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The war in Ukraine has caused massive destruction of the country’s infrastructure, particularly its electricity system. Discussions on how to rebuild the system are underway within the country and the international community. Yet, major uncertainties about patterns of destruction and rebuilding potential complicate this task. Here, we present the first comprehensive and geo-spatial mapping of the Ukrainian electricity system and its destruction during the ongoing war. We find that virtually all large, centralized power plants have been attacked since February 2022, reducing overall generation capacity to roughly a third of before-war levels and substantially weakening the grid via attacks on transmission lines and substations, particularly in the east. We argue that any effort to rebuild the electricity system should meet four criteria: fast rebuilding, increased resilience, lowered fuel import dependence, and abatement of polluting emissions. Based on an estimation of the country’s wind and solar potential, we argue that these renewables should form the backbone of a future electricity system, as only they meet all four criteria, and we discuss how Ukrainian and international policymakers can facilitate and direct investment.

Bessie Noll is a post doctoral researcher at the Energy and Technology Policy Group at ETH Zurich. Her research focuses on the effects of policy intervention on the development of clean energy technologies and transitional outcomes of modern energy systems. She holds a master’s degree in mechanical engineering from Stanford University and a PhD in energy and technology policy from ETH Zurich.

Several low-carbon technologies, such as solar photovoltaics or batteries, have experienced massive cost reductions in the recent past. However, non-mature technologies will also be required to meet the Paris climate targets. The cost of novel technologies, like direct air capture (DAC) technologies, remains highly uncertain. Here, we introduce a new method to project future costs of novel technologies by assigning empirically grounded experience rates to technology components based on their similarity to mature technologies in terms of design complexity and customization needs. After an ex-post validation of this method, we apply it to three DAC technologies combined with CO2transport and storage (DACCS) to provide probabilistic estimates of the cost of CO2net removed. At 1 Gt-CO2/year cumulative capacity, we project DACCS costs at $341/tCO2($226–$544 at 90% confidence) for liquid solvent DACCS, $374/tCO2($281–$579) for solid sorbent DACCS, and $371/tCO2($230–$835) for CaO ambient weathering DACCS.

ABSTRACTAs latecomers to the industrialization process, developing countries may face barriers to upgrading from the production of mass-produced goods to higher-value technologies. Scholars have suggested that ‘windows of opportunity’ can temporarily lower entry barriers and provide an opportunity for latecomers to catch up to or even leapfrog incumbents. In this paper, we use the literature on product architecture to build on the concept of windows of opportunity. Specifically, we explore how changes in a technology’s use environment can create opportunities for indigenous innovation and upgrading in specific sub-systems or components of complex technologies. Using a comparative qualitative case study focusing on three renewable energy technologies, we develop a typology of catching-up opportunities in green energy industries. Our findings suggest that policymakers should target certain technologies in their industrial strategies based on certain technology characteristics, and should seek opportunities to capitalize on a local niche that creates a need for innovation.

ObjectivesThe association of pulmonary hypertension (PH) with the outcome after mitral transcatheter edge-to-edge repair (M-TEER) focusing on the new ESC/ERS guidelines definition for PH.BackgroundPH is frequently found in patients with mitral regurgitation and is associated with lower survival rates. Recent studies were based on echocardiographic parameters, but results based on invasive haemodynamics differentiating distinct types of PH using the new definition for PH are missing.Methods449 consecutive M-TEER-treated patients from December 2009 to February 2015 were included in this retrospective analysis. All patients were stratified by the distinct types of PH (no PH, precapillary PH, isolated postcapillary PH, combined post-PH and precapillary PH) according to the definitions of the ESC/ERS guidelines for the diagnosis of PH from 2015 (meanPA cut-off <25 mm Hg, pulmonary capillary wedge pressure (PCWP) cut-off ≤15 mm Hg, diastolic pulmonary gradient cut-off ≥7 mm Hg or pulmonary vascular resistance (PVR) >3 WU) and 2022 (meanPA cut-off ≤20 mm Hg, PCWP cut-off ≤15 mm Hg, PVR cut-off ≥3 WU).ResultsPatients with any type of PH (2015: meanPA cut-off 25 mm Hg; 2022: meanPA cut-off >20 mm Hg) showed a higher risk of death after M-TEER compared with patients with no PH (2015: HR 1.61 (95% CI 1.25 to 2.07); p<0.001 and 2022: HR 2.09 (95% CI 1.54 to 2.83); p<0.001). Based on the new PH definition, each PH subgroup showed a lower survival after M-TEER compared with patients with no PH. Echocardiographic estimated systolic PAP showed a correlation with invasively measured mean pulmonary artery pressure (mPAP) (r=0.29, p<0.001) and systolic pulmonary arterial pressure (r=0.34,p<0.001). Cox-regression analysis showed higher invasive diastolic, systolic and mean pulmonary pressures were associated with higher all-cause mortality (p<0.001). In addition, invasive measured higher right atrial pressure, lower pulmonary arterial compliance, higher PVR and higher wedge pressure were identified as predictors of all-cause mortality after M-TEER.ConclusionsThe new PH definition discriminates PH groups and mortality better than the old definition. The lower threshold of mPAP of 20mmHg improved prognostication in this cohort of patients.

