and plays an extremely important role in protein characterization. Recently, two groups of researchers from DeepMind and the Baker lab have independently published protein structure prediction tools that can help us obtain predicted protein structures for the whole human proteome. This enabled us to visualize the entire human proteome using predicted 3D structures for the first time. To help other researchers best utilize these protein structure predictions in proteomics experiments, we present the Sequence Coverage Visualizer (SCV), http://scv.lab.gy, a web application for protein sequence coverage 3D visualization. Here we showed a few possible usages of the SCV, including the labeling of post-translational modifications and isotope labeling experiments. These results highlight the usefulness of such 3D visualization for proteomics experiments and how SCV can turn a regular proteomics experiment (identified peptide list) into structural insights. Furthermore, when used together with limited proteolysis, we demonstrated that SCV can help to compare different protein structures from different sources, including predicted ones and existing PDB entries. We hope our tool can provide help in the process of improving protein structure prediction accuracy. Overall, SCV is a convenient and powerful tool for visualizing proteomics results in 3D.

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased "hypothesis-free" analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.

Altered ureido protein modification profiles in seminal plasma extracellular vesicles of non-normozoospermic men

INTRODUCTION

Extracellular vesicles (EVs) have been recognized as key players in numerous physiological functions. These vesicles alter their compositions attuned to the health and disease states of the organism. In men, significant changes in the proteomic composition(s) of seminal plasma EVs (sEVs) have already been found to be related to infertility.

METHODS

Methods: In this study, we analyze the posttranslational configuration of sEV proteomes from normozoospermic (NZ) men and non-normozoospermic (non-NZ) men diagnosed with teratozoospermia and/or asthenozoospermia by unbiased, discovery-driven proteomics and advanced bioinformatics, specifically focusing on citrulline (Cit) and homocitrulline (hCit) posttranscriptional residues, both considered product of ureido protein modifications.

RESULTS AND DISCUSSION

Significant increase in the proteome-wide cumulative presence of hCit together with downregulation of Cit in specific proteins related to decisive molecular functions have been encountered in sEVs of non-NZ subjects. These findings identify novel culprits with a higher chance of affecting fundamental aspects of sperm functional quality and define potential specific diagnostic and prognostic non-invasive markers for male infertility.

MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is increasingly used to detect changes in posttranslational modifications (PTMs) in samples from different conditions. Analysis of data from such experiments faces numerous statistical challenges. These include the low abundance of modified proteoforms, the small number of observed peptides that span modification sites, and confounding between changes in the abundance of PTM and the overall changes in the protein abundance. Therefore, statistical approaches for detecting differential PTM abundance must integrate all the available information pertaining to a PTM site and consider all the relevant sources of confounding and variation. In this manuscript, we propose such a statistical framework, which is versatile, accurate, and leads to reproducible results. The framework requires an experimental design, which quantifies, for each sample, both peptides with PTMs and peptides from the same proteins with no modification sites. The proposed framework supports both label-free and tandem mass tag-based LC-MS/MS acquisitions. The statistical methodology separately summarizes the abundances of peptides with and without the modification sites, by fitting separate linear mixed effects models appropriate for the experimental design. Next, model-based inferences regarding the PTM and the protein-level abundances are combined to account for the confounding between these two sources. Evaluations on computer simulations, a spike-in experiment with known ground truth, and three biological experiments with different organisms, modification types, and data acquisition types demonstrate the improved fold change estimation and detection of differential PTM abundance, as compared to currently used approaches. The proposed framework is implemented in the free and open-source R/Bioconductor package MSstatsPTM.

Ubiquitin and ubiquitin-like conjugation systems in trypanosomatids

In eukaryotic cells, reversible attachment of ubiquitin and ubiquitin-like modifiers (Ubls) to specific target proteins is conducted by multicomponent systems whose collective actions control protein fate and cell behaviour in precise but complex ways. In trypanosomatids, attachment of ubiquitin and Ubls to target proteins regulates the cell cycle, endocytosis, protein sorting and degradation, autophagy and various aspects of infection and stress responses. The extent of these systems in trypanosomatids has been surveyed in recent reports, while in Leishmania mexicana, essential roles have been defined for many ubiquitin-system genes in deletion mutagenesis and life-cycle phenotyping campaigns. The first steps to elucidate the pathways of ubiquitin transfer among the ubiquitination components and to define the acceptor substrates and the downstream deubiquitinases are now being taken.

3D-Beacons: decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modeling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank.

