https://github.com/POVME/POVME

首先按照教程安裝povme3.0，每次使用的時候钮科，用一個單獨的python27環(huán)境退盯，source 路徑/miniconda2/bin/activate

（我GPU工作站設(shè)置了alias povme裤园，直接輸入povme即到python27環(huán)境）

第一步：將同一個蛋白（不同軌跡）align好撤师，生成pdb軌跡

gmx trjconv -s tpr 為reference structure，align的時候align到tpr結(jié)構(gòu)上拧揽，而不是第一幀

得到一個pdb軌跡

第二步：生成povme參數(shù)文件 *.ini

簡化版（斜體為必須設(shè)置的剃盾，其他參數(shù)默認(rèn)）：

GridSpacing? ? ? ? ? ? 1.0 （相鄰點之間的距離，單位為埃淤袜。降低這個數(shù)字可以提高精度痒谴，但會犧牲計算時間）

InclusionSphere? 65.0 98.0 50.0 16.0? （添加一個由點組成的球體到包圍區(qū)域，以(65.0 98.0 50.0)為中心铡羡，半徑為16.0积蔚。(65.0 98.0 50.0)是提供的樣品PDB軌跡文件中酶活性位點的位置(4NSS.pdb)）

PDBFileName?4NSS.pdb?

DistanceCutoff? ? ? ? ? ? ? 1.09 （默認(rèn)值為1.09埃，因為這是氫原子的范德華半徑蓖墅。）

ConvexHullExclusion? ? ? ? first? ?（first/max/each库倘，詳見下面解釋）

SeedSphere? 67.0 102.0 57.0 4.0 （如果設(shè)置了該seed球临扮，則測的即必須包含這個區(qū)域（如果有多個口袋的話）论矾；不設(shè)置，則測口袋總體積杆勇，即使有多個不相鄰的口袋贪壳。不要隨便用這個參數(shù)，當(dāng)該設(shè)置中所含的體積里面沒有口袋時蚜退，運算會報錯）

ContiguousPointsCriteria? ? 3 （如果兩個口袋卷至少共享這個ContiguousPointsCriteria的相鄰點闰靴，則認(rèn)為它們是“鄰接的”彪笼，用默認(rèn)即可）

NumProcessors? ? ? ? ? ? ? 16

OutputFilenamePrefix? ? ? ? ? ./POVME_test_run/example_? （所有POVME輸出的文件都以這個前綴開頭。POVME自動創(chuàng)建所需的目錄(./POVME_test_run/在本例中)）

####?The minimum input required for POVME to run is?the input trajectory name and inclusion region.

第三步：看輸出結(jié)果

We recommend that you visualize the results using VMD. Open the POVME output using VMD with the following command:

vmd -m POVME_test_run/example_volume_trajectory.pdb 4NSS.pdb

Under the?Graphics-->Representations menu?in VMD, show the 0: POVME_volume_trajectory.pdb molecule using the Drawing Method "VDW" (and consider reducing the sphere Scalevalue to something like0.3). Now press the play button in the bottom right corner of the VMD Main window to watch the pocket trajectory (For this short trajectory, it is probably better to advance manually through the frames).

第四步：PCA分析和聚類

（未完成蚂且。配猫。）

詳細(xì)參數(shù)：

# POVME 3.0 Sample Input File

# First, we need to define a point field that entirely encompasses all trajectory pockets.?

首先，我們需要定義一個完全包含所有軌跡口袋的點場杏死。

GridSpacing? ? ? ? ? ? 1.0? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?# The distance, in Angstroms, between adjacent points. Making this number lower improves accuracy at the expense of computing time.

#相鄰點之間的距離泵肄，單位為埃。降低這個數(shù)字可以提高精度淑翼，但會犧牲計算時間腐巢。

InclusionSphere? 65.0 98.0 50.0 16.0? ? ? ? ? ? ? ? # Add a sphere of points to the pocket- encompassing region, centered on (65.0 98.0 50.0) with radius 16.0.? (65.0 98.0 50.0) is the location of the enzymatic active site in the sample PDB? trajectory file provided (4NSS.pdb).

添加一個由點組成的球體到包圍區(qū)域，以(65.0 98.0 50.0)為中心玄括，半徑為16.0冯丙。(65.0 98.0 50.0)是提供的樣品PDB軌跡文件中酶活性位點的位置(4NSS.pdb)。

#InclusionSphere? -100.0 -100.0 -100.0 10.0? ? ? ? # Add a second sphere of points. Note that this sphere is included only for?demonstration?purposes. The point (-100.0 -100.0 -100.0) is actually far?from the 4NSS.pdb structure.

