Nanopore Analysis of Wild-Type and Mutant Prion Protein (PrPC): Single Molecule Discrimination and PrPC Kinetics
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{"title"=>"Nanopore Analysis of Wild-Type and Mutant Prion Protein (PrPC): Single Molecule Discrimination and PrPC Kinetics", "type"=>"journal", "authors"=>[{"first_name"=>"Nahid N.", "last_name"=>"Jetha", "scopus_author_id"=>"6602130078"}, {"first_name"=>"Valentyna", "last_name"=>"Semenchenko", "scopus_author_id"=>"6601927665"}, {"first_name"=>"David S.", "last_name"=>"Wishart", "scopus_author_id"=>"7005602630"}, {"first_name"=>"Neil R.", "last_name"=>"Cashman", "scopus_author_id"=>"16169124200"}, {"first_name"=>"Andre", "last_name"=>"Marziali", "scopus_author_id"=>"6701614670"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pui"=>"368294462", "sgr"=>"84873504625", "doi"=>"10.1371/journal.pone.0054982", "scopus"=>"2-s2.0-84873504625", "pmid"=>"23393562"}, "id"=>"a8caaae2-73aa-3706-9a95-a34e3bf9d965", "abstract"=>"Prion diseases are fatal neurodegenerative diseases associated with the conversion of cellular prion protein (PrP(C)) in the central nervous system into the infectious isoform (PrP(Sc)). The mechanics of conversion are almost entirely unknown, with understanding stymied by the lack of an atomic-level structure for PrP(Sc). A number of pathogenic PrP(C) mutants exist that are characterized by an increased propensity for conversion into PrP(Sc) and that differ from wild-type by only a single amino-acid point mutation in their primary structure. These mutations are known to perturb the stability and conformational dynamics of the protein. Understanding of how this occurs may provide insight into the mechanism of PrP(C) conversion. In this work we sought to explore wild-type and pathogenic mutant prion protein structure and dynamics by analysis of the current fluctuations through an organic α-hemolysin nanometer-scale pore (nanopore) in which a single prion protein has been captured electrophoretically. In doing this, we find that wild-type and D178N mutant PrP(C), (a PrP(C) mutant associated with both Fatal Familial Insomnia and Creutzfeldt-Jakob disease), exhibit easily distinguishable current signatures and kinetics inside the pore and we further demonstrate, with the use of Hidden Markov Model signal processing, accurate discrimination between these two proteins at the single molecule level based on the kinetics of a single PrP(C) capture event. Moreover, we present a four-state model to describe wild-type PrP(C) kinetics in the pore as a first step in our investigation on characterizing the differences in kinetics and conformational dynamics between wild-type and D178N mutant PrP(C). These results demonstrate the potential of nanopore analysis for highly sensitive, real-time protein and small molecule detection based on single molecule kinetics inside a nanopore, and show the utility of this technique as an assay to probe differences in stability between wild-type and mutant prion proteins at the single molecule level.", "link"=>"http://www.mendeley.com/research/nanopore-analysis-wildtype-mutant-prion-protein-prpc-single-molecule-discrimination-prpc-kinetics", "reader_count"=>35, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>4, "Student > Master"=>3, "Student > Bachelor"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>4, "Student > Master"=>3, "Student > Bachelor"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>3, "Agricultural and Biological Sciences"=>13, "Medicine and Dentistry"=>3, "Neuroscience"=>1, "Physics and Astronomy"=>8, "Chemistry"=>3, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>8}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>13}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Japan"=>1, "United Kingdom"=>1, "Mexico"=>1, "Australia"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/482484"], "description"=>"<div><p>Prion diseases are fatal neurodegenerative diseases associated with the conversion of cellular prion protein (PrP<sup>C</sup>) in the central nervous system into the infectious isoform (PrP<sup>Sc</sup>). The mechanics of conversion are almost entirely unknown, with understanding stymied by the lack of an atomic-level structure for PrP<sup>Sc</sup>. A number of pathogenic PrP<sup>C</sup> mutants exist that are characterized by an increased propensity for conversion into PrP<sup>Sc</sup> and that differ from wild-type by only a single amino-acid point mutation in their primary structure. These mutations are known to perturb the stability and conformational dynamics