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Statistical limits to the identification of ion channel domains by sequence similarity.

ABSTRACT: The study of ion channel function is constrained by the availability of structures for only a small number of channels. A commonly used bioinformatics technique is to assert, based on sequence similarity, that a domain within a channel of interest has the same structure as a reference domain for which the structure is known. This technique, while useful, is often employed when there is only a slight similarity between the channel of interest and the domain of known structure. In this study, we exploit recent advances in structural genomics to calculate the sequence-based probability of the presence of putative domains in a number of ion channels. We find strong support for the presence of many domains that have been proposed in the literature. For example, eukaryotic and prokaryotic CLC proteins almost certainly share a common structure. A number of proposed domains, however, are not as well supported. In particular, for the COOH terminus of the BK channel we find a number of literature proposed domains for which the assertion of common structure based on common sequence has a nontrivial probability of error.

SUBMITTER: Fodor AA

PROVIDER: S-EPMC2151544 | biostudies-literature | 2006 Jun

REPOSITORIES: biostudies-literature

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Statistical limits to the identification of ion channel domains by sequence similarity.

Fodor Anthony A AA Aldrich Richard W RW

The Journal of general physiology 20060601 6

The study of ion channel function is constrained by the availability of structures for only a small number of channels. A commonly used bioinformatics technique is to assert, based on sequence similarity, that a domain within a channel of interest has the same structure as a reference domain for which the structure is known. This technique, while useful, is often employed when there is only a slight similarity between the channel of interest and the domain of known structure. In this study, we e ...[more]

PMID: 16735758

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Project description:The stochastic behavior of single ion channels is most often described as an aggregated continuous-time Markov process with discrete states. For ligand-gated channels each state can represent a different conformation of the channel protein or a different number of bound ligands. Single-channel recordings show only whether the channel is open or shut: states of equal conductance are aggregated, so transitions between them have to be inferred indirectly. The requirement to filter noise from the raw signal further complicates the modeling process, as it limits the time resolution of the data. The consequence of the reduced bandwidth is that openings or shuttings that are shorter than the resolution cannot be observed; these are known as missed events. Postulated models fitted using filtered data must therefore explicitly account for missed events to avoid bias in the estimation of rate parameters and therefore assess parameter identifiability accurately. In this article, we present the first, to our knowledge, Bayesian modeling of ion-channels with exact missed events correction. Bayesian analysis represents uncertain knowledge of the true value of model parameters by considering these parameters as random variables. This allows us to gain a full appreciation of parameter identifiability and uncertainty when estimating values for model parameters. However, Bayesian inference is particularly challenging in this context as the correction for missed events increases the computational complexity of the model likelihood. Nonetheless, we successfully implemented a two-step Markov chain Monte Carlo method that we called "BICME", which performs Bayesian inference in models of realistic complexity. The method is demonstrated on synthetic and real single-channel data from muscle nicotinic acetylcholine channels. We show that parameter uncertainty can be characterized more accurately than with maximum-likelihood methods. Our code for performing inference in these ion channel models is publicly available.

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Statistical limits to the identification of ion channel domains by sequence similarity.

Publications

Statistical limits to the identification of ion channel domains by sequence similarity.

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