Understudied proteins: opportunities and challenges for functional proteomics

Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

Protein subcellular localization prediction tools

Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.

Improving quantitative prediction of protein subcellular locations in fluorescence images through deep generative models

Machine learning has been employed in recognizing protein localization at the subcellular level, which highly facilitates the protein function studies, especially for those multi-label proteins that localize in more than one organelle. However, existing works mostly study the qualitative classification of protein subcellular locations, ignoring fraction of one multi-label protein in different locations. Actually, about 50 % proteins are multi-label proteins, and the ignorance of quantitative information highly restricts the understanding of their spatial distribution and functional mechanism. One reason of the lack of quantitative study is the insufficiency of quantitative annotations. To address the data shortage problem, here we proposed a generative model, PLocGAN, which could generate cell images with conditional quantitative annotation of the fluorescence distribution. The model was a conditional generative adversarial network, in which the condition learning utilized partial label learning to overcome the lack of training labels and allowed training with only qualitative labels. Meanwhile, it used contrastive learning to enhance diversity of the generated images. We assessed the PLocGAN on four pixel-fused synthetic datasets and one real dataset, and demonstrated that the model could generate images with good fidelity and diversity, outperforming existing state-of-the-art generative methods. To verify the utility of PLocGAN in the quantitative prediction of protein subcellular locations, we replaced the training images with generated quantitative images and built prediction models, and found that they had a boosting effect on the quantitative estimation. This work demonstrates the effectiveness of deep generative models in bioimage analysis, and provides a new solution for quantitative subcellular proteomics.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas, PINNACLE learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PINNACLE's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. PINNACLE outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models. PINNACLE's ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, Pinnacle learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. Pinnacle's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. Pinnacle outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. Pinnacle's ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

Effects of vitamin D signaling in cardiovascular disease: centrality of macrophage polarization

Among the leading causes of natural death are cardiovascular diseases, cancer, and respiratory diseases. Factors causing illness include genetic predisposition, aging, stress, chronic inflammation, environmental factors, declining autophagy, and endocrine abnormalities including insufficient vitamin D levels. Inconclusive clinical outcomes of vitamin D supplements in cardiovascular diseases demonstrate the need to identify cause-effect relationships without bias. We employed a spectral clustering methodology capable of analyzing large diverse datasets for examining the role of vitamin D's genomic and non-genomic signaling in disease in this study. The results of this investigation showed the following: (1) vitamin D regulates multiple reciprocal feedback loops including p53, macrophage autophagy, nitric oxide, and redox-signaling; (2) these regulatory schemes are involved in over 2,000 diseases. Furthermore, the balance between genomic and non-genomic signaling by vitamin D affects autophagy regulation of macrophage polarization in tissue homeostasis. These findings provide a deeper understanding of how interactions between genomic and non-genomic signaling affect vitamin D pharmacology and offer opportunities for increasing the efficacy of vitamin D-centered treatment of cardiovascular disease and healthy lifespans.

On knowing a gene: A distributional hypothesis of gene function

As words can have multiple meanings that depend on sentence context, genes can have various functions that depend on the surrounding biological system. This pleiotropic nature of gene function is limited by ontologies, which annotate gene functions without considering biological contexts. We contend that the gene function problem in genetics may be informed by recent technological leaps in natural language processing, in which representations of word semantics can be automatically learned from diverse language contexts. In contrast to efforts to model semantics as "is-a" relationships in the 1990s, modern distributional semantics represents words as vectors in a learned semantic space and fuels current advances in transformer-based models such as large language models and generative pre-trained transformers. A similar shift in thinking of gene functions as distributions over cellular contexts may enable a similar breakthrough in data-driven learning from large biological datasets to inform gene function.

Unveiling the power of proteomics in advancing tropical animal health and production

Proteomics, the large-scale study of proteins in biological systems has emerged as a pivotal tool in the field of animal and veterinary sciences, mainly for investigating local and rustic breeds. Proteomics provides valuable insights into biological processes underlying animal growth, reproduction, health, and disease. In this review, we highlight the key proteomics technologies, methodologies, and their applications in domestic animals, particularly in the tropical context. We also discuss advances in proteomics research, including integration of multi-omics data, single-cell proteomics, and proteogenomics, all of which are promising for improving animal health, adaptation, welfare, and productivity. However, proteomics research in domestic animals faces challenges, such as sample preparation variation, data quality control, privacy and ethical considerations relating to animal welfare. We also provide recommendations for overcoming these challenges, emphasizing the importance of following best practices in sample preparation, data quality control, and ethical compliance. We therefore aim for this review to harness the full potential of proteomics in advancing our understanding of animal biology and ultimately improve animal health and productivity in local breeds of diverse animal species in a tropical context.