添加第二個點球體遭京。請注意胃惜，包含此范圍僅用于演示目的。這個點(-100.0 -100.0 -100.0)實際上離4NSS pdb的結(jié)構(gòu)很遠(yuǎn)洁墙。

#InclusionBox? ? 100.0 100.0 100.0 10.0 10.0 10.0? ? # Add a rectangular prism ("box") of? points to the pocket-encompassing region, centered on? (100.0 100.0 100.0) and spanning 10.0 Angstroms in the x, y, and z directions, respectively.? ? Again, this box is far from the?4NSS.pdb structure and is included only for demonstration purposes.

#添加一個矩形棱柱(“方框”)到包含口袋的區(qū)域蛹疯，以(100.0 100.0 100.0)為中心，在x热监、y和z方向上分別跨越10.0埃捺弦。同樣，這個盒子離4NSS很遠(yuǎn)孝扛，僅用于演示目的列吼。

InclusionCylinder? 65.0 98.0 50.0 1.0 1.0 1.0 16.0 10.0? # Add a cylinder of points centered?at [65.0 98.0 50.0], with its length along the axis? ? [1.0 1.0 1.0], a radius of 16.0 angstroms, and a height of 10.0 angstroms.

#添加一個以[65.0 98.0 50.0]為中心的點圓柱體，其長度沿軸[1.0 1.0 1.0]苦始，半徑16.0埃寞钥，高度10.0埃。

#ExclusionSphere? -100.0 -100.0 -100.0 10.0? ? ? ? ? # Remove all points from the pocket- encompassing region that fall within a sphere centered at? (-100.0 -100.0 -100.0) with radius 10.0.

#從環(huán)繞口袋的區(qū)域中移除所有位于半徑為10.0圓心(-100.0 -100.0 -100.0)的球體內(nèi)的點陌选。

#ExclusionBox? ? 100.0 100.0 100.0 10.0 10.0 10.0? ? # Remove all points from the pocket-encompassing region that fall within a box, centered at? ?(100.0 100.0 100.0) and spanning 10.0 Angstroms in the x, y, and z directions, respectively.

#從包含口袋的區(qū)域中移除位于方框內(nèi)的所有點理郑，在x、y和z方向上分別以(100.0 100.0 100.0)為中心和跨越10.0埃咨油。

# Saving and loading the POVME points of the pocket-encompassing region.

#SavePoints?? ? ? true? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? # You can optionally save the point field to a PDB file. As you can imagine, identifying just the right set of inclusion and exclusion spheres and boxes to encompass the binding pocket is challenging. On approach is to define an initial geometry, visualize that geometry together with the protein using a? program like VMD, and then iteratively add new inclusion and exclusion regions as required. The ability to save the points for visualization is helpful. Additionally, if your point field contains many points, generating the field may be computationally intensive. If you want?to use the same field for multiple POVME runs, using a saved copy of the field rather than repeatedly generating it from scratch is more efficient. Note that POVME also saves a NPY file, which contains the same data as the PDB file but can be loaded much faster in subsequent POVME runs. The point-field PDB file is saved to? ?{PREFIX}point_field.pdb (see OutputFilenamePrefix below). Additionally, if you specify a? contiguous- pocket seed region (see ContiguousPocketSeedSphere and ContiguousPocketSeedBox? below), POVME will also save those points to {PREFIX}contiguous_pocket_seed_points.pdb? for? visualization.

您可以選擇將POINT field 保存到PDB文件中您炉。正如你可以想象的那樣，確定正確的包含和排除球體和盒子來包含binding口袋是一項挑戰(zhàn)役电。方法是定義一個初始的幾何圖形赚爵，使用像VMD這樣的程序?qū)⒃搸缀螆D形與蛋白質(zhì)可視化，然后根據(jù)需要迭代地添加新的包含和排除區(qū)域。保存點以便可視化的能力很有幫助冀膝。此外唁奢，如果點場包含許多點，則生成的場可能會非常密集窝剖。如果您希望在多個POVME運行時使用相同的字段麻掸，那么使用保存的字段副本而不是從頭重復(fù)生成該字段更有效。注意赐纱，POVME還保存了一個NPY文件论笔，該文件包含與PDB文件相同的數(shù)據(jù)，但在隨后的POVME運行中可以更快地加載千所。點字段PDB文件被保存到{PREFIX}point_field.pdb(參見下面的OutputFilenamePrefix)狂魔。另外，如果您指定一個連續(xù)的口袋種子區(qū)域(參見下面的?ContiguousPocketSeedSphere和ContiguousPocketSeedBox?)淫痰，POVME也會將這些點保存為{PREFIX}contiguous_pocket_seed_points.pdb來可視化最楷。