A gap persists between the emissions reductions pledged by countries under the Paris Agreement and those resulting from their domestic policies. We argue that this gap in fact contains two parts: one in the policies that countries adopt, and the other in the outcomes that those policies achieve.

Energy system models suggest optimal investment pathways and guide policymakers in implementing the low-carbon energy transition. Although model accuracy greatly improved over the last decade, it is unclear whether the cost of capital (CoC) is well represented, despite the parameter’s strong influence on model outcomes. In this perspective, we review 58 model-based publications that explicitly refer to CoC and observe that simply stating unfounded assumptions remains the most important “method” for determining the CoC. Overall, there appears to be a clear lack of guiding principles for how to appropriately determine CoC for use within models. To close this gap, we identify four barriers to a more accurate CoC reflection and suggest practical steps for overcoming them, including heuristic guidelines to support modelers in determining when differentiated CoC rates ought to be applied. Overall, we believe more rigorous treatment of the CoC will improve the quality of model-based policy advice.

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

Cancers arise through the accumulation of genetic and epigenetic alterations that enable cells to evade telomere-based proliferative barriers and achieve immortality. One such barrier is replicative crisis—an autophagy-dependent program that eliminates checkpoint-deficient cells with unstable telomeres and other cancer-relevant chromosomal aberrations1,2. However, little is known about the molecular events that regulate the onset of this important tumour-suppressive barrier. Here we identified the innate immune sensor Z-DNA binding protein 1 (ZBP1) as a regulator of the crisis program. A crisis-associated isoform of ZBP1 is induced by the cGAS–STING DNA-sensing pathway, but reaches full activation only when associated with telomeric-repeat-containing RNA (TERRA) transcripts that are synthesized from dysfunctional telomeres. TERRA-bound ZBP1 oligomerizes into filaments on the outer mitochondrial membrane of a subset of mitochondria, where it activates the innate immune adapter protein mitochondrial antiviral-signalling protein (MAVS). We propose that these oligomerization properties of ZBP1 serve as a signal amplification mechanism, where few TERRA–ZBP1 interactions are sufficient to launch a detrimental MAVS-dependent interferon response. Our study reveals a mechanism for telomere-mediated tumour suppression, whereby dysfunctional telomeres activate innate immune responses through mitochondrial TERRA–ZBP1 complexes to eliminate cells destined for neoplastic transformation.

Data set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC) coupled to mass spectrometry (MS) data sets, due to the high levels of data reduction that occur between raw data and machine learning-ready data. Since predictive proteomics is an emerging field, when predicting peptide behavior in LC-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based data sets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides data sets that are useful for comparing state-of-the-art machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available at https://www.proteomicsml.org/, and we welcome the entire proteomics community to contribute to the project at https://github.com/ProteomicsML/ProteomicsML.