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

PomBase: a Global Core Biodata Resource-growth, collaboration, and sustainability

PomBase (https://www.pombase.org), the model organism database (MOD) for fission yeast, was recently awarded Global Core Biodata Resource (GCBR) status by the Global Biodata Coalition (GBC; https://globalbiodata.org/) after a rigorous selection process. In this MOD review, we present PomBase's continuing growth and improvement over the last 2 years. We describe these improvements in the context of the qualitative GCBR indicators related to scientific quality, comprehensivity, accelerating science, user stories, and collaborations with other biodata resources. This review also showcases the depth of existing connections both within the biocuration ecosystem and between PomBase and its user community.

The Landscape and Perspectives of the Human Gut Metaproteomics

The human gut microbiome is closely associated with human health and diseases. Metaproteomics has emerged as a valuable tool for studying the functionality of the gut microbiome by analyzing the entire proteins present in microbial communities. Recent advancements in liquid chromatography and tandem mass spectrometry (LC-MS/MS) techniques have expanded the detection range of metaproteomics. However, the overall coverage of the proteome in metaproteomics is still limited. While metagenomics studies have revealed substantial microbial diversity and functional potential of the human gut microbiome, few studies have summarized and studied the human gut microbiome landscape revealed with metaproteomics. In this article, we present the current landscape of human gut metaproteomics studies by re-analyzing the identification results from 15 published studies. We quantified the limited proteome coverage in metaproteomics and revealed a high proportion of annotation coverage of metaproteomics-identified proteins. We conducted a preliminary comparison between the metaproteomics view and the metagenomics view of the human gut microbiome, identifying key areas of consistency and divergence. Based on the current landscape of human gut metaproteomics, we discuss the feasibility of using metaproteomics to study functionally unknown proteins and propose a whole workflow peptide-centric analysis. Additionally, we suggest enhancing metaproteomics analysis by refining taxonomic classification and calculating confidence scores, as well as developing tools for analyzing the interaction between taxonomy and function.

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

One substrate many enzymes virtual screening uncovers missing genes of carnitine biosynthesis in human and mouse

The increasing availability of experimental and computational protein structures entices their use for function prediction. Here we develop an automated procedure to identify enzymes involved in metabolic reactions by assessing substrate conformations docked to a library of protein structures. By screening AlphaFold-modeled vitamin B6-dependent enzymes, we find that a metric based on catalytically favorable conformations at the enzyme active site performs best (AUROC Score=0.84) in identifying genes associated with known reactions. Applying this procedure, we identify the mammalian gene encoding hydroxytrimethyllysine aldolase (HTMLA), the second enzyme of carnitine biosynthesis. Upon experimental validation, we find that the top-ranked candidates, serine hydroxymethyl transferase (SHMT) 1 and 2, catalyze the HTMLA reaction. However, a mouse protein absent in humans (threonine aldolase; Tha1) catalyzes the reaction more efficiently. Tha1 did not rank highest based on the AlphaFold model, but its rank improved to second place using the experimental crystal structure we determined at 2.26 Å resolution. Our findings suggest that humans have lost a gene involved in carnitine biosynthesis, with HTMLA activity of SHMT partially compensating for its function.

Predicting Protein Functions Based on Heterogeneous Graph Attention Technique

In bioinformatics, protein function prediction stands as a fundamental area of research and plays a crucial role in addressing various biological challenges, such as the identification of potential targets for drug discovery and the elucidation of disease mechanisms. However, known functional annotation databases usually provide positive experimental annotations that proteins carry out a given function, and rarely record negative experimental annotations that proteins do not carry out a given function. Therefore, existing computational methods based on deep learning models focus on these positive annotations for prediction and ignore these scarce but informative negative annotations, leading to an underestimation of precision. To address this issue, we introduce a deep learning method that utilizes a heterogeneous graph attention technique. The method first constructs a heterogeneous graph that covers the protein-protein interaction network, ontology structure, and positive and negative annotation information. Then, it learns embedding representations of proteins and ontology terms by using the heterogeneous graph attention technique. Finally, it leverages these learned representations to reconstruct the positive protein-term associations and score unobserved functional annotations. It can enhance the predictive performance by incorporating these known limited negative annotations into the constructed heterogeneous graph. Experimental results on three species (i.e., Human, Mouse, and Arabidopsis) demonstrate that our method can achieve better performance in predicting new protein annotations than state-of-the-art methods.

Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods

Protein-protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain-motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post-translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high-throughput PPI detection methods can be a reliable source for predicting DMIs.

Meta-Research: Understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of -omics studies. To promote the investigation of understudied genes, we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

DeepSS2GO: protein function prediction from secondary structure

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system

The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.

The previously uncharacterized RnpM (YlxR) protein modulates the activity of ribonuclease P in Bacillus subtilis in vitro

Even though Bacillus subtilis is one of the most studied organisms, no function has been identified for about 20% of its proteins. Among these unknown proteins are several RNA- and ribosome-binding proteins suggesting that they exert functions in cellular information processing. In this work, we have investigated the RNA-binding protein YlxR. This protein is widely conserved in bacteria and strongly constitutively expressed in B. subtilis suggesting an important function. We have identified the RNA subunit of the essential RNase P as the binding partner of YlxR. The main activity of RNase P is the processing of 5' ends of pre-tRNAs. In vitro processing assays demonstrated that the presence of YlxR results in reduced RNase P activity. Chemical cross-linking studies followed by in silico docking analysis and experiments with site-directed mutant proteins suggest that YlxR binds to the region of the RNase P RNA that is important for binding and cleavage of the pre-tRNA substrate. We conclude that the YlxR protein is a novel interaction partner of the RNA subunit of RNase P that serves to finetune RNase P activity to ensure appropriate amounts of mature tRNAs for translation. We rename the YlxR protein RnpM for RNase P modulator.

Meta-Research: understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results

Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of - omics studies. To promote the investigation of understudied genes we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.

Interactome Analysis Identifies the Role of BZW2 in Promoting Endoplasmic Reticulum-Mitochondria Contact and Mitochondrial Metabolism

Understanding the molecular functions of less-studied proteins is an important task of life science research. Despite reports of basic leucine zipper and W2 domain-containing protein 2 (BZW2) promoting cancer progression first emerging in 2017, little is known about its molecular function. Using a quantitative proteomic approach to identify its interacting proteins, we found that BZW2 interacts with both endoplasmic reticulum (ER) and mitochondrial proteins. We thus hypothesized that BZW2 localizes to and promotes the formation of ER-mitochondria contact sites and that such localization would promote calcium transport from ER to the mitochondria and promote ATP production. Indeed, we found that BZW2 localized to ER-mitochondria contact sites and that BZW2 knockdown decreased ER-mitochondria contact, mitochondrial calcium levels, and ATP production. These findings provide key insights into molecular functions of BZW2, the potential role of BZW2 in cancer progression, and highlight the utility of interactome data in understanding the function of less-studied proteins.

A computational pipeline elucidating functions of conserved hypothetical Trypanosoma cruzi proteins based on public proteomic data

The proteome is complex, dynamic, and functionally diverse. Functional proteomics aims to characterize the functions of proteins in biological systems. However, there is a delay in annotating the function of proteins, even in model organisms. This gap is even greater in other organisms, including Trypanosoma cruzi, the causative agent of the parasitic, systemic, and sometimes fatal disease called Chagas disease. About 99.8% of Trypanosoma cruzi proteome is not manually annotated (unreviewed), among which>25% are conserved hypothetical proteins (CHPs), calling attention to the knowledge gap on the protein content of this organism. CHPs are conserved proteins among different species of various evolutionary lineages; however, they lack functional validation. This study describes a bioinformatics pipeline applied to public proteomic data to infer possible biological functions of conserved hypothetical Trypanosoma cruzi proteins. Here, the adopted strategy consisted of collecting differentially expressed proteins between the epimastigote and metacyclic trypomastigotes stages of Trypanosoma cruzi; followed by the functional characterization of these CHPs applying a manifold learning technique for dimension reduction and 3D structure homology analysis (Spalog). We found a panel of 25 and 26 upregulated proteins in the epimastigote and metacyclic trypomastigote stages, respectively; among these, 18 CHPs (8 in the epimastigote stage and 10 in the metacyclic stage) were characterized. The data generated corroborate the literature and complement the functional analyses of differentially regulated proteins at each stage, as they attribute potential functions to CHPs, which are frequently identified in Trypanosoma cruzi proteomics studies. However, it is important to point out that experimental validation is required to deepen our understanding of the CHPs.

