update literature

src/content/publications/2018_Zimmer.bib

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+@article{Zimmer.2018,
+ abstract = {Targeted mass spectrometry has become the method of choice to gain absolute quantification information of high quality, which is essential for a quantitative understanding of biological systems. However, the design of absolute protein quantification assays remains challenging due to variations in peptide observability and incomplete knowledge about factors influencing peptide detectability. Here, we present a deep learning algorithm for peptide detectability prediction, d::pPop, which allows the informed selection of synthetic proteotypic peptides for the successful design of targeted proteomics quantification assays. The deep neural network is able to learn a regression model that relates the physicochemical properties of a peptide to its ion intensity detected by mass spectrometry. The approach makes use of experimentally detected deviations from the assumed equimolar abundance of all peptides derived from a given protein. Trained on extensive proteomics datasets, d::pPop's plant and non-plant specific models can predict the quality of proteotypic peptides for not yet experimentally identified proteins. Interrogating the deep neural network after learning from {\~{}}76,000 peptides per model organism allows to investigate the impact of different physicochemical properties on the observability of a peptide, thus providing insights into peptide observability as a multifaceted process. Empirical evaluation with rank accuracy metrics showed that our prediction approach outperforms existing algorithms. We circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the need for selecting the top most promising peptides for targeting a protein of interest. Further, we used an artificial QconCAT protein to experimentally validate the observability prediction. Our proteotypic peptide prediction approach not only facilitates the design of absolute protein quantification assays via a user-friendly web interface but also enables the selection of proteotypic peptides for not yet observed proteins, hence rendering the tool especially useful for plant research.},
+ author = {Zimmer, David and Schneider, Kevin and Sommer, Frederik and Schroda, Michael and Mühlhaus, Timo},
+ year = {2018},
+ title = {Artificial Intelligence Understands Peptide Observability and Assists With Absolute Protein Quantification},
+ volume = {9},
+ issn = {1664-462X},
+ journal = {Frontiers in plant science},
+ doi = {10.3389/fpls.2018.01559},
+ file = {Zimmer, Schneider et al. 2018 - Artificial Intelligence Understands Peptide Observability:Attachments/Zimmer, Schneider et al. 2018 - Artificial Intelligence Understands Peptide Observability.pdf:application/pdf}
+}

src/content/publications/2021_Zimmer.bib

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+@article{Zimmer.2021,
+ abstract = {Photosynthetically produced electrons provide energy for various metabolic pathways, including carbon reduction. Four Calvin-Benson cycle enzymes and several other plastid proteins are activated in the light by reduction of specific cysteines via thioredoxins, a family of electron transporters operating in redox regulation networks. How does this network link the photosynthetic chain with cellular metabolism? Using a time-resolved redox proteomic method, we have investigated the redox network in vivo during the dark–to–low light transition. We show that redox states of some thioredoxins follow the photosynthetic linear electron transport rate. While some redox targets have kinetics compatible with an equilibrium with one thioredoxin (TRXf), reduction of other proteins shows specific kinetic limitations, allowing fine-tuning of each redox-regulated step of chloroplast metabolism. We identified five new redox-regulated proteins, including proteins involved in Mg2+ transport and 1O2 signaling. Our results provide a system-level functional view of the photosynthetic redox regulation network.},
+ author = {Zimmer, David and Swart, Corné and Graf, Alexander and Arrivault, Stéphanie and Tillich, Michael and Proost, Sebastian and Nikoloski, Zoran and Stitt, Mark and Bock, Ralph and Mühlhaus*, Timo and Boulouis*, Alix},
+ year = {2021},
+ title = {Topology of the redox network during induction of photosynthesis as revealed by time-resolved proteomics in tobacco},
+ pages = {eabi8307},
+ volume = {7},
+ number = {51},
+ journal = {Science advances},
+ doi = {10.1126/sciadv.abi8307},
+ file = {Zimmer, Swart et al. 2021 - Topology of the redox network:Attachments/Zimmer, Swart et al. 2021 - Topology of the redox network.pdf:application/pdf}
+}

