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.
Key wordsDrug discovery Binding sites Enzyme binding Virtual screening Ligand homology modeling Protein sequence variants Bioisosterism Maximum clique algorithm Scaffold hopping ProBiS ProBiS-CHARMMing GenProBiS LiSiCA BoBER
Financial support through Slovenian Research Agency grant L7-8269 is gratefully acknowledged.
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