{"name":"MEDUSA","description":"MEDUSA (Multiclass flexibility prediction from sequences of amino acids) is a deep learning approach for prediction of protein flexibility from sequence. MEDUSA takes as input an amino acid sequence and returns a flexibility class of each residue in terms of the expected normalized B-factor value range. Prediction is simultaneously performed in two-, three- and five classes using a convolutional neural network trained on a dataset of non-redundant X-ray structures.","homepage":"https://www.dsimb.inserm.fr/MEDUSA","biotoolsID":"medusa-protein","biotoolsCURIE":"biotools:medusa-protein","version":[],"otherID":[],"relation":[],"function":[{"operation":[{"uri":"http://edamontology.org/operation_0244","term":"Simulation analysis"},{"uri":"http://edamontology.org/operation_0474","term":"Protein structure prediction"},{"uri":"http://edamontology.org/operation_2476","term":"Molecular dynamics"}],"input":[],"output":[],"note":null,"cmd":null}],"toolType":["Web application"],"topic":[{"uri":"http://edamontology.org/topic_2828","term":"X-ray diffraction"},{"uri":"http://edamontology.org/topic_0130","term":"Protein folding, stability and design"},{"uri":"http://edamontology.org/topic_0176","term":"Molecular dynamics"},{"uri":"http://edamontology.org/topic_0154","term":"Small molecules"},{"uri":"http://edamontology.org/topic_0736","term":"Protein folds and structural domains"}],"operatingSystem":[],"language":["Python","Perl"],"license":null,"collectionID":[],"maturity":null,"cost":null,"accessibility":null,"elixirPlatform":[],"elixirNode":[],"elixirCommunity":[],"link":[{"url":"https://github.com/DSIMB/medusa","type":["Repository"],"note":null},{"url":"https://github.com/DSIMB/medusa/issues","type":["Issue tracker"],"note":null}],"download":[{"url":"https://hub.docker.com/r/dsimb/medusa","type":"Container file","note":null,"version":null}],"documentation":[],"publication":[{"doi":"10.1016/j.jmb.2021.166882","pmid":"33972018","pmcid":null,"type":["Primary"],"version":null,"note":null,"metadata":{"title":"MEDUSA: Prediction of Protein Flexibility from Sequence","abstract":"Information on the protein flexibility is essential to understand crucial molecular mechanisms such as protein stability, interactions with other molecules and protein functions in general. B-factor obtained in the X-ray crystallography experiments is the most common flexibility descriptor available for the majority of the resolved protein structures. Since the gap between the number of the resolved protein structures and available protein sequences is continuously growing, it is important to provide computational tools for protein flexibility prediction from amino acid sequence. In the current study, we report a Deep Learning based protein flexibility prediction tool MEDUSA (https://www.dsimb.inserm.fr/MEDUSA). MEDUSA uses evolutionary information extracted from protein homologous sequences and amino acid physico-chemical properties as input for a convolutional neural network to assign a flexibility class to each protein sequence position. Trained on a non-redundant dataset of X-ray structures, MEDUSA provides flexibility prediction in two, three and five classes. MEDUSA is freely available as a web-server providing a clear visualization of the prediction results as well as a standalone utility (https://github.com/DSIMB/medusa). Analysis of the MEDUSA output allows a user to identify the potentially highly deformable protein regions and general dynamic properties of the protein.","date":"2021-05-28T00:00:00Z","citationCount":36,"authors":[{"name":"Vander Meersche Y."},{"name":"Cretin G."},{"name":"de Brevern A.G."},{"name":"Gelly J.-C."},{"name":"Galochkina T."}],"journal":"Journal of Molecular Biology"}}],"credit":[{"name":"Tatiana Galochkina","email":"tatiana.galochkina@u-paris.fr","url":null,"orcidid":null,"gridid":null,"rorid":null,"fundrefid":null,"typeEntity":"Person","typeRole":["Primary contact"],"note":null},{"name":"Jean-Christophe Gelly","email":"jean-christophe.gelly@u-paris.fr","url":null,"orcidid":null,"gridid":null,"rorid":null,"fundrefid":null,"typeEntity":"Person","typeRole":["Primary contact"],"note":null}],"owner":"TatianaGalochkina","additionDate":"2021-10-09T18:53:52.135931Z","lastUpdate":"2024-11-24T14:46:31.350586Z","editPermission":{"type":"group","authors":["TatianaGalochkina"]},"validated":0,"homepage_status":0,"elixir_badge":0,"confidence_flag":"tool"}