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Quantifying hub-like behavior in protein folding networks.

ABSTRACT: The free energy landscape of a protein is a function of many interdependent degrees of freedom. For this reason, conceptual constructs (e.g., funnels) have been useful to visualize these landscapes. One relatively new construct is the idea of a hub-like native state that is the final destination of many non-interconverting folding pathways. This is in contrast to the idea of a single predominant folding pathway connecting the native state to a rapidly interconverting ensemble of unfolded states. The key quantity to distinguish between these two ideas is the connectivity of the unfolded ensemble. We present a metric to determine this connectivity for a given network, which can be calculated either from continuous folding trajectories, or a Markov model. The metric determines how often a region of space is used as an intermediate on transition paths that connect two other regions of space, and we use it here to determine how often two parts of the unfolded ensemble are connected directly, versus how often these transitions are mediated by the native state.

SUBMITTER: Dickson A

PROVIDER: S-EPMC3767461 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Quantifying hub-like behavior in protein folding networks.

Dickson Alex A Brooks Charles L CL

Journal of chemical theory and computation 20120101 9

The free energy landscape of a protein is a function of many interdependent degrees of freedom. For this reason, conceptual constructs (e.g., funnels) have been useful to visualize these landscapes. One relatively new construct is the idea of a hub-like native state that is the final destination of many non-interconverting folding pathways. This is in contrast to the idea of a single predominant folding pathway connecting the native state to a rapidly interconverting ensemble of unfolded states. ...[more]

PMID: 24027492

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Project description:Multistep protein-protein interactions underlie most biological processes, but their characterization through methods such as isothermal titration calorimetry (ITC) is largely confined to simple models that provide little information on the intermediate, individual steps. In this study, we primarily examine the essential hub protein LC8, a small dimer that binds disordered regions of 100+ client proteins in two symmetrical grooves at the dimer interface. Mechanistic details of LC8 binding have remained elusive, hampered in part by ITC data analyses employing simple models that treat bivalent binding as a single event with a single binding affinity. We build on existing Bayesian ITC approaches to quantify thermodynamic parameters for multi-site binding interactions impacted by significant uncertainty in protein concentration. Using a two-site binding model, we identify positive cooperativity with high confidence for LC8 binding to multiple client peptides. In contrast, application of an identical model to the two-site binding between the coiled-coil NudE dimer and the intermediate chain of dynein reveals little evidence of cooperativity. We propose that cooperativity in the LC8 system drives the formation of saturated induced-dimer structures, the functional units of most LC8 complexes. In addition to these system-specific findings, our work advances general ITC analysis in two ways. First, we describe a previously unrecognized mathematical ambiguity in concentrations in standard binding models and clarify how it impacts the precision with which binding parameters are determinable in cases of high uncertainty in analyte concentrations. Second, building on observations in the LC8 system, we develop a system-agnostic heat map of practical parameter identifiability calculated from synthetic data which demonstrates that the ability to determine microscopic binding parameters is strongly dependent on both the parameters themselves and experimental conditions. The work serves as a foundation for determination of multi-step binding interactions, and we outline best practices for Bayesian analysis of ITC experiments.

Project description:BackgroundProtein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) 12. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets.Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions.ResultsA hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO) annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy.A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species.ConclusionThe successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets - even in those organisms that currently lack large-scale protein interaction data.

Dataset Information

Quantifying hub-like behavior in protein folding networks.

Publications

Quantifying hub-like behavior in protein folding networks.

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