Computational model predicts protein binding sites of a luminescent ligand equipped with guanidiniocarbonyl-pyrrole groups

The 14-3-3 protein family, one of the first discovered phosphoserine/phosphothreonine binding proteins, has attracted interest not only because of its important role in the cell regulatory processes but also due to its enormous number of interactions with other proteins. Here, we use a computational approach to predict the binding sites of the designed hybrid compound featuring aggregation-induced emission luminophores as a potential supramolecular ligand for 14-3-3ζ in the presence and absence of C-Raf peptides. Our results suggest that the area above and below the central pore of the dimeric 14-3-3ζ protein is the most probable binding site for the ligand. Moreover, we predict that the position of the ligand is sensitive to the presence of phosphorylated C-Raf peptides. With a series of experiments, we confirmed the computational prediction of two C2 related, dominating binding sites on 14-3-3ζ that may bind to two of the supramolecular ligand molecules.

[1]  J. Voskuhl,et al.  Mesogens with aggregation‐induced emission properties: Materials with a bright future , 2021, Aggregate.

[2]  Diogo Santos-Martins,et al.  AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings , 2021, J. Chem. Inf. Model..

[3]  J. Voskuhl,et al.  Guanidiniocarbonyl-Pyrroles (GCP) - 20 Years of the Schmuck Binding Motif. , 2020, ChemPlusChem.

[4]  C. Schmuck,et al.  Functional Disruption of the Cancer‐Relevant Interaction between Survivin and Histone H3 with a Guanidiniocarbonyl Pyrrole Ligand , 2020, Angewandte Chemie.

[5]  Yasser B. Ruiz-Blanco,et al.  Multivalent Ligands with Tailor‐Made Anion Binding Motif as Stabilizers of Protein–Protein Interactions , 2019, Chembiochem : a European journal of chemical biology.

[6]  Muhammad Waheed Iqbal,et al.  Recent advances and perspectives of aggregation-induced emission as an emerging platform for detection and bioimaging , 2019, TrAC Trends in Analytical Chemistry.

[7]  C. Ottmann,et al.  Arginine mimetic appended peptide-based probes for fluorescence turn-on detection of 14-3-3 proteins. , 2019, Organic & biomolecular chemistry.

[8]  D. Hoffmann,et al.  A new class of supramolecular ligands stabilizes 14-3-3 protein-protein interactions by up to two orders of magnitude. , 2019, Chemical communications.

[9]  D. Hoffmann,et al.  Rational Design, Binding Studies, and Crystal‐Structure Evaluation of the First Ligand Targeting the Dimerization Interface of the 14‐3‐3ζ Adapter Protein , 2018, Chembiochem : a European journal of chemical biology.

[10]  Daniel Hoffmann,et al.  Locating Large, Flexible Ligands on Proteins , 2017, J. Chem. Inf. Model..

[11]  A. Fournier,et al.  14‐3‐3 adaptor protein‐protein interactions as therapeutic targets for CNS diseases , 2017, Pharmacological research.

[12]  G. Jansen,et al.  Aromatic Thioethers as Novel Luminophores with Aggregation-Induced Fluorescence and Phosphorescence. , 2017, Chemistry.

[13]  Daniel Hoffmann,et al.  Quantitative Assessment of Molecular Dynamics Sampling for Flexible Systems. , 2017, Journal of chemical theory and computation.

[14]  Alan Edelman,et al.  Julia: A Fresh Approach to Numerical Computing , 2014, SIAM Rev..

[15]  Ryan T. K. Kwok,et al.  Aggregation-Induced Emission: Together We Shine, United We Soar! , 2015, Chemical reviews.

[16]  C. Ottmann,et al.  Modulators of protein-protein interactions. , 2014, Chemical reviews.

[17]  Tod D Romo,et al.  Unknown unknowns: the challenge of systematic and statistical error in molecular dynamics simulations. , 2014, Biophysical journal.

[18]  C. Yip,et al.  Indolicidin binding induces thinning of a lipid bilayer. , 2014, Biophysical journal.

[19]  M. Skwarczynska,et al.  Stabilization of physical RAF/14-3-3 interaction by cotylenin A as treatment strategy for RAS mutant cancers. , 2013, ACS chemical biology.

[20]  Ben Zhong Tang,et al.  Specific detection of integrin αvβ3 by light-up bioprobe with aggregation-induced emission characteristics. , 2012, Journal of the American Chemical Society.

[21]  C. Schalley Analytical Methods in Supramolecular Chemistry: SCHALLEY:SUPR.CHEM.2E2VOL O-BK , 2012 .

[22]  Chris Morley,et al.  Open Babel: An open chemical toolbox , 2011, J. Cheminformatics.

[23]  D. Morrison,et al.  14-3-3 Proteins: diverse functions in cell proliferation and cancer progression. , 2011, Seminars in cell & developmental biology.

[24]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[25]  Bin Liu,et al.  An effective bead-spring model for polymer simulation , 2008, J. Comput. Phys..

[26]  C. Schmuck,et al.  Oxoanion binding by flexible guanidiniocarbonyl pyrrole-ammonium bis-cations in water. , 2007, The Journal of organic chemistry.

[27]  Gerhard Klebe,et al.  PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations , 2007, Nucleic Acids Res..

[28]  Christoph A. Schalley,et al.  Analytical methods in supramolecular chemistry , 2006 .

[29]  K. Schug,et al.  Noncovalent binding between guanidinium and anionic groups: focus on biological- and synthetic-based arginine/guanidinium interactions with phosph[on]ate and sulf[on]ate residues. , 2005, Chemical reviews.

[30]  P. Mhawech,et al.  14-3-3 proteins—an update , 2005, Cell Research.

[31]  Patrick S. Doyle,et al.  On the coarse-graining of polymers into bead-spring chains , 2004 .

[32]  J. Wittmer,et al.  Monte Carlo Simulation of Polymers: Coarse-Grained Models , 2004, cond-mat/0407717.

[33]  Nathan A. Baker,et al.  PDB2PQR: an automated pipeline for the setup of Poisson-Boltzmann electrostatics calculations , 2004, Nucleic Acids Res..

[34]  Sheldon Howard Jacobson,et al.  The Theory and Practice of Simulated Annealing , 2003, Handbook of Metaheuristics.

[35]  P. Tseng,et al.  Statistical Data Analysis Based on the L1-Norm and Related Methods , 2002 .

[36]  Nathan A. Baker,et al.  Electrostatics of nanosystems: Application to microtubules and the ribosome , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[37]  S. Masters,et al.  14-3-3 proteins: structure, function, and regulation. , 2000, Annual review of pharmacology and toxicology.

[38]  Kurt Binder,et al.  Static and Dynamic Properties of Adsorbed Chains at Surfaces: Monte Carlo Simulation of a Bead-Spring Model , 1996 .

[39]  T. Blundell,et al.  Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.

[40]  V. Cerný Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[41]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[42]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.