MENSAdb (Data-Driven Molecular Design) is a thorough structural analysis of membrane protein dimers.
Center for Neuroscience and Cell Biology (CNC)
CNC is a scientific institute that fosters biomedical research and multidisciplinary graduate training at the University of Coimbra
A multi-step approach including models’ construction (multiple sequence alignment, homology modeling), complex assembling (protein complex refinement with HADDOCK and complex equilibration), and protein-protein interface (PPI) characterization (including both structural and dynamics analysis) were performed. Our database can be easily applied to several GPCR sub-families, to determine the key structural and dynamical determinants involved in GPCR coupling selectivity.
SpotOn is a robust algorithm developed to identify and classify the interfacial residues as Hot-Spots (HS) and Null-Spots (NS) with a final accuracy of 0.95 and a sensitivity of 0.95 on an independent test set.
SynPred (Data-Driven Molecular Design) is a tool for prediction of drug combination effects in cancer using full-agreement synergy metrics and deep learning. SynPred, which leverages state-of-the-art AI advances, specifically designed ensembles of ML and DL algorithms to link in an interdisciplinary approach omics and biophysical traits to predict anticancer drug synergy.
SPOTONE is a new Machine-Learning (ML) predictor able to accurately classify protein Hot-Spots (HS) via sequence-only features. This algorithm shows an accuracy, AUROC, precision, recall and F1-score of 0.82, 0.83, 0.91, 0.82 and 0.85, respectively, in an independent testing set.