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Web-based computational chemistry education with CHARMMing II: Coarse-grained protein folding.

Pickard FC, Miller BT, Schalk V, Lerner MG, Woodcock HL, Brooks BR - PLoS Comput. Biol. (2014)

Bottom Line: As a proof of concept, this lesson demonstrates the construction of a CG model of a small globular protein, its simulation via Langevin dynamics, and the analysis of the resulting data.New functionality has been added to CHARMMing to facilitate this process.The implementation of these features into CHARMMing helps automate many of the tedious aspects of constructing a CG Gō model.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT
A lesson utilizing a coarse-grained (CG) Gō-like model has been implemented into the CHARMM INterface and Graphics (CHARMMing) web portal (www.charmming.org) to the Chemistry at HARvard Macromolecular Mechanics (CHARMM) molecular simulation package. While widely used to model various biophysical processes, such as protein folding and aggregation, CG models can also serve as an educational tool because they can provide qualitative descriptions of complex biophysical phenomena for a relatively cheap computational cost. As a proof of concept, this lesson demonstrates the construction of a CG model of a small globular protein, its simulation via Langevin dynamics, and the analysis of the resulting data. This lesson makes connections between modern molecular simulation techniques and topics commonly presented in an advanced undergraduate lecture on physical chemistry. It culminates in a straightforward analysis of a short dynamics trajectory of a small fast folding globular protein; we briefly describe the thermodynamic properties that can be calculated from this analysis. The assumptions inherent in the model and the data analysis are laid out in a clear, concise manner, and the techniques used are consistent with those employed by specialists in the field of CG modeling. One of the major tasks in building the Gō-like model is determining the relative strength of the nonbonded interactions between coarse-grained sites. New functionality has been added to CHARMMing to facilitate this process. The implementation of these features into CHARMMing helps automate many of the tedious aspects of constructing a CG Gō model. The CG model builder and its accompanying lesson should be a valuable tool to chemistry students, teachers, and modelers in the field.

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An example melting curve.Each point on the plot represents  calculated from a full trajectory.  occurs when , and the curvature of the plot is related to .
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pcbi-1003738-g003: An example melting curve.Each point on the plot represents calculated from a full trajectory. occurs when , and the curvature of the plot is related to .

Mentions: When analyzing trajectory data, an important consideration is the choice of reaction coordinate employed in data analysis. As illustrated in Table 1, the choice of reaction coordinate can have an uncomfortably large effect upon the computed properties of the simulation. The choice of a reaction coordinate is very complex and depends heavily on what scientific questions are being asked about the system under study. Users are encouraged to consult the literature broadly when choosing a reaction coordinate for a new system. In this lesson, we will consider the fraction of native contacts (Q), a reaction coordinate which has been shown to be robust [37]. This reaction coordinate is often used when mapping the thermodynamic landscape of various folding pathways of a protein. When , a protein is considered folded; when , a protein is considered unfolded. The of a protein occurs when it is equally likely to be folded as unfolded. Figure 2 shows an example trajectory of a protein below its melting point. From the relative frequencies of folded versus unfolded structures, we can calculate . Furthermore, by considering how changes with respect to temperature, we can plot a melting curve and apply the Gibbs-Helmholtz equation to determine the protein's heat capacity () and enthalpy of fusion () (see equation below). Figure 3 gives an illustrative example of a computed melting curve.


Web-based computational chemistry education with CHARMMing II: Coarse-grained protein folding.

Pickard FC, Miller BT, Schalk V, Lerner MG, Woodcock HL, Brooks BR - PLoS Comput. Biol. (2014)

An example melting curve.Each point on the plot represents  calculated from a full trajectory.  occurs when , and the curvature of the plot is related to .
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109841&req=5

pcbi-1003738-g003: An example melting curve.Each point on the plot represents calculated from a full trajectory. occurs when , and the curvature of the plot is related to .
Mentions: When analyzing trajectory data, an important consideration is the choice of reaction coordinate employed in data analysis. As illustrated in Table 1, the choice of reaction coordinate can have an uncomfortably large effect upon the computed properties of the simulation. The choice of a reaction coordinate is very complex and depends heavily on what scientific questions are being asked about the system under study. Users are encouraged to consult the literature broadly when choosing a reaction coordinate for a new system. In this lesson, we will consider the fraction of native contacts (Q), a reaction coordinate which has been shown to be robust [37]. This reaction coordinate is often used when mapping the thermodynamic landscape of various folding pathways of a protein. When , a protein is considered folded; when , a protein is considered unfolded. The of a protein occurs when it is equally likely to be folded as unfolded. Figure 2 shows an example trajectory of a protein below its melting point. From the relative frequencies of folded versus unfolded structures, we can calculate . Furthermore, by considering how changes with respect to temperature, we can plot a melting curve and apply the Gibbs-Helmholtz equation to determine the protein's heat capacity () and enthalpy of fusion () (see equation below). Figure 3 gives an illustrative example of a computed melting curve.

Bottom Line: As a proof of concept, this lesson demonstrates the construction of a CG model of a small globular protein, its simulation via Langevin dynamics, and the analysis of the resulting data.New functionality has been added to CHARMMing to facilitate this process.The implementation of these features into CHARMMing helps automate many of the tedious aspects of constructing a CG Gō model.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT
A lesson utilizing a coarse-grained (CG) Gō-like model has been implemented into the CHARMM INterface and Graphics (CHARMMing) web portal (www.charmming.org) to the Chemistry at HARvard Macromolecular Mechanics (CHARMM) molecular simulation package. While widely used to model various biophysical processes, such as protein folding and aggregation, CG models can also serve as an educational tool because they can provide qualitative descriptions of complex biophysical phenomena for a relatively cheap computational cost. As a proof of concept, this lesson demonstrates the construction of a CG model of a small globular protein, its simulation via Langevin dynamics, and the analysis of the resulting data. This lesson makes connections between modern molecular simulation techniques and topics commonly presented in an advanced undergraduate lecture on physical chemistry. It culminates in a straightforward analysis of a short dynamics trajectory of a small fast folding globular protein; we briefly describe the thermodynamic properties that can be calculated from this analysis. The assumptions inherent in the model and the data analysis are laid out in a clear, concise manner, and the techniques used are consistent with those employed by specialists in the field of CG modeling. One of the major tasks in building the Gō-like model is determining the relative strength of the nonbonded interactions between coarse-grained sites. New functionality has been added to CHARMMing to facilitate this process. The implementation of these features into CHARMMing helps automate many of the tedious aspects of constructing a CG Gō model. The CG model builder and its accompanying lesson should be a valuable tool to chemistry students, teachers, and modelers in the field.

Show MeSH