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                                    "term": "CSV"
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                    "cmd": "./prolfqua_dea.sh -i data_dir/ -d annotation.xlsx -y config.yaml -w NameOfAnalysis -s DIANN\n# and again you run the version within the docker container with\n# ./prolfquapp_docker.sh prolfqua_dea.sh -i data_dir/ -d annotation.xlsx -y config.yaml -w NameOfAnalysis -s DIANN"
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                {
                    "doi": "10.1021/acs.jproteome.4c00911",
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                    "metadata": {
                        "title": "prolfquapp ─ A User-Friendly Command-Line Tool Simplifying Differential Expression Analysis in Quantitative Proteomics",
                        "abstract": "Mass spectrometry is a cornerstone of quantitative proteomics, enabling relative protein quantification and differential expression analysis (DEA) of proteins. As experiments grow in complexity, involving more samples, groups, and identified proteins, interactive differential expression analysis tools become impractical. The prolfquapp addresses this challenge by providing a command-line interface that simplifies DEA, making it accessible to nonprogrammers and seamlessly integrating it into workflow management systems. Prolfquapp streamlines data processing and result visualization by generating dynamic HTML reports that facilitate the exploration of differential expression results. These reports allow for investigating complex experiments, such as those involving repeated measurements or multiple explanatory variables. Additionally, prolfquapp supports various output formats, including XLSX files, SummarizedExperiment objects and rank files, for further interactive analysis using spreadsheet software, the exploreDE Shiny application, or gene set enrichment analysis software, respectively. By leveraging advanced statistical models from the prolfqua R package, prolfquapp offers a user-friendly, integrated solution for large-scale quantitative proteomics studies, combining efficient data processing with insightful, publication-ready outputs.",
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                            {
                                "name": "Wolski W.E."
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                            {
                                "name": "Grossmann J."
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                            {
                                "name": "Schwarz L."
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                            {
                                "name": "Leary P."
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                            {
                                "name": "Turker C."
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                            {
                                "name": "Nanni P."
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                            {
                                "name": "Schlapbach R."
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                            {
                                "name": "Panse C."
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                        "journal": "Journal of Proteome Research"
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                    "term": "Protein folding, stability and design"
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                    "term": "Genetic variation"
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                    "url": "https://ddgemb.biocomp.unibo.it/help/",
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                        "title": "DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning",
                        "abstract": "Motivation: The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimental approaches and allow a fast screening of large datasets of variations. Results: In this work, we present DDGemb, a novel method combining protein language model embeddings and transformer architectures to predict protein ΔΔG upon both single- and multi-point variations. DDGemb has been trained on a high-quality dataset derived from literature and tested on available benchmark datasets of single- and multi-point variations. DDGemb performs at the state of the art in both single- and multi-point variations.",
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                                "name": "Savojardo C."
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                            {
                                "name": "Martelli P.L."
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                            {
                                "name": "Casadio R."
