Wiki source code of Distance-Fluctuations

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5 = Distance Fluctuation (DF) Analysis =
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7 Giorgio Colombo Group (UNIPV)
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15 = What DF matrices are? =
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17 The analysis of the results of a MD simulation can performed using the Distance Fluctuation matrices (DF), based on the Coordination Propensity hypothesis:
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20 [[image:CodeCogsEqn-58.png||height="34" width="181"]]
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22 low CP values, corresponding to low pair-distance fluctuations, highlight groups of residues that move in a mechanically coordinated way.
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25 = How to use the script =
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27 • __Requisites__
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29 - Python 3.0 (or newer version)
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31 - Numpy
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33 - Scipy
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36 • __Usage__
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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.
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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).
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42 For more information run:
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44 {{{ python3 distance_fluctuation.py -h}}}
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47 {{{optional arguments:
48 -h, --help show this help message and exit
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50 Required arguments:
51 -ext {pdb,xyz}, --file_ext {pdb,xyz}
52 Input trajectory file iformat (options: .pdb or .xyz)
53 -n N_AA, --n_aa N_AA Number of aminoacids in each frame of the .pdb
54 trajectory
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56 Optional arguments:
57 -i IN_NAME, --in_name IN_NAME
58 Name of the trajectory file (default: trj)
59 -s S_FRAME, --s_frame S_FRAME
60 Index of the initial frame (default = 0)
61 -e E_FRAME, --e_frame E_FRAME
62 Index of the last frame (default = end)
63 -c CUTOFF, --cutoff CUTOFF
64 Cutoff for the distance fluctuation analysis (default = 5)
65 -t TOLERANCE, --tolerance TOLERANCE
66 Tolerance for the distance fluctuation analysis (default = 0)
67 -p {all,c-a}, --pdb_type {all,c-a}
68 Type of pdb file submitted (only C-alpha, all protein atoms)
69 -l {s,seq,v,volume}, --local {s,seq,v,volume}
70 Type of local cutoff used, sequence or volume (default = seq)
71 -r RADIUS, --radius RADIUS
72 Radius of the area cutoff (default = 7A)
73 -rs RES_START, --res_start RES_START
74 Starting residue if the PDB does not start with res.num. 1
75 -ro RMS_OUT, --rms_out RMS_OUT
76 RMS distance output filename (without extension)
77 -ao AVG_OUT, --avg_out AVG_OUT
78 Average distance output filename (without extension)
79 -so SEQ_OUT, --seq_out SEQ_OUT
80 Profile sequence output filename (without extension)
81 -da DIST_AN, --dist_an DIST_AN
82 Distance analysis output filename (without extension)
83 --blocks BLOCKS File with domains borders
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85 -rb RMS_B_OUT, --rms_b_out RMS_B_OUT
86 RMS distance blocks output filename}}}
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94 {{box title="**Contents**"}}
95 {{toc/}}
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98 {{/box}}
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104 • __How to read the output__
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106 The script generate different output file.
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109 A) Average Distance (avgdist) file:
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111 The name of the file is avgdist_out_x_y_type.dat with x = start frame of the analysis, y = end frame of the analysis, type = type of analysis (CA or sidechains)
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113 The file contains a matrix using the residue indexes as axes and the average value of the distance between the residues as the data (r1 r2  avgdist).
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115 The distance is calculated as the average of the euclidean distance between the residues.
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118 B) Distance Fluctuation (rmsdist) file:
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120 The name of the file is rmsdist_out_x_y_type.dat with x = start frame of the analysis, y = end frame of the analysis, type = type of analysis (CA or sidechains).
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122 The file contains a matrix using the residue indexes as axes and the distance fluctuation between the residues as the data (r1 r2 rmsdist).
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124 The distance fluctuation is calculated for the residues that are at least 3 residues away from each other (x-2 to x+2) as follow:
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126 1) Calculate the average euclidean distance between the residues (either CA or center of mass)
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128 2) Calculate the average distance vector
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130 3) Substract the distance fluctuation to the average distance
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132 4) Calculate the power of the difference between distance fluctuation and local fluctuation
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134 5) Filter the values of the close residues (1-x, x, 1+x)
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136 6) Divide the obtained value for the number of frames
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139 C) Profile Sequence (profile_sequence) file:
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141 The name of the file is profile_sequence_x_y_type.dat with x = start frame of the analysis, y = end frame of the analysis, type = type of analysis (CA or sidechains).
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143 The file is a vector containing the residue number and the local flucutation value.
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145 The local fluctuation is calculated as the average fluctuation of the residues close to each other (that is the residues ranging from x-2 to x+2 with x = residue number).
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147 Since the value contains the average distance fluctuation for a range of residues the output starts from residue 4 and ends at residue n-3).
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150 D) Distance Analysis (distance_analysis_out) file:
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152 The name of the file is distance_analysis_out_c_x_y_type.dat with c = cutoff value, x = start frame of the analysis, y = end frame of the analysis, type = type of analysis (CA or sidechains).
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154 This fail contains the distance fluctuation filtered by the cutoff value (the value are kept if the distance fluctuation is smaller than the cutoff value).
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156 The cutoff value is calculated as the sum between the nearcutoff and the tolerance value specified by the user.
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158 The nearcutoff can be calcolated either using sequence proximity or 3d proximity and can be specified by command line using the option -l or ~-~-local (see the command line help for further details).
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160 In the case of sequence proximity it is calculated as the average of the distance fluctuation value for residues in the range of x-2,x+2 (x = residue index)
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162 In the case of 3d proximity is calculated as the average distance fluctuation of the residues within a certain radius from the current one (default value = 6.5 A)
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165 E) Blocks Averaging: (rmsdist_out_b)
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167 It is possible to obtain a distance fluctuation matrix average on protein domains ( or blocks).
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169 The script requires an input file with the borders of the protein blocks defined by the user, in the form of two columns: the first with the lower limit and the second with the upper limit.
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172 = References =
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174 Morra, G.; Potestio, R.; Micheletti, C.; Colombo, G., Corresponding Functional Dynamics across the Hsp90 Chaperone Family: Insights from a Multiscale Analysis of MD Simulations, PLOS Computational Biology 8(3): e1002433. [[https:~~/~~/doi.org/10.1371/journal.pcbi.1002433>>url:https://doi.org/10.1371/journal.pcbi.1002433]]