of the protein. Understanding of how this occurs may provide insight into the mechanism of PrP<sup>C</sup> conversion. In this work we sought to explore wild-type and pathogenic mutant prion protein structure and dynamics by analysis of the current fluctuations through an organic α-hemolysin nanometer-scale pore (nanopore) in which a single prion protein has been captured electrophoretically. In doing this, we find that wild-type and D178N mutant PrP<sup>C</sup>, (a PrP<sup>C</sup> mutant associated with both Fatal Familial Insomnia and Creutzfeldt-Jakob disease), exhibit easily distinguishable current signatures and kinetics inside the pore and we further demonstrate, with the use of Hidden Markov Model signal processing, accurate discrimination between these two proteins at the single molecule level based on the kinetics of a single PrP<sup>C</sup> capture event. Moreover, we present a four-state model to describe wild-type PrP<sup>C</sup> kinetics in the pore as a first step in our investigation on characterizing the differences in kinetics and conformational dynamics between wild-type and D178N mutant PrP<sup>C</sup>. These results demonstrate the potential of nanopore analysis for highly sensitive, real-time protein and small molecule detection based on single molecule kinetics inside a nanopore, and show the utility of this technique as an assay to probe differences in stability between wild-type and mutant prion proteins at the single molecule level.</p> </div>", "links"=>[], "tags"=>["nanopore", "wild-type", "mutant", "prion", "kinetics"], "article_id"=>155832, "categories"=>["Neuroscience", "Physics", "Biochemistry", "Infectious Diseases", "Biophysics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Nahid N. Jetha", "Valentyna Semenchenko", "David S. Wishart", "Neil R. Cashman", "Andre Marziali"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0054982"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Nanopore_Analysis_of_Wild_Type_and_Mutant_Prion_Protein_PrP_C_Single_Molecule_Discrimination_and_PrP_C_Kinetics__/155832", "title"=>"Nanopore Analysis of Wild-Type and Mutant Prion Protein (PrP<sup>C</sup>): Single Molecule Discrimination and PrP<sup>C</sup> Kinetics", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-05 01:37:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/494803"], "description"=>"<p>PrP<sup>C</sup> is electrophoretically driven into the α-HL nanopore (voltage polarity given by the plus and minus signs) via its positively charged N-terminus. The <i>trans</i> chamber contains 0.3 M KCl, 45 mM NaPO<sub>4</sub>, 10 mM HEPES, pH 8.0 solution at a PrP<sup>C</sup> concentration of 13.4 µM. The <i>cis</i> chamber contains 3 M KCl, 10 mM HEPES, pH 8.0 solution. The salt-concentration gradient across the pore generates an osmotic flow from <i>trans</i>-to-<i>cis</i> and enhances the electric field around the entrance of the <i>trans</i>-side of the pore <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone.0054982-Wanunu1\" target=\"_blank\">[30]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone.0054982-Hatlo1\" target=\"_blank\">[31]</a> thereby substantially increasing the nanopore-capture rate of PrP<sup>C</sup> in solution relative to symmetric salt conditions <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone.0054982-Wanunu1\" target=\"_blank\">[30]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone.0054982-Hatlo1\" target=\"_blank\">[31]</a>. Experiments were conducted (and maintained) at a temperature of 20°C ±0.1°C.</p>", "links"=>[], "tags"=>["illustrating"], "article_id"=>165323, "categories"=>["Neuroscience", "Physics", "Biochemistry", "Infectious Diseases", "Biophysics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Nahid N. Jetha", "Valentyna Semenchenko", "David S. Wishart", "Neil R. Cashman", "Andre Marziali"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0054982.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cartoon_illustrating_the_capture_of_an_individual_PrP_C_molecule_into_an_945_hemolysin_nanopore_/165323", "title"=>"Cartoon illustrating the capture of an individual PrP<sup>C</sup> molecule into an α-hemolysin nanopore.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:28:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/494892"], "description"=>"<p> <b>M KCl </b><b><i>cis</i></b><b>, 0.3 </b><b>M KCl </b><b><i>trans</i></b><b>).</b> (<b>a</b>) Shown is the capture rate as a function of voltage for KKRR-ShPrP(120–232) (blue), ShPrP(120–232) (red) and the buffer-only control (green). In the case of the buffer-only control the capture rate represents the rate of pore gating as a function of voltage. Both KKRR-ShPrP(120–232) and ShPrP(120–232) exhibit capture kinetics that are exponentially dependent on voltage consistent with the applied voltage acting on the positively charged residues at the N-terminus (five in the case of KKRR-ShPrP(120–232) and one in the case of ShPrP(120–232)) to decrease the energy barrier height for entry into the pore, thereby exponentially increasing the capture rate. In addition, the capture rate for KKRR-ShPrP(120–232) is between one and one-and-a-half orders of magnitude higher than ShPrP(120–232) indicating that the large majority of captures of KKRR-ShPrP(120–232) involve threading of the N-terminus through the pore. Error bars represent the standard error on the mean, and were determined via bootstrapping in the case of ShPrP(120–232) and the buffer-only control, whereas in the case of KKRR-ShPrP(120–232) error bars were determined based on two separate datasets. (<b>b</b>) The average event lifetime for KKRR-ShPrP(120–232) increases exponentially with voltage consistent with PrP<sup>C</sup> escape from the pore (as opposed to translocation) over an electrostatic energy barrier (governed by the applied voltage). The standard deviation of the event lifetime distribution indicates the presence of both short and very long time events (i.e. >1 s). Error bars represent standard error on the mean and were determined based on two separate datasets.</p>", "links"=>[], "tags"=>["voltage"], "article_id"=>165420, "categories"=>["Neuroscience", "Physics", "Biochemistry", "Infectious Diseases", "Biophysics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Nahid N. Jetha", "Valentyna Semenchenko", "David S. Wishart", "Neil R. Cashman", "Andre Marziali"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0054982.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Nanopore_capture_rate_and_KKRR_ShPrP_120_8211_232_average_event_lifetime_as_a_function_of_voltage_3_/165420", "title"=>"Nanopore capture rate and KKRR-ShPrP(120–232) average event lifetime as a function of voltage (3", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:30:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/495027"], "description"=>"<p>Ionic current histogram of all PrP<sup>C</sup> capture events from all voltages, with ionic current normalized by the open-pore current (I0) at a given voltage, for KKRR-ShPrP(120–232) (blue) and KKRR-ShPrP(120–232)-D178N (red). Ionic current is median filtered to 2.99 ms per data-point. The histograms exhibit multiple peaks with varying degrees of overlap indicative of complex PrP<sup>C</sup> kinetics in the pore. Moreover, the histograms exhibit clear distinguishable features (e.g. the near absence of the peak at I/I0 ∼0 pA with respect to KKRR-ShPrP(120–232)-D178N).</p>", "links"=>[], "tags"=>["histogram"], "article_id"=>165550, "categories"=>["Neuroscience", "Physics", "Biochemistry", "Infectious Diseases", "Biophysics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Nahid N. Jetha", "Valentyna Semenchenko", "David S. Wishart", "Neil R. Cashman", "Andre Marziali"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0054982.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_All_point_current_histogram_for_KKRR_ShPrP_120_8211_232_and_KKRR_ShPrP_120_8211_232_D178N_/165550", "title"=>"All-point current histogram for KKRR-ShPrP(120–232) and KKRR-ShPrP(120–232)-D178N.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:32:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/495156"], "description"=>"<p>(<b>a</b>) <b>(Left)</b> The KKRR-ShPrP(120–232) event histogram (blue) is divided into three regimes/states a high-state (black), mid-state (green) and low-state (red). The location of the peak and width of the distribution for each state in our initial model represents our best guess of the location and size of a given regime. <b>(Right)</b> The model parameters: <i>π</i> (i.e. the initial condition or probability that an event begins in a given state), <i>q</i> (the location of the peak of the Gaussian distribution, in terms of I/I0, for a given state), <i>b</i> (the standard deviation on the Gaussian of each state, which defines a state’s noise properties), and <i>A</i> (the state-to-state transition probability matrix). In our initial model we assume ignorance of the probabilities and therefore assume <i>π</i> to be uniformly distributed (i.e. an event is assumed equally likely to begin in any of the three states). Similarly with the transition probability matrix <i>A</i>, we