Changes for page Distance-Fluctuations

Last modified by emacasali on 2022/09/15 13:34
From version 7.3
edited by emacasali
on 2022/09/14 21:51
Change comment: There is no comment for this version
To version 3.1
edited by emacasali
on 2022/09/14 17:25
Change comment: There is no comment for this version

Summary

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2 2  (((
3 3  (% class="container" %)
4 4  (((
5 -= Distance Fluctuation (DF) Analysis =
5 += My Collab's Extended Title =
6 6  
7 -Giorgio Colombo Group (UNIPV)
7 +My collab's subtitle
8 8  )))
9 9  )))
10 10  
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13 13  (% class="col-xs-12 col-sm-8" %)
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15 -= What DF matrices are? =
15 += What can I find here? =
16 16  
17 -The analysis of the results of a MD simulation can performed using the Distance Fluctuation matrices (DF), based on the Coordination Propensity hypothesis:
17 +* Notice how the table of contents on the right
18 +* is automatically updated
19 +* to hold this page's headers
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20 -[[image:CodeCogsEqn-58.png||height="34" width="181"]]
21 += Who has access? =
21 21  
22 -low CP values, corresponding to low pair-distance fluctuations, highlight groups of residues that move in a mechanically coordinated way.
23 -
24 -
25 -= How to use the script =
26 -
27 -• __Requisites__
28 -
29 - - Python 3.0 (or newer version)
30 -
31 - - Numpy
32 -
33 - - Scipy
34 -
35 -
36 -• __Usage__
37 -
38 -The script can analyze a MD trajectory and identify the coordinated motions between residues. It can then filter the output matrix based on the distance to identify long-range coordinated motions.
39 -
40 -The script can work both using only C-alphas (using either a pdb or a xyz file) or the sidechains (using a pdb file).
41 -
42 -For more information run:
43 -
44 -{{{ python3 distance_fluctuation.py -h}}}
23 +Describe the audience of this collab.
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46 46  
47 47  
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49 49  (((
50 50  {{box title="**Contents**"}}
51 51  {{toc/}}
52 -
53 -
54 54  {{/box}}
55 55  
56 56  
57 57  )))
58 58  )))
59 -
60 -
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