Dataset Information

An in silico analysis of the binding modes and binding affinities of small molecule modulators of PDZ-peptide interactions.

ABSTRACT: Inhibitors of PDZ-peptide interactions have important implications in a variety of biological processes including treatment of cancer and Parkinson's disease. Even though experimental studies have reported characterization of peptidomimetic inhibitors of PDZ-peptide interactions, the binding modes for most of them have not been characterized by structural studies. In this study we have attempted to understand the structural basis of the small molecule-PDZ interactions by in silico analysis of the binding modes and binding affinities of a set of 38 small molecules with known K(i) or K(d) values for PDZ2 and PDZ3 domains of PSD-95 protein. These two PDZ domains show differential selectivity for these compounds despite having a high degree of sequence similarity and almost identical peptide binding pockets. Optimum binding modes for these ligands for PDZ2 and PDZ3 domains were identified by using a novel combination of semi-flexible docking and explicit solvent molecular dynamics (MD) simulations. Analysis of the binding modes revealed most of the peptidomimectic ligands which had high K(i) or K(d) moved away from the peptide binding pocket, while ligands with high binding affinities remained in the peptide binding pocket. The differential specificities of the PDZ2 and PDZ3 domains primarily arise from differences in the conformation of the loop connecting ?B and ?C strands, because this loop interacts with the N-terminal chemical moieties of the ligands. We have also computed the MM/PBSA binding free energy values for these 38 compounds with both the PDZ domains from multiple 5 ns MD trajectories on each complex i.e. a total of 228 MD trajectories of 5 ns length each. Interestingly, computational binding free energies show good agreement with experimental binding free energies with a correlation coefficient of approximately 0.6. Thus our study demonstrates that combined use of docking and MD simulations can help in identification of potent inhibitors of PDZ-peptide complexes.

SUBMITTER: Tiwari G

PROVIDER: S-EPMC3738590 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An in silico analysis of the binding modes and binding affinities of small molecule modulators of PDZ-peptide interactions.

Tiwari Garima G Mohanty Debasisa D

PloS one 20130808 8

Inhibitors of PDZ-peptide interactions have important implications in a variety of biological processes including treatment of cancer and Parkinson's disease. Even though experimental studies have reported characterization of peptidomimetic inhibitors of PDZ-peptide interactions, the binding modes for most of them have not been characterized by structural studies. In this study we have attempted to understand the structural basis of the small molecule-PDZ interactions by in silico analysis of th ...[more]

PMID: 23951139

Similar Datasets

Project description:BACKGROUND: PDZ domains are one of the most promiscuous protein recognition modules that bind with short linear peptides and play an important role in cellular signaling. Recently, few high-throughput techniques (e.g. protein microarray screen, phage display) have been applied to determine in-vitro binding specificity of PDZ domains. Currently, many computational methods are available to predict PDZ-peptide interactions but they often provide domain specific models and/or have a limited domain coverage. RESULTS: Here, we composed the largest set of PDZ domains derived from human, mouse, fly and worm proteomes and defined binding models for PDZ domain families to improve the domain coverage and prediction specificity. For that purpose, we first identified a novel set of 138 PDZ families, comprising of 548 PDZ domains from aforementioned organisms, based on efficient clustering according to their sequence identity. For 43 PDZ families, covering 226 PDZ domains with available interaction data, we built specialized models using a support vector machine approach. The advantage of family-wise models is that they can also be used to determine the binding specificity of a newly characterized PDZ domain with sufficient sequence identity to the known families. Since most current experimental approaches provide only positive data, we have to cope with the class imbalance problem. Thus, to enrich the negative class, we introduced a powerful semi-supervised technique to generate high confidence non-interaction data. We report competitive predictive performance with respect to state-of-the-art approaches. CONCLUSIONS: Our approach has several contributions. First, we show that domain coverage can be increased by applying accurate clustering technique. Second, we developed an approach based on a semi-supervised strategy to get high confidence negative data. Third, we allowed high order correlations between the amino acid positions in the binding peptides. Fourth, our method is general enough and will easily be applicable to other peptide recognition modules such as SH2 domains and finally, we performed a genome-wide prediction for 101 human and 102 mouse PDZ domains and uncovered novel interactions with biological relevance. We make all the predictive models and genome-wide predictions freely available to the scientific community.

Project description:The eight mammalian Src-family tyrosine kinases are dynamic, multi-domain structures, which adopt distinct "open" and "closed" conformations. In the closed conformation, the regulatory SH3 and SH2 domains pack against the back of the kinase domain, providing allosteric control of kinase activity. Small molecule ligands that engage the regulatory SH3-SH2 region have the potential to modulate Src-family kinase activity for therapeutic advantage. Here we describe an HTS-compatible fluorescence polarization assay to identify small molecules that interact with the unique-SH3-SH2-linker (U32L) region of Hck, a Src-family member expressed exclusively in cells of myeloid lineage. Hck has significant potential as a drug target in acute myeloid leukemia, an aggressive form of cancer with substantial unmet clinical need. The assay combines recombinant Hck U32L protein with a fluorescent probe peptide that binds to the SH3 domain in U32L, resulting in an increased FP signal. Library compounds that interact with the U32L protein and interfere with probe binding reduce the FP signal, scoring as hits. Automated 384-well high-throughput screening of 60,000 compounds yielded Z'-factor coefficients > 0.7 across nearly 200 assay plates, and identified a series of hit compounds with a shared pyrimidine diamine substructure. Surface plasmon resonance assays confirmed direct binding of hit compounds to the Hck U32L target protein as well as near-full-length Hck. Binding was not observed with the individual SH3 and SH2 domains, demonstrating that these compounds recognize a specific three-dimensional conformation of the regulatory regions. This conclusion is supported by computational docking studies, which predict ligand contacts with a pocket formed by the juxtaposition of the SH3 domain, the SH3-SH2 domain connector, and the SH2-kinase linker. Each of the four validated hits stimulated recombinant, near-full-length Hck activity in vitro, providing evidence for allosteric effects on the kinase domain. These results provide a path to discovery and development of chemical scaffolds to target the regulatory regions of Hck and other Src family kinases as a new approach to pharmacological kinase control.

Dataset Information

An in silico analysis of the binding modes and binding affinities of small molecule modulators of PDZ-peptide interactions.

Publications

An in silico analysis of the binding modes and binding affinities of small molecule modulators of PDZ-peptide interactions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets