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

Computer-Aided Drug Discovery for Undruggable Targets

Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the presence of highly conserved active sites, and functional modulation by protein-protein interactions. Recent advances in computational simulations and artificial intelligence have revolutionized the drug design landscape, giving rise to innovative strategies for overcoming these obstacles. In this review, we highlight the latest progress in computational approaches for drug design against undruggable targets, present several successful case studies, and discuss remaining challenges and future directions. Special emphasis is placed on four primary target categories: intrinsically disordered proteins, protein allosteric regulation, protein-protein interactions, and protein degradation, along with discussion of emerging target types. We also examine how AI-driven methodologies have transformed the field, from applications in protein-ligand complex structure prediction and virtual screening to de novo ligand generation for undruggable targets. Integration of computational methods with experimental techniques is expected to bring further breakthroughs to overcome the hurdles of undruggable targets. As the field continues to evolve, these advancements hold great promise to expand the druggable space, offering new therapeutic opportunities for previously untreatable diseases.

Leveraging AI to explore structural contexts of post-translational modifications in drug binding

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can significantly influence various aspects of drug interactions including binding affinity. The structural consequences of PTMs, such as phosphorylation-induced conformational changes or their effects on ligand binding affinity, have historically been challenging to study on a large scale, primarily due to reliance on experimental methods. Recent advancements in computational power and artificial intelligence, particularly in deep learning algorithms and protein structure prediction tools like AlphaFold3, have opened new possibilities for exploring the structural context of interactions between PTMs and drugs. These AI-driven methods enable accurate modeling of protein structures including prediction of PTM-modified regions and simulation of ligand-binding dynamics on a large scale. In this work, we identified small molecule binding-associated PTMs that can influence drug binding across all human proteins listed as small molecule targets in the DrugDomain database, which we developed recently. 6,131 identified PTMs were mapped to structural domains from Evolutionary Classification of Protein Domains (ECOD) database.Scientific contribution: Using recent AI-based approaches for protein structure prediction (AlphaFold3, RoseTTAFold All-Atom, Chai-1), we generated 14,178 models of PTM-modified human proteins with docked ligands. Our results demonstrate that these methods can predict PTM effects on small molecule binding, but precise evaluation of their accuracy requires a much larger benchmarking set. We also found that phosphorylation of NADPH-Cytochrome P450 Reductase, observed in cervical and lung cancer, causes significant structural disruption in the binding pocket, potentially impairing protein function. All data and generated models are available from DrugDomain database v1.1 ( http://prodata.swmed.edu/DrugDomain/ ) and GitHub ( https://github.com/kirmedvedev/DrugDomain ). This resource is the first to our knowledge in offering structural context for small molecule binding-associated PTMs on a large scale.

Finding the brakes on the Toxoplasma life cycle

Toxoplasma gondii motility is an all-or-nothing response. Claywell et al. identify negative-feedback loops in cyclic-nucleotide signaling that allow parasites to turn off motility and commit to intracellular replication. Feedback mechanisms and bistability are useful frameworks for describing, modeling, and testing T. gondii motility signaling pathways.

Leveraging AI to Explore Structural Contexts of Post-Translational Modifications in Drug Binding

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can significantly influence various aspects of drug interactions including binding affinity. The structural consequences of PTMs, such as phosphorylation-induced conformational changes or their effects on ligand binding affinity, have historically been challenging to study on a large scale, primarily due to reliance on experimental methods. Recent advancements in computational power and artificial intelligence, particularly in deep learning algorithms and protein structure prediction tools like AlphaFold3, have opened new possibilities for exploring the structural context of interactions between PTMs and drugs. These AI-driven methods enable accurate modeling of protein structures including prediction of PTM-modified regions and simulation of ligand-binding dynamics on a large scale. In this work, we identified small molecule binding-associated PTMs that can influence drug binding across all human proteins listed as small molecule targets in the DrugDomain database, which we developed recently. 6,131 identified PTMs were mapped to structural domains from Evolutionary Classification of Protein Domains (ECOD) database. Scientific contribution. Using recent AI-based approaches for protein structure prediction (AlphaFold3, RoseTTAFold All-Atom, Chai-1), we generated 14,178 models of PTM-modified human proteins with docked ligands. Our results demonstrate that these methods can predict PTM effects on small molecule binding, but precise evaluation of their accuracy requires a much larger benchmarking set. We also found that phosphorylation of NADPH-Cytochrome P450 Reductase, observed in cervical and lung cancer, causes significant structural disruption in the binding pocket, potentially impairing protein function. All data and generated models are available from DrugDomain database v1.1 (http://prodata.swmed.edu/DrugDomain/) and GitHub (https://github.com/kirmedvedev/DrugDomain). This resource is the first to our knowledge in offering structural context for small molecule binding-associated PTMs on a large scale.

PTMVision: An Interactive Visualization Webserver for Post-translational Modifications of Proteins

Recent improvements in methods and instruments used in mass spectrometry have greatly enhanced the detection of protein post-translational modifications (PTMs). On the computational side, the adoption of open modification search strategies now allows for the identification of a wide variety of PTMs, potentially revealing hundreds to thousands of distinct modifications in biological samples. While the observable part of the proteome is continuously growing, the visualization and interpretation of this vast amount of data in a comprehensive fashion is not yet possible. There is a clear need for methods to easily investigate the PTM landscape and to thoroughly examine modifications on proteins of interest from acquired mass spectrometry data. We present PTMVision, a web server providing an intuitive and simple way to interactively explore PTMs identified in mass spectrometry-based proteomics experiments and to analyze the modification sites of proteins within relevant context. It offers a variety of tools to visualize the PTM landscape from different angles and at different levels, such as 3D structures and contact maps, UniMod classification summaries, and site specific overviews. The web server's user-friendly interface ensures accessibility across diverse scientific backgrounds. PTMVision is available at https://ptmvision-tuevis.cs.uni-tuebingen.de/.

