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Small Molecule Inhibitor Starting Points

详细技术说明

None

*Abstract

BackgroundProteinΓÇôprotein interactions (PPIs) are a promising, but challenging target for pharmaceutical intervention. One approach for addressing these difficult targets is the rational design of small-molecule inhibitors that mimic the chemical and physical properties of small clusters of key residues at the protein-protein interface. The identification of appropriate clusters of interface residues provides starting points for inhibitor design and supports an overall assessment of the susceptibility of PPIs to small-molecule inhibition.TechnologyResearchers have performed a systematic analysis of protein-ligand and protein-protein complexes in the Protein Data Bank (PDB) to identify clusters of interface residues that delineate the binding site of a high affinity small molecule at the PPI interface. These clusters are referred to as Small Molecule Inhibitor Starting Points (SMISPs), since the binding mode of these residues in the acceptor protein provides a starting point for small molecule inhibitor design. The SMISPs form the basis of a training set where each cluster is characterized using a variety of properties commonly used to predict hot spots in PPIs, e.g., free energies, solvent accessible surface area, and sequence conservation. The training set is used to train two distinct classifiers, a support vector machine and an easily interpreted rule classifier. Both classifiers achieve better than 70% leave-one-complex-out cross validation accuracy and correctly predict SMISPs of known PPI inhibitors not in the training set. Application to non-redundant PPIs in the PDB predicts that 48% possess a sufficient quantity and quality of SMISPs to be susceptible to small molecule intervention. Application* Drug DiscoveryAdvantages* The SMISPs provide an immediate computational hypothesis to initiate efforts which have the potential to ultimately culminate in the design of novel therapeutics.Stage of Development* In silico

*Principal Investigation

Name: Carlos Camacho, Associate Professor

Department: Med-Computational and Systems Biology

Name: David Koes

Department: Med-Computational and Systems Biology

国家/地区

美国