assume all transitions to be equally likely (e.g. if in the low-state there is an equal probability for remaining in the low-state as there is for transitioning into the mid-state or the high-state). (<b>b</b>) <b>(Left)</b> After 40 iterations of the EM algorithm the optimal three-state model that best describes the data (i.e. the maximum likelihood model estimate) is converged upon. The low-state, far from encompassing all of the low current (as was presumed in our initial model) is very narrow and well defined, while the mid and high states both broaden out (the peak of the mid-state also shifts to a deeper conductance level relative to the initial model). <b>(Right)</b> The corresponding optimal model parameters.</p>", "links"=>[], "tags"=>["histogram", "optimal", "hmm"], "article_id"=>165673, "categories"=>["Neuroscience", "Physics", "Biochemistry", "Infectious Diseases", "Biophysics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Nahid N. Jetha", "Valentyna Semenchenko", "David S. Wishart", "Neil R. Cashman", "Andre Marziali"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0054982.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_KKRR_ShPrP_120_8211_232_event_histogram_and_initial_and_optimal_HMM_models_/165673", "title"=>"KKRR-ShPrP(120–232) event histogram and initial and optimal HMM models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:34:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/495256"], "description"=>"<p>(<b>a</b>) <b>(Left)</b> The KKRR-ShPrP(120–232)-D178N event histogram (red), and the corresponding high-state (black), mid-state (green) and low-state (blue) that make up the initial model for HMM analysis. The location of the peak (<i>q</i>) and width of the distribution (<i>b</i>) for each state are the same as for the optimal KKRR-ShPrP(120–232) model (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone-0054982-g004\" target=\"_blank\">Figure 4B</a>). This choice for <i>q</i> and <i>b</i> serves to highlight how the individual states evolve and differ from that of wild-type PrP<sup>C</sup>. <b>(Right)</b> The corresponding initial model parameters. Similar to the initial model for KKRR-ShPrP(120–232) (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone-0054982-g004\" target=\"_blank\">Figure 4A</a>) we assume ignorance of the probabilities and therefore assume <i>π</i> to be uniformly distributed. Likewise, with the transition probability matrix (<i>A</i>), we assume all transitions to be equally likely. (<b>b</b>) <b>(Left)</b> After 36 iterations of the EM algorithm the optimal three-state model that best describes the data (i.e. the maximum likelihood model estimate) is converged upon. The individual states (properties and kinetics) are significantly different from wild-type PrP<sup>C</sup> (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone-0054982-g004\" target=\"_blank\">Figure 4B</a>), highlighting the importance of amino-acid residue D178 to the dynamics and structural stability of PrP<sup>C </sup><b>(Right)</b> The corresponding optimal model parameters.</p>", "links"=>[], "tags"=>["histogram", "optimal", "hmm"], "article_id"=>165782, "categories"=>["Neuroscience", "Physics", "Biochemistry", "Infectious Diseases", "Biophysics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Nahid N. Jetha", "Valentyna Semenchenko", "David S. Wishart", "Neil R. Cashman", "Andre Marziali"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0054982.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_KKRR_ShPrP_120_8211_232_D178N_event_histogram_and_initial_and_optimal_HMM_models_/165782", "title"=>"KKRR-ShPrP(120–232)-D178N event histogram and initial and optimal HMM models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:36:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/495346"], "description"=>"<p>A sample KKRR-ShPrP(120–232) event (blue) with the most likely state sequence (i.e. Viterbi path) overlayed (red) as determined by a Viterbi analysis of the event. The sample event highlights the dependence of mid-state statistics (i.e. the transition rates out of the mid-state) on how the mid-state is entered. The event qualitatively shows that if the mid-state is entered from the high state then a transition back to the high-state is more likely than a transition into the low-state. Likewise, transitions into the mid-state from the low-state are more likely to return to the low-state as opposed to entering the high-state. <b>(Bottom left)</b> Mid-state transition rate into the high-state as a function of voltage, depending on how the mid-state