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            "description": "MANTI is an tool for automated annotation of protein N-termini for rapid interpretation of N-terminome data sets.",
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                {
                    "doi": "10.1021/ACS.ANALCHEM.1C00310",
                    "pmid": "33729755",
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                        "title": "MANTI: Automated Annotation of Protein N-Termini for Rapid Interpretation of N-Terminome Data Sets",
                        "abstract": "© 2021 The Authors. Published by American Chemical Society.Site-specific proteolytic processing is an important, irreversible post-translational protein modification with implications in many diseases. Enrichment of protein N-terminal peptides followed by mass spectrometry-based identification and quantification enables proteome-wide characterization of proteolytic processes and protease substrates but is challenged by the lack of specific annotation tools. A common problem is, for example, ambiguous matches of identified peptides to multiple protein entries in the databases used for identification. We developed MaxQuant Advanced N-termini Interpreter (MANTI), a standalone Perl software with an optional graphical user interface that validates and annotates N-terminal peptides identified by database searches with the popular MaxQuant software package by integrating information from multiple data sources. MANTI utilizes diverse annotation information in a multistep decision process to assign a conservative preferred protein entry for each N-terminal peptide, enabling automated classification according to the likely origin and determines significant changes in N-terminal peptide abundance. Auxiliary R scripts included in the software package summarize and visualize key aspects of the data. To showcase the utility of MANTI, we generated two large-scale TAILS N-terminome data sets from two different animal models of chemically and genetically induced kidney disease, puromycin adenonucleoside-treated rats (PAN), and heterozygous Wilms Tumor protein 1 mice (WT1). MANTI enabled rapid validation and autonomous annotation of >10 000 identified terminal peptides, revealing novel proteolytic proteoforms in 905 and 644 proteins, respectively. Quantitative analysis indicated that proteolytic activities with similar sequence specificity are involved in the pathogenesis of kidney injury and proteinuria in both models, whereas coagulation processes and complement activation were specifically induced after chemical injury.",
                        "date": "2021-04-06T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Demir F."
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                            {
                                "name": "Kizhakkedathu J.N."
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                                "name": "Rinschen M.M."
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                            {
                                "name": "Huesgen P.F."
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                        "journal": "Analytical Chemistry"
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                    "name": "Fatih Demir",
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            "description": "Novel bioinformatics approach to predict kinase-specific phosphorylation substrates and sites in the human proteome by combining informative protein sequence and functional features to build the prediction models using random forest (RF).",
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                    "term": "Proteins"
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                    "term": "Enzymes"
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                    "uri": "http://edamontology.org/topic_2815",
                    "term": "Human biology"
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                    "term": "Protein sites, features and motifs"
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                    "url": "http://phosphopredict.erc.monash.edu/",
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                        "title": "PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection",
                        "abstract": "© 2017 The Author(s).Protein phosphorylation is a major form of post-translational modification (PTM) that regulates diverse cellular processes. In silico methods for phosphorylation site prediction can provide a useful and complementary strategy for complete phosphoproteome annotation. Here, we present a novel bioinformatics tool, PhosphoPredict, that combines protein sequence and functional features to predict kinase-specific substrates and their associated phosphorylation sites for 12 human kinases and kinase families, including ATM, CDKs, GSK-3, MAPKs, PKA, PKB, PKC, and SRC. To elucidate critical determinants, we identified feature subsets that were most informative and relevant for predicting substrate specificity for each individual kinase family. Extensive benchmarking experiments based on both five-fold cross-validation and independent tests indicated that the performance of PhosphoPredict is competitive with that of several other popular prediction tools, including KinasePhos, PPSP, GPS, and Musite. We found that combining protein functional and sequence features significantly improves phosphorylation site prediction performance across all kinases. Application of PhosphoPredict to the entire human proteome identified 150 to 800 potential phosphorylation substrates for each of the 12 kinases or kinase families. PhosphoPredict significantly extends the bioinformatics portfolio for kinase function analysis and will facilitate high-throughput identification of kinase-specific phosphorylation sites, thereby contributing to both basic and translational research programs.",
                        "date": "2017-12-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Song J."
                            },
                            {
                                "name": "Wang H."
                            },
                            {
                                "name": "Wang J."
                            },
                            {
                                "name": "Leier A."
                            },
                            {
                                "name": "Marquez-Lago T."
                            },
                            {
                                "name": "Yang B."
                            },
                            {
                                "name": "Zhang Z."
                            },
                            {
                                "name": "Akutsu T."
                            },
                            {
                                "name": "Webb G.I."
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                            {
                                "name": "Daly R.J."
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                        "journal": "Scientific Reports"
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                    "email": "jiangning.song@monash.edu",
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                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
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                    "term": "Sequencing"
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                    "term": "Protein modifications"
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                    "doi": "10.1093/BIOINFORMATICS/BTAA055",
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                        "title": "The Feature-Viewer: A visualization tool for positional annotations on a sequence",
                        "abstract": "© 2020 The Author(s). Published by Oxford University Press. All rights reserved.The Feature-Viewer is a lightweight library for the visualization of biological data mapped to a protein or nucleotide sequence. It is designed for ease of use while allowing for a full customization. The library is already used by several biological data resources and allows intuitive visual mapping of a full spectra of sequence features for different usages.",
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                                "name": "Paladin L."
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                            {
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                            {
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                            {
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            "name": "GPCRsclass",
            "description": "Tool for predicting amine-binding receptors based on a protein sequence provided by the user.",
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                            "term": "Transmembrane protein prediction"
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                        "title": "GPCRsclass: A web tool for the classification of amine type of G-protein-coupled receptors",
                        "abstract": "The receptors of amine subfamily are specifically major drug targets for therapy of nervous disorders and psychiatric diseases. The recognition of novel amine type of receptors and their cognate ligands is of paramount interest for pharmaceutical companies. In the past, Chou and co-workers have shown that different types of amine receptors are correlated with their amino acid composition and are predictable on its basis with considerable accuracy [Elrod and Chou (2002) Protein Eng., 15, 713-715]. This motivated us to develop a better method for the recognition of novel amine receptors and for their further classification. The method was developed on the basis of amino acid composition and dipeptide composition of proteins using support vector machine. The method was trained and tested on 167 proteins of amine subfamily of G-protein-coupled receptors (GPCRs). The method discriminated amine subfamily of GPCRs from globular proteins with Matthew's correlation coefficient of 0.98 and 0.99 using amino acid composition and dipeptide composition, respectively. In classifying different types of amine receptors using amino acid composition and dipeptide composition, the method achieved an accuracy of 89.8 and 96.4%, respectively. The performance of the method was evaluated using 5-fold cross-validation. The dipeptide composition based method predicted 67.6% of protein sequences with an accuracy of 100% with a reliability index ≥5. A web server GPCRsclass has been developed for predicting amine-binding receptors from its amino acid sequence [http://www.imtech.res.in/raghava/gpcrsclass/ and http://bioinformatics.uams.edu/raghava/gpersclass/ (mirror site)]. © 2005 Oxford University Press.",
                        "date": "2005-07-01T00:00:00Z",
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                        "authors": [
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                                "name": "Bhasin M."
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                                "name": "Raghava G.P.S."
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                    "note": "Prof Raghava is know to develop open source in bioinformatics"
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                    "metadata": {
                        "title": "SPACEPro: A Software Tool for Analysis of Protein Sample Cleavage for Tandem Mass Spectrometry",
                        "abstract": "© 2020 American Chemical Society. All rights reserved.The efficiency of shotgun proteomic analysis is dependent on the reproducibility of the peptide cleavage process during sample preparation. To generate rapid and useful metrics for peptide cleavage efficiency, as in enzymatic or chemical cleavage, SPACEPro was developed to evaluate efficiency and reproducibility of protein cleavage in peptide samples following data-dependent analysis by mass spectrometry. SPACEPro analyzes samples at the peptide-spectrum match (PSM), peptide, and protein levels to provide a comprehensive representation of the overall sample processing to peptides. All output is provided in human-readable text and JSON files that can be further processed to assess the cleavage efficiency on proteins within the sample. SPACEPro provides a snapshot of the protein cleavage efficiency through very minimal effort so that the user is informed of the quality of the sample processing efficiency and can accordingly develop protocols to improve the initial sample preparation for subsequent analyses.",
                        "date": "2021-01-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Kailash V."
                            },
                            {
                                "name": "Mendoza L."
                            },
                            {
                                "name": "Moritz R.L."
                            },
                            {
                                "name": "Hoopmann M.R."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                }
            ],
            "credit": [],
            "community": null,
            "owner": "mhoopmann",
            "additionDate": "2021-02-02T22:32:53Z",
            "lastUpdate": "2021-05-27T06:50:48Z",
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            },
            "validated": 0,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "Scansite",
            "description": "Searches for motifs within proteins that are likely to be phosphorylated or that bind to common cellular signaling domains.",
            "homepage": "http://scansite.mit.edu/",
            "biotoolsID": "scansite",
            "biotoolsCURIE": "biotools:scansite",
            "version": [
                "4.0"
            ],
            "otherID": [
                {
                    "value": "RRID:SCR_007026",
                    "type": "rrid",
                    "version": null
                }
            ],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0239",
                            "term": "Sequence motif recognition"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0240",
                            "term": "Sequence motif comparison"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0303",
                            "term": "Protein fold recognition"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0246",
                            "term": "Protein domain recognition"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0128",
                    "term": "Protein interactions"
                },
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_0160",
                    "term": "Sequence sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "Java"
            ],
            "license": "MIT",
            "collectionID": [],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://scansite4.mit.edu/webservice/",
                    "type": [
                        "Service"
                    ],
                    "note": null
                },
                {
                    "url": "https://github.com/kkrismer/scansite4",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [
                {
                    "url": "https://scansite4.mit.edu/4.0/#tutorial",
                    "type": [
                        "Training material"
                    ],
                    "note": "Tutorial material"
                },
                {
                    "url": "https://scansite4.mit.edu/4.0/#faq",
                    "type": [
                        "FAQ"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": null,
                    "pmid": "7874496",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Use of an oriented peptide library to determine the optimal substrates of protein kinases",
                        "abstract": "Background: Phosphorylation by protein kinases is an important general mechanism for controlling intracellular processes, and plays an essential part in the signal transduction pathways that regulate cell growth in response to extracellular signals. A great number of protein kinases have been discovered, and the identification of their biological targets is still a very active research area. Protein kinases must have the appropriate substrate specificity to ensure that signals are transmitted correctly. Previous studies have demonstrated the importance of primary sequences within substrate proteins in determining protein kinase specificity, but efficient ways of identifying these sequences are lacking. Results We have developed a new technique for determining the substrate specificity of protein kinases, using an oriented library of more than 2.5 billion peptide substrates. In this approach, the consensus sequence of optimal substrates is determined by sequencing the mixture of products generated during a brief reaction with the kinase of interest. The optimal substrate predicted for cAMP-dependent protein kinase (PKA) by this technique is consistent with the sequences of known PKA substrates. The optimal sequences predicted for cyclin-dependent kinases (CDKs) cyclin B-Cdc2 and cyclin A-CDK2 also agree well with sites thought to be phosphorylated in vivo by these kinases. In addition, we determined the optimal substrate for SLK1, a homologue of the STE20 protein serine kinase of hitherto unknown substrate specificity. We also discuss a model incorporating the optimal cyclin B-Cdc2 substrate into the known crystal structure of this kinase. Conclusion Using the new technique we have developed, the sequence specificity of protein kinases can rapidly be predicted and, from this information, potential targets of the kinases can be identified. © 1994 Elsevier Science Ltd. All rights reserved.",
                        "date": "1994-01-01T00:00:00Z",
                        "citationCount": 513,
                        "authors": [
                            {
                                "name": "Songyang Z."
                            },
                            {
                                "name": "Blechner S."
                            },
                            {
                                "name": "Hoagland N."
                            },
                            {
                                "name": "Hoekstra M.F."
                            },
                            {
                                "name": "Piwnica-Worms H."
                            },
                            {
                                "name": "Cantley L.C."
                            }
                        ],
                        "journal": "Current Biology"
                    }
                },
                {
                    "doi": null,
                    "pmid": "15064475",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Computational prediction of protein-protein interactions.",
                        "abstract": "Eukaryotic proteins typically contain one or more modular domains such as kinases, phosphatases, and phoshopeptide-binding domains, as well as characteristic sequence motifs that direct post-translational modifications such as phosphorylation, or mediate binding to specific modular domains. A computational approach to predict protein interactions on a proteome-wide basis would therefore consist of identifying modular domains and sequence motifs from protein primary sequence data, creating sequence specificity-based algorithms to connect a domain in one protein with a motif in another in \"interaction space,\" and then graphically constructing possible interaction networks. Computational methods for predicting modular domains in proteins have been quite successful, but identifying the short sequence motifs these domains recognize has been more difficult. We are developing improved methods to identify these motifs by combining experimental and computational techniques with databases of sequences and binding information. Scansite is a web-accessible program that predicts interactions between proteins using experimental binding data from peptide library and phage display experiments. This program focuses on domains important in cell signaling, but it can, in principle, be used for other interactions if the domains and binding motifs are known. This chapter describes in detail how to use Scansite to predict the binding partners of an input protein, and how to find all proteins that contain a given sequence motif.",
                        "date": "2004-01-01T00:00:00Z",
                        "citationCount": 36,
                        "authors": [
                            {
                                "name": "Obenauer J.C."