src/content/publications/2022_Garth.bib

@@ -0,0 +1,8 @@
+@misc{Garth.2022,
+ author = {Garth, Christoph and Lukasczyk, Jonas and Mühlhaus, Timo and Venn, Benedikt and Krüger, Jens and Glogowski, Kolja and {Martins Rodrigues}, Cristina and von Suchodoletz, Dirk},
+ date = {2022},
+ title = {Immutable yet evolving: ARCs for permanent sharing in the research data-time continuum},
+ publisher = {heiBOOKS},
+ doi = {10.11588/heibooks.979.c13751},
+ file = {979-4-98015-1-10-20220413:Attachments/979-4-98015-1-10-20220413.pdf:application/pdf}
+}

src/content/publications/2022_Muehlhaus.bib

@@ -0,0 +1,8 @@
+@misc{Muhlhaus.2022,
+ author = {Mühlhaus, Timo and Brillhaus, Dominik and Tschöpe, Marcel and Maus, Oliver and Grüning, Björn and Garth, Christoph and {Martins Rodrigues}, Cristina and von Suchodoletz, Dirk},
+ date = {2022},
+ title = {DataPLANT – Tools and Services to structure the Data Jungle for fundamental plant researchers},
+ publisher = {heiBOOKS},
+ doi = {10.11588/heibooks.979.c13724},
+ file = {979-4-97973-1-10-20220413:Attachments/979-4-97973-1-10-20220413.pdf:application/pdf}
+}

src/content/publications/2022_Schneider.bib

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+@article{Schneider.2022,
+ author = {Schneider, Kevin and Venn, Benedikt and Mühlhaus, Timo},
+ year = {2022},
+ title = {Plotly.NET: A fully featured charting library for .NET programming languages},
+ pages = {1094},
+ volume = {11},
+ journal = {F1000Research},
+ doi = {10.12688/f1000research.123971.1},
+ file = {Schneider, Venn et al. 2022 - Plotly.NET:Attachments/Schneider, Venn et al. 2022 - Plotly.NET.pdf:application/pdf}
+}

src/content/publications/2022_Spaniol.bib

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+@article{Spaniol.2022,
+ abstract = {While the composition and function of the major thylakoid membrane complexes are well understood, comparatively little is known about their biogenesis. The goal of this work was to shed more light on the role of auxiliary factors in the biogenesis of photosystem II (PSII). Here we have identified the homolog of LOW PSII ACCUMULATION 2 (LPA2) in Chlamydomonas. A Chlamydomonas reinhardtii lpa2 mutant grew slower in low light, was hypersensitive to high light, and exhibited aberrant structures in thylakoid membrane stacks. Chlorophyll fluorescence (Fv/Fm) was reduced by 38{\%}. Synthesis and stability of newly made PSII core subunits D1, D2, CP43, and CP47 were not impaired. However, complexome profiling revealed that in the mutant CP43 was reduced to {\~{}}23{\%} and D1, D2, and CP47 to {\~{}}30{\%} of wild type levels. Levels of PSI and the cytochrome b6f complex were unchanged, while levels of the ATP synthase were increased by {\~{}}29{\%}. PSII supercomplexes, dimers, and monomers were reduced to {\~{}}7{\%}, {\~{}}26{\%}, and {\~{}}60{\%} of wild type levels, while RC47 was increased {\~{}}6-fold and LHCII by {\~{}}27{\%}. We propose that LPA2 catalyses a step during PSII assembly without which PSII monomers and further assemblies become unstable and prone to degradation. The LHCI antenna was more disconnected from PSI in the lpa2 mutant, presumably as an adaptive response to reduce excitation of PSI. From the co-migration profiles of 1734 membrane-associated proteins, we identified three novel putative PSII associated proteins with potential roles in regulating PSII complex dynamics, assembly, and chlorophyll breakdown.},
+ author = {Spaniol, Benjamin and Lang, Julia and Venn, Benedikt and Schake, Lara and Sommer, Frederik and Mustas, Matthieu and Geimer, Stefan and Wollman, Francis-André and Choquet, Yves and Mühlhaus, Timo and Schroda, Michael},
+ year = {2022},
+ title = {Complexome profiling on the Chlamydomonas lpa2 mutant reveals insights into PSII biogenesis and new PSII associated proteins},
+ pages = {245--262},
+ volume = {73},
+ number = {1},
+ issn = {1460-2431},
+ journal = {Journal of experimental botany},
+ doi = {10.1093/jxb/erab390},
+ file = {Spaniol, Lang et al. 2022 - Complexome profiling on the Chlamydomonas:Attachments/Spaniol, Lang et al. 2022 - Complexome profiling on the Chlamydomonas.pdf:application/pdf}
+}

