PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures.

Knowledge of protein-ligand binding sites (LBSs) enables research ranging from protein function annotation to structure-based drug design. To this end, we have previously developed a stand-alone tool, P2Rank, and the web server PrankWeb (https://prankweb.cz/) for fast and accurate LBS prediction.

PrankWeb: a web server for ligand binding site prediction and visualization.

PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability centered on points placed on a solvent-accessible protein surface. Points with a high ligandability score are then clustered to form the resulting ligand binding sites.

Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

Background: Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect.

Results: We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank.

Molpher: a software framework for systematic chemical space exploration.

Background: Chemical space is virtual space occupied by all chemically meaningful organic compounds. It is an important concept in contemporary chemoinformatics research, and its systematic exploration is vital to the discovery of either novel drugs or new tools for chemical biology.

Results: In this paper, we describe Molpher, an open-source framework for the systematic exploration of chemical space.