Acetylation of proximal cysteine-lysine pairs by alcohol metabolism

Alcohol consumption induces hepatocyte damage through complex processes involving oxidative stress and disrupted metabolism. These factors alter proteomic and epigenetic marks, including alcohol-induced protein acetylation, which is a key post-translational modification (PTM) that regulates hepatic metabolism and is associated with the pathogenesis of alcohol-associated liver disease (ALD). Recent evidence suggests lysine acetylation occurs when a proximal cysteine residue is within ∼15 Å of a lysine residue, referred to as a cysteine-lysine (Cys-Lys) pair. Here, acetylation can occur through the transfer of an acetyl moiety via an S → N transfer reaction. Alcohol-mediated redox stress is known to occur coincidentally with lysine acetylation, yet the biochemical mechanisms related to cysteine and lysine crosstalk within ALD remain unexplored. A murine model of ALD was employed to quantify hepatic cysteine redox changes and lysine acetylation, revealing that alcohol metabolism significantly reduced the cysteine thiol proteome and increased protein acetylation. Interrogating both cysteine redox and lysine acetylation datasets, 1280 protein structures generated by AlphaFold2 represented by a 3D spatial matrix were used to quantify the distances between 557,815 cysteine and lysine residues. Our analysis revealed that alcohol metabolism induces redox changes and acetylation selectively on proximal Cys-Lys pairs with an odds ratio of 1.88 (p < 0.0001). Key Cys-Lys redox signaling hubs were impacted in metabolic pathways associated with ALD, including lipid metabolism and the electron transport chain. Proximal Cys-Lys pairs exist as sets with four major motifs represented by the number of Cys and Lys residues that are pairing (Cys1:Lys1, Cysx:Lys1, Cys1:Lysx and Cysx:Lysx) each with a unique microenvironment. The motifs are composed of functionally relevant Cys-Ly altered within ALD, identifying potential therapeutic targets. Furthermore, these unique Cys-Lys redox signatures are translationally relevant as revealed by orthologous comparison with severe alcohol-associated hepatitis (SAH) explants, revealing numerous pathogenic thiol redox signals in these patients.

Mass-spectrometry-based proteomics: from single cells to clinical applications

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

Covalent targeting of splicing in T cells

Despite significant interest in therapeutic targeting of splicing, few chemical probes are available for the proteins involved in splicing. Here, we show that elaborated stereoisomeric acrylamide EV96 and its analogues lead to a selective T cell state-dependent loss of interleukin 2-inducible T cell kinase (ITK) by targeting one of the core splicing factors SF3B1. Mechanistic investigations suggest that the state-dependency stems from a combination of differential protein turnover rates and extensive ITK mRNA alternative splicing. We further introduce the most comprehensive list to date of proteins involved in splicing and leverage cysteine- and protein-directed activity-based protein profiling with electrophilic scout fragments to demonstrate covalent ligandability for many classes of splicing factors and splicing regulators in T cells. Taken together, our findings show how chemical perturbation of splicing can lead to immune state-dependent changes in protein expression and provide evidence for the broad potential to target splicing factors with covalent chemistry.

Infection-induced lysine lactylation enables herpesvirus immune evasion

Aerobic glycolysis is a hallmark of many viral infections, leading to substantial accumulation of lactate. However, the regulatory roles of lactate during viral infections remain poorly understood. Here, we report that human cytomegalovirus (HCMV) infection leverages lactate to induce widespread protein lactylation and promote viral spread. We establish that lactyllysine is enriched in intrinsically disordered regions, regulating viral protein condensates and immune signaling transduction. Dynamic lactylation of immune factors suppresses immunity, a feature we show to be shared for HCMV and herpes simplex virus 1 infections, through regulation of RNA binding protein 14 and interferon-γ-inducible protein 16 (IFI16). K90 lactylation of the viral DNA sensor IFI16 inhibits recruitment of the DNA damage response kinase DNA-PK, preventing IFI16-driven virus gene repression and cytokine induction. Together, we characterize global protein lactylation dynamics during virus infection, finding that virus-induced lactate contributes to its immune evasion through direct inhibition of immune signaling pathways.

Artificial Intelligence Transforming Post-Translational Modification Research

Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.

Integrative Multi-PTM Proteomics Reveals Dynamic Global, Redox, Phosphorylation, and Acetylation Regulation in Cytokine-Treated Pancreatic Beta Cells

Studying regulation of protein function at a systems level necessitates an understanding of the interplay among diverse posttranslational modifications (PTMs). A variety of proteomics sample processing workflows are currently used to study specific PTMs but rarely characterize multiple types of PTMs from the same sample inputs. Method incompatibilities and laborious sample preparation steps complicate large-scale physiological investigations and can lead to variations in results. The single-pot, solid-phase-enhanced sample preparation (SP3) method for sample cleanup is compatible with different lysis buffers and amenable to automation, making it attractive for high-throughput multi-PTM profiling. Herein, we describe an integrative SP3 workflow for multiplexed quantification of protein abundance, cysteine thiol oxidation, phosphorylation, and acetylation. The broad applicability of this approach is demonstrated using cell and tissue samples, and its utility for studying interacting regulatory networks is highlighted in a time-course experiment of cytokine-treated β-cells. We observed a swift response in the global regulation of protein abundances consistent with rapid activation of JAK-STAT and NF-κB signaling pathways. Regulators of these pathways as well as proteins involved in their target processes displayed multi-PTM dynamics indicative of complex cellular response stages: acute, adaptation, and chronic (prolonged stress). PARP14, a negative regulator of JAK-STAT, had multiple colocalized PTMs that may be involved in intraprotein regulatory crosstalk. Our workflow provides a high-throughput platform that can profile multi-PTMomes from the same sample set, which is valuable in unraveling the functional roles of PTMs and their co-regulation.