src/content/publications/2022_Suchodoletz.bib

@@ -0,0 +1,8 @@
+@misc{Suchodoletz.2022,
+ author = {von Suchodoletz, Dirk and Mühlhaus, Timo and Brillhaus, Dominik and Jabeen, Hajira and Usadel, Björn and Krüger, Jens and Gauza, Holger and {Martins Rodrigues}, Cristina},
+ date = {2022},
+ title = {Data Stewards as ambassadors between the NFDI and the community},
+ publisher = {heiBOOKS},
+ doi = {10.11588/heibooks.979.c13750},
+ file = {979-4-98014-1-10-20220413:Attachments/979-4-98014-1-10-20220413.pdf:application/pdf}
+}

src/content/publications/2023_Arend.bib

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+@article{Arend.2023,
+ abstract = {Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.},
+ author = {Arend, Marius and Zimmer, David and Xu, Rudan and Sommer, Frederik and Mühlhaus, Timo and Nikoloski, Zoran},
+ year = {2023},
+ title = {Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale},
+ pages = {4781},
+ volume = {14},
+ number = {1},
+ journal = {Nature Communications},
+ doi = {10.1038/s41467-023-40498-1},
+ file = {Arend, Zimmer et al. 2023 - Proteomics and constraint-based modelling reveal:Attachments/Arend, Zimmer et al. 2023 - Proteomics and constraint-based modelling reveal.pdf:application/pdf}
+}

src/content/publications/2023_Jung.bib

@@ -0,0 +1,11 @@
+@article{Jung.2023,
+ author = {Jung, Felix and Frey, Kevin and Zimmer, David and Mühlhaus, Timo},
+ year = {2023},
+ title = {DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability},
+ pages = {7444},
+ volume = {24},
+ number = {8},
+ journal = {International Journal of Molecular Sciences},
+ doi = {10.3390/ijms24087444},
+ file = {Jung, Frey et al. 2023 - DeepSTABp A Deep Learning Approach:Attachments/Jung, Frey et al. 2023 - DeepSTABp A Deep Learning Approach.pdf:application/pdf}
+}