A dive into the unknome

We may never understand the function of all genes, findings by Freeman, Munro and colleagues suggest, unless we rethink our approaches. They make a thorough attempt at quantifying the unknownness of protein-coding genes and experimentally prove that many neglected genes hold the seed of important discoveries.

Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry

We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

Locus-specific proteome decoding reveals Fpt1 as a chromatin-associated negative regulator of RNA polymerase III assembly

Transcription of tRNA genes by RNA polymerase III (RNAPIII) is tuned by signaling cascades. The emerging notion of differential tRNA gene regulation implies the existence of additional regulatory mechanisms. However, tRNA gene-specific regulators have not been described. Decoding the local chromatin proteome of a native tRNA gene in yeast revealed reprogramming of the RNAPIII transcription machinery upon nutrient perturbation. Among the dynamic proteins, we identified Fpt1, a protein of unknown function that uniquely occupied RNAPIII-regulated genes. Fpt1 binding at tRNA genes correlated with the efficiency of RNAPIII eviction upon nutrient perturbation and required the transcription factors TFIIIB and TFIIIC but not RNAPIII. In the absence of Fpt1, eviction of RNAPIII was reduced, and the shutdown of ribosome biogenesis genes was impaired upon nutrient perturbation. Our findings provide support for a chromatin-associated mechanism required for RNAPIII eviction from tRNA genes and tuning the physiological response to changing metabolic demands.

Evaluation of input data modality choices on functional gene embeddings

Functional gene embeddings, numerical vectors capturing gene function, provide a promising way to integrate functional gene information into machine learning models. These embeddings are learnt by applying self-supervised machine-learning algorithms on various data types including quantitative omics measurements, protein-protein interaction networks and literature. However, downstream evaluations comparing alternative data modalities used to construct functional gene embeddings have been lacking. Here we benchmarked functional gene embeddings obtained from various data modalities for predicting disease-gene lists, cancer drivers, phenotype-gene associations and scores from genome-wide association studies. Off-the-shelf predictors trained on precomputed embeddings matched or outperformed dedicated state-of-the-art predictors, demonstrating their high utility. Embeddings based on literature and protein-protein interactions inferred from low-throughput experiments outperformed embeddings derived from genome-wide experimental data (transcriptomics, deletion screens and protein sequence) when predicting curated gene lists. In contrast, they did not perform better when predicting genome-wide association signals and were biased towards highly-studied genes. These results indicate that embeddings derived from literature and low-throughput experiments appear favourable in many existing benchmarks because they are biased towards well-studied genes and should therefore be considered with caution. Altogether, our study and precomputed embeddings will facilitate the development of machine-learning models in genetics and related fields.

Co-option of a non-retroviral endogenous viral element in planthoppers

Non-retroviral endogenous viral elements (nrEVEs) are widely dispersed throughout the genomes of eukaryotes. Although nrEVEs are known to be involved in host antiviral immunity, it remains an open question whether they can be domesticated as functional proteins to serve cellular innovations in arthropods. In this study, we found that endogenous toti-like viral elements (ToEVEs) are ubiquitously integrated into the genomes of three planthopper species, with highly variable distributions and polymorphism levels in planthopper populations. Three ToEVEs display exon‒intron structures and active transcription, suggesting that they might have been domesticated by planthoppers. CRISPR/Cas9 experiments revealed that one ToEVE in Nilaparvata lugens, NlToEVE14, has been co-opted by its host and plays essential roles in planthopper development and fecundity. Large-scale analysis of ToEVEs in arthropod genomes indicated that the number of arthropod nrEVEs is currently underestimated and that they may contribute to the functional diversity of arthropod genes.

Proteomics and Peptidomics As a Tool to Compare the Proteins and Endogenous Peptides in Human, Cow, and Donkey Milk

Cow's milk is the most widely used ingredient in infant formulas. However, its specific protein composition can cause allergic reactions. Finding alternatives to replace cow's milk and fill the nutritional gap with human milk is essential for the health of infants. Proteomic and peptidomic techniques have supported the elucidation of milk's nutritional ingredients. Recently, omics approaches have attracted increasing interest in the investigation of milk because of their high throughput, precision, sensitivity, and reproducibility. This review offers a significant overview of recent developments in proteomics and peptidomics used to study the differences in human, cow, and donkey milk. All three types of milks were identified to have critical biological functions in human health, particularly in infants. Donkey milk proteins were closer in composition to human milk, were less likely to cause allergic reactions, and may be developed as novel raw materials for formula milk powders.

