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Integrating protein structural dynamics and evolutionary analysis with Bio3D.

Skjærven L, Yao XQ, Scarabelli G, Grant BJ - BMC Bioinformatics (2014)

Bottom Line: These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis.New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included.We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedicine, University of Bergen, Bergen, Norway. lars.skjarven@biomed.uib.no.

ABSTRACT

Background: Popular bioinformatics approaches for studying protein functional dynamics include comparisons of crystallographic structures, molecular dynamics simulations and normal mode analysis. However, determining how observed displacements and predicted motions from these traditionally separate analyses relate to each other, as well as to the evolution of sequence, structure and function within large protein families, remains a considerable challenge. This is in part due to the general lack of tools that integrate information of molecular structure, dynamics and evolution.

Results: Here, we describe the integration of new methodologies for evolutionary sequence, structure and simulation analysis into the Bio3D package. This major update includes unique high-throughput normal mode analysis for examining and contrasting the dynamics of related proteins with non-identical sequences and structures, as well as new methods for quantifying dynamical couplings and their residue-wise dissection from correlation network analysis. These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis. New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included. We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case.

Conclusions: The integration of structural dynamics and evolutionary analysis in Bio3D enables researchers to go beyond a prediction of single protein dynamics to investigate dynamical features across large protein families. The Bio3D package is distributed with full source code and extensive documentation as a platform independent R package under a GPL2 license from http://thegrantlab.org/bio3d/ .

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Related in: MedlinePlus

Investigating functional dynamics in heterotrimeric G-proteins. (A) Prediction of large-scale opening motions. (B) Prediction of dynamically coupled sub-domains (colored regions) from correlation network analysis of NMA results. Inter-subdomain couplings are highlighted with thick black lines. (C) Characterization of distinct GTP-active and GDP-inactive states from a clustering of NMA RMSIP results. (D) Fluctuation analysis reveals structural regions with significantly distinct flexibilities (highlighted with a blue shaded background are sites with a p-value < 0.005) between the active (red) and inactive (green) states. Full details for the reproduction of this analysis along with PCA that distinguishes GDP and GTP states can be found in the Additional file 1.
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Fig4: Investigating functional dynamics in heterotrimeric G-proteins. (A) Prediction of large-scale opening motions. (B) Prediction of dynamically coupled sub-domains (colored regions) from correlation network analysis of NMA results. Inter-subdomain couplings are highlighted with thick black lines. (C) Characterization of distinct GTP-active and GDP-inactive states from a clustering of NMA RMSIP results. (D) Fluctuation analysis reveals structural regions with significantly distinct flexibilities (highlighted with a blue shaded background are sites with a p-value < 0.005) between the active (red) and inactive (green) states. Full details for the reproduction of this analysis along with PCA that distinguishes GDP and GTP states can be found in the Additional file 1.

Mentions: In the current application, we collected 53 PDB structures of Gα (from application of the blast.pdb() function). These structures were aligned with the function pdbaln() and their modes of motion calculated with nma() (Figure 1 and Additional file 1). Results from RMSIP, fluctuation, and correlation analysis indicate that the structural dynamics are nucleotide state dependent (Figure 4). The modes of motion clearly distinguish the GTP (active) and GDP (inactive) states (Figure 4C). Predicted residue fluctuations reveal areas of conserved dynamics interspersed with areas of significantly distinct flexibilities in the active and inactive states (Figure 4D). Specifically, the P-loop and switch I, switch II and switch III regions are predicted to be significantly more flexible in the GDP than in GTP state. These results are consistent with our previous structural and MD simulation studies, in which these regions were found to be strongly coupled only in the active GTP state [42]. The stabilized P-loop and switch regions are thus a potential prerequisite for GTP hydrolysis and the binding of effectors.Figure 4


Integrating protein structural dynamics and evolutionary analysis with Bio3D.

Skjærven L, Yao XQ, Scarabelli G, Grant BJ - BMC Bioinformatics (2014)

Investigating functional dynamics in heterotrimeric G-proteins. (A) Prediction of large-scale opening motions. (B) Prediction of dynamically coupled sub-domains (colored regions) from correlation network analysis of NMA results. Inter-subdomain couplings are highlighted with thick black lines. (C) Characterization of distinct GTP-active and GDP-inactive states from a clustering of NMA RMSIP results. (D) Fluctuation analysis reveals structural regions with significantly distinct flexibilities (highlighted with a blue shaded background are sites with a p-value < 0.005) between the active (red) and inactive (green) states. Full details for the reproduction of this analysis along with PCA that distinguishes GDP and GTP states can be found in the Additional file 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4279791&req=5

Fig4: Investigating functional dynamics in heterotrimeric G-proteins. (A) Prediction of large-scale opening motions. (B) Prediction of dynamically coupled sub-domains (colored regions) from correlation network analysis of NMA results. Inter-subdomain couplings are highlighted with thick black lines. (C) Characterization of distinct GTP-active and GDP-inactive states from a clustering of NMA RMSIP results. (D) Fluctuation analysis reveals structural regions with significantly distinct flexibilities (highlighted with a blue shaded background are sites with a p-value < 0.005) between the active (red) and inactive (green) states. Full details for the reproduction of this analysis along with PCA that distinguishes GDP and GTP states can be found in the Additional file 1.
Mentions: In the current application, we collected 53 PDB structures of Gα (from application of the blast.pdb() function). These structures were aligned with the function pdbaln() and their modes of motion calculated with nma() (Figure 1 and Additional file 1). Results from RMSIP, fluctuation, and correlation analysis indicate that the structural dynamics are nucleotide state dependent (Figure 4). The modes of motion clearly distinguish the GTP (active) and GDP (inactive) states (Figure 4C). Predicted residue fluctuations reveal areas of conserved dynamics interspersed with areas of significantly distinct flexibilities in the active and inactive states (Figure 4D). Specifically, the P-loop and switch I, switch II and switch III regions are predicted to be significantly more flexible in the GDP than in GTP state. These results are consistent with our previous structural and MD simulation studies, in which these regions were found to be strongly coupled only in the active GTP state [42]. The stabilized P-loop and switch regions are thus a potential prerequisite for GTP hydrolysis and the binding of effectors.Figure 4

Bottom Line: These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis.New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included.We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedicine, University of Bergen, Bergen, Norway. lars.skjarven@biomed.uib.no.

ABSTRACT

Background: Popular bioinformatics approaches for studying protein functional dynamics include comparisons of crystallographic structures, molecular dynamics simulations and normal mode analysis. However, determining how observed displacements and predicted motions from these traditionally separate analyses relate to each other, as well as to the evolution of sequence, structure and function within large protein families, remains a considerable challenge. This is in part due to the general lack of tools that integrate information of molecular structure, dynamics and evolution.

Results: Here, we describe the integration of new methodologies for evolutionary sequence, structure and simulation analysis into the Bio3D package. This major update includes unique high-throughput normal mode analysis for examining and contrasting the dynamics of related proteins with non-identical sequences and structures, as well as new methods for quantifying dynamical couplings and their residue-wise dissection from correlation network analysis. These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis. New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included. We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case.

Conclusions: The integration of structural dynamics and evolutionary analysis in Bio3D enables researchers to go beyond a prediction of single protein dynamics to investigate dynamical features across large protein families. The Bio3D package is distributed with full source code and extensive documentation as a platform independent R package under a GPL2 license from http://thegrantlab.org/bio3d/ .

Show MeSH
Related in: MedlinePlus