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Minimalist models for proteins: a comparative analysis

Published online by Cambridge University Press:  16 August 2010

Valentina Tozzini
Valentina Tozzini*
Affiliation:
NEST, Istituto Nanoscienze – CNR Scuola Normale Superiore, Piazza San Silvestro 12, I-56127 Pisa, Italy
*
*Email: tozzini@nest.sns.it
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Abstract

The last decade has witnessed a renewed interest in the coarse-grained (CG) models for biopolymers, also stimulated by the needs of modern molecular biology, dealing with nano- to micro-sized bio-molecular systems and larger than microsecond timescale. This combination of size and timescale is, in fact, hard to access by atomic-based simulations. Coarse graining the system is a route to be followed to overcome these limits, but the ways of practically implementing it are many and different, making the landscape of CG models very vast and complex.

In this paper, the CG models are reviewed and their features, applications and performances compared. This analysis, restricted to proteins, focuses on the minimalist models, namely those reducing at minimum the number of degrees of freedom without losing the possibility of explicitly describing the secondary structures. This class includes models using a single or a few interacting centers (beads) for each amino acid.

From this analysis several issues emerge. The difficulty in building these models resides in the need for combining transferability/predictive power with the capability of accurately reproducing the structures. It is shown that these aspects could be optimized by accurately choosing the force field (FF) terms and functional forms, and combining different parameterization procedures. In addition, in spite of the variety of the minimalist models, regularities can be found in the parameters values and in FF terms. These are outlined and schematically presented with the aid of a generic phase diagram of the polypeptide in the parameter space and, hopefully, could serve as guidelines for the development of minimalist models incorporating the maximum possible level of predictive power and structural accuracy.

Type
Review Article
Information
Quarterly Reviews of Biophysics , Volume 43 , Issue 3 , August 2010 , pp. 333 - 371
Copyright
Copyright © Cambridge University Press 2010

