ArticleBiophysical journal2000
A direct optimization approach to hidden Markov modeling for single channel kinetics.
Article in Biophysical journal, 2000. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers.
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Who cites it
119 citing papers in PubMed.
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- Molecular basis for the increased fusion activity of the Ebola virus glycoprotein epidemic variant A82V: Insights from simulations and experiments.Cell reports · 2025Article
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- The combination of three CD4-induced antibodies targeting highly conserved Env regions with a small CD4-mimetic achieves potent ADCC activity.bioRxiv : the preprint server for biology · 2024Article
- Increasing the accuracy of single-molecule data analysis using tMAVEN.bioRxiv : the preprint server for biology · 2024Article
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- Regulation of Ebola GP conformation and membrane binding by the chemical environment of the late endosome.PLoS pathogens · 2023Article
- Detergent modulates the conformational equilibrium of SARS-CoV-2 Spike during cryo-EM structural determination.Nature communications · 2023Article
- Cholesterol inhibits human voltage-gated proton channel hHv1.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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- Structural dynamics determine voltage and pH gating in human voltage-gated proton channel.eLife · 2022Article
59 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Hidden Markov modeling (HMM) provides an effective approach for modeling single channel kinetics. Standard HMM is based on Baum's reestimation. As applied to single channel currents, the algorithm has the inability to optimize the rate constants directly. We present here an alternative approach by considering the problem as a general optimization problem. The quasi-Newton method is used for searching the likelihood surface. The analytical derivatives of the likelihood function are derived, thereby maximizing the efficiency of the optimization. Because the rate constants are optimized directly, the approach has advantages such as the allowance for model constraints and the ability to simultaneously fit multiple data sets obtained at different experimental conditions. Numerical examples are presented to illustrate the performance of the algorithm. Comparisons with Baum's reestimation suggest that the approach has a superior convergence speed when the likelihood surface is poorly defined due to, for example, a low signal-to-noise ratio or the aggregation of multiple states having identical conductances.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.