Abstract
Members of protein families often share conserved structural subsites for interaction with chemically similar moieties despite low sequence identity. We propose a core site-moiety map of multiple proteins (called CoreSiMMap) to discover inhibitors and mechanisms by profiling subsite-moiety interactions of immense screening compounds. The consensus anchor, the subsite-moiety interactions with statistical significance, of a CoreSiMMap can be regarded as a “hot spot” that represents the conserved binding environments involved in biological functions. Here, we derive the CoreSiMMap with six consensus anchors and identify six inhibitors (IC50<8.0 µM) of shikimate kinases (SKs) of Mycobacterium tuberculosis and Helicobacter pylori from the NCI database (236,962 compounds). Studies of site-directed mutagenesis and analogues reveal that these conserved interacting residues and moieties contribute to pocket-moiety interaction spots and biological functions. These results reveal that our multi-target screening strategy and the CoreSiMMap can increase the accuracy of screening in the identification of novel inhibitors and subsite-moiety environments for elucidating the binding mechanisms of targets.
Highlights
The expanding number of protein structures and advances in bioinformatics tools have offered an exciting opportunity for structure-based virtual screening in drug discovery [1]
The major enhancements of the CoreSiMMap for multi-target inhibitors from SiMMap are as follows: 1) we developed the robust theoretical model for the SiMMap; 2) the CoreSiMMap is designed for multiple target proteins by modifying the SiMMap on a single target protein; 3) we added an anchor alignment method to identify core binding environments among multiple targets to reveal binding mechanisms; 4) we added a rankbased consensus score (RCS) of multiple targets to improve the enrichment of true positives
Overview of CoreSiMMap A CoreSiMMap is the consensus site-moiety maps, which consist of several consensus anchors derived from multiple targets, to represent essential features that are involved in the common biological functions of these targets (Figs. 1 and 2)
Summary
The expanding number of protein structures and advances in bioinformatics tools have offered an exciting opportunity for structure-based virtual screening in drug discovery [1]. Screening tools are often designed for one-target paradigm and the scoring methods are highly target-dependent and energy-based As a result, they cannot consistently and persuasively identify true leads, leading to a low success rate [4,5,6]. Orthologous proteins often perform similar functions, despite low sequence identity They frequently share conserved binding environments for interacting with partners. These proteins and their interacting partners (inhibitors or substrates) can be regarded as a pharmacophore family, which is a group of protein-compound complexes that share similar physical-chemical features and interaction patterns between the proteins and their partners. Developing an efficient method for identifying new adaptive inhibitors against multiple targets from public compound libraries is becoming an important task [13,14,15]
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