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References

8. References

Alemani, D., Collu, F., Cascella, M. & Dal Peraro, M. (2010). A nonradial coarse-grained potential for proteins produces naturally stable secondary structure elements. J. Chem. Theor. Comput. 6, 315–324.CrossRefGoogle ScholarPubMed
Arcangeli, C. & Tozzini, V. (in preparation). Multi-scale modeling molecular dynamics of the Artichoke Mottled. Crinkle Virus, in preparation.Google Scholar
Arora, N. & Jayaram, B. (1996). Strength of hydrogen bonds in alpha helices. J. Comput. Chem. 18, 1246–1252.Google Scholar
Atilgan, A. R., Durell, S. R., Jernigan, R. L., Demirel, M. C., Keskin, O. & Bahar, I. (2001). Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys. J. 80, 505–515.CrossRefGoogle ScholarPubMed
Ayton, G. S., Noid, W. G. & Voth, G. A. (2007). Multiscale modeling of biomolecular systems: in serial and in parallel. Curr. Opin. Struct. Biol. 17, 192–198.CrossRefGoogle ScholarPubMed
Bahar, I. & Jernigan, R. L. (1997). Inter-residue potentials in globular proteins and the dominance of highly specific hydrophilic interactions at close separation. J. Mol. Biol. 266, 195–214.CrossRefGoogle ScholarPubMed
Bahar, I., Kaplan, M. & Jernigan, R. L. (1997). Short-range conformational energies, secondary structure propensities, and recognition of correct sequence–structure matches. Proteins 29, 292–308.3.0.CO;2-D>CrossRefGoogle ScholarPubMed
Banachowicz, E., Gapinski, J. & Patkowski, A. (2000). Solution structure of biopolymers: a new method of constructing a bead model. Biophys. J. 78, 70–78.CrossRefGoogle ScholarPubMed
Bay, Y. & Englander, W. (1994). Hydrogen bond strength and beta-sheet propensities: the role of a side chain blocking effect. Proteins 18, 262–266.CrossRefGoogle Scholar
Betancourt, M. R. & Thirumalai, D. (1999). Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes. Protein Sci. 8, 361–369.CrossRefGoogle ScholarPubMed
Buck, P. M. & Bystroff, C. (2009). Simulating protein folding initiation sites using an alpha-carbon-only knowledge-based force field. Proteins 76, 331–342.CrossRefGoogle ScholarPubMed
Cascella, M. & Peraro, M. D. (2008). Challenges and perspectives in biomolecular simulations: from the atomistic picture to multiscale modeling. Curr. Opin. Struct. Biol. 18, 630–640.Google Scholar
Chang, C.-E., Trylska, J., Tozzini, V. & McCammon, J. A. (2007). Binding pathways of ligands to HIV-1 protease: coarse-grained and atomistic simulations. Chem. Biol. Drug Des. 69, 5–13.CrossRefGoogle ScholarPubMed
Chennubhotla, C., Rader, A. J., Lee-Wei Yang, L.-W. & Bahar, I. (2005). Elastic network models for understanding biomolecular machinery: from enzymes to supramolecular assemblies. Phys. Biol. 2, S173–S180.CrossRefGoogle ScholarPubMed
Chou, P. Y. & Fasman, G. D. (1978). Empirical prediction of protein conformation. Annu. Rev. Biochem. 47, 251–276.CrossRefGoogle ScholarPubMed
Chu, J.-W. & Voth, G. A. (2007). Coarse-grained free energy functions for studying protein conformational changes: a double-well network model. Biophys. J. 93, 3860–3871.CrossRefGoogle ScholarPubMed
Clementi, C., Nymeyer, H. & Onuchic, J. N. (2000). Topological and energetic factors: what determines the structural details of the transition state ensemble and ‘en-route’ intermediates for protein folding? An investigation for small globular proteins. J. Mol. Biol. 298, 937–953.CrossRefGoogle ScholarPubMed
Das, A. & Andersen, H. C. (2009). The multiscale coarse-graining method. III. A test of pairwise additivity of the coarse-grained potential and of new basis functions for the variational calculation. J. Chem. Phys. 131, 034102.CrossRefGoogle Scholar
Das, P., Matysiak, S. & Clementi, C. (2005). Balancing energy and entropy: a minimalist model for the characterization of protein folding landscapes. Proc. Natl. Acad. Sci. U.S.A. 102, 10141–10146.CrossRefGoogle ScholarPubMed
Demirel, M. C. & Keskin, O. (2005). Protein interactions and fluctuations in a proteomic network using an elastic network model. J. Biomol. Struct. Dyn. 22, 381–386.CrossRefGoogle Scholar
Di Fenza, A., Rocchia, W. & Tozzini, V. (2009). Complexes of HIV-1 Integrase with HAT proteins: multiscale models, dynamics and hypotheses on allosteric sites of inhibition. Proteins 76, 946–958.CrossRefGoogle ScholarPubMed
