e FindSite: Enhanced Fingerprint‐Based Virtual Screening Against Predicted Ligand Binding Sites in Protein Models

A standard practice for lead identification in drug discovery is ligand virtual screening, which utilizes computing technologies to detect small compounds that likely bind to target proteins prior to experimental screens. A high accuracy is often achieved when the target protein has a resolved crystal structure; however, using protein models still renders significant challenges. Towards this goal, we recently developed e FindSite that predicts ligand binding sites using a collection of effective algorithms, including meta‐threading, machine learning and reliable confidence estimation systems. Here, we incorporate fingerprint‐based virtual screening capabilities in e FindSite in addition to its flagship role as a ligand binding pocket predictor. Virtual screening benchmarks using the enhanced Directory of Useful Decoys demonstrate that e FindSite significantly outperforms AutoDock Vina as assessed by several evaluation metrics. Importantly, this holds true regardless of the quality of target protein structures. As a first genome‐wide application of e FindSite, we conduct large‐scale virtual screening of the entire proteome of Escherichia coli with encouraging results. In the new approach to fingerprint‐based virtual screening using remote protein homology, e FindSite demonstrates its compelling proficiency offering a high ranking accuracy and low susceptibility to target structure deformations. The enhanced version of e FindSite is freely available to the academic community at http://www.brylinski.org/efindsite.