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            "homepage": "https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4",
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                            "term": "Modelling and simulation"
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                            "uri": "http://edamontology.org/operation_0476",
                            "term": "Ab initio structure prediction"
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                                "term": "Protein sequence"
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                                "uri": "http://edamontology.org/data_2884",
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                                    "uri": "http://edamontology.org/format_1475",
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                                "uri": "http://edamontology.org/data_2048",
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                    "term": "Molecular modelling"
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                    "note": "Link to the PEP-FOLD4 web page service"
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                    "url": "https://owncloud.rpbs.univ-paris-diderot.fr/owncloud/index.php/s/7NHDSJXiQA0V2zy",
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                    "note": "Source code and recipe for docker images allowing to run PEP-FOLD4 locally. All repository content is covered by a non-commercial license agreement and may be used for non-commercial and internal research purposes only.",
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            ],
            "documentation": [
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                    "url": "https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4/",
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                        "title": "PEP-FOLD4: A pH-dependent force field for peptide structure prediction in aqueous solution",
                        "abstract": "Accurate and fast structure prediction of peptides of less 40 amino acids in aqueous solution has many biological applications, but their conformations are pH-and salt concentration-dependent. In this work, we present PEP-FOLD4 which goes one step beyond many machine-learning approaches, such as AlphaFold2, TrRosetta and RaptorX. Adding the Debye-Hueckel formalism for charged-charged side chain interactions to a Mie formalism for all intramolecular (backbone and side chain) interactions, PEP-FOLD4, based on a coarse-grained representation of the peptides, performs as well as machine-learning methods on well-structured peptides, but displays significant improvements for poly-charged peptides. PEP-FOLD4 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD4. This server is free and there is no login requirement.",
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                        "title": "HSYMDOCK: A docking web server for predicting the structure of protein homo-oligomers with Cn or Dn symmetry",
                        "abstract": "A major subclass of protein-protein interactions is formed by homo-oligomers with certain symmetry. Therefore, computational modeling of the symmetric protein complexes is important for understanding the molecular mechanism of related biological processes. Although several symmetric docking algorithms have been developed for Cn symmetry, few docking servers have been proposed for Dn symmetry. Here, we present HSYMDOCK, a web server of our hierarchical symmetric docking algorithm that supports both Cn and Dn symmetry. The HSYMDOCK server was extensively evaluated on three benchmarks of symmetric protein complexes, including the 20 CASP11-CAPRI30 homo-oligomer targets, the symmetric docking benchmark of 213 Cn targets and 35 Dn targets, and a nonredundant test set of 55 transmembrane proteins. It was shown that HSYMDOCK obtained a significantly better performance than other similar docking algorithms. The server supports both sequence and structure inputs for the monomer/subunit. Users have an option to provide the symmetry type of the complex, or the server can predict the symmetry type automatically. The docking process is fast and on average consumes 101/420 min for a docking job. The HSYMDOCK web server is available at http://huanglab.phys.hust.edu.cn/hsymdock/.",
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                        "authors": [
                            {
                                "name": "Yan Y."
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                            {
                                "name": "Tao H."
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                            {
                                "name": "Huang S.-Y."
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                        "title": "MobiDB-lite: Fast and highly specific consensus prediction of intrinsic disorder in proteins",
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                                "name": "Necci M."
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                            {
                                "name": "Piovesan D."
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                            {
                                "name": "Dosztanyi Z."
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                            {
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                        "title": "RosettaES: A sampling strategy enabling automated interpretation of difficult cryo-EMmaps",
                        "abstract": "Accurate atomic modeling of macromolecular structures into cryo-electron microscopy (cryo-EM) maps is a major challenge, as the moderate resolution makes accurate placement of atoms difficult. We present Rosetta enumerative sampling (RosettaES), an automated tool that uses a fragment-based sampling strategy for de novo model completion of macromolecular structures from cryo-EM density maps at 3-5-Å resolution. On a benchmark set of nine proteins, RosettaES was able to identify near-native conformations in 85% of segments. RosettaES was also used to determine models for three challenging macromolecular structures.",
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                                "name": "Frenz B."
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                            "uri": "http://edamontology.org/operation_0476",
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                        "title": "UNRES server for physics-based coarse-grained simulations and prediction of protein structure, dynamics and thermodynamics",
                        "abstract": "A server implementation of the UNRES package (http://www.unres.pl) for coarse-grained simulations of protein structures with the physics-based UNRES model, coined a name UNRES server, is presented. In contrast to most of the protein coarsegrained models, owing to its physics-based origin, the UNRES force field can be used in simulations, including those aimed at protein-structure prediction, without ancillary information from structural databases; however, the implementation includes the possibility of using restraints. Local energy minimization, canonical molecular dynamics simulations, replica exchange and multiplexed replica exchange molecular dynamics simulations can be run with the current UNRES server; the latter are suitable for protein-structure prediction. The user-supplied input includes protein sequence and, optionally, restraints from secondary-structure prediction or small x-ray scattering data, and simulation type and parameters which are selected or typed in. Oligomeric proteins, as well as those containing D-amino-acid residues and disulfide links can be treated. The output is displayed graphically (minimized structures, trajectories, final models, analysis of trajectory/ensembles); however, all output files can be downloaded by the user.",
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                                "name": "Czaplewski C."