Phosphorylation of the HMGN1 Nucleosome Binding Domain Decreases Helicity and Interactions with the Acidic Patch

Intrinsically disordered proteins are abundant in the nucleus and are prime sites for posttranslational modifications that modulate transcriptional regulation. Lacking a defined three-dimensional structure, intrinsically disordered proteins populate an ensemble of several conformational states, which are dynamic and often altered by posttranslational modifications, or by binding to interaction partners. Although there is growing appreciation for the role that intrinsically disordered regions have in regulating protein-protein interactions, we still have a poor understanding of how to determine conformational population shifts, their causes under various conditions, and how to represent and model conformational ensembles. Here, we study the effects of serine phosphorylation in the nucleosome-binding domain of an intrinsically disordered protein - HMGN1 - using NMR spectroscopy, circular dichroism and modelling of protein complexes. We show that phosphorylation induces local conformational changes in the peptide backbone and decreases the helical propensity of the nucleosome binding domain. Modelling studies using AlphaFold3 suggest that phosphorylation disrupts the interface between HMGN1 and the nucleosome acidic patch, but that the models over-predict helicity in comparison to experimental data. These studies help us to build a picture of how posttranslational modifications might shift the conformational populations of disordered regions, alter access to histones, and regulate chromatin compaction.

An Atypical Mechanism of SUMOylation of Neurofibromin SecPH Domain Provides New Insights into SUMOylation Site Selection

Neurofibromin (Nf1) is a giant multidomain protein encoded by the tumour-suppressor gene NF1. NF1 is mutated in a common genetic disease, neurofibromatosis type I (NF1), and in various cancers. The protein has a Ras-GAP (GTPase activating protein) activity but is also connected to diverse signalling pathways through its SecPH domain, which interacts with lipids and different protein partners. We previously showed that Nf1 partially colocalized with the ProMyelocytic Leukemia (PML) protein in PML nuclear bodies, hotspots of SUMOylation, thereby suggesting the potential SUMOylation of Nf1. Here, we demonstrate that the full-length isoform 2 and a SecPH fragment of Nf1 are substrates of the SUMO pathway and identify a well-defined SUMOylation profile of SecPH with two main modified lysines. One of these sites, K1731, is highly conserved and surface-exposed. Despite the presence of an inverted SUMO consensus motif surrounding K1731, and a potential SUMO-interacting motif (SIM) within SecPH, we show that neither of these elements is necessary for K1731 SUMOylation, which is also independent of Ubc9 SUMOylation on K14. A 3D model of an interaction between SecPH and Ubc9 centred on K1731, combined with site-directed mutagenesis, identifies specific structural elements of SecPH required for K1731 SUMOylation, some of which are affected in reported NF1 pathogenic variants. This work provides a new example of SUMOylation dependent on the tertiary rather than primary protein structure surrounding the modified site, expanding our knowledge of mechanisms governing SUMOylation site selection.

The future of integrated structural biology

Instruct-ERIC, "the European Research Infrastructure Consortium for Structural biology research," is a pan-European distributed research infrastructure making high-end technologies and methods in structural biology available to users. Here, we describe the current state-of-the-art of integrated structural biology and discuss potential future scientific developments as an impulse for the scientific community, many of which are located in Europe and are associated with Instruct. We reflect on where to focus scientific and technological initiatives within the distributed Instruct research infrastructure. This review does not intend to make recommendations on funding requirements or initiatives directly, neither at the national nor the European level. However, it addresses future challenges and opportunities for the field, and foresees the need for a stronger coordination within the European and international research field of integrated structural biology to be able to respond timely to thematic topics that are often prioritized by calls for funding addressing societal needs.

Prediction of protein structure and AI

AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-function relationships from protein 3D structure models and can serve as a template or a reference for experimental structural analysis including X-ray crystallography, NMR and cryo-EM analysis. Its use is expanding among researchers, not only in structural biology but also in other research fields. Researchers are currently exploring the full potential of AlphaFold-generated protein models. Predicting disease severity caused by missense mutations is one such application. This article provides an overview of the 3D structural modeling of AlphaFold based on deep learning techniques and highlights the challenges in predicting the pathogenicity of missense mutations.

Post-translational modifications in the Protein Data Bank

Proteins frequently undergo covalent modification at the post-translational level, which involves the covalent attachment of chemical groups onto amino acids. This can entail the singular or multiple addition of small groups, such as phosphorylation; long-chain modifications, such as glycosylation; small proteins, such as ubiquitination; as well as the interconversion of chemical groups, such as the formation of pyroglutamic acid. These post-translational modifications (PTMs) are essential for the normal functioning of cells, as they can alter the physicochemical properties of amino acids and therefore influence enzymatic activity, protein localization, protein-protein interactions and protein stability. Despite their inherent importance, accurately depicting PTMs in experimental studies of protein structures often poses a challenge. This review highlights the role of PTMs in protein structures, as well as the prevalence of PTMs in the Protein Data Bank, directing the reader to accurately built examples suitable for use as a modelling reference.

