ArticleJournal of chemical theory and computation2015
Identification of Mutational Hot Spots for Substrate Diffusion: Application to Myoglobin.
Article in Journal of chemical theory and computation, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Gas Sensing by Bacterial H-NOX Proteins: An MD Study.Molecules (Basel, Switzerland) · 2020Article
- Correction to Identification of Mutational Hot Spots for Substrate Diffusion: Application to Myoglobin.Journal of chemical theory and computation · 2018Article
- Mechanism of ONature chemistry · 2017Article
- Markov state models of protein misfolding.The Journal of chemical physics · 2016Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
The pathways by which small molecules (substrates or inhibitors) access active sites are a key aspect of the function of enzymes and other proteins. A key problem in designing or altering such proteins is to identify sites for mutation that will have the desired effect on the substrate transport properties. While specific access channels have been invoked in the past, molecular simulations suggest that multiple routes are possible, complicating the analysis. This complexity, however, can be captured by a Markov State Model (MSM) of the ligand diffusion process. We have developed a sensitivity analysis of the resulting rate matrix, which identifies the locations where mutations should have the largest effect on the diffusive on rate. We apply this method to myoglobin, which is the best characterized example both from experiment and simulation. We validate the approach by translating the sensitivity parameter obtained from this method into the CO binding rates in myoglobin upon mutation, resulting in a semi-quantitative correlation with experiments. The model is further validated against an explicit simulation for one of the experimental mutants.
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Registered trials
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