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                        "title": "Prediction of site-specific interactions in antibody-antigen complexes: The proABC method and server",
                        "abstract": "Motivation: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode. Results: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen. © The Author 2013.",
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                            {
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                                "term": "Compound name"
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                                "term": "Protein sequence"
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                                    "term": "FASTA-like"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2600",
                                "term": "Pathway or network"
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                                    "uri": "http://edamontology.org/format_2330",
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                                    "term": "Image format"
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                            ]
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0954",
                                "term": "Database cross-mapping"
                            },
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                            ]
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                                {
                                    "uri": "http://edamontology.org/format_2330",
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                                }
                            ]
                        }
                    ],
                    "note": "STITCH is a database of protein – chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions. Its key function is to provide users with predictions about the interactions that their entities of interest (proteins or small molecules) may be making with other such entities, and to visualise where a set of entities of interest can be found within the STITCH network. Direct traceable links to the original sources on which interactions are described in the network, make it easy for users to trace the provenance of the data.",
                    "cmd": null
                }
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                "Command-line tool",
                "Web application",
                "Database portal"
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                    "term": "Chemistry"
                },
                {
                    "uri": "http://edamontology.org/topic_3344",
                    "term": "Biomedical science"
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                    "uri": "http://edamontology.org/topic_0602",
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                    "url": "http://stitch.embl.de/cgi/show_info_page.pl?UserId=pRfi11Qlkhnx&sessionId=sEfmbh_T7QLq",
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                        "General"
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            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gkm795",
                    "pmid": "18084021",
                    "pmcid": "PMC2238848",
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                    "version": null,
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                    "metadata": {
                        "title": "STITCH: Interaction networks of chemicals and proteins",
                        "abstract": "The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH ('search tool for interactions of chemicals') integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug-target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68 000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de/. © 2007 The Author(s).",
                        "date": "2008-01-01T00:00:00Z",
                        "citationCount": 754,
                        "authors": [
                            {
                                "name": "Kuhn M."
                            },
                            {
                                "name": "von Mering C."
                            },
                            {
                                "name": "Campillos M."
                            },
                            {
                                "name": "Jensen L.J."
                            },
                            {
                                "name": "Bork P."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkp937",
                    "pmid": "19897548",
                    "pmcid": "PMC2808890",
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "STITCH 2: An interaction network database for small molecules and proteins",
                        "abstract": "Over the last years, the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing. To create a network of interactions, STITCH aims to integrate the data dispersed over the literature and various databases of biological pathways, drug-target relationships and binding affinities. In STITCH 2, the number of relevant interactions is increased by incorporation of BindingDB, PharmGKB and the Comparative Toxicogenomics Database. The resulting network can be explored interactively or used as the basis for large-scale analyses. To facilitate links to other chemical databases, we adopt InChIKeys that allow identification of chemicals with a short, checksum-like string. STITCH 2.0 connects proteins from 630 organisms to over 74 000 different chemicals, including 2200 drugs. STITCH can be accessed at http://stitch.embl.de/. © The Author(s) 2009. Published by Oxford University Press.",
                        "date": "2009-11-06T00:00:00Z",
                        "citationCount": 231,
                        "authors": [
                            {
                                "name": "Kuhn M."
                            },
                            {
                                "name": "Szklarczyk D."
                            },
                            {
                                "name": "Franceschini A."
                            },
                            {
                                "name": "Campillos M."
                            },
                            {
                                "name": "Von Mering C."
                            },
                            {
                                "name": "Jensen L.J."
                            },
                            {
                                "name": "Beyer A."