Russia’s invasion of Ukraine brought a range of new challenges to the global economy, including affecting the inflation expectations of individuals. In this paper, we aim to quantify the effect of the invasion on short- and long-term inflation expectations of individuals in Germany. We use microdata from the Bundesbank Online Panel - Households (BOP-HH), for the period from February 15th to March 29th, 2022. Treating the unanticipated start of the war in Ukraine on the 24th of February 2022 as a natural experiment, we find that both short- and long-term inflation expectations increased as an immediate result of the invasion. Long-term inflation expectations increased by around 0.4 percentage points, while the impact on short-term inflation expectations was more than twice as large - around one percentage point. These significant deviations from the central bank’s target, highlight an elevated risk of de-anchoring of inflation expectations early in March 2022. Looking into the possible mechanisms of this increase, we suggest that it can be partially attributed to individuals’ fears of soaring energy prices and increasing pessimism about economic trends in general. Our results indicate that large geopolitical shocks can have a substantial impact on both short and long-term inflation expectations.

The Global Stocktake of the Paris Agreement on Climate Change assesses countries’ collective progress toward holding warming to well below 2°C, or 1.5°C. In this commentary, we argue that governments’ fiscal responses to global economic shocks provide an important opportunity to investigate the credibility of climate pledges and progress.

B-cell precursor acute lymphoblastic leukemias (B-ALL) are characterized by the activation of signaling pathways, which are involved in survival and proliferation of leukemia cells. Using an unbiased shRNA library screen enriched for targeting signaling pathways, we identified MTOR as the key gene on which human B-ALL E2A-PBX1+RCH-ACV cells are dependent. Using genetic and pharmacologic approaches, we investigated whether B-ALL cells depend on MTOR upstream signaling pathways including PI3K/AKT and the complexes MTORC1 or MTORC2 for proliferation and survival in vitro and in vivo. Notably, the combined inhibition of MTOR and AKT shows a synergistic effect on decreased cell proliferation in B-ALL with different karyotypes. Hence, B-ALL cells were more dependent on MTORC2 rather than MTORC1 complex in genetic assays. Using cell metabolomics, we identified changes in mitochondrial fuel oxidation after shRNA-mediated knockdown or pharmacological inhibition of MTOR. Dependence of the cells on fatty acid metabolism for their energy production was increased upon inhibition of MTOR and associated upstream signaling pathways, disclosing a possible target for a combination therapy. In conclusion, B-ALL are dependent on the PI3K/AKT/MTOR signaling pathway and the combination of specific small molecules targeting this pathway appears to be promising for the treatment of B-ALL patients.

Achieving most sustainable development goals (SDGs) and the Paris climate targets depends on the fast transformation of complex socio-technical systems. Recent research has highlighted the importance of crossing positive tipping points to accelerate the transformation of complex energy, food, and transport systems. Yet, there is a lack of research on the politics of enabling such tipping points. Here, we argue that policy strategies enabling the creation and crossing of such points are needed. Such strategies should harness political feedbacks from both technological and behavioral changes over time. To inform such strategies, we need a better empirical understanding of how these feedbacks unfold, eventually resulting in tipping points. We propose a novel framework to structure such feedback research by linking it to three core sustainability principles, namely efficiency, sufficiency, and substitution. Our framework advances ongoing debates about the politics of enabling tipping points for sustainable development.

Sepsis manifests due to the host's dysregulated immune response to infection. High‐dose ascorbic acid (AA) has emerged as a potential treatment of sepsis, yet little is known regarding how AA influences the immune system in sepsis, such as monocytes. The objective of this study is to investigate the effects of high‐dose AA on monocyte polarization and cytokine production in vitro. Monocytes isolated from healthy donors (n= 6) were polarized in vitro for 48 h using LPS or lipoteichoic acid (LTA). Polarization was confirmed by surface marker expression using flow cytometry. In parallel, monocytes from septic patients (n= 3) were analyzed for polarization markers as a comparison with the in vitro polarization. The effect of AA on monocyte polarization was then evaluated. Finally, monocytes were analyzed for cytokine production by intracellular staining. Both LPS and LTA induced polarization in healthy monocytes in vitro, with increased expression of both pro (M1) (CD40 and PDL1, p< 0.05) and anti‐inflammatory (M2) (CD16 and CD163, p< 0.05) polarization markers. This pattern resembled that of monocytes from septic patients. Treatment with AA significantly inhibited surface expression of CD16 and CD163 (p< 0.05) in a dose‐dependent manner. Finally, AA attenuated LPS‐ or LTA‐induced cytokine production of IL‐1ß, IL‐6, IL‐8, and TNF. In conclusion, AA attenuates proinflammatory cytokine production and diminishes up‐regulation of CD16 and CD163, but not of CD40 and PDL‐1 in LPS‐ or LTA‐polarized monocytes. This study provides important insight into the effects of high‐dose AA on monocytes and potential implications in sepsis. Ascorbic acid inhibits both the production of pro‐inflammatory cytokines and the upregulation of CD16 and CD163 in LPS or LTA polarized monocytes in vitro.