is entered. If the mid-state is entered from the high-state (blue) the transition rate back into the high-state is between one and two orders of magnitude higher (depending on voltage) than the transition rate into the high-state when the mid-state is entered from the low-state (red). <b>(Bottom right)</b> Mid-state transition rate into the low-state as a function of voltage, depending on how the mid-state is entered. If the mid-state is entered from the low-state (red) the transition rate back into the low-state is between one and two orders of magnitude higher (depending on voltage) than the transition rate into the low-state when the mid-state is entered from the high-state (blue).</p>", "links"=>[], "tags"=>["mid-state"], "article_id"=>165868, "categories"=>["Neuroscience", "Physics", "Biochemistry", "Infectious Diseases", "Biophysics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Nahid N. Jetha", "Valentyna Semenchenko", "David S. Wishart", "Neil R. Cashman", "Andre Marziali"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0054982.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_KKRR_ShPrP_120_8211_232_mid_state_statistics_as_a_function_of_voltage_Top_/165868", "title"=>"KKRR-ShPrP(120–232) mid-state statistics as a function of voltage. (Top)", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:37:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/495425"], "description"=>"<p>H, M<sub>H</sub>, L, and M<sub>L</sub> refer to the high, mid-high, low, and mid-low states respectively (N.B. PrP<sup>C</sup> can escape from the pore from each state, which is not explicitly shown in the four-state model). HMM analysis of KKRR-ShPrP(120–232) in combination with the mid-state analysis (refer to text) yields the information on how the states are connected.</p>", "links"=>[], "tags"=>["characterizing", "kinetics"], "article_id"=>165945, "categories"=>["Neuroscience", "Physics", "Biochemistry", "Infectious Diseases", "Biophysics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Nahid N. Jetha", "Valentyna Semenchenko", "David S. Wishart", "Neil R. Cashman", "Andre Marziali"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0054982.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Four_state_model_characterizing_KKRR_ShPrP_120_8211_232_kinetics_in_the_pore_/165945", "title"=>"Four-state model characterizing KKRR-ShPrP(120–232) kinetics in the pore.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-05 01:39:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/495457"], "description"=>"<p>Case 1 refers to the situation whereby all events, regardless of event lifetime, are analyzed. The simulated 50∶50 mix dataset forms the individual events to be protein-called by which wild-type and mutant predictive value is determined. These events are randomly selected from the total number of wild-type and mutant events. After selection (and removal from the total number of events), the remaining events form the training sets for both wild-type and mutant. Wild-type predictive value refers to the likelihood that an event is in fact a wild-type event given that the protein-calling algorithm has called it as wild-type. Similarly the mutant predictive value refers to the likelihood that an event is in fact a mutant event given that the protein-calling algorithm has called it as mutant. Case 2 refers to the case whereby only those events that have an event lifetime of ≥1 s are analyzed (i.e. only long time events makeup the training sets for both wild-type and mutant and the generated 50∶50 mix dataset). The predictive value, not surprisingly, improves when only considering long-time events which is primarily due to the fact that long-time events can be better assessed in terms of their kinetics than short-time events (i.e. the amount of data available to characterize an event is proportional to the event lifetime) thereby improving protein-calling accuracy.</p>", "links"=>[], "tags"=>["mutant", "predictive"], "article_id"=>165981, "categories"=>["Neuroscience", "Physics", "Biochemistry", "Infectious Diseases", "Biophysics", "Biotechnology", "Chemistry", "Genetics"], "users"=>["Nahid N. Jetha", "Valentyna Semenchenko", "David S. Wishart", "Neil R. Cashman", "Andre Marziali"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0054982.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Wild_type_and_mutant_predictive_value_/165981", "title"=>"Wild-type and mutant predictive value.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-05 01:39:41"}

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