Designing High-Affinity Peptides for Organic Molecules by Explicit Solvent Molecular Dynamics

Journal of Physical Chemistry B

Volumn 119, Issue 41, 2015, Pages 12963-12969

  Gladich, Ivan ^a   Rodriguez, Alex ^a   Hong Enriquez, Rolando P ^b   Guida, Filomena ^a   Berti, Federico ^c   Laio, Alessandro ^a  

^a SISSA   (Italy)

^b ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

^c UNIVERSITY OF TRIESTE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BINDING ENERGY; BINS; FREE ENERGY; MOLECULAR DYNAMICS; MOLECULES; SOLVENTS; STOCHASTIC SYSTEMS;

BINDING FREE ENERGY; COMPUTATIONAL PROCEDURES; CONCENTRATION MEASUREMENT; EXPLICIT SOLVENT MOLECULAR DYNAMICS; HIGH-AFFINITY PEPTIDES; MOLECULAR DYNAMICS SIMULATIONS; PRESSURE AND TEMPERATURE; SOLVENT ENVIRONMENTS;

PEPTIDES;

ORGANIC COMPOUND; PEPTIDE; SOLVENT;

ALGORITHM; AMINO ACID SEQUENCE; CHEMISTRY; MOLECULAR DYNAMICS; THERMODYNAMICS;

ALGORITHMS; AMINO ACID SEQUENCE; MOLECULAR DYNAMICS SIMULATION; ORGANIC CHEMICALS; PEPTIDES; SOLVENTS; THERMODYNAMICS;

EID: 84944339270     PISSN: 15206106     EISSN: 15205207     Source Type: Journal    
DOI: 10.1021/acs.jpcb.5b06227     Document Type: Article

References (29)

