Label-free quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis, and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web-based analysis environment that can analyze and visualize LFQ data produced by software such as MaxQuant. Unlike other tools, MD 1.0 utilizes a cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but also annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardization in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform is available globally via: https://app.massdynamics.com/.
The interaction between T cell receptor (TCR) and peptide (p)-Human Leukocyte Antigen (HLA) complexes is the critical first step in determining T cell responses. X-ray crystallographic studies of pHLA in TCR-bound and free states provide a structural perspective that can help understand T cell activation. These structures represent a static “snapshot”, yet the nature of pHLAs and their interactions with TCRs are highly dynamic. This has been demonstrated for HLA class I molecules with in silico techniques showing that some interactions, thought to stabilise pHLA-I, are only transient and prone to high flexibility. Here, we investigated the dynamics of HLA class II molecules by focusing on three allomorphs (HLA-DR1, -DR11 and -DR15) that are able to present the same epitope and activate CD4+ T cells. A single TCR (F24) has been shown to recognise all three HLA-DR molecules, albeit with different affinities. Using molecular dynamics and crystallographic ensemble refinement, we investigate the molecular basis of these different affinities and uncover hidden roles for HLA polymorphic residues. These polymorphisms were responsible for the widening of the antigen binding cleft and disruption of pHLA-TCR interactions, underpinning the hierarchy of F24 TCR binding affinity, and ultimately T cell activation. We expanded this approach to all available pHLA-DR structures and discovered that all HLA-DR molecules were inherently rigid. Together with in vitro protein stability and peptide affinity measurements, our results suggest that HLA-DR1 possesses inherently high protein stability, and low HLA-DM susceptibility.
Label Free Quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web based analysis environment that can analyze and visualize LFQ data produced by software such as Maxquant. Unlike other tools, MD 1.0 utilizes cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardisation in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform is available globally via: https://app.massdynamics.com/.
Data processing is essential to reliably generate knowledge from proteomics studies. The complexity of the proteomics data, as well as the ability of research teams to adopt complex analysis pipelines, have proven to be an obstacle to effective collaboration and more efficient biological insight generation. Here, we introduce MD 2.0, a cloud- and web-based platform for quantitative proteomics data, which implements a novel analysis workspace where statistical analyses, visualizations, and external knowledge generation are integrated into a modular framework. This modularity enables researchers the flexibility to test different hypotheses, and customize and template complex proteomics analyses, thereby expediting insight generation for complex datasets. The extensible MD 2.0 environment has been built with a scalable architecture to allow rapid development of future analysis modules and enhanced tools for remote collaboration, like experiment sharing and a live chat capability. The new drag-and-drop modules allow researchers to easily and quickly assess different aspects of an experiment, including quality control, differential expression and enrichment analysis. The modularity of MD 2.0 lays the foundation to support broader community-based analytical template generation and optimized sharing and collaboration between proteomics experts and biologists, thereby accelerating research teams' abilities to extract knowledge from complex proteomics datasets.
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