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<u>Molecular weight (MW) estimates from experiments and analysis</u>: true molecular weight can be compared to the Porod estimate from scattering profiles.
In a Porod-Debye plot, a clear plateau is observed for globular (partial or fully folded) domains, whereas, fully unfolded domains are devoid of any discernable plateau. For details, refer to Figure 5 in <ahref=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3103662/>Rambo and Tainer, 2011</a>. In a Kratky plot, a parabolic shape is observed for globular (partial or fully folded) domains and a hyperbolic shape is observed for fully unfolded domains.
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<u><aname=firstP>Pair-distance distribution analysis</a></u><aclass='help' title='help' style='background-color:#228B22;color:#000000;text-decoration:none font-size=16px' href='/validation_help.html#pofr'>?</a>
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<u><aname=firstP>3.1.4. Pair-distance distribution analysis</a></u>
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{{ format_help("pofr", current_task) }}
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<u>P(r) analysis:</u> P(r) represents the distribution of distances between all pairs of atoms within the particle weighted by the respective electron densities. P(r) is the Fourier transform of I(s) (and vice versa). R<sub>g</sub> can be estimated from integrating the P(r) function. Agreement between the P(r) and Guinier-determined R<sub>g</sub> (table below) is a good measure of the self-consistency of the SAS profile. R<sub>g</sub> is a measure for the overall size of a macromolecule; e.g. a protein with a smaller R<sub>g</sub> is more compact than a protein with a larger R<sub>g</sub>, provided both have the same molecular weight (MW). The point where P(r) is decaying to zero is called D<sub>max</sub> and represents the maximum size of the particle.
<u> Guinier analysis:</u> agreement between the P(r) and Guinier-determined R<sub>g</sub> (table below) is a good measure of the self-consistency of the SAS profile. Molecular weight estimates can also be compared to Porod and sample molecular weights for consistency.
Copy file name to clipboardExpand all lines: templates/formodeling.html
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{% extends "validation_report_layout.html" %}
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{% from "macro.html" import insert_json_plot, write_table with context %}
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{% from "macro.html" import insert_json_plot, write_table, format_help with context %}
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{% block body %}
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<!-- Template for writing fit to information webpage -->
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<!-- ganesans - Salilab - UCSF -->
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<divclass="row">
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<u><aname='first'>Fit of model to data used for modeling</a></u><aclass='help' title='help' style='background-color:#228B22;color:#000000;text-decoration:none font-size=16px' href='/validation_help.html#fittodata'>?</a>
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<u><aname='first'>5. Fit of model to data used for modeling</a></u>
<h5class= ex2align= center><u> Fit of model(s) to SAS data</u></h5>
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<h5class= ex2align= center>
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<u>5.1. SAS</u>
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{{ format_help("fq-sas", current_task) }}
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</h5>
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{% if sasdb_sascif|length <1%}
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SAS data used in this integrative model could not be validated as the sascif file is currently unavailable.
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<u><aname='second'>Cormap p-value analysis of fits </a></u><aclass='help' title='help' style='background-color:#228B22;color:#000000;text-decoration:none font-size=16px' href='/validation_help.html#cormap'>?</a>
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<u><aname='second'>5.1.1. Χ² Goodness of Fit Assessment</a></u>
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{{ format_help("goodness", current_task) }}
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</h5>
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{% if number_of_fits > 0 %}
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<i>ATSAS DATCMP was used for hypothesis testing. <i>All data sets are similar (i.e. the fit and the data collected) </i> is the null hypothesis. p-value is a measure of evidence against the null hypothesis, smaller the value, the stronger the evidence that you should reject the null hypothesis.</i>
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{{ write_table(p_val) }}
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{% else %}
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<i>Model(s) and/or fit for this entry has not been deposited.</i>
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{% endif %}
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<i>The experimental scattering curve (in blue) can be compared with the theoretical curve calculated from a model (in red). Residual value plot is a measure to assess fit to the data. Residual values should be equally and randomly spaced around the horizontal axis.</i>
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</p>
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{% endfor %}
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</div>
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<u><aname='second'>5.1.2. Cormap Analysis</a></u>
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{{ format_help("cormap", current_task) }}
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</h5>
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{% if number_of_fits > 0 %}
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<i>ATSAS DATCMP was used for hypothesis testing. <i>All data sets are similar (i.e. the fit and the data collected) </i> is the null hypothesis. p-value is a measure of evidence against the null hypothesis, smaller the value, the stronger the evidence that you should reject the null hypothesis.</i>
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</p>
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{{ write_table(p_val) }}
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{% else %}
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<i>Model(s) and/or fit for this entry has not been deposited.</i>
<h4class="ex2" align="center"><u><aname='data'>Datasets used for modeling </a></u><aclass='help' title='help' style='background-color:#228B22;color:#000000;text-decoration:none font-size=16px' href="/validation_help.html#datasets">?</a></h4>
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<h4class="ex2" align="center"><u><aname='data'>2.3. Datasets used for modeling </a></u><aclass='help' title='help' style='background-color:#228B22;color:#000000;text-decoration:none font-size=16px' href="/validation_help.html#datasets">?</a></h4>
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{% if number_of_datasets <2%}
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<i>There is {{number_of_datasets|int}} unique dataset used to build the models in this entry.</i>
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{% else %}
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