The laboratory mouse ranks among the most important experimental systems for biomedical research and molecular reference maps of such models are essential informational tools. Here, we present a quantitative draft of the mouse proteome and phosphoproteome constructed from 41 healthy tissues and several lines of analyses exemplify which insights can be gleaned from the data. For instance, tissue- and cell-type resolved profiles provide protein evidence for the expression of 17,000 genes, thousands of isoforms and 50,000 phosphorylation sites in vivo. Proteogenomic comparison of mouse, human and Arabidopsisreveal common and distinct mechanisms of gene expression regulation and, despite many similarities, numerous differentially abundant orthologs that likely serve species-specific functions. We leverage the mouse proteome by integrating phenotypic drug (n> 400) and radiation response data with the proteomes of 66 pancreatic ductal adenocarcinoma (PDAC) cell lines to reveal molecular markers for sensitivity and resistance. This unique atlas complements other molecular resources for the mouse and can be explored online via ProteomicsDB and PACiFIC.

Electricity storage systems can support the decarbonization of energy systems. However, the effect of electricity storage use on greenhouse gas emissions is complex because of roundtrip efficiency losses of the storage and its effects on the dispatch of different electricity-generation technologies. The limited literature examining this issue focus on individual electricity storage applications and the geographies where they are applied. Here, we systematically compare the effects of electricity storage on CO2emissions across four applications in electricity systems resembling seven European countries. Our findings reveal large emission impact differences between applications and countries. Among other findings, we find that when compared with wholesale arbitrage, storing electricity in other applications exhibits much lower effects on CO2emissions. We also investigate different policy options to reduce CO2emissions from storing electricity. We find that although a higher carbon price can have a substantial effect on reducing the CO2emissions from wholesale arbitrage, other applications require alternative approaches.

Here, we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can be either provided manually by the user (table format) or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high-quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other web sites (https://github.com/kusterlab/universal_spectrum_explorer).

The disease COVID‐19 has developed into a worldwide pandemic. Hyperinflammation and high levels of several cytokines, for example, IL‐6, are observed in severe COVID‐19 cases. However, little is known about the cellular origin of these cytokines. Here, we investigated whether circulating leukocytes from patients with COVID‐19 had spontaneous cytokine production. Patients with hyperinflammatory COVID‐19 (n= 6) and sepsis (n= 3) were included at Skåne University Hospital, Sweden. Healthy controls were also recruited (n= 5). Cytokines were measured in COVID‐19 and sepsis patients using an Immulite immunoassay system. PBMCs were cultured with brefeldin A to allow cytokine accumulation. In parallel, LPS was used as an activator. Cells were analyzed for cytokines and surface markers by flow cytometry. High levels of IL‐6 and measurable levels of IL‐8 and TNF, but not IL‐1β, were observed in COVID‐19 patients. Monocytes from COVID‐19 patients had spontaneous production of IL‐1β and IL‐8 (P= 0.0043), but not of TNF and IL‐6, compared to controls. No spontaneous cytokine production was seen in lymphocytes from either patients or controls. Activation with LPS resulted in massive cytokine production by monocytes from COVID‐19 patients and healthy controls, but not from sepsis patients. Finally, monocytes from COVID‐19 patients produced more IL‐1β than from healthy controls (P= 0.0087) when activated. In conclusion, monocytes contribute partly to the ongoing hyperinflammation by production of IL‐1β and IL‐8. Additionally, they are responsive to further activation. This data supports the notion of IL‐1β blockade in treatment of COVID‐19. However, the source of the high levels of IL‐6 remains to be determined. Circulating levels of IL‐6, IL‐1b, and inflammatory cytokines are mismatched with induced cytokine production by leukocytes in hyperinflammatory COVID‐19