                            },
                            {
                                "name": "Bork P."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkt1207",
                    "pmid": "24293645",
                    "pmcid": "PMC3964996",
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "STITCH 4: Integration of protein-chemical interactions with user data",
                        "abstract": "STITCH is a database of protein-chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions. Available at http://stitch.embl.de, the resulting interaction network includes 390 000 chemicals and 3.6 million proteins from 1133 organisms. Compared with the previous version, the number of high-confidence protein-chemical interactions in human has increased by 45%, to 367 000. In this version, we added features for users to upload their own data to STITCH in the form of internal identifiers, chemical structures or quantitative data. For example, a user can now upload a spreadsheet with screening hits to easily check which interactions are already known. To increase the coverage of STITCH, we expanded the text mining to include full-text articles and added a prediction method based on chemical structures. We further changed our scheme for transferring interactions between species to rely on orthology rather than protein similarity. This improves the performance within protein families, where scores are now transferred only to orthologous proteins, but not to paralogous proteins. STITCH can be accessed with a web-interface, an API and downloadable files. © 2013 The Author(s). Published by Oxford University Press.",
                        "date": "2014-01-01T00:00:00Z",
                        "citationCount": 413,
                        "authors": [
                            {
                                "name": "Kuhn M."
                            },
                            {
                                "name": "Szklarczyk D."
                            },
                            {
                                "name": "Pletscher-Frankild S."
                            },
                            {
                                "name": "Blicher T.H."
                            },
                            {
                                "name": "Von Mering C."
                            },
                            {
                                "name": "Jensen L.J."
                            },
                            {
                                "name": "Bork P."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkr1011",
                    "pmid": "22075997",
                    "pmcid": "PMC3245073",
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "STITCH 3: Zooming in on protein-chemical interactions",
                        "abstract": "To facilitate the study of interactions between proteins and chemicals, we have created STITCH, an aggregated database of interactions connecting over 300 000 chemicals and 2.6 million proteins from 1133 organisms. Compared to the previous version, the number of chemicals with interactions and the number of high-confidence interactions both increase 4-fold. The database can be accessed interactively through a web interface, displaying interactions in an integrated network view. It is also available for computational studies through downloadable files and an API. As an extension in the current version, we offer the option to switch between two levels of detail, namely whether stereoisomers of a given compound are shown as a merged entity or as separate entities. Separate display of stereoisomers is necessary, for example, for carbohydrates and chiral drugs. Combining the isomers increases the coverage, as interaction databases and publications found through text mining will often refer to compounds without specifying the stereoisomer. The database is accessible at http://stitch.embl.de/. © The Author(s) 2011.",
                        "date": "2012-01-01T00:00:00Z",
                        "citationCount": 250,
                        "authors": [
                            {
                                "name": "Kuhn M."
                            },
                            {
                                "name": "Szklarczyk D."
                            },
                            {
                                "name": "Franceschini A."
                            },
                            {
                                "name": "Von Mering C."
                            },
                            {
                                "name": "Jensen L.J."
                            },
                            {
                                "name": "Bork P."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
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                },
                {
                    "name": "SUND-KU",
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                    "url": "http://healthsciences.ku.dk/",
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            "name": "SODA",
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                    "term": "Protein sites, features and motifs"
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                    "uri": "http://edamontology.org/topic_3538",
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                    "doi": "10.1093/nar/gkx412",
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                    "pmcid": "PMC7059794",
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                    "version": null,
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                    "metadata": {
                        "title": "SODA: Prediction of protein solubility from disorder and aggregation propensity",
                        "abstract": "Solubility is an important, albeit not well understood, feature determining protein behavior. It is of paramount importance in protein engineering, where similar folded proteins may behave in very different ways in solution. Here we present SODA, a novel method to predict the changes of protein solubility based on several physico-chemical properties of the protein. SODA uses the propensity of the protein sequence to aggregate as well as intrinsic disorder, plus hydrophobicity and secondary structure preferences to estimate changes in solubility. It has been trained and benchmarked on two different datasets. The comparison to other recently published methods shows that SODA has state-of-the-art performance and is particularly well suited to predict mutations decreasing solubility. The method is fast, returning results for single mutations in seconds. A usage example estimating the full repertoire of mutations for a human germline antibody highlights several solubility hotspots on the surface.",
                        "date": "2017-07-03T00:00:00Z",
                        "citationCount": 47,
                        "authors": [
                            {
                                "name": "Paladin L."
                            },
                            {
                                "name": "Piovesan D."