                            },
                            {
                                "name": "Yaffe M.B."
                            }
                        ],
                        "journal": "Methods in molecular biology (Clifton, N.J.)"
                    }
                },
                {
                    "doi": null,
                    "pmid": "11283593",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "A motif-based profile scanning approach for genome-wide prediction of signaling pathways",
                        "abstract": "The rapid increase in genomic information requires new techniques to infer protein function and predict protein-protein interactions. Bioinformatics identifies modular signaling domains within protein sequences with a high degree of accuracy. In contrast, little success has been achieved in predicting short linear sequence motifs within proteins targeted by these domains to form complex signaling networks. Here we describe a peptide library-based searching algorithm, accessible over the World Wide Web, that identifies sequence motifs likely to bind to specific protein domains such as 14-3-3, SH2, and SH3 domains, or likely to be phosphorylated by specific protein kinases such as Src and AKT. Predictions from database searches for proteins containing motifs matching two different domains in a common signaling pathway provides a much higher success rate. This technology facilitates prediction of cell signaling networks within proteomes, and could aid in the identification of drug targets for the treatment of human diseases.",
                        "date": "2001-04-18T00:00:00Z",
                        "citationCount": 456,
                        "authors": [
                            {
                                "name": "Yaffe M.B."
                            },
                            {
                                "name": "Leparc G.G."
                            },
                            {
                                "name": "Lai J."
                            },
                            {
                                "name": "Obata T."
                            },
                            {
                                "name": "Volinia S."
                            },
                            {
                                "name": "Cantley L.C."
                            }
                        ],
                        "journal": "Nature Biotechnology"
                    }
                },
                {
                    "doi": null,
                    "pmid": "12824383",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Scansite 2.0: Proteome-wide prediction of cell signalling interactions using short sequence motifs",
                        "abstract": "Scansite identifies short protein sequence motifs that are recognized by modular signaling domains, phosphorylated by protein Ser/Thr- or Tyr-kinases or mediate specific interactions with protein or phospholipid ligands. Each sequence motif is represented as a position-specific scoring matrix (PSSM) based on results from oriented peptide library and phage display experiments. Predicted domain-motif interactions from Scansite can be sequentially combined, allowing segments of biological pathways to be constructed in silico. The current release of Scansite, version 2.0, includes 62 motifs characterizing the binding and/or substrate specificities of many families of Ser/Thr- or Tyr-kinases, SH2, SH3, PDZ, 14-3-3 and PTB domains, together with signature motifs for PtdIns(3,4,5)P3-specific PH domains. Scansite 2.0 contains significant improvements to its original interface, including a number of new generalized user features and significantly enhanced performance. Searches of all SWISS-PROT, TrEMBL, Genpept and Ensembl protein database entries are now possible with run times reduced by ∼60% when compared with Scansite version 1.0. Scansite 2.0 allows restricted searching of species-specific proteins, as well as isoelectric point and molecular weight sorting to facilitate comparison of predictions with results from two-dimensional gel electrophoresis experiments. Support for user-defined motifs has been increased, allowing easier input of user-defined matrices and permitting user-defined motifs to be combined with pre-compiled Scansite motifs for dual motif searching. In addition, a new series of Sequence Match programs for non-quantitative user-defined motifs has been implemented. Scansite is available via the World Wide Web at http://scansite.mit.edu.",
                        "date": "2003-07-01T00:00:00Z",
                        "citationCount": 1289,
                        "authors": [
                            {
                                "name": "Obenauer J.C."
                            },
                            {
                                "name": "Cantley L.C."