Ercolessi, F. & Adams, J. B. (1994). Interatomic potentials from first-principles calculations: the force-matching method. Europhys. Lett. 26, 583.CrossRefGoogle Scholar
Florence Tama, F. & Brooks, C. L. III (2005). Symmetry, form, and shape: guiding principles for robustness in macromolecular machines. Annu. Rev. Biophys. Biomol. Struct. 35, 115–133.CrossRefGoogle Scholar
Friedel, M. & Shea, J. M. (2004). Self-assembly of peptides into a β-barrel motif. J. Chem. Phys. 120, 5809.CrossRefGoogle ScholarPubMed
Friedel, M., Sheeler, D. J., & Shea, J.-E. (2003). Effects of confinement and crowding on the thermodynamics and kinetics of folding of a minimalist β-barrel protein. J. Chem. Phys. 118, 8106–8113.CrossRefGoogle Scholar
Go, N. & Scheraga, H. A. (1976). On the use of classical statistical mechanics in the treatment of polymer chain conformation. Macromolecules 9, 535–542.CrossRefGoogle Scholar
Ha-Duong, T. (2010). Protein backbone dynamics simulations using coarse-grained bonded potentials and simplified hydrogen bonds. J. Chem. Theory Comput. 6, 761–773.CrossRefGoogle ScholarPubMed
Hamacher, K. & McCammon, J. A. (2006). Computing the amino acid specificity of fluctuations in biomolecular systems. J. Chem. Theory Comput. 2, 873–878.CrossRefGoogle ScholarPubMed
Honeycutt, J. D. & Thirumalai, D. (1990). Metastability of the folded states of globular proteins. Proc. Natl. Acad. Sci. U.S.A. 87, 3526–3529.CrossRefGoogle ScholarPubMed
Izvekov, S. & Voth, G. A. (2006). Multiscale coarse-graining of mixed phospholipid/cholesterol bilayers. J. Chem. Theory Comput. 2, 637–648.CrossRefGoogle ScholarPubMed
Izvekov, S., Parrinello, M., Burnham, C. J. & Voth, G. A. (2004). Effective force fields for condensed phase systems from ab initio molecular dynamics simulation: a new method for force-matching. J. Chem. Phys. 120, 10896–10913.CrossRefGoogle ScholarPubMed
Izvekov, S. & Voth, G. A. (2005). Multiscale coarse graining of liquid-state systems. J. Chem. Phys. 123, 134105.CrossRefGoogle ScholarPubMed
Jang, H., Hall, C. K. & Zhou, Y. (2004). Assembly and kinetic folding pathways of a tetrameric β-sheet complex: molecular dynamics simulations on simplified off-lattice protein models. Biophys. J. 86, 31–49.CrossRefGoogle ScholarPubMed
Jeong, J. I., Jang, Y. & Kim, M. K. (2005). A connection rule for α-carbon coarse-grained elastic network models using chemical bond information. J. Mol. Graph Model 24, 296–306.CrossRefGoogle ScholarPubMed
Kaya, H. & Chan, H. S. (2003). Solvation effects and driving forces for proteinthermodynamic and kinetic cooperativity: how adequate is native-centric topological modeling? J. Mol. Biol. 326, 911–931.CrossRefGoogle ScholarPubMed
Keskin, O., Bahar, I., Badretdinov, A., Ptitsyn, O. & Jernigan, R. (1998). Empirical solvent-mediated potentials hold for both intra-molecular and inter-molecular inter-residue interactions. Protein Sci. 7, 2578.CrossRefGoogle ScholarPubMed
Klimov, D. K. & Thirumalai, D. (2000). Mechanisms and kinetics of β-hairpin formation. Proc. Natl. Acad. Sci. U.S.A. 97, 2544–2549.CrossRefGoogle ScholarPubMed
Klimov, D. K., Betancourt, M. R. & Thirumalai, D. (1998). Virtual atom representation of hydrogen bonds in minimal off-lattice models of alpha helices: effect on stability, cooperativity and kinetics. Folding Des. 3, 481–496.CrossRefGoogle ScholarPubMed
Koga, N. & Takada, S. (2001). Roles of native topology and chain-length scaling in protein folding: a simulation study. J. Mol. Biol. 313, 171–180.CrossRefGoogle ScholarPubMed
Korkuta, A. & Hendrickson, W. A. (2009). A force field for virtual atom molecular mechanics of proteins. Proc. Natl. Acad. Sci. U.S.A. 106, 15667–15672.CrossRefGoogle Scholar
Kundu, S., Sorensen, D. C., & Phillips, G. R. Jr. (2004). Automatic domain decomposition of proteins by a Gaussian network model. Proteins 57, 725–733.CrossRefGoogle ScholarPubMed
Levitt, M. & Warshel, A. (1975). Computer simulation of protein folding. Nature 253, 694–698.CrossRefGoogle ScholarPubMed
Levitt, M. (1976). A simplified representation of protein conformations for rapid simulation of protein folding. J. Mol. Biol. 104, 59–107.CrossRefGoogle ScholarPubMed
Liu, P., Izvekow, S. & Voth, G. A. (2007). Multi-scale coarse graining of monosaccharides. J. Phys. Chem. B 111, 11566–11575.CrossRefGoogle Scholar
