Protein Structure Prediction pp 235-250 | Cite as
Protocols for Efficient Simulations of Long-Time Protein Dynamics Using Coarse-Grained CABS Model
- 11 Citations
- 3.2k Downloads
Abstract
Coarse-grained (CG) modeling is a well-acknowledged simulation approach for getting insight into long-time scale protein folding events at reasonable computational cost. Depending on the design of a CG model, the simulation protocols vary from highly case-specific—requiring user-defined assumptions about the folding scenario—to more sophisticated blind prediction methods for which only a protein sequence is required. Here we describe the framework protocol for the simulations of long-term dynamics of globular proteins, with the use of the CABS CG protein model and sequence data. The simulations can start from a random or a selected (e.g., native) structure. The described protocol has been validated using experimental data for protein folding model systems—the prediction results agreed well with the experimental results.
Keywords
Folding pathway Folding mechanism Protein dynamics Protein folding Coarse-grained modelingNotes
Acknowledgments
The authors acknowledge support from a TEAM project (TEAM/2011-7/6) co-financed by the EU European Regional Development Fund operated within the Innovative Economy Operational Program and from Polish National Science Center (Grant No. NN301071140) and from Polish Ministry of Science and Higher Education (Grant No. IP2011 024371).
References
- 1.Kmiecik S, Jamroz M, Kolinski A (2011) Multiscale approach to protein folding dynamics. In: Kolinski A (ed) Multiscale approaches to protein modeling. Springer, New York, pp 281–294Google Scholar
- 2.Lindorff-Larsen K, Piana S, Dror RO, Shaw DE (2011) How fast-folding proteins fold. Science 334:517–520PubMedCrossRefGoogle Scholar
- 3.Schaeffer RD, Fersht A, Daggett V (2008) Combining experiment and simulation in protein folding: closing the gap for small model systems. Curr Opin Struct Biol 18:4–9PubMedCentralPubMedCrossRefGoogle Scholar
- 4.Rizzuti B, Daggett V (2013) Using simulations to provide the framework for experimental protein folding studies. Arch Biochem Biophys 531(1–2):128–135PubMedCrossRefGoogle Scholar
- 5.Kolinski A, Bujnicki JM (2005) Generalized protein structure prediction based on combination of fold-recognition with de novo folding and evaluation of models. Proteins 61(Suppl 7):84–90PubMedCrossRefGoogle Scholar
- 6.Debe DA, Danzer JF, Goddard WA, Poleksic A (2006) STRUCTFAST: protein sequence remote homology detection and alignment using novel dynamic programming and profile-profile scoring. Proteins 64:960–967PubMedCrossRefGoogle Scholar
- 7.Kmiecik S, Kolinski A (2007) Characterization of protein-folding pathways by reduced-space modeling. Proc Natl Acad Sci USA 104:12330–12335PubMedCentralPubMedCrossRefGoogle Scholar
- 8.Kmiecik S, Kolinski A (2008) Folding pathway of the B1 domain of protein G explored by multiscale modeling. Biophys J 94:726–736PubMedCentralPubMedCrossRefGoogle Scholar
- 9.Kmiecik S, Gront D, Kouza M, Kolinski A (2012) From coarse-grained to atomic-level characterization of protein dynamics: transition state for the folding of B domain of protein A. J Phys Chem B 116:7026–7032PubMedCrossRefGoogle Scholar
- 10.Kmiecik S, Kolinski A (2011) Simulation of chaperonin effect on protein folding: a shift from nucleation-condensation to framework mechanism. J Am Chem Soc 133:10283–10289PubMedCentralPubMedCrossRefGoogle Scholar
- 11.Kmiecik S, Kurcinski M, Rutkowska A, Gront D, Kolinski A (2006) Denatured proteins and early folding intermediates simulated in a reduced conformational space. Acta Biochim Pol 53:131–144PubMedGoogle Scholar
- 12.Jamroz M, Orozco M, Kolinski A, Kmiecik S (2013) Consistent view of protein fluctuations from all-atom molecular dynamics and coarse-grained dynamics with knowledge-based force-field. J Chem Theory Comput 9:119–125CrossRefGoogle Scholar
- 13.Kmiecik S, Gront D, Kolinski A (2007) Towards the high-resolution protein structure prediction. Fast refinement of reduced models with all-atom force field. BMC Struct Biol 7:43PubMedCentralPubMedCrossRefGoogle Scholar
- 14.Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637PubMedCrossRefGoogle Scholar
- 15.Kolinski A (2004) Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 51:349–371PubMedGoogle Scholar
- 16.Jamroz M, Kolinski A (2013) ClusCo: clustering and comparison of protein models. BMC Bioinformatics 14:62PubMedCentralPubMedCrossRefGoogle Scholar
- 17.Maisuradze GG, Liwo A, Scheraga HA (2009) Principal component analysis for protein folding dynamics. J Mol Biol 385:312–329PubMedCentralPubMedCrossRefGoogle Scholar
- 18.Xu D, Zhang Y (2011) Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J 101:2525–2534PubMedCentralPubMedCrossRefGoogle Scholar
- 19.Gront D, Kmiecik S, Kolinski A (2007) Backbone building from quadrilaterals: a fast and accurate algorithm for protein backbone reconstruction from alpha carbon coordinates. J Comput Chem 28:1593–1597PubMedCrossRefGoogle Scholar
- 20.Krivov GG, Shapovalov MV, Dunbrack RL Jr (2009) Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77:778–795PubMedCentralPubMedCrossRefGoogle Scholar
- 21.Pollastri G, McLysaght A (2005) Porter: a new, accurate server for protein secondary structure prediction. Bioinformatics 21:1719–1720PubMedCrossRefGoogle Scholar