BRD2 protects the rat H9C2 cardiomyocytes from hypoxia‑reoxygenation injury by targeting Nrf2/HO‑1 signaling pathway

Myocardial ischemia-reperfusion (I/R) injury is a common complication of acute myocardial infarction following percutaneous coronary intervention, but there are currently no effective pharmacological targets for adjuvant therapy due to a lack of knowledge of I/R injury mechanisms in cardiomyocytes. To evaluate the effects of hypoxia-reoxygenation on the plasma proteome of cardiomyocytes and prospective therapeutic targets, five sets of H9C2 cardiomyocytes from rats were cultured under various hypoxic circumstances. Using Cell Counting Kit-8 (CCK8) and lactose dehydrogenase (LDH) release assays, the cell viability and LDH release of H9C2 cells were analyzed. Proteome sequencing was then performed on cardiomyocytes to show the quantitative protein changes during the I/R injury process. After hypoxia/reoxygenation, bromodomain-containing protein 2 (BRD2) expression was evaluated. After administering the BRD2 inhibitor dBET1, the expression of nuclear factor erythroid 2-related factor 2/haem oxygenase-1 (Nrf2/HO-1) was identified. The results showed that in the group exposed to 4 h of hypoxia followed by 4 h of reoxygenation (H/R4), the cell survival rate was dramatically reduced, although the apoptotic rate and LDH were much higher than in the normal oxygen group. In addition, the expressions of 2,325 proteins differed considerably between these two groups, with 128 upregulated and 122 downregulated proteins being discovered in the H/R4 group. After 4 h of reoxygenation, the BRD2 expression was increased. Following the addition of dBET1 to suppress BRD2, the expression of Nrf2/HO-1 was reduced, but the rate of apoptosis increased. In conclusion, through the Nrf2/HO-1 signaling pathway, BRD2 protects cardiomyocytes from damage caused by hypoxia/reoxygenation. This may have implications for novel treatment targets to minimize I/R damage to the myocardium.

Beam search decoder for enhancing sequence decoding speed in single-molecule peptide sequencing data

Next-generation single-molecule protein sequencing technologies have the potential to significantly accelerate biomedical research. These technologies offer sensitivity and scalability for proteomic analysis. One auspicious method is fluorosequencing, which involves: cutting naturalized proteins into peptides, attaching fluorophores to specific amino acids, and observing variations in light intensity as one amino acid is removed at a time. The original peptide is classified from the sequence of light-intensity reads, and proteins can subsequently be recognized with this information. The amino acid step removal is achieved by attaching the peptides to a wall on the C-terminal and using a process called Edman Degradation to remove an amino acid from the N-Terminal. Even though a framework (Whatprot) has been proposed for the peptide classification task, processing times remain restrictive due to the massively parallel data acquisicion system. In this paper, we propose a new beam search decoder with a novel state formulation that obtains considerably lower processing times at the expense of only a slight accuracy drop compared to Whatprot. Furthermore, we explore how our novel state formulation may lead to even faster decoders in the future.

Cleavable Cross-Linkers Redefined by a Novel MS3-Trigger Algorithm

Cross-linking mass spectrometry (MS) is currently transitioning from a routine tool in structural biology to enabling structural systems biology. MS-cleavable cross-linkers could substantially reduce the associated search space expansion by allowing a MS3-based approach for identifying cross-linked peptides. However, MS2 (MS/MS)-based approaches currently outperform approaches utilizing MS3. We show here that the sensitivity and specificity of triggering MS3 have been hampered algorithmically. Our four-step MS3-trigger algorithm greatly outperformed currently employed methods and comes close to reaching the theoretical limit.

Explainable protein function annotation using local structure embeddings

The rapid expansion of protein sequence and structure databases has resulted in a significant number of proteins with ambiguous or unknown function. While advances in machine learning techniques hold great potential to fill this annotation gap, current methods for function prediction are unable to associate global function reliably to the specific residues responsible for that function. We address this issue by introducing PARSE (Protein Annotation by Residue-Specific Enrichment), a knowledge-based method which combines pre-trained embeddings of local structural environments with traditional statistical techniques to identify enriched functions with residue-level explainability. For the task of predicting the catalytic function of enzymes, PARSE achieves comparable or superior global performance to state-of-the-art machine learning methods (F1 score > 85%) while simultaneously annotating the specific residues involved in each function with much greater precision. Since it does not require supervised training, our method can make one-shot predictions for very rare functions and is not limited to a particular type of functional label (e.g. Enzyme Commission numbers or Gene Ontology codes). Finally, we leverage the AlphaFold Structure Database to perform functional annotation at a proteome scale. By applying PARSE to the dark proteome-predicted structures which cannot be classified into known structural families-we predict several novel bacterial metalloproteases. Each of these proteins shares a strongly conserved catalytic site despite highly divergent sequences and global folds, illustrating the value of local structure representations for new function discovery.

