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Quantitative analysis of ligand migration from transition networks.

ABSTRACT: In this work we use transition network analysis for the first time to investigate ligand migration in truncated hemoglobin (trHbN) and obtain kinetic information about the docking-site dynamics in the protein. A comparison with explicit water molecular dynamics simulations (100 ns in total) shows that the rate constants derived from the network analysis are realistic. The transition network analysis provides 1) The time-resolved connectivity network in the protein; 2) The half-lives of the docking sites; 3) The transition timescales between two given docking sites; and 4) The extent of population transfer among different docking sites of the protein as a function of lag time. We investigate the role of the Tyr33 and Gln58 residues in ligand migration by studying ligand migration in four mutants of trHbN. The mutation study suggests that residues Tyr33 and Gln58 stabilize the NO ligand in the Xe2 docking site of trHbN, thus facilitating the efficiency of the NO detoxification reaction.

SUBMITTER: Mishra S

PROVIDER: S-EPMC3000489 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Quantitative analysis of ligand migration from transition networks.

Mishra Sabyashachi S Meuwly Markus M

Biophysical journal 20101201 12

In this work we use transition network analysis for the first time to investigate ligand migration in truncated hemoglobin (trHbN) and obtain kinetic information about the docking-site dynamics in the protein. A comparison with explicit water molecular dynamics simulations (100 ns in total) shows that the rate constants derived from the network analysis are realistic. The transition network analysis provides 1) The time-resolved connectivity network in the protein; 2) The half-lives of the docki ...[more]

PMID: 21156139

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Project description:BACKGROUND: A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed. RESULTS: In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes. CONCLUSION: In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).

Dataset Information

Quantitative analysis of ligand migration from transition networks.

Publications

Quantitative analysis of ligand migration from transition networks.

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