Computational Screening for Active Compounds Targeting Protein Sequences: Methodology and Experimental Validation

The three-dimensional (3D) structures of most protein targets have not been determined so far, with many of them not even having a known ligand, a truly general method to predict ligand-protein interactions in the absence of three-dimensional information would be of great potential value in drug discovery. Using the support vector machine (SVM) approach, we constructed a model for predicting ligand-protein interaction based only on the primary sequence of proteins and the structural features of small molecules. The model, trained by using 15,000 ligand-protein interactions between 626 proteins and over 10,000 active compounds, was successfully used in discovering nine novel active compounds for four pharmacologically important targets (i.e., GPR40, SIRT1, p38, and GSK-3β). To our knowledge, this is the first example of a successful sequence-based virtual screening campaign, demonstrating that our approach has the potential to discover, with a single model, active ligands for any protein.