#LoadPointsFilename? ? points.pdb.npy? ? ? ? ? ? ? ? ? ? ? # You can optionally load previous point fields if you don't want to generate them using the comamnds above. Note that you should use the .pdb.npy file, not the pdb file.

?加載之前生成的point field 文件

# Load the PDB trajectory file

PDBFileName? ? ? ? ? ? ? ? 4NSS.pdb? ? ? ? ? ? ? ? ? ? ? # Load in the PDB trajectory file with the pocket you want to analyze.Tell POVME how to identify points that are within the binding pocket.

DistanceCutoff? ? ? ? ? ? ? 1.09? ? ? ? ? ? ? ? ? ?# Any point that comes within this distance of any receptor atom's van der Waals surface will not be considered part of the pocket volume. 1.09 Angstroms was chosen as the default value because that is the van der Waals radius of a hydrogen atom.

任何靠近任何受體原子的范德華表面這個距離內(nèi)的點都不會被認(rèn)為是口袋體積的一部分。默認(rèn)值為1.09埃待错，因為這是氫原子的范德華半徑籽孙。

ConvexHullExclusion? ? ? ? first? ? ? ? ? ? ? ? ? ? ? ? ? # The convex hull is a method of determining where a pocket "ends" on the outside of the protein.? Portions of the inclusion region which lie outside of the convex hull are removed. In previous versions, this would be recalculated for each individual frame, however this led to significant numerical noise as the motion of the protein in different frames could radically redefine the convex hull. A consistent convex hull can now be used in all frames by giving the "first" or "max" keywords here. "first" applied the convex hull from? the first frame to all others."max" draws a convex hull around all the frames superimposed simultaneously (and may take a while on large trajectories). To reproduce previous behavior, the "each" keyword may be used. Any other keyword will not use the convex hull exclusion method.

可寫參數(shù)：first max each none

凸殼是一種確定pocket在蛋白質(zhì)外部“末端”位置的方法。所述凸包外部的部分包含區(qū)域被移除火俄。在以前的版本中犯建，對于每個單獨的幀，這將被重新計算瓜客，但是這導(dǎo)致了顯著的數(shù)值噪聲适瓦，因為蛋白質(zhì)在不同幀中的運動可能會從根本上重新定義凸包。一個一致的凸殼現(xiàn)在可以使用在所有幀通過給出“first”或“max”關(guān)鍵字在這里谱仪〔Ｎ酰“first”將第一幀中的凸包應(yīng)用到所有其他幀中》柙埽“max”在同時疊加的所有幀周圍畫一個凸殼(在大軌跡上可能需要一段時間)嗦随。要重現(xiàn)以前的行為，可以使用“each”關(guān)鍵字敬尺。任何其他關(guān)鍵字都不會使用凸包排除方法枚尼。

SeedSphere? 67.0 102.0 57.0 4.0? ? ? ? ? ? ? ? ? ?# It's possible your pocket-encompassing point field defined above might include more than one pocket in at least some of the frames of your trajectory. You can instruct POVME to remove any points? that are not contiguous with a user-defined "contiguous pocket seed region." This region, which is typically just a small sphere placed in the center of your primary pocket of interest, tells POVME which pocket to measure. If no such regions are specified, POVME will calculate the total volume accross all pockets covered by your pocket-encompassing point field, even if they are not contiguous.

如果設(shè)置了該seed球，則測的即必須包含這個區(qū)域（如果有多個口袋的話）砂吞；不設(shè)置署恍，則測口袋總體積，即使有多個不相鄰的口袋呜舒。

在你的軌跡的至少一些框架中锭汛，上面定義的包含口袋的點場可能包含多個口袋。您可以指示POVME刪除不與用戶定義的“contiguous pocket seed region”相鄰的任何points袭蝗。這個區(qū)域唤殴，通常是放在你感興趣的主要口袋中心的一個小球體，告訴POVME要測量哪個口袋（如果有多個口袋的話）到腥。如果沒有指定這樣的區(qū)域朵逝，POVME將計算包含口袋的點場所覆蓋的所有口袋的總體積，即使它們不是相鄰的乡范。

ContiguousPointsCriteria? ? 3? ? ? ? ? ? ? ? ? ? ?# Two pocket volumes are considered "contiguous" if they share at least this number neighboring points in common. Note that points that are "kitty-corner" from each other count as neighbors.