                            },
                            {
                                "name": "Tosatto S.C.E."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
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                }
            ],
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                        "title": "SIB-BLAST: A web server for improved delineation of true and false positives in PSI-BLAST searches",
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                        "title": "Liquid chromatography at critical conditions: Comprehensive approach to sequence-dependent retention time prediction",
                        "abstract": "An approach to sequence-dependent retention time prediction of peptides based on the concept of liquid chromatography at critical conditions (LCCC) is presented. Within the LCCC approach applied to biopolymers (BioLCCC), the specific retention time corresponds to a particular sequence. In combination with mass spectrometry, this approach provides an efficient tool to solve problems wherein the protein sequencing is essential. In this work, we present a theoretical background of the BioLCCC concept and demonstrate experimentally its feasibility for sequence-dependent LC retention time prediction for peptides. BioLCCC model is based on three notions: (a) a random walk model for a macromolecule chain; (b) an entropy and energy compensation for the macromolecules within the adsorbent pore; and (c) a set of phenomenological parameters for the effective interaction energies of interactions between the amino acid residues and the adsorbent surface. In this work, the phenomenological parameters have been obtained for C18 reversed-phase HPLC. Note, that contrary to alternative additive models for retention time prediction based on summation of the so-called \"retention coefficients\", the BioLCCC approach takes into account the location of amino acids within the primary structure of a peptide and, thus, allows the identification of the peptides having the same composition of amino acids but differing by their arrangement. As a result, this new approach allows prediction of retention time for any possible amino acid sequence in particular HPLC experiments. In addition, the BioLCCC model lacks of main drawbacks of additive approaches that predict retention time for sequences of limited chain lengths and provide information about amino acid composition only. The proposed BioLCCC approach was characterized experimentally using LTQ FT LC-MS and LC-MS/MS data obtained earlier for Escherichia coli. The HPLC system calibration was performed using peptide retention standards. The results received show a linear correlation between predicted and experimental retention times, with a correlation coefficient, R2, of 0.97 for a peptide standard mixture and 0.9 for E. coli data, respectively, with the standard error below 1 min. The work presents the first description of a BioLCCC approach for high-throughput peptide characterization and preliminary results of its feasibility tests. © 2006 American Chemical Society.",
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                            {
                                "name": "Tarasova I.A."
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                            {
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                            {
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                            {
                                "name": "Nielsen M.L."
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                            {
                                "name": "Zubarev R.A."
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                            {
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                        "title": "Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations",
                        "abstract": "Researchers have several options when designing proteomics experiments. Primary among these are choices of experimental method, instrumentation and spectral interpretation software. To evaluate these choices on a proteome scale, we compared triplicate measurements of the yeast proteome by liquid chromatography tandem mass spectrometry (LC-MS/MS) using linear ion trap (LTQ) and hybrid quadrupole time-of-flight (QqTOF; QSTAR) mass spectrometers. Acquired MS/MS spectra were interpreted with Mascot and SEQUEST algorithms with and without the requirement that all returned peptides be tryptic. Using a composite target decoy database strategy, we selected scoring criteria yielding 1% estimated false positive identifications at maximum sensitivity for all data sets, allowing reasonable comparisons between them. These comparisons indicate that Mascot and SEQUEST yield similar results for LTQ-acquired spectra but less so for QSTAR spectra. Furthermore, low reproducibility between replicate data acquisitions made on one or both instrument platforms can be exploited to increase sensitivity and confidence in large-scale protein identifications.",
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                        "title": "massXpert 2: A cross-platform software environment for polymer chemistry modelling and simulation/analysis of mass spectrometric data",
                        "abstract": "Since the middle of the 90s, mass spectrometry has evolved into an almost indispensable tool in structural studies on an ever-growing variety of (bio-)polymers, of which proteins, sugars and nucleic acids are the most prominent. Since the first public release of massXpert, the advances of mass spectrometry have motivated continuous and thorough maintenance of that software, in the form of two full software rewrites, culminating with massXpert 2, which we describe in this report. We shall describe the profound changes in massXpert that were performed so as to keep up with the technical advances in mass spectrometry since a decade. © The Author 2009. Published by Oxford University Press. All rights reserved.",
                        "date": "2009-10-20T00:00:00Z",
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                        "authors": [
                            {
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                                "uri": "http://edamontology.org/data_1709",
                                "term": "Protein secondary structure image"
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                    "term": "Protein folds and structural domains"
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            ],
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                    "url": "http://wlab.ethz.ch/protter/help/",
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                        "title": "Protter: Interactive protein feature visualization and integration with experimental proteomic data",
                        "abstract": "Summary: The ability to integrate and visualize experimental proteomic evidence in the context of rich protein feature annotations represents an unmet need of the proteomics community. Here we present Protter, a web-based tool that supports interactive protein data analysis and hypothesis generation by visualizing both annotated sequence features and experimental proteomic data in the context of protein topology. Protter supports numerous proteomic file formats and automatically integrates a variety of reference protein annotation sources, which can be readily extended via modular plug-ins. A built-in export function produces publication-quality customized protein illustrations, also for large datasets. Visualizations of surfaceome datasets show the specific utility of Protter for the integrated visual analysis of membrane proteins and peptide selection for targeted proteomics. © 2013 The Author 2013. Published by Oxford University Press. All rights reserved.",
                        "date": "2014-03-01T00:00:00Z",
                        "citationCount": 948,
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                            {
                                "name": "Omasits U."
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                            {
                                "name": "Ahrens C.H."
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                            {
                                "name": "Muller S."
                            },
                            {
                                "name": "Wollscheid B."
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                        "journal": "Bioinformatics"
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                            "uri": "http://edamontology.org/operation_0346",
                            "term": "Sequence similarity search"
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                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
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                        },
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                            "data": {
                                "uri": "http://edamontology.org/data_3494",
                                "term": "DNA sequence"
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                    ],
                    "output": [
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                            "data": {
                                "uri": "http://edamontology.org/data_1277",
                                "term": "Protein features"
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                            "data": {
                                "uri": "http://edamontology.org/data_0857",
                                "term": "Sequence search results"
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                        }
                    ],
                    "note": "The output includes protein similarity matches with active site residues annotated",
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0821",
                    "term": "Enzymes"
                }
            ],
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                "Linux",
                "Windows",
                "Mac"
            ],
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            "collectionID": [
                "BLAST",
                "EBI Tools"
            ],
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            "documentation": [
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                    "url": "http://www.ebi.ac.uk/about/terms-of-use",
                    "type": [
                        "Terms of use"
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                {
                    "url": "http://merops.sanger.ac.uk/about/special_features.shtml#BLAST",
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            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gkv1118",
                    "pmid": "26527717",
                    "pmcid": "PMC4702814",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Twenty years of the MEROPS database of proteolytic enzymes, their substrates and inhibitors",
                        "abstract": "The MEROPS database (http://merops.sanger.ac.UK) is an integrated source of information about peptidases, their substrates and inhibitors, which are of great relevance to biology, medicine and biotechnology. The hierarchical classification of the database is as follows: homologous sets of sequences are grouped into a protein species; protein species are grouped into a family; families are grouped into clans. There is a type example for each protein species (known as a 'holotype'), family and clan, and each protein species, family and clan has its own unique identifier. Pages to show the involvement of peptidases and peptidase inhibitors in biological pathways have been created. Each page shows the peptidases and peptidase inhibitors involved in the pathway, along with the known substrate cleavages and peptidase-inhibitor interactions, and a link to the KEGG database of biological pathways. Links have also been established with the IUPHAR Guide to Pharmacology. A new service has been set up to allow the submission of identified substrate cleavages so that conservation of the cleavage site can be assessed. This should help establish whether or not a cleavage site is physiologically relevant on the basis that such a cleavage site is likely to be conserved.",
                        "date": "2016-01-01T00:00:00Z",
                        "citationCount": 558,
                        "authors": [
                            {
                                "name": "Rawlings N.D."
                            },
                            {
                                "name": "Barrett A.J."
                            },
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