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How does a drug molecule find its target binding site?

ABSTRACT: Although the thermodynamic principles that control the binding of drug molecules to their protein targets are well understood, detailed experimental characterization of the process by which such binding occurs has proven challenging. We conducted relatively long, unguided molecular dynamics simulations in which a ligand (the cancer drug dasatinib or the kinase inhibitor PP1) was initially placed at a random location within a box that also contained a protein (Src kinase) to which that ligand was known to bind. In several of these simulations, the ligand correctly identified its target binding site, forming a complex virtually identical to the crystallographically determined bound structure. The simulated trajectories provide a continuous, atomic-level view of the entire binding process, revealing persistent and noteworthy intermediate conformations and shedding light on the role of water molecules. The technique we employed, which does not assume any prior knowledge of the binding site's location, may prove particularly useful in the development of allosteric inhibitors that target previously undiscovered binding sites.

SUBMITTER: Shan Y

PROVIDER: S-EPMC3221467 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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How does a drug molecule find its target binding site?

Shan Yibing Y Kim Eric T ET Eastwood Michael P MP Dror Ron O RO Seeliger Markus A MA Shaw David E DE

Journal of the American Chemical Society 20110513 24

Although the thermodynamic principles that control the binding of drug molecules to their protein targets are well understood, detailed experimental characterization of the process by which such binding occurs has proven challenging. We conducted relatively long, unguided molecular dynamics simulations in which a ligand (the cancer drug dasatinib or the kinase inhibitor PP1) was initially placed at a random location within a box that also contained a protein (Src kinase) to which that ligand was ...[more]

PMID: 21545110

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Project description:BACKGROUND: Accurate small molecule binding site information for a protein can facilitate studies in drug docking, drug discovery and function prediction, but small molecule binding site protein sequence annotation is sparse. The Small Molecule Interaction Database (SMID), a database of protein domain-small molecule interactions, was created using structural data from the Protein Data Bank (PDB). More importantly it provides a means to predict small molecule binding sites on proteins with a known or unknown structure and unlike prior approaches, removes large numbers of false positive hits arising from transitive alignment errors, non-biologically significant small molecules and crystallographic conditions that overpredict ion binding sites. DESCRIPTION: Using a set of co-crystallized protein-small molecule structures as a starting point, SMID interactions were generated by identifying protein domains that bind to small molecules, using NCBI's Reverse Position Specific BLAST (RPS-BLAST) algorithm. SMID records are available for viewing at http://smid.blueprint.org. The SMID-BLAST tool provides accurate transitive annotation of small-molecule binding sites for proteins not found in the PDB. Given a protein sequence, SMID-BLAST identifies domains using RPS-BLAST and then lists potential small molecule ligands based on SMID records, as well as their aligned binding sites. A heuristic ligand score is calculated based on E-value, ligand residue identity and domain entropy to assign a level of confidence to hits found. SMID-BLAST predictions were validated against a set of 793 experimental small molecule interactions from the PDB, of which 472 (60%) of predicted interactions identically matched the experimental small molecule and of these, 344 had greater than 80% of the binding site residues correctly identified. Further, we estimate that 45% of predictions which were not observed in the PDB validation set may be true positives. CONCLUSION: By focusing on protein domain-small molecule interactions, SMID is able to cluster similar interactions and detect subtle binding patterns that would not otherwise be obvious. Using SMID-BLAST, small molecule targets can be predicted for any protein sequence, with the only limitation being that the small molecule must exist in the PDB. Validation results and specific examples within illustrate that SMID-BLAST has a high degree of accuracy in terms of predicting both the small molecule ligand and binding site residue positions for a query protein.

Project description:BackgroundSeveral human cancers, especially cervical cancer are caused by the infection of high risk strains of human papillomaviruses (HPV), notably HPV16. It is implicated that the oncoprotein E6 expressed from HPV, is inhibiting the apoptotic pathway by binding to adaptor molecule FADD (Fas-associated death domain). Inhibiting E6 interactions with FADD could provide a promising treatment for cervical cancer. There are few small molecules reported to inhibit such interactions. However, the FADD binding site information on the HPV E6 is not currently available. This binding site information may provide an opportunity to design new small molecule inhibitors to treat E6 mediated cancers. In this study we report the possible binding pocket on HPV16 E6 oncoprotein by using activity data of reported inhibitors through a stepwise molecular modeling approach.ResultsBlind docking and removing duplicates followed by visual inspection to determine ligand-receptor interactions provided 68 possible binding sites on the E6 protein. Individual docking of all known inhibitors lead to the identification of 28 pockets having some kind of correlation with their activity data. It was also observed that several of these pockets overlapped with each other, having some amino acids in common. Amino acids Leu50 and Cys51 were identified as key E6 residues for high affinity ligand binding which are seen in most of these pockets. In most cases, ligands demonstrated a hydrogen bond interaction with Cys51. Ala61, Arg131 and Gln107 were also frequently observed showing interactions among these pockets. A few amino acids unique to each ligand were also identified representing additional interactions at the receptor site.ConclusionsAfter determining receptor-ligand interactions between E6 oncoprotein and the six known inhibitors, the amino acids Cys51, Leu50, Arg102, Arg131, Leu67, Val62, and Gln107 were identified to have importance in E6 inhibition. It was generally observed that Leu50 and Cys51 are necessary for high binding affinity with Cys51 being essential for hydrogen bonding. This study identified a potential binding pocket for the E6 inhibitors. Identification of the ligand binding pocket helps to design novel inhibitors of HPV16 E6 oncoprotein as a promising treatment for cervical cancer.

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How does a drug molecule find its target binding site?

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How does a drug molecule find its target binding site?

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