Intrinsically Disordered Proteins: An Overview

Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called "Disorder-function paradigm" of proteins. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

PDBe and PDBe-KB: Providing high-quality, up-to-date and integrated resources of macromolecular structures to support basic and applied research and education

The archiving and dissemination of protein and nucleic acid structures as well as their structural, functional and biophysical annotations is an essential task that enables the broader scientific community to conduct impactful research in multiple fields of the life sciences. The Protein Data Bank in Europe (PDBe; pdbe.org) team develops and maintains several databases and web services to address this fundamental need. From data archiving as a member of the Worldwide PDB consortium (wwPDB; wwpdb.org), to the PDBe Knowledge Base (PDBe-KB; pdbekb.org), we provide data, data-access mechanisms, and visualizations that facilitate basic and applied research and education across the life sciences. Here, we provide an overview of the structural data and annotations that we integrate and make freely available. We describe the web services and data visualization tools we offer, and provide information on how to effectively use or even further develop them. Finally, we discuss the direction of our data services, and how we aim to tackle new challenges that arise from the recent, unprecedented advances in the field of structure determination and protein structure modeling.

Nutrient-sensitive protein O-GlcNAcylation shapes daily biological rhythms

O-linked-N-acetylglucosaminylation (O-GlcNAcylation) is a nutrient-sensitive protein modification that alters the structure and function of a wide range of proteins involved in diverse cellular processes. Similar to phosphorylation, another protein modification that targets serine and threonine residues, O-GlcNAcylation occupancy on cellular proteins exhibits daily rhythmicity and has been shown to play critical roles in regulating daily rhythms in biology by modifying circadian clock proteins and downstream effectors. We recently reported that daily rhythm in global O-GlcNAcylation observed in Drosophila tissues is regulated via the integration of circadian and metabolic signals. Significantly, mistimed feeding, which disrupts coordination of these signals, is sufficient to dampen daily O-GlcNAcylation rhythm and is predicted to negatively impact animal biological rhythms and health span. In this review, we provide an overview of published and potential mechanisms by which metabolic and circadian signals regulate hexosamine biosynthetic pathway metabolites and enzymes, as well as O-GlcNAc processing enzymes to shape daily O-GlcNAcylation rhythms. We also discuss the significance of functional interactions between O-GlcNAcylation and other post-translational modifications in regulating biological rhythms. Finally, we highlight organ/tissue-specific cellular processes and molecular pathways that could be modulated by rhythmic O-GlcNAcylation to regulate time-of-day-specific biology.

Substrate spectrum of PPM1D in the cellular response to DNA double-strand breaks

PPM1D is a p53-regulated protein phosphatase that modulates the DNA damage response (DDR) and is frequently altered in cancer. Here, we employed chemical inhibition of PPM1D and quantitative mass spectrometry-based phosphoproteomics to identify the substrates of PPM1D upon induction of DNA double-strand breaks (DSBs) by etoposide. We identified 73 putative PPM1D substrates that are involved in DNA repair, regulation of transcription, and RNA processing. One-third of DSB-induced S/TQ phosphorylation sites are dephosphorylated by PPM1D, demonstrating that PPM1D only partially counteracts ATM/ATR/DNA-PK signaling. PPM1D-targeted phosphorylation sites are found in a specific amino acid sequence motif that is characterized by glutamic acid residues, high intrinsic disorder, and poor evolutionary conservation. We identified a functionally uncharacterized protein Kanadaptin as ATM and PPM1D substrate upon DSB induction. We propose that PPM1D plays a role during the response to DSBs by regulating the phosphorylation of DNA- and RNA-binding proteins in intrinsically disordered regions.

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MS-based phosphoproteomic profiling of PPM1D substrates in U2OS and HCT116 cells

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PPM1D counteracts ATM in the cellular response to DNA double-strand breaks

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PPM1D target sites localize to glutamic acid-rich regions with high intrinsic disorder

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Kanadaptin is a putative DNA damage response factor regulated by ATM and PPM1D

The evolution of post-translational modifications

Post-translational modifications (PTMs) are chemical modifications that can regulate the activity and function of proteins. From an evolutionary perspective, they also represent a fast mechanism for the generation of phenotypic diversity and divergence. Advances in mass spectrometry have now enabled the identification of over 600 distinct PTM classes collectively spanning an order of 10⁶ unique sites. However, the chemical detection of PTMs has lagged far behind their functional characterisation, and relatively little is still known about the selective constraints that govern PTM evolution. In particular, the true fraction of PTM sites that are functional - and thus subject to selection - remains an open question. Here, I review advances made in the past two years towards understanding the evolution of PTMs and their associated enzymes.

Whole-proteome structures shed new light on posttranslational modifications

Accurate but protein-only AlphaFold models may show structural fingerprints of likely posttranslational modifications (PTMs). In this issue of PLOS Biology, Bludau and colleagues add a functional context to models by combining them with readily available proteomics results.