{"id":137,"date":"2023-02-10T13:24:19","date_gmt":"2023-02-10T13:24:19","guid":{"rendered":"https:\/\/model3dbio.csic.es\/?page_id=137"},"modified":"2025-06-25T10:14:03","modified_gmt":"2025-06-25T10:14:03","slug":"tools","status":"publish","type":"page","link":"https:\/\/model3dbio.csic.es\/?page_id=137","title":{"rendered":"\ud83d\udee0 Tools and Web Services"},"content":{"rendered":"<p>Some portals and servers we maintain for the community:<\/p>\n<ul>\n<li><a href=\"https:\/\/life.bsc.es\/pid\/ccharppi\" target=\"_blank\" rel=\"noopener\"><b>CCharPPI<\/b><\/a><br \/>Computational Characterisation of Protein\u2013Protein Interactions.<\/li>\n<li><a href=\"https:\/\/life.bsc.es\/pid\/opra\" target=\"_blank\" rel=\"noopener\"><b>OPRA Server<\/b><\/a><br \/>OPRA (Optimal Protein\u2013RNA Area): identifies potential RNA-binding sites on proteins and facilitates modeling of biologically or therapeutically relevant protein\u2013RNA interactions.<\/li>\n<li><a href=\"https:\/\/life.bsc.es\/pid\/protein-rna-benchmark\/\" target=\"_blank\" rel=\"noopener\"><b>Protein\u2013RNA Benchmark v1.0<\/b><\/a><br \/>Dataset of 106 cases for benchmarking protein\u2013RNA docking (unbound\u2013unbound, unbound\u2013bound, model\u2013bound, etc.).<\/li>\n<li><a href=\"https:\/\/life.bsc.es\/pid\/pydock\/\" target=\"_blank\" rel=\"noopener\"><b>pyDockWEB<\/b><\/a><br \/>Fast protocol using electrostatics and desolvation energy to score FFT-generated docking conformations.<\/li>\n<li><a href=\"https:\/\/life.bsc.es\/pid\/pydockeneres\" target=\"_blank\" rel=\"noopener\"><b>pyDockEneRes<\/b><\/a><br \/>Calculates per-residue energy contributions in protein\u2013protein interactions.<\/li>\n<li><a href=\"https:\/\/life.bsc.es\/pid\/pydockrescoring\" target=\"_blank\" rel=\"noopener\"><b>pyDockRescoring<\/b><\/a><br \/>Web service for rescoring jobs from the pyDockWEB server.<\/li>\n<li><a href=\"https:\/\/life.bsc.es\/pid\/pydocksaxs\" target=\"_blank\" rel=\"noopener\"><b>pyDockSAXS<\/b><\/a><br \/>Rigid-body protein\u2013protein docking server integrating SAXS experimental data to refine predictions.<\/li>\n<li><a href=\"https:\/\/life.bsc.es\/pid\/pydockweb\" target=\"_blank\" rel=\"noopener\"><b>pyDockWEB<\/b><\/a><br \/>Web server for structural prediction of protein\u2013protein interactions via computational docking.<\/li>\n<li><a href=\"https:\/\/model3dbio.csic.es\/pydockdna\" target=\"_blank\" rel=\"noopener\"><b>pyDockDNA<\/b><\/a><br \/>Web server for structural prediction of protein\u2013DNA interactions using computational docking.<\/li>\n<li><a href=\"https:\/\/life.bsc.es\/pid\/skempi2\" target=\"_blank\" rel=\"noopener\"><b>SKEMPI 2.0<\/b><\/a><br \/>Database of 7000+ protein\u2013protein mutation data, including thermodynamic and kinetic binding changes with available interaction structures.<\/li>\n<li><a href=\"https:\/\/model3dbio.csic.es\/wines\/\"><strong>Machine Learning Classification of Rioja Wines by Origin and Ageing<\/strong><\/a><br data-start=\"88\" data-end=\"91\" \/>This study applies voltammetry, absorbance, and fluorescence to classify 130 Rioja wines by sub-region (Alta, Alavesa, Oriental) and ageing (Joven, Crianza, Reserva). Machine learning models, particularly XGBoost, showed high accuracy with fluorescence data, which improved further when combined with absorbance. Voltammetry alone distinguished young from aged wines but lacked finer detail. The method is transferable to other wine regions. <a href=\"https:\/\/model3dbio.csic.es\/?page_id=225\">Request the password to view and collaborate.