Robust, scalable, and informative clustering for diverse biological networks

Clustering molecular data into informative groups is a primary step in extracting robust conclusions from big data. However, due to foundational issues in how they are defined and detected, such clusters are not always reliable, leading to unstable conclusions. We compare popular clustering algorithms across thousands of synthetic and real biological datasets, including a new consensus clustering algorithm-SpeakEasy2: Champagne. These tests identify trends in performance, show no single method is universally optimal, and allow us to examine factors behind variation in performance. Multiple metrics indicate SpeakEasy2 generally provides robust, scalable, and informative clusters for a range of applications.

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition

We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data-independent acquisition, the Thermo Scientific Orbitrap Astral mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific Orbitrap mass spectrometers, which have long been the gold standard for high-resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high-quality quantitative measurements across a wide dynamic range. We also use a newly developed extracellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5000 plasma proteins in a 60 min gradient with the Orbitrap Astral mass spectrometer.

Broad functional profiling of fission yeast proteins using phenomics and machine learning

Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of 'priority unstudied' proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through 'guilt by association' with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions.

Protein network analysis links the NSL complex to Parkinson's disease via mitochondrial and nuclear biology

Whilst the majority of Parkinson's Disease (PD) cases are sporadic, much of our understanding of the pathophysiological basis of the disease can be traced back to the study of rare, monogenic forms of PD. In the past decade, the availability of genome-wide association studies (GWAS) has facilitated a shift in focus, toward identifying common risk variants conferring increased risk of developing PD across the population. A recent mitophagy screening assay of GWAS candidates has functionally implicated the non-specific lethal (NSL) complex in the regulation of PINK1-mitophagy. Here, a bioinformatics approach has been taken to investigate the proteome of the NSL complex, to unpick its relevance to PD pathogenesis. The NSL interactome has been built, using 3 online tools: PINOT, HIPPIE and MIST, to mine curated, literature-derived protein-protein interaction (PPI) data. We built (i) the 'mitochondrial' NSL interactome exploring its relevance to PD genetics and (ii) the PD-oriented NSL interactome to uncover biological pathways underpinning the NSL/PD association. In this study, we find the mitochondrial NSL interactome to be significantly enriched for the protein products of PD-associated genes, including the Mendelian PD genes LRRK2 and VPS35. In addition, we find nuclear processes to be amongst those most significantly enriched within the PD-associated NSL interactome. These findings strengthen the role of the NSL complex in sporadic and familial PD, mediated by both its mitochondrial and nuclear functions.

A proteogenomics data-driven knowledge base of human cancer

By combining mass-spectrometry-based proteomics and phosphoproteomics with genomics, epi-genomics, and transcriptomics, proteogenomics provides comprehensive molecular characterization of cancer. Using this approach, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has characterized over 1,000 primary tumors spanning 10 cancer types, many with matched normal tissues. Here, we present LinkedOmicsKB, a proteogenomics data-driven knowledge base that makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data available to the public through ∼40,000 gene-, protein-, mutation-, and phenotype-centric web pages. Visualization techniques facilitate efficient exploration and reasoning of complex, interconnected data. Using three case studies, we illustrate the practical utility of LinkedOmicsKB in providing new insights into genes, phosphorylation sites, somatic mutations, and cancer phenotypes. With precomputed results of 19,701 coding genes, 125,969 phosphosites, and 256 genotypes and phenotypes, LinkedOmicsKB provides a comprehensive resource to accelerate proteogenomics data-driven discoveries to improve our understanding and treatment of human cancer. A record of this paper's transparent peer review process is included in the supplemental information.

Did the early full genome sequencing of yeast boost gene function discovery?

BACKGROUND

Although the genome of Saccharomyces cerevisiae (S. cerevisiae) was the first one of a eukaryote organism that was fully sequenced (in 1996), a complete understanding of the potential of encoded biomolecular mechanisms has not yet been achieved. Here, we wish to quantify how far the goal of a full list of S. cerevisiae gene functions still is.