Accelerating innovation in low-carbon technologies is fundamental in order to achieve global climate targets. However, only few technologies are currently on track to meet these targets. Here, we review and synthesize the innovation literature to develop a technology typology that helps explain systematic differences in technologies’ experience rates. Based on the typology, we derive implications for policy makers and climate and energy modelers. More specifically, we argue that the Paris Agreement’s focus on the national green industrial policy only suits innovation in certain types of technologies, and thus, should be complemented by top-down measures to coordinate research, development, demonstration, deployment, and regulations across countries.

Low-cost electricity-storage technologies (ESTs) enable rapid decarbonization of energy systems. However, current EST cost estimates lack meaningful models to assess alternative market and technology scenarios. Here, we project the competition between six ESTs until 2030 and derive cost benchmarks. To this end, a system-dynamic simulation model operationalizes technology costs using component-based experience curves with cost floors for battery materials. We find that lithium-ion batteries are likely to outcompete alternative ESTs by 2030 across applications and largely independent of selected scenarios. This dominance can pose risks associated with technological lock-in. We, therefore, analyze different policy options and contend that it seems most promising to combine: (1) the support for development and commercialization of breakthrough storage concepts and (2) piggybacking on the strong improvement dynamics in lithium-ion batteries by fostering technology and knowledge spillovers that benefit the development of alternative active materials; battery designs; and adjacent, non-lithium-based technologies.

Background: Although there is mounting evidence on sex-linked differences in paraspinal muscle function, it is unknown whether sex-based variations in mechanical and contractile characteristics of the lumbar erector spinae (LES) can be monitored noninvasively in healthy participants at rest using tensiomyography (TMG).Hypothesis: Sex-specific effects in muscle displacement (Dm) and velocity of muscle deformation (Vd) will be observed via TMG assessed in the LES.Study Design: Observational study.Level of Evidence: Level 3.Methods: LES response was measured in a relaxed state in 40 healthy adults (20 females). Possible differences between the conditions were investigated using mixed-model analyses of variance. Two-stage hierarchical linear regression analyses were performed to predict the outcome of TMG Dm and Vd based on participant sex.Results: There were significant main effects of sex with large effect sizes for both TMG parameters, resulting from lower mean values in women compared with men (Dm, P< 0.01; Vd, P< 0.01). In contrast, neither the main effect of side (left vs right LES) nor the interaction between the side and sex reached significance (all P> 0.3). Introducing the sex variable in stage 2 of the regression analyses significantly improved the prediction of the TMG parameters (all ∆R2≥ 0.18; all P< 0.01; all f2≥ 0.29).Conclusion: Sex-based differences in muscle stiffness and contractile characteristics could be observed by TMG on LES muscles in healthy individuals at rest. The data suggest that these disparities are not exclusively attributable to anthropometric measures but may be linked to intrinsic sex-based differences in skeletal muscle characteristics.Clinical Relevance: We recommend implementing TMG in a clinical setting using the obtained results as a basis to factor for the patient’s biological sex when assessing effects of therapeutic/exercise regimens aiming at the optimization of myofascial tissue regeneration and performance.

Bjarne Steffen is a senior researcher at ETH Zurich’s Energy Politics Group. His research addresses policies related to energy innovation and the role of finance in the energy transition. He previously worked at MIT’s Center for Energy and Environmental Policy Research, the World Economic Forum, and a strategy consultancy. Bjarne holds a Master’s in economics from the University of Mannheim and a PhD in energy economics from the University of Duisburg-Essen.

Plants are essential for life and are extremely diverse organisms with unique molecular capabilities1. Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana. Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsisproteins, their modifications and interactions.

