Some portals and servers we maintain for the community:

• CCharPPI
  Computational Characterisation of Protein–Protein Interactions.
• OPRA Server
  OPRA (Optimal Protein–RNA Area): identifies potential RNA-binding sites on proteins and facilitates modeling of biologically or therapeutically relevant protein–RNA interactions.
• Protein–RNA Benchmark v1.0
  Dataset of 106 cases for benchmarking protein–RNA docking (unbound–unbound, unbound–bound, model–bound, etc.).
• pyDockWEB
  Fast protocol using electrostatics and desolvation energy to score FFT-generated docking conformations.
• pyDockEneRes
  Calculates per-residue energy contributions in protein–protein interactions.
• pyDockRescoring
  Web service for rescoring jobs from the pyDockWEB server.
• pyDockSAXS
  Rigid-body protein–protein docking server integrating SAXS experimental data to refine predictions.
• pyDockWEB
  Web server for structural prediction of protein–protein interactions via computational docking.
• pyDockDNA
  Web server for structural prediction of protein–DNA interactions using computational docking.
• SKEMPI 2.0
  Database of 7000+ protein–protein mutation data, including thermodynamic and kinetic binding changes with available interaction structures.
• Machine Learning Classification of Rioja Wines by Origin and Ageing
  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. Request the password to view and collaborate.
• Bayesian mechanistic model of COVID-19 transmission dynamics
  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.
• Assessment of the Risk of Contracting a Tick-borne Disease in Urban Areas
  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.
  Request the password to view and collaborate.
• Assessment of the Risk of Root Rot in Vineyards Caused by Armillaria mellea
  This study aims to investigate the presence of the pathogenic fungus Armillaria mellea—commonly known as “Armillary”—in 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. Request the password to view and collaborate.