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            "name": "CoCoNat",
            "description": "CoCoNat is a novel deep-learning based method for predicting coiled-coil regions, register annotation and oligomenrization state. CoCoNat adopts a sequence encoding based on two state-of-the-art protein language models and a deep-learning architetcure to perform prediction.",
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                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
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                    "uri": "http://edamontology.org/topic_2814",
                    "term": "Protein structure analysis"
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                {
                    "doi": "10.1093/bioinformatics/btad495",
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                    "metadata": {
                        "title": "CoCoNat: a novel method based on deep learning for coiled-coil prediction",
                        "abstract": "Motivation: Coiled-coil domains (CCD) are widespread in all organisms and perform several crucial functions. Given their relevance, the computational detection of CCD is very important for protein functional annotation. State-of-the-art prediction methods include the precise identification of CCD boundaries, the annotation of the typical heptad repeat pattern along the coiled-coil helices as well as the prediction of the oligomerization state. Results: In this article, we describe CoCoNat, a novel method for predicting coiled-coil helix boundaries, residue-level register annotation, and oligomerization state. Our method encodes sequences with the combination of two state-of-the-art protein language models and implements a three-step deep learning procedure concatenated with a Grammatical-Restrained Hidden Conditional Random Field for CCD identification and refinement. A final neural network predicts the oligomerization state. When tested on a blind test set routinely adopted, CoCoNat obtains a performance superior to the current state-of-the-art both for residue-level and segment-level CCD. CoCoNat significantly outperforms the most recent state-of-the-art methods on register annotation and prediction of oligomerization states.",
                        "date": "2023-08-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Madeo G."
                            },
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Manfredi M."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Casadio R."
                            }
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                        "journal": "Bioinformatics"
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                    "doi": "10.21769/BioProtoc.4935",
                    "pmid": "38405078",
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                        "title": "CoCoNat: A Deep Learning–Based Tool for the Prediction of Coiled-coil Domains in Protein Sequences",
                        "abstract": "Coiled-coil domains (CCDs) are structural motifs observed in proteins in all organisms that perform several crucial functions. The computational identification of CCD segments over a protein sequence is of great importance for its functional characterization. This task can essentially be divided into three separate steps: the detection of segment boundaries, the annotation of the heptad repeat pattern along the segment, and the classification of its oligomerization state. Several methods have been proposed over the years addressing one or more of these predictive steps. In this protocol, we illustrate how to make use of CoCoNat, a novel approach based on protein language models, to characterize CCDs. CoCoNat is, at its release (August 2023), the state of the art for CCD detection. The web server allows users to submit input protein sequences and visualize the predicted domains after a few minutes. Optionally, precomputed segments can be provided to the model, which will predict the oligomerization state for each of them. CoCoNat can be easily integrated into biological pipelines by downloading the standalone version, which provides a single executable script to produce the output.",
                        "date": "2024-02-20T00:00:00Z",
                        "citationCount": 0,
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                            {
                                "name": "Manfredi M."
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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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                            "term": "Phylogenetic tree visualisation"
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                    "term": "Metabolomics"
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                    "metadata": {
                        "title": "Molecular phylogenetics by direct comparison of tandem mass spectra",
                        "abstract": "Rationale: Molecular phylogenetics is the study of evolution and relatedness of organisms or genes. Mass spectrometry is used routinely for bacterial identification and has also been used for phylogenetic analysis, for instance from bone material. Unfortunately, only a small fraction of the acquired tandem mass spectra allow direct interpretation. Methods: We describe a new algorithm and software for molecular phylogenetics using pairwise comparisons of tandem mass spectra from enzymatically digested proteins. The spectra need not be annotated and all acquired data is used in the analysis. To demonstrate the method, we analyzed tryptic digests of sera from four great apes and two other primates. Results: The distribution of spectra dot products for thousands of tandem mass spectra collected from two samples provides a measure on the fraction of shared peptides between the two samples. When inverted, this becomes a distance metric. By pairwise comparison between species and averaging over four individuals per species, it was possible to reconstruct the unique correct phylogenetic tree for the great apes and other primates. Conclusions: The new method described here has several attractive features compared with existing methods, among them simplicity, the unbiased use of all acquired data rather than a small subset of spectra, and the potential use of heavily degraded proteins or proteins with a priori unknown modifications. © 2012 John Wiley & Sons, Ltd.",
                        "date": "2012-04-15T00:00:00Z",
                        "citationCount": 28,
                        "authors": [
                            {
                                "name": "Palmblad M."
                            },
                            {
                                "name": "Deelder A.M."
                            }
                        ],
                        "journal": "Rapid Communications in Mass Spectrometry"
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                {
                    "doi": "10.1021/acs.jproteome.2c00457",
                    "pmid": "36173614",
                    "pmcid": "PMC9903320",
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                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets",
                        "abstract": "It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.",
                        "date": "2023-02-03T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Marissen R."
                            },
                            {
                                "name": "Varunjikar M.S."
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                            {
                                "name": "Laros J.F.J."
                            },
                            {
                                "name": "Rasinger J.D."
                            },
                            {
                                "name": "Neely B.A."
                            },
                            {
                                "name": "Palmblad M."
                            }
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                        "journal": "Journal of Proteome Research"
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                    "doi": "10.1021/acs.jproteome.1c00528",
                    "pmid": "34523928",
                    "pmcid": "PMC8491155",
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                        "title": "Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics Experiments in the Light of Proteomics",
                        "abstract": "Science is full of overlooked and undervalued research waiting to be rediscovered. Proteomics is no exception. In this perspective, we follow the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing tryptic peptides across animal species. This pioneering work directly led to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics. In the decades following, proteins continued to provide essential clues on evolutionary history. While technology has continued to improve, contemporary proteomics has strayed from this larger biological context, rarely comparing species or asking how protein structure, function, and interactions have evolved. Here we recombine proteomics with molecular phylogenetics, highlighting the value of framing proteomic results in a larger biological context and how almost forgotten research, though technologically surpassed, can still generate new ideas and illuminate our work from a different perspective. Though it is infeasible to read all research published on a large topic, looking up older papers can be surprisingly rewarding when rediscovering a \"gem\"at the end of a long citation chain, aided by digital collections and perpetually helpful librarians. Proper literature study reduces unnecessary repetition and allows research to be more insightful and impactful by truly standing on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/) as dataset MSV000087993.",
                        "date": "2021-10-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Neely B.A."
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                                "name": "Palmblad M."
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