Fast and deep phosphoproteome analysis with the Orbitrap Astral mass spectrometer

Owing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method, we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology is benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We apply this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detect 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of phosphorylation events relevant to mitochondrial and brain biology.

Presynaptic Rac1 in the hippocampus selectively regulates working memory

One of the most extensively studied members of the Ras superfamily of small GTPases, Rac1 is an intracellular signal transducer that remodels actin and phosphorylation signaling networks. Previous studies have shown that Rac1-mediated signaling is associated with hippocampal-dependent working memory and longer-term forms of learning and memory and that Rac1 can modulate forms of both pre- and postsynaptic plasticity. How these different cognitive functions and forms of plasticity mediated by Rac1 are linked, however, is unclear. Here, we show that spatial working memory in mice is selectively impaired following the expression of a genetically encoded Rac1 inhibitor at presynaptic terminals, while longer-term cognitive processes are affected by Rac1 inhibition at postsynaptic sites. To investigate the regulatory mechanisms of this presynaptic process, we leveraged new advances in mass spectrometry to identify the proteomic and post-translational landscape of presynaptic Rac1 signaling. We identified serine/threonine kinases and phosphorylated cytoskeletal signaling and synaptic vesicle proteins enriched with active Rac1. The phosphorylated sites in these proteins are at positions likely to have regulatory effects on synaptic vesicles. Consistent with this, we also report changes in the distribution and morphology of synaptic vesicles and in postsynaptic ultrastructure following presynaptic Rac1 inhibition. Overall, this study reveals a previously unrecognized presynaptic role of Rac1 signaling in cognitive processes and provides insights into its potential regulatory mechanisms.

The impact of IDR phosphorylation on the RNA binding profiles of proteins

Due to their capacity to mediate repetitive protein interactions, intrinsically disordered regions (IDRs) are crucial for the formation of various types of protein-RNA complexes. The functions of IDRs are strongly modulated by post-translational modifications (PTMs). Phosphorylation is the most common and well-studied modification of IDRs, which can alter homomeric or heteromeric interactions of proteins and impact their ability to phase separate. Moreover, phosphorylation can influence the RNA-binding properties of proteins, and recent studies demonstrated its selective impact on the global profiles of protein-RNA binding and regulation. These findings highlight the need for further integrative approaches to understand how signalling remodels protein-RNA networks in cells.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics

Lysine deacetylase inhibitors (KDACis) are approved drugs for cutaneous T cell lymphoma (CTCL), peripheral T cell lymphoma (PTCL), and multiple myeloma, but many aspects of their cellular mechanism of action (MoA) and substantial toxicity are not well understood. To shed more light on how KDACis elicit cellular responses, we systematically measured dose-dependent changes in acetylation, phosphorylation, and protein expression in response to 21 clinical and pre-clinical KDACis. The resulting 862,000 dose-response curves revealed, for instance, limited cellular specificity of histone deacetylase (HDAC) 1, 2, 3, and 6 inhibitors; strong cross-talk between acetylation and phosphorylation pathways; localization of most drug-responsive acetylation sites to intrinsically disordered regions (IDRs); an underappreciated role of acetylation in protein structure; and a shift in EP300 protein abundance between the cytoplasm and the nucleus. This comprehensive dataset serves as a resource for the investigation of the molecular mechanisms underlying KDACi action in cells and can be interactively explored online in ProteomicsDB.

Global, site-resolved analysis of ubiquitylation occupancy and turnover rate reveals systems properties

Ubiquitylation regulates most proteins and biological processes in a eukaryotic cell. However, the site-specific occupancy (stoichiometry) and turnover rate of ubiquitylation have not been quantified. Here we present an integrated picture of the global ubiquitylation site occupancy and half-life. Ubiquitylation site occupancy spans over four orders of magnitude, but the median ubiquitylation site occupancy is three orders of magnitude lower than that of phosphorylation. The occupancy, turnover rate, and regulation of sites by proteasome inhibitors are strongly interrelated, and these attributes distinguish sites involved in proteasomal degradation and cellular signaling. Sites in structured protein regions exhibit longer half-lives and stronger upregulation by proteasome inhibitors than sites in unstructured regions. Importantly, we discovered a surveillance mechanism that rapidly and site-indiscriminately deubiquitylates all ubiquitin-specific E1 and E2 enzymes, protecting them against accumulation of bystander ubiquitylation. The work provides a systems-scale, quantitative view of ubiquitylation properties and reveals general principles of ubiquitylation-dependent governance.

Fast and Deep Phosphoproteome Analysis with the Orbitrap Astral Mass Spectrometer

Owing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology was benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We applied this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detected 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of novel phosphorylation events relevant to mitochondrial and brain biology.

FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics

Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhos-STR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.

