Sequence, structure, and cooperativity in folding of elementary protein structural motifs

Lai, Jason; Kubelka, Ginka S.; Kubelka, Jan

doi:10.1073/pnas.1506309112

Cited by 24 publications

(63 citation statements)

References 56 publications

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“…4 Whether or not analogous cooperativity applies to β-sheet formation is a function of the geometric details of any particular such structure, and remains a matter of debate. 5–8 Cooperativity of α-helical H-bonding has been studied experimentally by multiple spectroscopic techniques, including time resolved or 2D infra-red spectroscopy 9–14 and T-jump fluorescence. 15–18 Only mass spectrometry has been proposed as a potential tool for studying cooperativity of H-bonding in intact proteins, relying on the exploitation of hydrogen/deuterium (H/D) exchange phenomena, 19–20 but to the best of our knowledge no quantitative analysis of H-bond cooperativity of such data has yet been published.…”

mentioning

confidence: 99%

Observation of α-Helical Hydrogen-Bond Cooperativity in an Intact Protein

Li¹,

Wang²,

Chen³

et al. 2016

J. Am. Chem. Soc.

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The presence and extent of hydrogen bonding cooperativity in proteins remains a fundamental question, which in the past has been studied extensively, mostly by infrared and fluorescence measurements on model peptides. We demonstrate that such cooperativity can be studied in an intact protein by hydrogen/deuterium exchange NMR spectroscopy. The method is based on the fact that substitution of NH by ND in a backbone amide group slightly weakens the N-H…O=C hydrogen bond. Our results show that such substitution at position i in an α-helix impacts the 1H and 15N chemical shifts of the amide sites of residues i − 3 to i + 3. Quantum mechanical calculations indicate that the upfield shifts of 1H and 15N resonances at site i, observed upon H/D exchanges at sites i − 3, i + 1, i + 2, and i + 3, correspond to a decrease of the ith backbone amide electric dipole moment, which weakens its hydrogen bonding and long range electrostatic interactions with other backbone amides in the α-helix. These results provide new quantitative insights into the cooperativity of H-bonding in protein α-helices.

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mentioning

confidence: 99%

Observation of α-Helical Hydrogen-Bond Cooperativity in an Intact Protein

Li¹,

Wang²,

Chen³

et al. 2016

J. Am. Chem. Soc.

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“… 2004 ; Kubelka and Kubelka 2014 ; Lai et al. 2015 ; Naganathan and Munoz 2014 ; Munoz et al. 1997 ; Irback et al.…”

Section: Model Description and Biological Motivationunclassified

A modified Ising model of Barabási–Albert network with gene-type spins

et al. 2020

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The central question of systems biology is to understand how individual components of a biological system such as genes or proteins cooperate in emerging phenotypes resulting in the evolution of diseases. As living cells are open systems in quasi-steady state type equilibrium in continuous exchange with their environment, computational techniques that have been successfully applied in statistical thermodynamics to describe phase transitions may provide new insights to the emerging behavior of biological systems. Here we systematically evaluate the translation of computational techniques from solid-state physics to network models that closely resemble biological networks and develop specific translational rules to tackle problems unique to living systems. We focus on logic models exhibiting only two states in each network node. Motivated by the apparent asymmetry between biological states where an entity exhibits boolean states i.e. is active or inactive, we present an adaptation of symmetric Ising model towards an asymmetric one fitting to living systems here referred to as the modified Ising model with gene-type spins. We analyze phase transitions by Monte Carlo simulations and propose a mean-field solution of a modified Ising model of a network type that closely resembles a real-world network, the Barabási–Albert model of scale-free networks. We show that asymmetric Ising models show similarities to symmetric Ising models with the external field and undergoes a discontinuous phase transition of the first-order and exhibits hysteresis. The simulation setup presented herein can be directly used for any biological network connectivity dataset and is also applicable for other networks that exhibit similar states of activity. The method proposed here is a general statistical method to deal with non-linear large scale models arising in the context of biological systems and is scalable to any network size.

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“…Subsequently, they have been used with great success in both biological and nonbiological systems to describe order-disorder transitions (12). Within the field of protein folding and design, they have been used in a number of instances to model phenomena such as helixto-coil transitions, b-hairpin formation, prediction of protein folding rates/thermodynamics, and with regards to the postulation of downhill folding (6,12,20,(29)(30)(31)(32)(33)(34). Most recently, two types of one-dimensional (1-D) variants have been used to probe the equilibrium and kinetic un/folding of repeat proteins (3,12,17,21,22,35,36).…”

Section: More Complex Fitting Evaluation and Simulations Using The mentioning

confidence: 99%

PyFolding: Open-Source Graphing, Simulation, and Analysis of the Biophysical Properties of Proteins

Lowe

Perez-Riba

Itzhaki

et al. 2018

Biophysical Journal

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For many years, curve-fitting software has been heavily utilized to fit simple models to various types of biophysical data. Although such software packages are easy to use for simple functions, they are often expensive and present substantial impediments to applying more complex models or for the analysis of large data sets. One field that is reliant on such data analysis is the thermodynamics and kinetics of protein folding. Over the past decade, increasingly sophisticated analytical models have been generated, but without simple tools to enable routine analysis. Consequently, users have needed to generate their own tools or otherwise find willing collaborators. Here we present PyFolding, a free, open-source, and extensible Python framework for graphing, analysis, and simulation of the biophysical properties of proteins. To demonstrate the utility of PyFolding, we have used it to analyze and model experimental protein folding and thermodynamic data. Examples include: 1) multiphase kinetic folding fitted to linked equations, 2) global fitting of multiple data sets, and 3) analysis of repeat protein thermodynamics with Ising model variants. Moreover, we demonstrate how PyFolding is easily extensible to novel functionality beyond applications in protein folding via the addition of new models. Example scripts to perform these and other operations are supplied with the software, and we encourage users to contribute notebooks and models to create a community resource. Finally, we show that PyFolding can be used in conjunction with Jupyter notebooks as an easy way to share methods and analysis for publication and among research teams.

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Sequence, structure, and cooperativity in folding of elementary protein structural motifs

Cited by 24 publications

References 56 publications

Observation of α-Helical Hydrogen-Bond Cooperativity in an Intact Protein

Observation of α-Helical Hydrogen-Bond Cooperativity in an Intact Protein

A modified Ising model of Barabási–Albert network with gene-type spins

PyFolding: Open-Source Graphing, Simulation, and Analysis of the Biophysical Properties of Proteins

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