<\/a><\/li>\n<li><a href=\"https:\/\/model3dbio.csic.es\/covid19model\/\" target=\"_blank\" rel=\"noopener\"><b>Bayesian mechanistic model of COVID-19 transmission dynamics<\/b><\/a><br \/>A mechanistic model using Bayesian analysis was developed to assess the impact of non-pharmacological measures, later extended to include vaccination and virus variants. Applied to data from 30 European countries, the model accurately described diverse outbreak patterns, confirming its reliability for analyzing current and future disease evolution.<\/li>\n<li><a href=\"https:\/\/model3dbio.csic.es\/garrapatas\/\" target=\"_blank\" rel=\"noopener\"><b>Assessment of the Risk of Contracting a Tick-borne Disease in Urban Areas<\/b><\/a><br \/>This study aims to investigate the presence of hard ticks and their associated microorganisms in urban parks and green areas of major Spanish cities, with the goal of evaluating the risk of tick-borne diseases for urban residents, given the diagnostic challenges and the scarcity of previous studies in these environments. <a href=\"https:\/\/model3dbio.csic.es\/?page_id=225\"><br \/>Request the password to view and collaborate.<\/a><\/li>\n<li><a href=\"https:\/\/model3dbio.csic.es\/armillaria\"><strong data-start=\"206\" data-end=\"285\">Assessment of the Risk of Root Rot in Vineyards Caused by <em>Armillaria mellea<\/em><\/strong><\/a><br data-start=\"285\" data-end=\"288\" \/>This study aims to investigate the presence of the pathogenic fungus <em data-start=\"357\" data-end=\"376\">Armillaria mellea<\/em>\u2014commonly known as \u201cArmillary\u201d\u2014in vineyard soils and root systems, in order to evaluate the risk of root rot and vine death for grape growers, given the diagnostic difficulties and the limited prior research conducted in these agricultural environments. <a href=\"https:\/\/model3dbio.csic.es\/?page_id=225\">Request the password to view and collaborate.<\/a><\/li>\n<\/ul>\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Some portals and servers we maintain for the community: CCharPPIComputational Characterisation of Protein\u2013Protein Interactions. OPRA ServerOPRA (Optimal Protein\u2013RNA Area): identifies potential RNA-binding sites on proteins and facilitates modeling of biologically or therapeutically relevant protein\u2013RNA interactions. Protein\u2013RNA Benchmark v1.0Dataset of 106 cases for benchmarking protein\u2013RNA docking (unbound\u2013unbound, unbound\u2013bound, model\u2013bound, etc.). pyDockWEBFast protocol using electrostatics and desolvation &hellip; <a href=\"https:\/\/model3dbio.csic.es\/?page_id=137\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">\ud83d\udee0 Tools and Web Services<\/span> <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-137","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/model3dbio.csic.es\/index.php?rest_route=\/wp\/v2\/pages\/137","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/model3dbio.csic.es\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/model3dbio.csic.es\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/model3dbio.csic.es\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/model3dbio.csic.es\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=137"}],"version-history":[{"count":29,"href":"https:\/\/model3dbio.csic.es\/index.php?rest_route=\/wp\/v2\/pages\/137\/revisions"}],"predecessor-version":[{"id":286,"href":"https:\/\/model3dbio.csic.es\/index.php?rest_route=\/wp\/v2\/pages\/137\/revisions\/286"}],"wp:attachment":[{"href":"https:\/\/model3dbio.csic.es\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=137"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}