Chemo-enzymatic synthesis of tetrasaccharide linker peptides to study the divergent step in glycosaminoglycan biosynthesis

Glycosaminoglycans are extended linear polysaccharides present on cell surfaces and within the extracellular matrix that play crucial roles in various biological processes. Two prominent glycosaminoglycans, heparan sulfate and chondroitin sulfate, are covalently linked to proteoglycan core proteins through a common tetrasaccharide linker comprising glucuronic acid, galactose, galactose, and xylose moities. This tetrasaccharide linker is meticulously assembled step by step by four Golgi-localized glycosyltransferases. The addition of the fifth sugar moiety, either N-acetylglucosamine or N-acetylgalactosamine, initiates further chain elongation, resulting in the formation of heparan sulfate or chondroitin sulfate, respectively. Despite the fundamental significance of this step in glycosaminoglycan biosynthesis, its regulatory mechanisms have remained elusive. In this study, we detail the expression and purification of the four linker-synthesizing glycosyltransferases and their utilization in the production of fluorescent peptides carrying the native tetrasaccharide linker. We generated five tetrasaccharide peptides, mimicking the core proteins of either heparan sulfate or chondroitin sulfate proteoglycans. These peptides were readily accepted as substrates by the EXTL3 enzyme, which adds an N-acetylglucosamine moiety, thereby initiating heparan sulfate biosynthesis. Importantly, EXTL3 showed a preference towards peptides mimicking the core proteins of heparan sulfate proteoglycans over the ones from chondroitin sulfate proteoglycans. This suggests that EXTL3 could play a role in the decision-making step during glycosaminoglycan biosynthesis. The innovative strategy for chemo-enzymatic synthesis of fluorescent-labeled linker-peptides promises to be instrumental in advancing future investigations into the initial steps and the divergent step of glycosaminoglycan biosynthesis.

Pathogenic mutations of human phosphorylation sites affect protein-protein interactions

Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated mutations affecting phosphorylation sites. Our results unveil significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We find that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments reveal the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering insights into potential molecular mechanisms underlying pathogenesis.

Cross-prediction-powered inference

While reliable data-driven decision-making hinges on high-quality labeled data, the acquisition of quality labels often involves laborious human annotations or slow and expensive scientific measurements. Machine learning is becoming an appealing alternative as sophisticated predictive techniques are being used to quickly and cheaply produce large amounts of predicted labels; e.g., predicted protein structures are used to supplement experimentally derived structures, predictions of socioeconomic indicators from satellite imagery are used to supplement accurate survey data, and so on. Since predictions are imperfect and potentially biased, this practice brings into question the validity of downstream inferences. We introduce cross-prediction: a method for valid inference powered by machine learning. With a small labeled dataset and a large unlabeled dataset, cross-prediction imputes the missing labels via machine learning and applies a form of debiasing to remedy the prediction inaccuracies. The resulting inferences achieve the desired error probability and are more powerful than those that only leverage the labeled data. Closely related is the recent proposal of prediction-powered inference [A. N. Angelopoulos, S. Bates, C. Fannjiang, M. I. Jordan, T. Zrnic, Science 382, 669-674 (2023)], which assumes that a good pretrained model is already available. We show that cross-prediction is consistently more powerful than an adaptation of prediction-powered inference in which a fraction of the labeled data is split off and used to train the model. Finally, we observe that cross-prediction gives more stable conclusions than its competitors; its CIs typically have significantly lower variability.

Accelerating multiplexed profiling of protein-ligand interactions: High-throughput plate-based reactive cysteine profiling with minimal input

Chemoproteomics has made significant progress in investigating small-molecule-protein interactions. However, the proteome-wide profiling of cysteine ligandability remains challenging to adapt for high-throughput applications, primarily due to a lack of platforms capable of achieving the desired depth using low input in 96- or 384-well plates. Here, we introduce a revamped, plate-based platform which enables routine interrogation of either ∼18,000 or ∼24,000 reactive cysteines based on starting amounts of 10 or 20 μg, respectively. This represents a 5-10X reduction in input and 2-3X improved coverage. We applied the platform to screen 192 electrophiles in the native HEK293T proteome, mapping the ligandability of 38,450 reactive cysteines from 8,274 human proteins. We further applied the platform to characterize new cellular targets of established drugs, uncovering that ARS-1620, a KRASG12C inhibitor, binds to and inhibits an off-target adenosine kinase ADK. The platform represents a major step forward to high-throughput proteome-wide evaluation of reactive cysteines.

Presynaptic Rac1 in the hippocampus selectively regulates working memory

One of the most extensively studied members of the Ras superfamily of small GTPases, Rac1 is an intracellular signal transducer that remodels actin and phosphorylation signaling networks. Previous studies have shown that Rac1-mediated signaling is associated with hippocampal-dependent working memory and longer-term forms of learning and memory and that Rac1 can modulate forms of both pre- and postsynaptic plasticity. How these different cognitive functions and forms of plasticity mediated by Rac1 are linked, however, is unclear. Here, we show that spatial working memory is selectively impaired following the expression of a genetically encoded Rac1-inhibitor at presynaptic terminals, while longer-term cognitive processes are affected by Rac1 inhibition at postsynaptic sites. To investigate the regulatory mechanisms of this presynaptic process, we leveraged new advances in mass spectrometry to identify the proteomic and post-translational landscape of presynaptic Rac1 signaling. We identified serine/threonine kinases and phosphorylated cytoskeletal signaling and synaptic vesicle proteins enriched with active Rac1. The phosphorylated sites in these proteins are at positions likely to have regulatory effects on synaptic vesicles. Consistent with this, we also report changes in the distribution and morphology of synaptic vesicles and in postsynaptic ultrastructure following presynaptic Rac1 inhibition. Overall, this study reveals a previously unrecognized presynaptic role of Rac1 signaling in cognitive processes and provides insights into its potential regulatory mechanisms.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Spray-type modifications: an emerging paradigm in post-translational modifications

A post-translational modification (PTM) occurs when a nucleophilic residue (e.g., lysine of a target protein) attacks electrophilic substrate molecules (e.g., acyl-AMP), involving writer enzymes or even occurring spontaneously. Traditionally, this phenomenon was thought to be sequence specific; however, recent research suggests that PTMs can also occur in a non-sequence-specific manner confined to a specific location in a cell. In this Opinion, we compile the accumulated evidence of spray-type PTMs and propose a mechanism for this phenomenon based on the exposure level of reactive electrophilic substrate molecules at the active site of the PTM writers. Overall, a spray-type PTM conceptual framework is useful for comprehending the promiscuous PTM writer events that cannot be adequately explained by the traditional concept of sequence-dependent PTM events.

