Markov modeling of peptide folding in the presence of protein crowders

Nilsson, Daniel; Mohanty, Sandipan; Irbäck, Anders

doi:10.1063/1.5017031

Cited by 6 publications

(5 citation statements)

References 47 publications

(64 reference statements)

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“…The other edge strand of PGB1, β3, also shows some propensity to form SOD1 contacts. Interestingly, these regions of PGB1 and BPTI are largely identical to those found prone to interact with peptides in previous works. − …”

Section: Resultssupporting

confidence: 76%

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Stability and Local Unfolding of SOD1 in the Presence of Protein Crowders

et al. 2019

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Using NMR and Monte Carlo (MC) methods, we investigate the stability and dynamics of superoxide dismutase 1 (SOD1) in homogeneous crowding environments, where either bovine pancreatic trypsin inhibitor (BPTI) or the B1 domain of streptococcal protein G (PGB1) serves as a crowding agent. By NMR, we show that both crowders, and especially BPTI, cause a drastic loss in the overall stability of SOD1 in its apo monomeric form. Additionally, we determine chemical shift perturbations indicating that SOD1 interacts with the crowder proteins in a residue-specific manner that further depends on the identity of the crowding protein. Furthermore, the specificity of SOD1−crowder interactions is reciprocal: chemical shift perturbations on BPTI and PGB1 identify regions that interact preferentially with SOD1. By MC simulations, we investigate the local unfolding of SOD1 in the absence and presence of the crowders. We find that the crowders primarily interact with the long flexible loops of the folded SOD1 monomer. The basic mechanisms by which the SOD1 β-barrel core unfolds remain unchanged when adding the crowders. In particular, both with and without the crowders, the second β-sheet of the barrel is more dynamic and unfolding-prone than the first. Notably, the MC simulations (exploring the early stages of SOD1 unfolding) and the NMR experiments (under equilibrium conditions) identify largely the same set of PGB1 and BPTI residues as prone to form SOD1 contacts. Thus, contacts stabilizing the unfolded state of SOD1 in many cases appear to form early in the unfolding reaction.

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Section: Resultssupporting

confidence: 76%

“…A detailed description of the interaction potential can be found elsewhere . Previous works based on this model include folding/unfolding studies of SOD1 and several other proteins with >90 residues. − Recently, it was used by us to study peptide folding in the presence of PGB1 and BPTI crowders. − …”

Section: Experimental and Computational Methodsmentioning

confidence: 99%

Stability and Local Unfolding of SOD1 in the Presence of Protein Crowders

et al. 2019

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“…Second, the fact that we have chosen Monte Carlo as the basis for our approach makes it difficult to obtain realistic kinetic information. This could potentially be be mitigated by careful selection of moves or restricting the analysis to longer time-scales. , …”

Section: Discussionmentioning

confidence: 99%

“…This could potentially be be mitigated by careful selection of moves, or restricting the analysis to longer time-scales. 111,112 The Monte Carlo approach, however, provides substantial benefits in terms of computational efficiency. Our procedure requires only a few weeks of computation on a single CPU to obtain converged simulations on modest size proteins (with 40-80 residues), a dramatic improvement over comparable explicit solvent force field simulations.…”

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confidence: 99%

Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias

et al. 2018

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Energy landscape theory suggests that native interactions are a major determinant of the folding mechanism of a protein. Thus, structure-based (Go̅) models have, aided by coarse-graining techniques, shown great success in capturing the mechanisms of protein folding and conformational changes. In certain cases, however, non-native interactions and atomic details are also essential to describe the protein dynamics, prompting the development of a variety of structure-based models that include non-native interactions, and differentiate between different types of attractive potentials. Here, we describe an all-protein-atom hybrid model, termed ProfasiGo, that integrates an implicit solvent all-atom physics-based model (called Profasi) and a structure-based Go̅ potential and its implementation in two software packages (PHAISTOS and ProFASi) that are developed for Monte Carlo sampling of protein molecules. We apply the ProfasiGo model to study the folding free energy landscapes of four topologically similar proteins, one of which can be folded by the simplified potential Profasi and two that have been folded by explicit solvent, all-atom molecular dynamics simulations with the CHARMM22* force field. Our results reveal that the hybrid ProfasiGo model is able to capture many of the details present in the physics-based potentials while retaining the advantages of Go̅ models for sampling and guiding to the native state. We expect that the model will be widely applicable to the study of the folding of more complex proteins or to the study of conformational dynamics and integration with experimental data.

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“…These interactions can alter the thermodynamic folding barriers leaving the protein marginally stable. Both theoretical and experimental developments have been made to understand macromolecular crowding with enthalpic contributions [41][42][43][44][45]. Being able to separate out the entropic from enthalpic effects is a vital step towards the development of general principles of in vivo protein folding.…”

Section: Protein Folding In the Cellmentioning

confidence: 99%

Towards developing principles of protein folding and dynamics in the cell

Cheung

Gasic

2018

Phys. Biol.

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Proteins must fold and function in the immensely complex environment of a cell, i.e. the cytoplasm-this is far from the ideal test-tube setting of a dilute solution. Here we review the advances in protein folding and dynamics inside the cell. In developing principles of protein behavior in vivo, we also begin to understand the organization and dynamics of the cytoplasm, unifying the single protein scale with the many-protein architectures at the subcellular scale. Our group has significantly contributed to this frontier by characterizing the effect of macromolecular crowding on the distribution of protein conformations. Additionally, we provide a personal perspective on becoming a theoretical biological physicist in the era of interdisciplinary research that has been greatly influenced by Dr Kamal Shukla. We also share our view on the future direction of protein folding inside a cell.

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Markov modeling of peptide folding in the presence of protein crowders

Cited by 6 publications

References 47 publications

Stability and Local Unfolding of SOD1 in the Presence of Protein Crowders

Stability and Local Unfolding of SOD1 in the Presence of Protein Crowders

Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias

Towards developing principles of protein folding and dynamics in the cell

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