                            },
                            {
                                "name": "Yaffe M.B."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": null,
                    "email": "scansite@mit.edu",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Project",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "ELIXIR-EE",
            "additionDate": "2017-03-24T09:33:58Z",
            "lastUpdate": "2021-05-15T13:20:41Z",
            "editPermission": {
                "type": "private",
                "authors": []
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "IMPACT",
            "description": "A Graphical User Interface for a software used to assess adaptive evolution in protein-coding genes. This software makes use of several known bioinformatics software, e.g., ConTest, Jmol, PhyML, ATV, etc.",
            "homepage": "http://impact-gui.sourceforge.net/",
            "biotoolsID": "impact",
            "biotoolsCURIE": "biotools:impact",
            "version": [
                "1.2.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0492",
                            "term": "Multiple sequence alignment"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0539",
                            "term": "Phylogenetic inference (method centric)"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2479",
                            "term": "Protein sequence analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0570",
                            "term": "Structure visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2403",
                            "term": "Sequence analysis"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Workflow",
                "Desktop application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3945",
                    "term": "Molecular evolution"
                },
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_2814",
                    "term": "Protein structure analysis"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "Java"
            ],
            "license": "GPL-3.0",
            "collectionID": [],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [
                {
                    "url": "http://impact-gui.sourceforge.net/",
                    "type": "Software package",
                    "note": null,
                    "version": "1.2.0"
                }
            ],
            "documentation": [
                {
                    "url": "http://impact-gui.sourceforge.net/IMPACT_tutorial.pdf",
                    "type": [
                        "User manual"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/jhered/esr003",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0.0",
                    "note": null,
                    "metadata": {
                        "title": "IMPACT: Integrated multiprogram platform for analyses in con test",
                        "abstract": "In this paper, we introduce a new Graphical User Interface that estimates evolutionary rates on protein sequences by assessing changes in biochemical constraints. We describe IMPACT, a platform-independent (tested in Linux, Windows, and MacOS) easy to install software written in Java. IMPACT integrates the use of a built-in multiple sequence alignment editor, with programs that perform phylogenetic and protein structure analyses (ConTest, PhyML, ATV, and Jmol) allowing the user to quickly and efficiently perform evolutionary analyses on protein sequences, including the detection of selection (negative and positive) signatures at the amino acid scale, which can provide fundamental insight about species evolution and ecological fitness. IMPACT provides the user with a working platform that combines a number of bioinformatics tools and utilities in one place, transferring information directly among the various programs and therefore increasing the overall performance of evolutionary analyses on proteins. © 2011 The American Genetic Association. All rights reserved.",
                        "date": "2011-05-01T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Maldonado E."
                            },
                            {
                                "name": "Dutheil J.Y."
                            },
                            {
                                "name": "Da Fonseca R.R."
                            },
                            {
                                "name": "Vasconcelos V."
                            },
                            {
                                "name": "Antunes A."
                            }
                        ],
                        "journal": "Journal of Heredity"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Emanuel Maldonado",
                    "email": "emaldonado@ciimar.up.pt",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-0084-6116",
                    "gridid": null,
                    "rorid": null,
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                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "emaldonado",
            "additionDate": "2021-05-06T16:14:40Z",
            "lastUpdate": "2021-05-10T14:48:08Z",
            "editPermission": {
                "type": "private",
                "authors": []
            },
            "validated": 0,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "P2Rank",
            "description": "Novel machine learning-based method for prediction of ligand binding sites from protein structure.",
            "homepage": "http://siret.ms.mff.cuni.cz/p2rank",
            "biotoolsID": "p2rank",
            "biotoolsCURIE": "biotools:p2rank",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2575",
                            "term": "Protein binding site prediction"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Desktop application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_2258",
                    "term": "Cheminformatics"
                },
                {
                    "uri": "http://edamontology.org/topic_1317",
                    "term": "Structural biology"
                },
                {
                    "uri": "http://edamontology.org/topic_0128",
                    "term": "Protein interactions"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "Java",
                "Groovy"
            ],
            "license": "MIT",
            "collectionID": [
                "ELIXIR-CZ"
            ],
            "maturity": null,
            "cost": null,
            "accessibility": null,
            "elixirPlatform": [],
            "elixirNode": [
                "Czech Republic"
            ],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "http://siret.ms.mff.cuni.cz/p2rank",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1186/s13321-018-0285-8",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure",
                        "abstract": "© 2018, The Author(s).Background: Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers. Results: We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein. We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation. Conclusions: P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines.",
                        "date": "2018-12-01T00:00:00Z",
                        "citationCount": 26,
                        "authors": [
                            {
                                "name": "Krivak R."
                            },
                            {
                                "name": "Hoksza D."