RESULTS

The scientific literature about S. cerevisiae protein-coding genes has been mapped onto the yeast genome via the mentioning of names for genomic regions in scientific publications. The match was quantified with the ratio of a given gene name's occurrences to those of any gene names in the article. We find that ~ 230 elite genes with ≥ 75 full publication equivalents (FPEs, FPE = 1 is an idealized publication referring to just a single gene) command ~ 45% of all literature. At the same time, about two thirds of the genes (each with less than 10 FPEs) are described in just 12% of the literature (in average each such gene has just ~ 1.5% of the literature of an elite gene). About 600 genes have not been mentioned in any dedicated article. Compared with other groups of genes, the literature growth rates were highest for uncharacterized or understudied genes until late nineties of the twentieth century. Yet, these growth rates deteriorated and became negative thereafter. Thus, yeast function discovery for previously uncharacterized genes has returned to the level of ~ 1980. At the same time, literature for anyhow well-studied genes (with a threshold T10 (≥ 10 FPEs) and higher) remains steadily growing.

CONCLUSIONS

Did the early full genome sequencing of yeast boost gene function discovery? The data proves that the moment of publishing the full genome in reality coincides with the onset of decline of gene function discovery for previously uncharacterized genes. If the current status of literature about yeast molecular mechanisms can be extrapolated into the future, it will take about another ~ 50 years to complete the yeast gene function list. We found that a small group of scientific journals contributed extraordinarily to publishing early reports relevant to yeast gene function discoveries.

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data Independent Acquisition

We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data independent acquisition, the Thermo Scientific™ Orbitrap™ Astral™ mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific™ Orbitrap™ mass spectrometers, which have long been the gold standard for high resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high quality quantitative measurements across a wide dynamic range. We also use a newly developed extra-cellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5,000 plasma proteins in a 60-minute gradient with the Orbitrap Astral mass spectrometer.

Molecular complex detection in protein interaction networks through reinforcement learning

BACKGROUND

Proteins often assemble into higher-order complexes to perform their biological functions. Such protein-protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein-protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks.

RESULTS

The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling.

CONCLUSIONS

Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.

Uncharacterized protein C17orf80 - a novel interactor of human mitochondrial nucleoids

Molecular functions of many human proteins remain unstudied, despite the demonstrated association with diseases or pivotal molecular structures, such as mitochondrial DNA (mtDNA). This small genome is crucial for the proper functioning of mitochondria, the energy-converting organelles. In mammals, mtDNA is arranged into macromolecular complexes called nucleoids that serve as functional stations for its maintenance and expression. Here, we aimed to explore an uncharacterized protein C17orf80, which was previously detected close to the nucleoid components by proximity labelling mass spectrometry. To investigate the subcellular localization and function of C17orf80, we took advantage of immunofluorescence microscopy, interaction proteomics and several biochemical assays. We demonstrate that C17orf80 is a mitochondrial membrane-associated protein that interacts with nucleoids even when mtDNA replication is inhibited. In addition, we show that C17orf80 is not essential for mtDNA maintenance and mitochondrial gene expression in cultured human cells. These results provide a basis for uncovering the molecular function of C17orf80 and the nature of its association with nucleoids, possibly leading to new insights about mtDNA and its expression.

Functional unknomics: Systematic screening of conserved genes of unknown function

The human genome encodes approximately 20,000 proteins, many still uncharacterised. It has become clear that scientific research tends to focus on well-studied proteins, leading to a concern that poorly understood genes are unjustifiably neglected. To address this, we have developed a publicly available and customisable "Unknome database" that ranks proteins based on how little is known about them. We applied RNA interference (RNAi) in Drosophila to 260 unknown genes that are conserved between flies and humans. Knockdown of some genes resulted in loss of viability, and functional screening of the rest revealed hits for fertility, development, locomotion, protein quality control, and resilience to stress. CRISPR/Cas9 gene disruption validated a component of Notch signalling and 2 genes contributing to male fertility. Our work illustrates the importance of poorly understood genes, provides a resource to accelerate future research, and highlights a need to support database curation to ensure that misannotation does not erode our awareness of our own ignorance.

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction

BACKGROUND

Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles.

METHODS

We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature.

RESULTS

We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance.

CONCLUSIONS

We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.