src/content/publications/2023_Ries.bib

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+@article{Ries.2023,
+ abstract = {The functionality of all metabolic processes in chloroplasts depends on a balanced integration of nuclear- and chloroplast-encoded polypeptides into the plastid's proteome. The chloroplast chaperonin machinery is an essential player in chloroplast protein folding under ambient and stressful conditions, with a more intricate structure and subunit composition compared to the orthologous GroEL/ES chaperonin of Escherichia coli. However, its exact role in chloroplasts remains obscure, mainly because of very limited knowledge about the interactors. We employed the competition immunoprecipitation method for the identification of the chaperonin's interactors in Chlamydomonas reinhardtii. Co-immunoprecipitation of the target complex in the presence of increasing amounts of isotope-labelled competitor epitope and subsequent mass spectrometry analysis specifically allowed to distinguish true interactors from unspecifically co-precipitated proteins. Besides known substrates such as RbcL and the expected complex partners, we revealed numerous new interactors with high confidence. Proteins that qualify as putative substrate proteins differ from bulk chloroplast proteins by a higher content of beta-sheets, lower alpha-helical conformation and increased aggregation propensity. Immunoprecipitations targeted against a subunit of the co-chaperonin lid revealed the ClpP protease as a specific partner complex, pointing to a close collaboration of these machineries to maintain protein homeostasis in the chloroplast.},
+ author = {Ries, Fabian and Weil, Heinrich Lukas and Herkt, Claudia and Mühlhaus, Timo and Sommer, Frederik and Schroda, Michael and Willmund, Felix},
+ year = {2023},
+ title = {Competition co-immunoprecipitation reveals the interactors of the chloroplast CPN60 chaperonin machinery},
+ pages = {3371--3391},
+ volume = {46},
+ number = {11},
+ issn = {0140-7791},
+ journal = {Plant Cell and Environment},
+ doi = {10.1111/pce.14697},
+ file = {Ries, Weil et al. 2023 - Competition co-immunoprecipitation reveals the interactors:Attachments/Ries, Weil et al. 2023 - Competition co-immunoprecipitation reveals the interactors.pdf:application/pdf}
+}

src/content/publications/2023_Scherhag.bib

@@ -0,0 +1,11 @@
+@article{Scherhag.2023,
+ abstract = {Studies of protein-protein interactions in membranes are very important to fully understand the biological function of a cell. The extraction of proteins from the native membrane environment is a critical step in the preparation of membrane proteins that might affect the stability of protein complexes. In this work, we used the amphiphilic diisobutylene/maleic acid copolymer to extract the membrane proteome of the opportunistic pathogen Pseudomonas aeruginosa, thereby creating a soluble membrane-protein library within a native-like lipid-bilayer environment. Size fractionation of nanodisc-embedded proteins and subsequent mass spectrometry enabled the identification of 3358 proteins. The native membrane-protein library showed a very good overall coverage compared to previous proteome data. The pattern of size fractionation indicated that protein complexes were preserved in the library. More than 20 previously described complexes, e.g. the SecYEG and Pili complexes, were identified and analyzed for coelution. Although the mass-spectrometric dataset alone did not reveal new protein complexes, combining pulldown assays with mass spectrometry was successful in identifying new protein interactions in the native membrane-protein library. Thus, we identified several candidate proteins for interactions with the membrane phosphodiesterase NbdA, a member of the c-di-GMP network. We confirmed the candidate proteins CzcR, PA4200, SadC, and PilB as novel interaction partners of NbdA using the bacterial adenylate cyclase two-hybrid assay. Taken together, this work demonstrates the usefulness of the native membrane-protein library of P. aeruginosa for the investigation of protein interactions and membrane-protein complexes. Data are available via ProteomeXchange with identifiers PXD039702 and PXD039700.},
+ author = {Scherhag, Anna and Räschle, Markus and Unbehend, Niklas and Venn, Benedikt and Glueck, David and Mühlhaus, Timo and Keller, Sandro and {Pérez Patallo}, Eugenio and Zehner, Susanne and Frankenberg-Dinkel, Nicole},
+ year = {2023},
+ title = {Characterization of a soluble library of the Pseudomonas aeruginosa PAO1 membrane proteome with emphasis on c-di-GMP turnover enzymes},
+ pages = {uqad028},
+ volume = {4},
+ journal = {microLife},
+ doi = {10.1093/femsml/uqad028},
+ file = {Scherhag, Räschle et al. 2023 - Characterization of a soluble library:Attachments/Scherhag, Räschle et al. 2023 - Characterization of a soluble library.pdf:application/pdf}
+}