                            }
                        ],
                        "journal": "Journal of Cheminformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "David Hoksza",
                    "email": "hoksza@ksi.mff.cuni.cz",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
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                    "typeEntity": "Person",
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                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "davidhokszamff",
            "additionDate": "2018-08-24T13:49:33Z",
            "lastUpdate": "2021-04-26T06:27:07Z",
            "editPermission": {
                "type": "private",
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            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "PRED-TMBB",
            "description": "PRED-TMBB is a tool that takes a Gram-negative bacteria protein sequence as input and predicts the transmembrane strands and the probability of it being an outer membrane beta-barrel protein.  The user has a choice of three different decoding methods.",
            "homepage": "http://bioinformatics.biol.uoa.gr/PRED-TMBB/",
            "biotoolsID": "pred-tmbb",
            "biotoolsCURIE": "biotools:pred-tmbb",
            "version": [],
            "otherID": [
                {
                    "value": "RRID:SCR_006190",
                    "type": "rrid",
                    "version": null
                }
            ],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0270",
                            "term": "Transmembrane protein analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0269",
                            "term": "Transmembrane protein prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0267",
                            "term": "Protein secondary structure prediction"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0820",
                    "term": "Membrane and lipoproteins"
                },
                {
                    "uri": "http://edamontology.org/topic_0736",
                    "term": "Protein folds and structural domains"
                },
                {
                    "uri": "http://edamontology.org/topic_2269",
                    "term": "Statistics and probability"
                },
                {
                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                },
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "Java"
            ],
            "license": null,
            "collectionID": [],
            "maturity": null,
            "cost": null,
            "accessibility": null,
            "elixirPlatform": [],
            "elixirNode": [],
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            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "http://bioinformatics.biol.uoa.gr/PRED-TMBB/inputHelp.jsp",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": null,
                    "pmid": "15215419",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "PRED-TMBB: A web server for predicting the topology of β-barrel outer membrane proteins",
                        "abstract": "The β-barrel outer membrane proteins constitute one of the two known structural classes of membrane proteins. Whereas there are several different web-based predictors for α-helical membrane proteins, currently there is no freely available prediction method for β -barrel membrane proteins, at least with an acceptable level of accuracy. We present here a web server (PRED-TMBB, http://bioinformatics. biol.uoa.gr/PRED-TMBB) which is capable of predicting the transmembrane strands and the topology of β-barrel outer membrane proteins of Gram-negative bacteria. The method is based on a Hidden Markov Model, trained according to the Conditional Maximum Likelihood criterion. The model was retrained and the training set now includes 16 non-homologous outer membrane proteins with structures known at atomic resolution. The user may submit one sequence at a time and has the option of choosing between three different decoding methods. The server reports the predicted topology of a given protein, a score indicating the probability of the protein being an outer membrane β-barrel protein, posterior probabilities for the transmembrane strand prediction and a graphical representation of the assumed position of the transmembrane strands with respect to the lipid bilayer. © Oxford University Press 2004; all rights reserved.",
                        "date": "2004-07-01T00:00:00Z",
                        "citationCount": 274,
                        "authors": [
                            {
                                "name": "Bagos P.G."
                            },
                            {
                                "name": "Liakopoulos T.D."
                            },
                            {
                                "name": "Spyropoulos I.C."
                            },
                            {
                                "name": "Hamodrakas S.J."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": null,
                    "pmid": "15070403",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "A Hidden Markov Model method, capable of predicting and discriminating β-barrel outer membrane proteins",
                        "abstract": "Background: Integral membrane proteins constitute about 20-30% of all proteins in the fully sequenced genomes. They come in two structural classes, the α-helical and the β-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the α-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the β-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane β-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences. Results: The training has been performed on a non-redundant database of 14 outer membrane proteins with structures known at atomic resolution; it has been tested with a jacknife procedure, yielding a per residue accuracy of 84.2% and a correlation coefficient of 0.72, whereas for the selfconsistency test the per residue accuracy was 88.1% and the correlation coefficient 0.824. The total number of correctly predicted topologies is 10 out of 14 in the self-consistency test, and 9 out of 14 in the jacknife. Furthermore, the model is capable of discriminating outer membrane from water-soluble proteins in large-scale applications, with a success rate of 88.8% and 89.2% for the correct classification of outer membrane and water-soluble proteins respectively, the highest rates obtained in the literature. That test has been performed independently on a set of known outer membrane proteins with low sequence identity with each other and also with the proteins of the training set. Conclusion: Based on the above, we developed a strategy, that enabled us to screen the entire proteome of E. coli for outer membrane proteins. The results were satisfactory, thus the method presented here appears to be suitable for screening entire proteomes for the discovery of novel outer membrane proteins. A web interface available for non-commercial users is located at: http:/ /bioinformatics.biol.uoa.gr/PRED-TMBB, and it is the only freely available HMM-based predictor for β-barrel outer membrane protein topology. © 2004 Bagos et al; licensee BioMed Central Ltd.",
                        "date": "2004-03-15T00:00:00Z",
                        "citationCount": 139,
                        "authors": [
                            {
                                "name": "Bagos P.G."
