Ligand cluster-based protein network and ePlatton, a multi-target ligand finder

Background: Small molecules are information carriers that make cells aware of external changes and couple internal metabolic and signalling pathway systems with each other. In some specific physiological status, natural or artificial molecules are used to interact with selective biological targets to activate or inhibit their functions to achieve expected biological and physiological output. Millions of years of evolution have optimized biological processes and pathways and now the endocrine and immune system cannot work properly without some key small molecules. In the past thousands of years, the human race has managed to find many medicines against diseases by trail-and-error experience. In the recent decades, with the deepening understanding of life and the progress of molecular biology, researchers spare no effort to design molecules targeting one or two key enzymes and receptors related to corresponding diseases. But recent studies in pharmacogenomics have shown that polypharmacology may be necessary for the effects of drugs, which challenge the paradigm, 'one drug, one target, one disease'. Nowadays, cheminformatics and structural biology can help us reasonably take advantage of the polypharmacology to design next-generation promiscuous drugs and drug combination therapies. Results: 234,591 protein-ligand interactions were extracted from ChEMBL. By the 2D structure similarity, 13,769 ligand emerged from 156,151 distinct ligands which were recognized by 1477 proteins. Ligand cluster-and sequencebased protein networks (LCBN, SBN) were constructed, compared and analysed. For assisting compound designing, exploring polypharmacology and finding possible drug combination, we integrated the pathway, disease, drug adverse reaction and the relationship of targets and ligand clusters into the web platform, ePlatton, which is available at http://www.megabionet.org/eplatton. Conclusions: Although there were some disagreements between the LCBN and SBN, communities in both networks were largely the same with normalized mutual information at 0.9. The study of target and ligand cluster promiscuity underlying the LCBN showed that light ligand clusters were more promiscuous than the heavy one and that highly connected nodes tended to be protein kinases and involved in phosphorylation. ePlatton considerably reduced the redundancy of the ligand set of targets and made it easy to deduce the possible relationship between compounds and targets, pathways and side effects. ePlatton behaved reliably in validation experiments and also fast in virtual screening and information retrieval.