Liwo, A., Oldziej, S., Pincus, M. R., Wawak, R. J., Rackowsky, S. & Scheraga, H. A. (1997a). A united-residue force field for off-lattice protein structure simulations. I. Functional forms and parameters of long range side chain interactions potentials from protein crystal data. J. Comput. Chem. 18, 849–873.3.0.CO;2-R>CrossRefGoogle Scholar
Liwo, A., Pincus, M. R., Wawak, R. J., Rackowsky, S., Oldziej, S. & Scheraga, H. A. (1997b). A united-residue force field for off-lattice protein structure simulations. II. Parameterization of short-range interactions and determination of weights of energy terms by z-score optimization. J. Comput. Chem. 18, 874–887.3.0.CO;2-O>CrossRefGoogle Scholar
Lyman, E., Pfaendtner, J., & Voth, G. A. (2008). Systematic multiscale parameterization of heterogeneous elastic network models of proteins. Biophys. J. 95, 4183–4192.CrossRefGoogle ScholarPubMed
Májek, P. & Elber, R. (2009). A coarse-grained potential for fold recognition and molecular dynamics simulations of proteins. Proteins 76, 822–836.CrossRefGoogle ScholarPubMed
Maragakis, P. & Karplus, M. (2005). Large amplitude conformational change in proteins explored with a plastic network model: adenylate kinase. J. Mol. Biol. 352, 807–822.CrossRefGoogle ScholarPubMed
Mathews, C., van Holde, K. E. & Ahern, K. G. (2000). Biochemistry. 3rd edn. San Francisco: Addison Wesley Longman Inc.Google Scholar
Matysiak, S. & Clementi, C. (2006). Minimalist protein model as a diagnostic tool for misfolding and aggregation. J. Mol. Biol. 363, 297–308.CrossRefGoogle ScholarPubMed
McCammon, J. A. & Northrup, S. H. (1980). Helix–coil transition in a simple polypeptide model. Biopolymers 19, 2033–2045.CrossRefGoogle Scholar
Miyazawa, S. & Jernigan, R. L. (1996). Residue–residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J. Mol. Biol. 256, 623.CrossRefGoogle Scholar
Monticelli, L., Kandasamy, S. K., Periole, X., Larson, R. G., Tieleman, D. P., & Marrink, S.-J. (2008). The MARTINI coarse-grained force field: extension to proteins. J. Chem. Theory Comput. 4, 819–834.CrossRefGoogle ScholarPubMed
Mukherjee, A. & Bagchi, B. (2002). Correlation between rate of folding, energy landscape and topology in the folding of a model protein HP-36. J. Chem. Phys. 118, 4733–4747.CrossRefGoogle Scholar
Mukherjee, A., Bhimalapuram, P. & Bagchia, B. (2005). Orientation-dependent potential of mean force for protein folding. J. Chem. Phys. 123, 014901.CrossRefGoogle ScholarPubMed
Nakagawa, N. & Peyrard, M. (2006). Modeling protein thermodynamics and fluctuations at the mesoscale. Phys. Rev. E 74, 041916.CrossRefGoogle ScholarPubMed
Noid, W. G., Chu, J.-W., Ayton, G. S., Krishna, V., Izvekov, S., Voth, G. A., Das, A. & Andersen, H. C. (2008a). The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models. J Chem. Phys. 128, 244114.CrossRefGoogle Scholar
Noid, W. G., Liu, P., Wang, Y., Chu, J.-W., Ayton, G. S., Izvekov, S., Andersen, H. C., & Voth, G. A. (2008b). The multiscale coarse-graining method. II. Numerical implementation for coarse-grained molecular models. J. Chem. Phys. 128, 244115.CrossRefGoogle ScholarPubMed
Nymeyer, H., Garcia, A. E. & Onuchic, J. N. (1998). Folding funnels and frustration in off-lattice minimalist protein landscapes. Proc. Natl. Acad. Sci. U.S.A. 95, 5921–5928.CrossRefGoogle ScholarPubMed
Okur, A., Strockbine, B., Hornak, V. & Simmerling, C. (2003). Using PC clusters to evaluate the transferability of molecular mechanics force fields for proteins. J. Comput. Chem. 24, 21–31.CrossRefGoogle ScholarPubMed
Ono, S., Nakajima, N., Higo, J. & Nakamura, H. (2000). Peptide free-energy profile is strongly dependent on the force field: comparison of C96 and AMBER95. J. Comput. Chem. 21, 748–762.3.0.CO;2-2>CrossRefGoogle Scholar
Reith, D., Pütz, M. & Müller-Plathe, F. (2003). Deriving effective mesoscale potentials from atomistic simulations. J. Comput. Chem. 24, 1624–1636.CrossRefGoogle ScholarPubMed
Russell, D., Lasker, K., Phillips, J., Schneidman-Duhovny, D., Velaszquez-Muriel, J. A. & Sali, A. (2009). The structural dynamics of macromolecular processes. Curr. Opin. Cell Biol. 21, 1–12.Google Scholar
Sherwood, P., Brooks, B. R. & Sansom, M. S. (2008). Multiscale methods for macromolecular simulations. Curr. Opin. Struct. Biol. 18, 630–640.CrossRefGoogle ScholarPubMed
Shi, Q., Izvekov, S., & Voth, G. A. (2006). Mixed atomistic and coarse grained molecular dynamics: simulation of membrane a bound ion channel. J. Phys. Chem. B. 110, 15045–15048.CrossRefGoogle ScholarPubMed