A multi-scale map of protein assemblies in the DNA damage response

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

LINC00493-encoded microprotein SMIM26 exerts anti-metastatic activity in renal cell carcinoma

Human microproteins encoded by long non-coding RNAs (lncRNA) have been increasingly discovered, however, complete functional characterization of these emerging proteins is scattered. Here, we show that LINC00493-encoded SMIM26, an understudied microprotein localized in mitochondria, is tendentiously downregulated in clear cell renal cell carcinoma (ccRCC) and correlated with poor overall survival. LINC00493 is recognized by RNA-binding protein PABPC4 and transferred to ribosomes for translation of a 95-amino-acid protein SMIM26. SMIM26, but not LINC00493, suppresses ccRCC growth and metastatic lung colonization by interacting with acylglycerol kinase (AGK) and glutathione transport regulator SLC25A11 via its N-terminus. This interaction increases the mitochondrial localization of AGK and subsequently inhibits AGK-mediated AKT phosphorylation. Moreover, the formation of the SMIM26-AGK-SCL25A11 complex maintains mitochondrial glutathione import and respiratory efficiency, which is abrogated by AGK overexpression or SLC25A11 knockdown. This study functionally characterizes the LINC00493-encoded microprotein SMIM26 and establishes its anti-metastatic role in ccRCC, and therefore illuminates the importance of hidden proteins in human cancers.

Data, measurement and empirical methods in the science of science

The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding 'science of science'. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field's diverse methodologies and expand researchers' toolkits. Overall, new empirical developments provide enormous capacity to test traditional beliefs and conceptual frameworks about science, discover factors associated with scientific productivity, predict scientific outcomes and design policies that facilitate scientific progress.

Performance benchmarking microplate-immunoassays for quantifying target-specific cysteine oxidation reveals their potential for understanding redox-regulation and oxidative stress

The antibody-linked oxi-state assay (ALISA) for quantifying target-specific cysteine oxidation can benefit specialist and non-specialist users. Specialists can benefit from time-efficient analysis and high-throughput target and/or sample n-plex capacities. The simple and accessible "off-the-shelf" nature of ALISA brings the benefits of oxidative damage assays to non-specialists studying redox-regulation. Until performance benchmarking establishes confidence in the "unseen" microplate results, ALISA is unlikely to be widely adopted. Here, we implemented pre-set pass/fail criteria to benchmark ALISA by evaluating immunoassay performance in diverse contexts. ELISA-mode ALISA assays were accurate, reliable, and sensitive. For example, the average inter-assay CV for detecting 20%- and 40%-oxidised PRDX2 or GAPDH standards was 4.6% (range: 3.6-7.4%). ALISA displayed target-specificity. Immunodepleting the target decreased the signal by ∼75%. Single-antibody formatted ALISA failed to quantify the matrix-facing alpha subunit of the mitochondrial ATP synthase. However, RedoxiFluor quantified the alpha subunit displaying exceptional performance in the single-antibody format. ALISA discovered that (1) monocyte-to-macrophage differentiation amplified PRDX2-oxidation in THP-1 cells and (2) exercise increased GAPDH-specific oxidation in human erythrocytes. The "unseen" microplate data were "seen-to-be-believed" via orthogonal visually displayed immunoassays like the dimer method. Finally, we established target (n = 3) and sample (n = 100) n-plex capacities in ∼4 h with 50-70 min hands-on time. Our work showcases the potential of ALISA to advance our understanding of redox-regulation and oxidative stress.

PAE viewer: a webserver for the interactive visualization of the predicted aligned error for multimer structure predictions and crosslinks

The development of AlphaFold for protein structure prediction has opened a new era in structural biology. This is even more the case for AlphaFold-Multimer for the prediction of protein complexes. The interpretation of these predictions has become more important than ever, but it is difficult for the non-specialist. While an evaluation of the prediction quality is provided for monomeric protein predictions by the AlphaFold Protein Structure Database, such a tool is missing for predicted complex structures. Here, we present the PAE Viewer webserver (http://www.subtiwiki.uni-goettingen.de/v4/paeViewerDemo), an online tool for the integrated visualization of predicted protein complexes using a 3D structure display combined with an interactive representation of the Predicted Aligned Error (PAE). This metric allows an estimation of the quality of the prediction. Importantly, our webserver also allows the integration of experimental cross-linking data which helps to interpret the reliability of the structure predictions. With the PAE Viewer, the user obtains a unique online tool which for the first time allows the intuitive evaluation of the PAE for protein complex structure predictions with integrated crosslinks.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.