src/content/publications/2023_Weil.bib

@@ -0,0 +1,10 @@
+@article{Weil.2023,
+ abstract = {In modern reproducible, hypothesis-driven plant research, scientists are increasingly relying on research data management (RDM) services and infrastructures to streamline the processes of collecting, processing, sharing, and archiving research data. FAIR (i.e., findable, accessible, interoperable, and reusable) research data play a pivotal role in enabling the integration of interdisciplinary knowledge and facilitating the comparison and synthesis of a wide range of analytical findings. The PLANTdataHUB offers a solution that realizes RDM of scientific (meta)data as evolving collections of files in a directory - yielding FAIR digital objects called ARCs - with tools that enable scientists to plan, communicate, collaborate, publish, and reuse data on the same platform while gaining continuous quality control insights. The centralized platform is scalable from personal use to global communities and provides advanced federation capabilities for institutions that prefer to host their own satellite instances. This approach borrows many concepts from software development and adapts them to fit the challenges of the field of modern plant science undergoing digital transformation. The PLANTdataHUB supports researchers in each stage of a scientific project with adaptable continuous quality control insights, from the early planning phase to data publication. The central live instance of PLANTdataHUB is accessible at (https://git.nfdi4plants.org), and it will continue to evolve as a community-driven and dynamic resource that serves the needs of contemporary plant science.},
+ author = {Weil, Heinrich Lukas and Schneider, Kevin and Tschöpe, Marcel and Bauer, Jonathan and Maus, Oliver and Frey, Kevin and Brilhaus, Dominik and {Martins Rodrigues}, Cristina and Doniparthi, Gajendra and Wetzels, Florian and Lukasczyk, Jonas and Kranz, Angela and Grüning, Björn and Zimmer, David and Deßloch, Stefan and von Suchodoletz, Dirk and Usadel, Björn and Garth, Christoph and Mühlhaus, Timo},
+ year = {2023},
+ title = {PLANTdataHUB: a collaborative platform for continuous FAIR data sharing in plant research},
+ issn = {1365-313X},
+ journal = {The Plant journal : for cell and molecular biology},
+ doi = {10.1111/tpj.16474},
+ file = {Weil, Schneider et al. 2023 - PLANTdataHUB:Attachments/Weil, Schneider et al. 2023 - PLANTdataHUB.pdf:application/pdf}
+}

src/content/publications/2023_Zhou.bib

@@ -0,0 +1,11 @@
+@article{Zhou.2023,
+ author = {Zhou, Xiao-Ran and Beier, Sebastian and Brilhaus, Dominik and {Martins Rodrigues}, Cristina and Mühlhaus, Timo and von Suchodoletz, Dirk and Twyman, Richard M. and Usadel, Björn and Kranz, Angela},
+ year = {2023},
+ title = {DataPLAN: A Web-Based Data Management Plan Generator for the Plant Sciences},
+ pages = {159},
+ volume = {8},
+ number = {11},
+ journal = {Data},
+ doi = {10.3390/data8110159},
+ file = {Zhou, Beier et al. 2023 - DataPLAN A Web-Based Data Management:Attachments/Zhou, Beier et al. 2023 - DataPLAN A Web-Based Data Management.pdf:application/pdf}
+}

src/content/publications/2024_Venn.bib

@@ -0,0 +1,11 @@
+@article{Venn.2024,
+ author = {Venn, Benedikt and Leifeld, Thomas and Zhang, Ping and Mühlhaus, Timo},
+ year = {2024},
+ title = {Temporal classification of short time series data},
+ volume = {25},
+ number = {1},
+ issn = {1471-2105},
+ journal = {BMC bioinformatics},
+ doi = {10.1186/s12859-024-05636-6},
+ file = {Venn, Leifeld et al. 2024 - Temporal classification of short time:Attachments/Venn, Leifeld et al. 2024 - Temporal classification of short time.pdf:application/pdf}
+}