The molecular mechanism by which Ca 2+ binding and phosphorylation regulate muscle contraction through Troponin is not yet fully understood. Revealing the differences between the relaxed and active structure of cTn, as well as the conformational changes that follow phosphorylation has remained a challenge for structural biologists over the years. Here we review the current understanding of how Ca 2+ , phosphorylation and disease-causing mutations affect the structure and dynamics of troponin to regulate the thin filament based on electron microscopy, X-ray diffraction, NMR and molecular dynamics methodologies.

The only available crystal structure of the human cardiac troponin molecule (cTn) in the Ca(2+) activated state does not include crucial segments, including the N-terminus of the cTn inhibitory subunit (cTnI). We have applied all-atom molecular dynamics (MD) simulations to study the structure and dynamics of cTn, both in the unphosphorylated and bis-phosphorylated states at Ser23/Ser24 of cTnI. We performed multiple microsecond MD simulations of wild type (WT) cTn (6, 5 μs) and bisphosphorylated (SP23/SP24) cTn (9 μs) on a 419 amino acid cTn model containing human sequence cTnC (1-161), cTnI (1-171) and cTnT (212-298), including residues not present in the crystal structure. We have compared our results to previous computational studies, and proven that longer simulations and a water box of at least 25 Å are needed to sample the interesting conformational shifts both in the native and bis-phosphorylated states. As a consequence of the introduction into the model of the C-terminus of cTnT that was missing in previous studies, cTnC-cTnI interactions that are responsible for the cTn dynamics are altered. We have also shown that phosphorylation does not increase cTn fluctuations, and its effects on the protein-protein interaction profiles cannot be assessed in a significant way. Finally, we propose that phosphorylation could provoke a loss of Ca(2+) by stabilizing out-of-coordination distances of the cTnC's EF hand II residues, and in particular Ser 69.

Summary ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. Availability and implementation http://chembioserver.vi-seem.eu.

<p>ChemBioServer 2.0 is the advanced sequel of a web-server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of chemical compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. The updated web server is available in the URL: <a href="http://chembioserver.vi-seem.eu/">http://chembioserver.vi-seem.eu/</a> </p>

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