The logic of protein post-translational modifications (PTMs): Chemistry, mechanisms and evolution of protein regulation through covalent attachments

Protein post-translational modifications (PTMs) play a crucial role in all cellular functions by regulating protein activity, interactions and half-life. Despite the enormous diversity of modifications, various PTM systems show parallels in their chemical and catalytic underpinnings. Here, focussing on modifications that involve the addition of new elements to amino-acid sidechains, I describe historical milestones and fundamental concepts that support the current understanding of PTMs. The historical survey covers selected key research programmes, including the study of protein phosphorylation as a regulatory switch, protein ubiquitylation as a degradation signal and histone modifications as a functional code. The contribution of crucial techniques for studying PTMs is also discussed. The central part of the essay explores shared chemical principles and catalytic strategies observed across diverse PTM systems, together with mechanisms of substrate selection, the reversibility of PTMs by erasers and the recognition of PTMs by reader domains. Similarities in the basic chemical mechanism are highlighted and their implications are discussed. The final part is dedicated to the evolutionary trajectories of PTM systems, beginning with their possible emergence in the context of rivalry in the prokaryotic world. Together, the essay provides a unified perspective on the diverse world of major protein modifications.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

N-glycosylation as a eukaryotic protective mechanism against protein aggregation

The tendency for proteins to form aggregates is an inherent part of every proteome and arises from the self-assembly of short protein segments called aggregation-prone regions (APRs). While posttranslational modifications (PTMs) have been implicated in modulating protein aggregation, their direct role in APRs remains poorly understood. In this study, we used a combination of proteome-wide computational analyses and biophysical techniques to investigate the potential involvement of PTMs in aggregation regulation. Our findings reveal that while most PTM types are disfavored near APRs, N-glycosylation is enriched and evolutionarily selected, especially in proteins prone to misfolding. Experimentally, we show that N-glycosylation inhibits the aggregation of peptides in vitro through steric hindrance. Moreover, mining existing proteomics data, we find that the loss of N-glycans at the flanks of APRs leads to specific protein aggregation in Neuro2a cells. Our findings indicate that, among its many molecular functions, N-glycosylation directly prevents protein aggregation in higher eukaryotes.

Posttranslational modifications in bacteria during phage infection

During phage infection, both virus and bacteria attempt to gain and/or maintain control over critical bacterial functions, through a plethora of strategies. These strategies include posttranslational modifications (PTMs, including phosphorylation, ribosylation, and acetylation), as rapid and dynamic regulators of protein behavior. However, to date, knowledge on the topic remains scarce and fragmented, while a more systematic investigation lies within reach. The release of AlphaFold, which advances PTM enzyme discovery and functional elucidation, and the increasing inclusivity and scale of mass spectrometry applications to new PTM types, could significantly accelerate research in the field. In this review, we highlight the current knowledge on PTMs during phage infection, and conceive a possible pipeline for future research, following an enzyme-target-function scheme.

Lactylation prediction models based on protein sequence and structural feature fusion

Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread attention due to the Warburg effect in tumor cells. In this work, we first design a natural language processing method to automatically extract the 3D structural features of Kla sites, avoiding potential biases caused by manually designed structural features. Then, we establish two Kla prediction frameworks, Attention-based feature fusion Kla model (ABFF-Kla) and EBFF-Kla, to integrate the sequence features and the structure features based on the attention layer and embedding layer, respectively. The results indicate that ABFF-Kla and Embedding-based feature fusion Kla model (EBFF-Kla), which fuse features from protein sequences and spatial structures, have better predictive performance than that of models that use only sequence features. Our work provides an approach for the automatic extraction of protein structural features, as well as a flexible framework for Kla prediction. The source code and the training data of the ABFF-Kla and the EBFF-Kla are publicly deposited at: https://github.com/ispotato/Lactylation_model.

LAFEM: A Scoring Model to Evaluate Functional Landscape of Lysine Acetylome

Protein lysine acetylation is a critical post-translational modification involved in a wide range of biological processes. To date, about 20,000 acetylation sites of Homo sapiens were identified through mass spectrometry-based proteomic technology, but more than 95% of them have unclear functional annotations because of the lack of existing prioritization strategy to assess the functional importance of the acetylation sites on large scale. Hence, we established a lysine acetylation functional evaluating model (LAFEM) by considering eight critical features surrounding lysine acetylation site to high-throughput estimate the functional importance of given acetylation sites. This was achieved by selecting one of the random forest models with the best performance in 10-fold cross-validation on undersampled training dataset. The global analysis demonstrated that the molecular environment of acetylation sites with high acetylation functional scores (AFSs) mainly had the features of larger solvent-accessible surface area, stronger hydrogen bonding-donating abilities, near motif and domain, higher homology, and disordered degree. Importantly, LAFEM performed well in validation dataset and acetylome, showing good accuracy to screen out fitness directly relevant acetylation sites and assisting to explain the core reason for the difference between biological models from the perspective of acetylome. We further used cellular experiments to confirm that, in nuclear casein kinase and cyclin-dependent kinase substrate 1, acetyl-K35 with higher AFS was more important than acetyl-K9 with lower AFS in the proliferation of A549 cells. LAFEM provides a prioritization strategy to large scale discover the fitness directly relevant acetylation sites, which constitutes an unprecedented resource for better understanding of functional acetylome.

Covalent Targeting of Splicing in T Cells

Despite significant interest in therapeutic targeting of splicing, few chemical probes are available for the proteins involved in splicing. Here, we show that elaborated stereoisomeric acrylamide chemical probe EV96 and its analogues lead to a selective T cell state-dependent loss of interleukin 2-inducible T cell kinase (ITK) by targeting one of the core splicing factors SF3B1. Mechanistic investigations suggest that the state-dependency stems from a combination of differential protein turnover rates and availability of functional mRNA pools that can be depleted due to extensive alternative splicing. We further introduce a comprehensive list of proteins involved in splicing and leverage both cysteine- and protein-directed activity-based protein profiling (ABPP) data with electrophilic scout fragments to demonstrate covalent ligandability for many classes of splicing factors and splicing regulators in primary human T cells. Taken together, our findings show how chemical perturbation of splicing can lead to immune state-dependent changes in protein expression and provide evidence for the broad potential to target splicing factors with covalent chemistry.

Unraveling the battle for lysine: A review of the competition among post-translational modifications

Proteins play a critical role as key regulators in various biological systems, influencing crucial processes such as gene expression, cell cycle progression, and cellular proliferation. However, the functions of proteins can be further modified through post-translational modifications (PTMs), which expand their roles and contribute to disease progression when dysregulated. In this review, we delve into the methodologies employed for the characterization of PTMs, shedding light on the techniques and tools utilized to help unravel their complexity. Furthermore, we explore the prevalence of crosstalk and competition that occurs between different types of PTMs, specifically focusing on both histone and non-histone proteins. The intricate interplay between different modifications adds an additional layer of regulation to protein function and cellular processes. To gain insights into the competition for lysine residues among various modifications, computational systems such as MethylSight have been developed, allowing for a comprehensive analysis of the modification landscape. Additionally, we provide an overview of the exciting developments in the field of inhibitors or drugs targeting PTMs, highlighting their potential in combatting prevalent diseases. The discovery and development of drugs that modulate PTMs present promising avenues for therapeutic interventions, offering new strategies to address complex diseases. As research progresses in this rapidly evolving field, we anticipate remarkable advancements in our understanding of PTMs and their roles in health and disease, ultimately paving the way for innovative treatment approaches.

Prediction-powered inference

Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system. The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients without making any assumptions about the machine-learning algorithm that supplies the predictions. Furthermore, more accurate predictions translate to smaller confidence intervals. Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning. The benefits of prediction-powered inference were demonstrated with datasets from proteomics, astronomy, genomics, remote sensing, census analysis, and ecology.

The fitness cost of spurious phosphorylation

The fidelity of signal transduction requires the binding of regulatory molecules to their cognate targets. However, the crowded cell interior risks off-target interactions between proteins that are functionally unrelated. How such off-target interactions impact fitness is not generally known, but quantifying this is required to understand the constraints faced by cell systems as they evolve. Here, we use the model organism S. cerevisiae to inducibly express tyrosine kinases. Because yeast lacks bona fide tyrosine kinases, most of the resulting tyrosine phosphorylation is spurious. This provides a suitable system to measure the impact of artificial protein interactions on fitness. We engineered 44 yeast strains each expressing a tyrosine kinase, and quantitatively analysed their phosphoproteomes. This analysis resulted in ~30,000 phosphosites mapping to ~3,500 proteins. Examination of the fitness costs in each strain revealed a strong correlation between the number of spurious pY sites and decreased growth. Moreover, the analysis of pY effects on protein structure and on protein function revealed over 1000 pY events that we predict to be deleterious. However, we also find that a large number of the spurious pY sites have a negligible effect on fitness, possibly because of their low stoichiometry. This result is consistent with our evolutionary analyses demonstrating a lack of phosphotyrosine counter-selection in species with bona fide tyrosine kinases. Taken together, our results suggest that, alongside the risk for toxicity, the cell can tolerate a large degree of non-functional crosstalk as interaction networks evolve.

Deciphering the functional landscape of phosphosites with deep neural network

Current biochemical approaches have only identified the most well-characterized kinases for a tiny fraction of the phosphoproteome, and the functional assignments of phosphosites are almost negligible. Herein, we analyze the substrate preference catalyzed by a specific kinase and present a novel integrated deep neural network model named FuncPhos-SEQ for functional assignment of human proteome-level phosphosites. FuncPhos-SEQ incorporates phosphosite motif information from a protein sequence using multiple convolutional neural network (CNN) channels and network features from protein-protein interactions (PPIs) using network embedding and deep neural network (DNN) channels. These concatenated features are jointly fed into a heterogeneous feature network to prioritize functional phosphosites. Combined with a series of in vitro and cellular biochemical assays, we confirm that NADK-S48/50 phosphorylation could activate its enzymatic activity. In addition, ERK1/2 are discovered as the primary kinases responsible for NADK-S48/50 phosphorylation. Moreover, FuncPhos-SEQ is developed as an online server.