Silverstein, K. A. T., Haymet, A. D. J. & Dill, K. A. (1998). A simple model of water and the hydrophobic effect. J. Am. Chem. Soc. 120, 3166–3175.CrossRefGoogle Scholar
Soheilifard, R., Makarov, D. E. & Rodin, G. J. (2008). Critical evaluation of simple network models of protein dynamics and their comparison with crystallographic B-factors. Phys. Biol. 5, 026008.CrossRefGoogle ScholarPubMed
Sorenson, J. M. & Head-Gordon, T. (2002a). Protein engineering study of protein L by simulation. J. Comput. Biol. 9, 35–54.CrossRefGoogle ScholarPubMed
Sorenson, J. M. & Head-Gordon, T. (2002b). Toward minimalist models of larger proteins: a ubiquitin-like protein. Proteins 46, 368–379.CrossRefGoogle ScholarPubMed
Thorpe, I. F., Zhou, J. & Voth, G. A. (2008). Peptide folding using multiscale coarse-grained models. J. Phys. Chem. B 112, 13079–13090.CrossRefGoogle ScholarPubMed
Tirion, M. M. (1996). Large amplitude elastic motions in proteins from a single-parameter, atomic analysis. Phys. Rev. Lett. 77, 1905.CrossRefGoogle ScholarPubMed
Tozzini, V. (2005). Coarse grained models for proteins. Curr. Opin. Struct. Biol. 15, 144–150.CrossRefGoogle ScholarPubMed
Tozzini, V. (2010). Multi-scale modeling of proteins. Acc. Chem. Res. 43, 220–230.CrossRefGoogle Scholar
Tozzini, V. (in preparation). The phase diagram of a minimalist polypeptide model, in preparation.Google Scholar
Tozzini, V. & McCammon, J. A. (2005). A coarse grained model for the dynamics of flap opening in HIV-1 protease. Chem. Phys. Lett. 413, 123–128.CrossRefGoogle Scholar
Tozzini, V. & McCammon, J. A. (2008). One-bead models for proteins. In Coarse Graining of Condensed Phase and Biomolecular Systems (ed. Voth, G. A.), p. 285. Washington, DC: CRC Press.Google Scholar
Tozzini, V., Rocchia, W. & McCammon, J. A. (2006). Mapping AA models onto one-bead coarse grained models: general properties and applications to a minimal polypeptide model. J. Chem. Theory Comput. 2, 667–673.CrossRefGoogle Scholar
Tozzini, V., Trylska, J., Chang, C.-E. & McCammon, J. A. (2007). Flap opening dynamics in HIV-1 protease explored with a coarse-grained model. J. Struct. Biol. 157, 606–615.CrossRefGoogle ScholarPubMed
Trovato, F. & Tozzini, V. A. (in preparation). Coarse grained model for the dynamic of the aggregation of the green fluorescent proteins, in preparation.Google Scholar
Trylska, J., Tozzini, V., Chang, C.-E. & McCammon, J. A. (2007). HIV-1 protease substrate binding and product release pathways explored with coarse-grained molecular dynamics. Biophys. J. 92, 4179–4187.CrossRefGoogle ScholarPubMed
Trylska, J., Tozzini, V. & McCammon, J. A. (2005). Exploring global motions and correlations in the ribosome. Biophys. J. 89, 1455–1463.CrossRefGoogle ScholarPubMed
Van Aalten, D. M. F., De Groot, B. L., Findlay, J. B. C., Berendsen, H. J. C. & Amadei, A. (1997). A comparison of techniques for calculating protein essential dynamics. J. Comput. Chem. 18, 169–181.3.0.CO;2-T>CrossRefGoogle Scholar
Voet, D. & Voet, J. G. (2005). Biochemistry. 3rd edn. New York: Wiley.Google Scholar
Voltz, K., Trylska, J., Tozzini, V., Kurkal-Siebert, V., Langowski, J. & Smith, J. (2008). Coarse-grained force field for the nucleosome from self-consistent multiscaling. J. Comput. Chem. 29, 1429–1439.CrossRefGoogle ScholarPubMed
Wang, Y., Noid, W. G., Liu, P. & Voth, G. A. (2009). Effective force coarse-graining. Phys. Chem. Chem. Phys. 11, 2002–2015.CrossRefGoogle ScholarPubMed
Wu, Y., Lu, M., Chen, M., Li, J. & Ma, J. (2007). OPUS-Ca: a knowledge-based potential function requiring only Cα positions. Protein Sci. 16, 1449–1463.CrossRefGoogle ScholarPubMed
Yap, E.-H., Fawzi, N. L., & Head-Gordon, T. (2008). A coarse-grained α-carbon protein model with anisotropic hydrogen-bonding. Proteins 70, 626–638.CrossRefGoogle ScholarPubMed
Zacharias, M. (2003). Protein–protein docking with a reduced protein model accounting for side-chain flexibility. Protein Sci. 12, 1271–1282.CrossRefGoogle ScholarPubMed
Zhou, H. & Zhou, Y. (2002). Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci. 11, 2714–2726.CrossRefGoogle ScholarPubMed
Zhou, J., Thorpe, I. F., Izvekov, S. & Voth, G. A. (2007). Coarse-grained peptide modeling using a systematic multiscale approach. Biophys. J. 92, 4289–4303.CrossRefGoogle ScholarPubMed