1. Crotti, S.; Enzo, M. V.; Bedin, C.; Pucciarelli, S.; Maretto, I.; Del Bianco, P.; Traldi, P.; Tasciotti, E.; Ferrari, M.; Rizzolio, F. et al. Clinical Predictive Circulating Peptides in Rectal Cancer Patients Treated with Neoadjuvant Chemoradiotherapy J. Cell. Physiol. 2015, 230, 1822-1828 10.1002/jcp.24894
2. Wang, W.; Li, M.; Wei, Z.; Wang, Z.; Bu, X.; Lai, W.; Yang, S.; Gong, H.; Zheng, H.; Wang, Y. et al. Bimodal Imprint Chips for Peptide Screening: Integration of High-Throughput Sequencing by MS and Affinity Analyses by Surface Plasmon Resonance Imaging Anal. Chem. 2014, 86, 3703-3707 10.1021/ac500465e
3. Gu, G.; Hu, Q.; Feng, X.; Gao, X.; Menglin, J.; Kang, T.; Jiang, D.; Song, Q.; Chen, H.; Chen, J. PEG-PLA Nanoparticles Modified with APTEDB Peptide for Enhanced Anti-Angiogenic and Anti-Glioma Therapy Biomaterials 2014, 35, 8215-8226 10.1016/j.biomaterials.2014.06.022
4. Vanhee, P.; van der Sloot, A.; Verschueren, E.; Serrano, L.; Rousseau, F.; Schymkowitz, J. Computational Design of Peptide Ligands Trends Biotechnol. 2011, 29, 231 10.1016/j.tibtech.2011.01.004
5. Nakamura, C.; Inuyama, Y.; Shirai, K.; Sugimoto, N.; Miyake, J. Detection of Porphyrin using a Short Peptide Immobilized on a Surface Plasmon Resonance Sensor Chip Biosens. Bioelectron. 2001, 16, 1095 10.1016/S0956-5663(01)00188-9
6. Sugimoto, N.; Miyoshi, D.; Zou, J. Development of Small Peptides Recognizing a Monosaccharide by Combinatorial Chemistry Chem. Commun. 2000, 2295 10.1039/b006714h
7. Wu, T.; Lo, Y. Synthetic Peptide Mimicking of Binding Sites on Olfactory Receptor Protein for Use in Electronic Nose J. Biotechnol. 2000, 80, 63 10.1016/S0168-1656(00)00228-5
8. Pavan, S.; Berti, F. Short Peptides as Biosensor Transducers Anal. Bioanal. Chem. 2012, 402, 3055-3070 10.1007/s00216-011-5589-8
9. Sinisi, V.; Forzato, C.; Cefarin, N.; Navarini, L.; Berti, F. Interaction of Chlorogenic Acids and Quinides from Coffee with Human Serum Albumin Food Chem. 2015, 168, 332-340 10.1016/j.foodchem.2014.07.080
10. Lim, J. H.; Park, J.; Ahn, J. H.; Jin, H. J.; Hong, S.; Park, T. H. A Peptide Receptor-Based Bioelectronic Nose for the Real-Time Determination of Seafood Quality Biosens. Bioelectron. 2013, 39, 244-249 10.1016/j.bios.2012.07.054
11. Sankaran, S.; Khot, L. R.; Panigrahi, S. Biology and Applications of Olfactory Sensing System: A Review Sens. Actuators, B 2012, 171, 1-17 10.1016/j.snb.2012.03.029
12. Tinberg, C. E.; Khare, S. D.; Dou, J.; Doyle, L.; Nelson, J. W.; Schena, A.; Jankowski, W.; Kalodimos, C. G.; Johnsson, K.; Stoddard, B. L. et al. Computational Design of Ligand-Binding Proteins with High Affinity and Selectivity Nature 2013, 501, 212-6 10.1038/nature12443
13. Hong Enriquez, R. P.; Pavan, S.; Benedetti, F.; Tossi, A.; Savoini, A.; Berti, F.; Laio, A. Designing Short Peptides with High Affinity for Organic Molecules: A Combined Docking, Molecular Dynamics, and Monte Carlo Approach J. Chem. Theory Comput. 2012, 8, 1121-1128 10.1021/ct200873y
14. Aumentado-Armstrong, T. T.; Istrate, B.; Murgita, R. a. Algorithmic Approaches to Protein-Protein Interaction Site Prediction Algorithms Mol. Biol. 2015, 10, 1-21 10.1186/s13015-015-0033-9
15. Sirin, S.; Pearlman, D. A.; Sherman, W. Physics-based enzyme design: Predicting binding affinity and catalytic activity Proteins: Struct., Funct., Genet. 2014, 82, 3397-3409 10.1002/prot.24694
16. Damborsky, J.; Brezovsky, J. Computational Tools for Designing and Engineering Enzymes Curr. Opin. Chem. Biol. 2014, 19, 8-16 10.1016/j.cbpa.2013.12.003
17. Rivory, L. P.; Chatelut, E.; Canal, P.; Mathieuboue, A.; Robert, J. Kinetics of the In-Vivo Interconversion of the Carboxylate and Lactone Forms of Irinotecan (CPT-11) and of Its Metabolite SN-38 in Patients Cancer Res. 1994, 54, 6330-6333
18. Yang, X. X.; Hu, Z. P.; Chan, S. Y.; Goh, B. C.; Duan, W.; Chan, E.; Zhou, S. F. Simultaneous Determination of the Lactone and Carboxylate Forms of Irinotecan (CPT-11) and Its Active Metabolite SN-38 by High-Performance Liquid Chromatography: Application to Plasma Pharmacokinetic Studies in the Rat J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2005, 821, 221-228 10.1016/j.jchromb.2005.05.010
19. Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of Simple Potential Functions for Simulating Liquid Water J. Chem. Phys. 1983, 79, 926-935 10.1063/1.445869
20. Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of Multiple Amber Force Fields and Development of Improved Protein Backbone Parameters Proteins: Struct., Funct., Genet. 2006, 65, 712-725 10.1002/prot.21123
21. Daura, X.; Gademann, K.; Jaun, B.; Seebach, D.; van Gunsteren, W. F.; Mark, A. E. Peptide Folding: When Simulation Meets Experiment Angew. Chem., Int. Ed. 1999, 38, 236-240 10.1002/(SICI)1521-3773(19990115)38:1/2<236::AID-ANIE236>3.3.CO;2-D
22. Trott, O.; Olson, A. J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading J. Comput. Chem. 2010, 31, 455-461 10.1002/jcc.21334
23. Frenkel, D.; Smit, B. Understanding Molecular Dynamics; Academic Press: London, 2002.
24. Wang, J. M.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and Testing of a General Amber Force Field J. Comput. Chem. 2004, 25, 1157-1174 10.1002/jcc.20035
25. Jakalian, A.; Jack, D. B.; Bayly, C. I. Fast, Efficient Generation of High-Quality Atomic Charges. AM1-BCC model: II. Parameterization and Validation J. Comput. Chem. 2002, 23, 1623-1641 10.1002/jcc.10128
26. Wang, J.; Wang, W.; Kollman, P. A.; Case, D. A. Automatic Atom Type and Bond Type Perception in Molecular Mechanical Calculations J. Mol. Graphics Modell. 2006, 25, 247-260 10.1016/j.jmgm.2005.12.005
27. Hess, B.; Kutzner, C.; van der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation J. Chem. Theory Comput. 2008, 4, 435-447 10.1021/ct700301q
28. Bussi, G.; Donadio, D.; Parrinello, M. Canonical Sampling through Velocity Rescaling J. Chem. Phys. 2007, 126, 014101 10.1063/1.2408420
29. Berendsen, H. J. C.; Postma, J. P. M.; Vangunsteren, W. F.; Dinola, A.; Haak, J. R. Molecular Dynamics with Coupling to an External Bath J. Chem. Phys. 1984, 81, 3684-3690 10.1063/1.448118