                            },
                            {
                                "name": "Liakopoulos T.D."
                            },
                            {
                                "name": "Spyropoulos I.C."
                            },
                            {
                                "name": "Hamodrakas S.J."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "P.G. Bagos",
                    "email": "pbagos@biol.uoa.gr",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
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                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "babissavakis",
            "additionDate": "2017-02-10T11:35:54Z",
            "lastUpdate": "2021-04-17T14:45:51Z",
            "editPermission": {
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            },
            "validated": 1,
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            "confidence_flag": null
        },
        {
            "name": "BOMP",
            "description": "The beta-barrel Outer Membrane protein Predictor (BOMP) takes one or more fasta-formatted polypeptide sequences from Gram-negative bacteria as input and predicts whether or not they are beta-barrel integral outer membrane proteins.",
            "homepage": "http://www.bioinfo.no/tools/bomp",
            "biotoolsID": "bomp",
            "biotoolsCURIE": "biotools:bomp",
            "version": [],
            "otherID": [
                {
                    "value": "RRID:SCR_007268",
                    "type": "rrid",
                    "version": null
                }
            ],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0408",
                            "term": "Protein globularity prediction"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0820",
                    "term": "Membrane and lipoproteins"
                },
                {
                    "uri": "http://edamontology.org/topic_0736",
                    "term": "Protein folds and structural domains"
                },
                {
                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                },
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [],
            "license": null,
            "collectionID": [],
            "maturity": null,
            "cost": null,
            "accessibility": null,
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            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "http://services.cbu.uib.no/tools/bomp/Information/instructions",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": null,
                    "pmid": "15215418",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BOMP: A program to predict integral β-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria",
                        "abstract": "This work describes the development of a program that predicts whether or not a polypeptide sequence from a Gram-negative bacterium is an integral β-barrel outer membrane protein. The program, called the β-barrel Outer Membrane protein Predictor (BOMP), is based on two separate components to recognize integral β-barrel proteins. The first component is a C-terminal pattern typical of many integral β -barrel proteins. The second component calculates an integral β -barrel score of the sequence based on the extent to which the sequence contains stretches of amino acids typical of transmembrane β -strands. The precision of the predictions was found to be 80% with a recall of 88% when tested on the proteins with SwissProt annotated subcellular localization in Escherichia coli K 12 (788 sequences) and Salmonella typhimurium (366 sequences). When tested on the predicted proteome of E. coli, BOMP found 103 of a total of 4346 polypeptide sequences to be possible integral β-barrel proteins. Of these, 36 were found by BLAST to lack similarity (E-value score < 1e-10) to proteins with annotated subcellular localization in SwissProt. BOMP predicted the content of integral β-barrels per predicted proteome of 10 different bacteria to range from 1.8 to 3%. BOMP is available at http://www.bioinfo.no/tools/bomp. © Oxford University Press 2004; all rights reserved.",
                        "date": "2004-07-01T00:00:00Z",
                        "citationCount": 143,
                        "authors": [
                            {
                                "name": "Berven F.S."
                            },
                            {
                                "name": "Flikka K."
                            },
                            {
                                "name": "Jensen H.B."
                            },
                            {
                                "name": "Eidhammer I."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "BOMP Support",
                    "email": "services@cbu.uib.no",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                    "fundrefid": null,
                    "typeEntity": "Person",
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                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "ELIXIR-EE",
            "additionDate": "2017-02-10T11:35:27Z",
            "lastUpdate": "2021-04-15T18:57:15Z",
            "editPermission": {
                "type": "private",
                "authors": []
            },
            "validated": 1,
            "homepage_status": 0,
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            "confidence_flag": null
        }
    ]
}