The impact of AlphaFold Protein Structure Database on the fields of life sciences

Arguably, 2020 was the year of high-accuracy protein structure predictions, with AlphaFold 2.0 achieving previously unseen accuracy in the Critical Assessment of Protein Structure Prediction (CASP). In 2021, DeepMind and EMBL-EBI developed the AlphaFold Protein Structure Database to make an unprecedented number of reliable protein structure predictions easily accessible to the broad scientific community. We provide a brief overview and describe the latest developments in the AlphaFold database. We highlight how the fields of data services, bioinformatics, structural biology, and drug discovery are directly affected by the influx of protein structure data. We also show examples of cutting-edge research that took advantage of the AlphaFold database. It is apparent that connections between various fields through protein structures are now possible, but the amount of data poses new challenges. Finally, we give an outlook regarding the future direction of the database, both in terms of data sets and new functionalities.

Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation

Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology in both normal and cancer cells. Advances in mass spectrometry enable high-throughput, accurate, and sensitive measurement of PTM levels to better understand their role, prevalence, and crosstalk. Here, we analyze the largest collection of proteogenomics data from 1,110 patients with PTM profiles across 11 cancer types (10 from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns of changes in protein acetylation and phosphorylation involved in hallmark cancer processes. These patterns revealed subsets of tumors, from different cancer types, including those with dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated with immune response driven by acetylation, affected kinase specificity by crosstalk between acetylation and phosphorylation, and modified histone regulation. Overall, this resource highlights the rich biology governed by PTMs and exposes potential new therapeutic avenues.

Protein diversification through post-translational modifications, alternative splicing, and gene duplication

Proteins provide the basis for cellular function. Having multiple versions of the same protein within a single organism provides a way of regulating its activity or developing novel functions. Post-translational modifications of proteins, by means of adding/removing chemical groups to amino acids, allow for a well-regulated and controlled way of generating functionally distinct protein species. Alternative splicing is another method with which organisms possibly generate new isoforms. Additionally, gene duplication events throughout evolution generate multiple paralogs of the same genes, resulting in multiple versions of the same protein within an organism. In this review, we discuss recent advancements in the study of these three methods of protein diversification and provide illustrative examples of how they affect protein structure and function.

SHEPHARD: a modular and extensible software architecture for analyzing and annotating large protein datasets

MOTIVATION

The emergence of high-throughput experiments and high-resolution computational predictions has led to an explosion in the quality and volume of protein sequence annotations at proteomic scales. Unfortunately, sanity checking, integrating, and analyzing complex sequence annotations remains logistically challenging and introduces a major barrier to entry for even superficial integrative bioinformatics.

RESULTS

To address this technical burden, we have developed SHEPHARD, a Python framework that trivializes large-scale integrative protein bioinformatics. SHEPHARD combines an object-oriented hierarchical data structure with database-like features, enabling programmatic annotation, integration, and analysis of complex datatypes. Importantly SHEPHARD is easy to use and enables a Pythonic interrogation of largescale protein datasets with millions of unique annotations. We use SHEPHARD to examine three orthogonal proteome-wide questions relating protein sequence to molecular function, illustrating its ability to uncover novel biology.

AVAILABILITY AND IMPLEMENTATION

We provided SHEPHARD as both a stand-alone software package (https://github.com/holehouse-lab/shephard), and as a Google Colab notebook with a collection of precomputed proteome-wide annotations (https://github.com/holehouse-lab/shephard-colab).

Proteome-wide structural analysis identifies warhead- and coverage-specific biases in cysteine-focused chemoproteomics

Covalent drug discovery has undergone a resurgence over the past two decades and reactive cysteine profiling has emerged in parallel as a platform for ligand discovery through on- and off-target profiling; however, the scope of this approach has not been fully explored at the whole-proteome level. We combined AlphaFold2-predicted side-chain accessibilities for >95% of the human proteome with a meta-analysis of eighteen public cysteine profiling datasets, totaling 44,187 unique cysteine residues, revealing accessibility biases in sampled cysteines primarily dictated by warhead chemistry. Analysis of >3.5 million cysteine-fragment interactions further showed that hit elaboration and optimization drives increased bias against buried cysteine residues. Based on these data, we suggest that current profiling approaches cover a small proportion of potential ligandable cysteine residues and propose future directions for increasing coverage, focusing on high-priority residues and depth. All analysis and produced resources are freely available and extendable to other reactive amino acids.

Orchestration of Mitochondrial Function and Remodeling by Post-Translational Modifications Provide Insight into Mechanisms of Viral Infection

The regulation of mitochondria structure and function is at the core of numerous viral infections. Acting in support of the host or of virus replication, mitochondria regulation facilitates control of energy metabolism, apoptosis, and immune signaling. Accumulating studies have pointed to post-translational modification (PTM) of mitochondrial proteins as a critical component of such regulatory mechanisms. Mitochondrial PTMs have been implicated in the pathology of several diseases and emerging evidence is starting to highlight essential roles in the context of viral infections. Here, we provide an overview of the growing arsenal of PTMs decorating mitochondrial proteins and their possible contribution to the infection-induced modulation of bioenergetics, apoptosis, and immune responses. We further consider links between PTM changes and mitochondrial structure remodeling, as well as the enzymatic and non-enzymatic mechanisms underlying mitochondrial PTM regulation. Finally, we highlight some of the methods, including mass spectrometry-based analyses, available for the identification, prioritization, and mechanistic interrogation of PTMs.

Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization

The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery of protein complexes. However, validation of these novel protein complexes as well as elucidation of their molecular interaction mechanisms are still challenging. Recently, native top-down MS (nTDMS) is rapidly developed for the structural analysis of protein complexes. In this review, we discuss the integration of AP-MS and nTDMS in the discovery and structural characterization of functional protein complexes. Further, we think the emerging artificial intelligence (AI)-based protein structure prediction is highly complementary to nTDMS and can promote each other. We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.

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.