Kazmirski, S. L. & Daggett, V. Simulations of the structural and dynamical properties of denatured proteins: the 'molten coil' state of bovine pancreatic trypsin inhibitor. J. Mol. Biol. 277, 487-506
Department of Medicinal Chemistry, University of Washington, Seattle WA 98195-7610, USAJournal of Molecular Biology (Impact Factor: 4.33). 03/1998; 277(2):487-506. DOI: 10.1006/jmbi.1998.1634
The dynamic nature of denatured, unfolded proteins makes it difficult to characterize their structures experimentally. To complement experiment and to obtain more detailed information about the structure and dynamic behavior of the denatured state, we have performed eleven 2.5 ns molecular dynamics simulations of reduced bovine pancreatic trypsin inhibitor (BPTI) at high temperature in water and a control simulation at 298 K, for a total of 30 ns of simulation time. In a neutral pH environment (acidic residues ionized), the unfolded protein structures were compact with an average radius of gyration 9% greater than the native state. The compact conformations resulted from the transient formation of non-native hydrophobic clusters, turns and salt bridges. However, when the acidic residues were protonated, the protein periodically expanded to a radius of gyration of 18 to 20 Å. The early steps in unfolding were similar in the different simulations until passing through the major transition state of unfolding. Afterwards, unfolding proceeded through one of two general pathways with respect to secondary structure: loss of the C-terminal helix followed by loss of β-structure or the opposite. To determine whether the protein preferentially sampled particular conformational substates in the denatured state, pairwise Cα root-mean-square deviations were measured between all structures, but similar structures were found between only two trajectories. Yet, similar composite properties (secondary structure content, side-chain and water contacts, solvent accessible surface area, etc.) were observed for the structures that unfolded through different pathways. Somewhat surprisingly, the unfolded structures are in agreement with both past experiments suggesting that reduced BPTI is a random coil and more recent experiments providing evidence for non-random structure, demonstrating how ensembles of fluctuating structures can give rise to experimental observables that are seemingly at odds.
Advances in Protein and Peptide Sciences Volume 1, Bentham e Books edited by Ben M. Dunn, 01/2013: chapter 9: pages 318-381; Bentham Science., ISBN: 978-1-60805-487-9
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ABSTRACT: Since experimental determination of protein folding pathways remains difficult, computational techniques are often used to simulate protein folding. Most current techniques to predict protein folding pathways are computationally intensive and are suitable only for small proteins. By assuming that the native structure of a protein is known and representing each intermediate conformation as a collection of fully folded structures in which each of them contains a set of interacting secondary structure elements, we show that it is possible to significantly reduce the conformation space while still being able to predict the most energetically favorable folding pathway of large proteins with hundreds of residues at the mesoscopic level, including the pig muscle phosphoglycerate kinase with 416 residues. The model is detailed enough to distinguish between different folding pathways of structurally very similar proteins, including the streptococcal protein G and the peptostreptococcal protein L. The model is also able to recognize the differences between the folding pathways of protein G and its two structurally similar variants NuG1 and NuG2, which are even harder to distinguish. We show that this strategy can produce accurate predictions on many other proteins with experimentally determined intermediate folding states. Our technique is efficient enough to predict folding pathways for both large and small proteins at the mesoscopic level. Such a strategy is often the only feasible choice for large proteins. A software program implementing this strategy (SSFold) is available at http://faculty.cs.tamu.edu/shsze/ssfold.BMC Bioinformatics 07/2008; 9(1):320. DOI:10.1186/1471-2105-9-320 · 2.58 Impact Factor
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ABSTRACT: The 'new view' of protein folding is based on a statistical analysis of the landscape for protein folding. This perspective leads to the investigation of the statistical distributions of protein conformations as the folding protein approaches the native state from unfolded states. Molecular dynamics simulations of both the thermodynamics of protein folding and the kinetics of unfolding are beginning to explore the statistical nature of these distributions. They also provide connections between the theory of protein folding landscapes and the experimental observations of the properties of key ensembles of the conformations populated as folding progresses.Current Opinion in Structural Biology 05/1998; 8(2):222-6. DOI:10.1016/S0959-440X(98)80043-2 · 7.20 Impact Factor
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