Modeling Protein Folding Pathways

Towse, Clare‐Louise; Daggett, Valerie

doi:10.1002/9781118889886.ch3

Cited by 2 publications

(3 citation statements)

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“…For example, a study of the polyalanine pentapeptide revealed the commonly used force fields, CHARMM27/CMAP, AMBER03, AMBER94, and AMBER99, to have helical content spanning 58%–98%, far in excess of the experimental estimate of ~20% (Best et al, 2008; Firestine et al, 2008; Jiang et al, 2013). We recently examined this behavior for our own force field using the same peptide system and confirmed our helical content to be 19%, in line with experiments (Towse et al, 2015). We have also shown that by incorporating dynamics we pick up features not well captured in the PDB, such as the differential behaviors of mobile surface residues whereby we observed flexible residues to have larger increases in the number of rotamers occupied at surfaces (+0.85 rotamers on average) than other residues (+0.10) (Scouras and Daggett, 2011).…”

Section: Discussionsupporting

confidence: 82%

“…In addition, our methods provide natural Boltzmann sampling, negating the need for weighting or culling of conformers. We have had sustained success at capturing protein dynamics in agreement with experimental observations over the last 20 years or so (Beck and Daggett, 2004; Beck et al, 2005; Rizzuti and Daggett, 2013; Schaeffer and Daggett, 2011; Schaeffer et al, 2008; Toofanny and Daggett, 2012; Towse and Daggett, 2015), with only one minor modification to our approach (Armen et al, 2005). For example, previous assessments of our simulation quality show low mean C α root-mean-square deviations between simulation and experimental structures with good agreement with spectroscopic observables, such as nuclear Overhauser effects (NOEs) (>90%) and chemical shifts (R > 0.9) (Beck and Daggett, 2004; Beck et al, 2008).…”

Section: Introductionsupporting

confidence: 54%

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New Dynamic Rotamer Libraries: Data-Driven Analysis of Side-Chain Conformational Propensities

et al. 2016

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SUMMARY Most rotamer libraries are generated from subsets of the PDB and do not fully represent the conformational scope of protein side chains. Previous attempts to rectify this sparse coverage of conformational space have involved application of weighting and smoothing functions. We resolve these limitations by using physics-based molecular dynamics simulations to determine more accurate frequencies of rotameric states. This work forms part of our Dynameomics initiative and uses a set of 807 proteins selected to represent 97% of known autonomous protein folds, thereby eliminating the bias toward common topologies found within the PDB. Our Dynameomics derived rotamer libraries encompass 4.8 × 109 rotamers, sampled from at least 51,000 occurrences of each of 93,642 residues. Here, we provide a backbone-dependent rotamer library, based on secondary structure ϕ/ψ regions, and an update to our 2011 backbone-independent library that addresses the doubling of our dataset since its original publication.

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Section: Discussionsupporting

confidence: 82%

Section: Introductionsupporting

confidence: 54%

New Dynamic Rotamer Libraries: Data-Driven Analysis of Side-Chain Conformational Propensities

et al. 2016

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“…Better understanding of protein unfolding under temperature variation has motivated many theoretical and experimental studies, and in the last few years, some progress has been made toward this goal . It has previously been reported that the folding or unfolding pathways are similar under temperature variation, thereby enabling the study of unfolding pathways to extrapolate information regarding protein folding. Unfolding processes are theoretically studied mostly through MD and Monte Carlo simulations .…”

Section: Introductionmentioning

confidence: 99%

Temperature-induced unfolding behavior of proteins studied by tensorial elastic network model

Srivastava

Granek

2016

Proteins

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Motivated by single molecule experiments and recent molecular dynamics (MD) studies, we propose a simple and computationally efficient method based on a tensorial elastic network model to investigate the unfolding pathways of proteins under temperature variation. The tensorial elastic network model, which relies on the native state topology of a protein, combines the anisotropic network model, the bond bending elasticity, and the backbone twist elasticity to successfully predicts both the isotropic and anisotropic fluctuations in a manner similar to the Gaussian network model and anisotropic network model. The unfolding process is modeled by breaking the native contacts between residues one by one, and by assuming a threshold value for strain fluctuation. Using this method, we simulated the unfolding processes of four well-characterized proteins: chymotrypsin inhibitor, barnase, ubiquitein, and adenalyate kinase. We found that this step-wise process leads to two or more cooperative, first-order-like transitions between partial denaturation states. The sequence of unfolding events obtained using this method is consistent with experimental and MD studies. The results also imply that the native topology of proteins "encrypts" information regarding their unfolding process. Proteins 2016; 84:1767-1775. © 2016 Wiley Periodicals, Inc.

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Modeling Protein Folding Pathways

Cited by 2 publications

References 253 publications

New Dynamic Rotamer Libraries: Data-Driven Analysis of Side-Chain Conformational Propensities

New Dynamic Rotamer Libraries: Data-Driven Analysis of Side-Chain Conformational Propensities

Temperature-induced unfolding behavior of proteins studied by tensorial elastic network model

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