Discovery of new MurA inhibitors using induced-fit simulation and docking.

We report on the successful application of ProBiS-CHARMMing web server in the discovery of new inhibitors of MurA, an enzyme that catalyzes the first committed cytoplasmic step of bacterial peptidoglycan synthesis. The available crystal structures of Escherichia coli MurA in the Protein Data Bank have binding sites whose small volume does not permit the docking of drug-like molecules. To prepare the binding site for docking, the ProBiS-CHARMMing web server was used to simulate the induced-fit effect upon ligand binding to MurA, resulting in a larger, more holo-like binding site. The docking of a filtered ZINC compound library to this enlarged binding site was then performed and resulted in three compounds with promising inhibitory potencies against MurA. Compound 1 displayed significant inhibitory potency with IC50 value of 1µM. All three compounds have novel chemical structures, which could be used for further optimization of small-molecule MurA inhibitors.

Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface.

Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a web-based tool for structure activity relationship and quantitative structure activity relationship modeling to add to the services provided by CHARMMing (www.charmming.org). This new module implements some of the most recent advances in modern machine learning algorithms-Random Forest, Support Vector Machine, Stochastic Gradient Descent, Gradient Tree Boosting, so forth. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity.

In Silico Laboratory: Tools for Similarity-Based Drug Discovery.

Computational methods that predict and evaluate binding of ligands to receptors implicated in different pathologies have become crucial in modern drug design and discovery. Here, we describe protocols for using the recently developed package of computational tools for similarity-based drug discovery. The ProBiS stand-alone program and web server allow superimposition of protein structures against large protein databases and predict ligands based on detected binding site similarities. GenProBiS allows mapping of human somatic missense mutations related to cancer and non-synonymous single nucleotide polymorphisms and subsequent visual exploration of specific interactions in connection to these mutations. We describe protocols for using LiSiCA, a fast ligand-based virtual screening software that enables easy screening of large databases containing billions of small molecules. Finally, we show the use of BoBER, a web interface that enables user-friendly access to a large database of bioisosteric and scaffold hopping replacements.