Blood is the primary source of protein biomarkers because it is readily accessible and reflects both physiological and pathological changes. However, ion suppression by high-abundance plasma proteins has long limited the coverage of mass spectrometry (MS)-based proteomics. Recent advances in nanomaterial-based enrichment have expanded blood proteome coverage to more than 6000 proteins. Nevertheless, whether the quantities of the enriched proteins proportionally reflect their true levels in neat blood – and thereby faithfully represent disease-associated variation – remains debated. We therefore developed NCTPro (Nanomaterial-enhanced Cross-Channel Transfer strategy for neat blood Proteomics), which enables in-depth plasma proteome quantification without enrichment-induced perturbation of endogenous protein concentrations. Using non-isobaric mass tags, NCTPro combines a reference channel of enriched low-abundance plasma proteins with a neat plasma channel in a single data-independent acquisition mass spectrometry (DIA-MS) run. Deep proteome coverage in the reference channel is transferred to the neat plasma channel through MS1 feature matching and MS2-assisted elucidation. In this way, NCTPro identifies more than 3000 proteins in neat plasma using a 23-min elution gradient – at least a three-fold increase in coverage over recent reports at similar throughput. Spike-in experiments further demonstrate that NCTPro provides accurate fold-change quantification, high precision, and high linearity, making it well suited to a wide range of clinical plasma proteomics studies.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.