Given the decline in investment costs for renewable energy technologies, other cost components have become increasingly important. In 2017, operations and maintenance (O&M) accounted for 20%–25% of lifecycle costs for wind and solar plants in Europe, but the understanding of O&M dynamics is limited. Presenting new data from Germany, here, we consider cumulative operating experience to estimate O&M experience curves for onshore wind and solar photovoltaic (PV). Results indicate O&M cost reductions with each doubling of cumulative experience of 9.2%–12.8% for onshore wind and of 15.7%–18.2% for solar. Drawing on expert interviews and archival information, we describe mechanisms regarding how O&M experience led to lower costs, including economies of scale, upsizing, learning by doing, and learning by using and learning by interacting, comparing both technologies. Moreover, regulatory changes fostered competitive pressure toward cost reductions. Our results suggest that dynamic cost reduction potentials for O&M need to be considered in energy system planning, modeling, and optimization.

IgG4-related disease is an immune-mediated condition characterized by tissue infiltration of IgG4-positive plasma cells. Involvement of the spinal meninges results in hypertrophic spinal pachymeningitis (HSP), causing spinal cord and nerve root compression.

Right heart catheterization (RHC) is a common diagnostic tool and of special importance in the diagnosis of pulmonary hypertension (PH). Until today there are no clear instructions or guidelines on which venous access to prefer.

ABSTRACTThe RNA-binding protein AUF1 regulates post-transcriptional gene expression by affecting the steady state and translation levels of numerous target RNAs. Remodeling of RNA structures by the largest isoform AUF1 p45 was recently demonstrated in the context of replicating RNA viruses, and involves two RNA remodeling activities, i.e. an RNA chaperone and an RNA annealing activity. AUF1 contains two non-identical RNA recognition motifs (RRM) and one RGG/RG motif located in the C-terminus. In order to determine the functional significance of each motif to AUF1’s RNA-binding and remodeling activities we performed a comprehensive mutagenesis study and characterized the wildtype AUF1, and several variants thereof. We demonstrate that each motif contributes to efficient RNA binding and remodeling by AUF1 indicating a tight cooperation of the RRMs and the RGG/RG motif. Interestingly, the data identify two distinct roles for the arginine residues of the RGG/RG motif for each RNA remodeling activity. First, arginine-mediated stacking interactions promote AUF1’s helix-destabilizing RNA chaperone activity. Second, the electropositive character of the arginine residues is the major driving force for the RNA annealing activity. Thus, we provide the first evidence that arginine residues of an RGG/RG motif contribute to the mechanism of RNA annealing and RNA chaperoning.

In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic peptide library to 550,000 tryptic peptides and 21 million high-quality tandem mass spectra. We trained a deep neural network, termed Prosit, resulting in chromatographic retention time and fragment ion intensity predictions that exceed the quality of the experimental data. Integrating Prosit into database search pipelines led to more identifications at >10× lower false discovery rates. We show the general applicability of Prosit by predicting spectra for proteases other than trypsin, generating spectral libraries for data-independent acquisition and improving the analysis of metaproteomes. Prosit is integrated into ProteomicsDB, allowing search result re-scoring and custom spectral library generation for any organism on the basis of peptide sequence alone. A deep learning–based tool, Prosit, predicts high-quality peptide tandem mass spectra, improving peptide-identification performance compared with that of traditional proteomics analysis methods.

Despite the increasing use of high-throughput experiments in molecular biology, methods for evaluating and classifying the acquired results have not kept pace, requiring significant manual efforts to do so. Here, we present CiRCus, a framework to generate custom machine learning models to classify results from high-throughput proteomics binding experiments. We show the experimental procedure that guided us to the layout of this framework as well as the usage of the framework on an example data set consisting of 557 166 protein/drug binding curves achieving an AUC of 0.9987. By applying our classifier to the data, only 6% of the data might require manual investigation. CiRCus bundles two applications, a minimal interface to label a training data set (CindeR) and an interface for the generation of random forest classifiers with optional optimization of pretrained models (CurveClassification). CiRCus is available on https://github.com/kusterlabaccompanied by an in-depth user manual and video tutorial.