如果兩個口袋卷至少共享這個ContiguousPointsCriteria的相鄰點配名，則認(rèn)為它們是“鄰接的”。注意晋辆，點是“kitty-corner”從對方算作相鄰渠脉。

# Tell POVME how to perform the calculations.

NumProcessors? ? ? ? ? ? ? 1? ? ? ? ? ? ? ? ? ? ? ? ? ? ? # POVME can use multiple processors on Unix-based systems.

# Tell POVME how to save the output

OutputFilenamePrefix? ? ? ./POVME_test_run/example_? # All the files POVME outputs will start with this prefix. POVME automatically creates any? ?required directory (./POVME_test_run/ in this case).

所有POVME輸出的文件都以這個前綴開頭。POVME自動創(chuàng)建所需的目錄(./POVME_test_run/在本例中)瓶佳。

CompressOutput? ? ? ? ? ? ? ? false? ? ? ? ? # If you're short on disk space, POVME can automatically compress all output files using gz compression.

安裝POVME3.0

wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh

bash Miniconda2-latest-Linux-x86_64.sh -b -p miniconda2

source miniconda2/bin/activate

pip install povme

https://github.com/POVME/POVME

Basic example

cd basic_example

POVME3.py sample_input.ini

This example shows the "classic" operation of POVME, using a geometrically-defined inclusion sphere. If you open the "sample_input.ini" text file, you will find the operating parameters. The minimum input required for POVME to run is the input trajectory name and inclusion region.

Once this runs, you will have an output directory named POVME_test_run.

Ligand-defined inclusion region example

cd ligand_example/

POVME3.py sample_POVME_input.ini

To visualize:

vmd -m POVME_test_run/POVME_volume_trajectory.pdb 1BYQ_every250.pdb

POVME 3.0 now allows users to define the inclusion region of a pocket using a ligand residue name. The pocket will then be defined in all grid points within 3 Angstroms of the ligand atoms in the loaded PDB trajectory. Note that this residue name must match the one given in the input PDB trajectory.

Clustering and PCA example

cd analysis_workflow_example/

source runWorkflow.sh

The bulk of the new capabilities of POVME 3.0 are in separate scripts. Three of these are showcased in the analysis workflow example.

This example runs POVME on 5 trajectories taken from the POVME 3.0 paper's HSP90 simulations. Each of these trajectory PDB files has 5 frames, and has had the ligand removed. After running POVME on these trajectories, three post-processing scripts are run:

binding_site_overlap.py calculates the similarity of all of the analyzed frames

cluster.py processes the binding_site_overlap matrix and performs hierarchical clustering.

1. This example is programmed to yield five clusters, as specified in the "-n" argument to cluster.py?// 這個示例被編程為生成5個集群芋膘，這在cluster.py的“-n”參數(shù)中指定

2. A heatmap showing which frames belong to which cluster is displayed when cluster.py finishes running. //py結(jié)束運行時將顯示一個熱圖，顯示哪個幀屬于哪個集群霸饲。

3. Combined and individual-simulation transition maps are displayed. //將顯示組合和單獨模擬的轉(zhuǎn)換映射为朋。

4. The most representative frames from each cluster are output in the 3-post_analysis/ALL/cluster#subdirectories. //每個集群中最具代表性的幀輸出在3-post_analysis/ALL/cluster#子目錄中。

5. The average pocket shape of each cluster can be visualized in vmd by running vmd -e visualizeAll.vmd in the 3-post_analysis/ALL subdirectory, and showing the second representation in each loaded object .//通過在3-post_analysis/ALL子目錄運行vmd -e visualizeAll.vmd厚脉，可以在vmd中顯示每個簇的平均?pocket shape习寸，并在每個加載的對象中顯示?second representation。

6. Text files of the cluster members and representatives are written, with each line corresponding to one cluster //text文件中寫cluter成員和代表的結(jié)構(gòu)傻工，每一行對應(yīng)一個集群

pocketPointsPca.py runs principal component analysis of the pocket points in the frames

1. Scatterplots of each simulations position in PC space are shown//給出了計算機空間中各模擬位置的散點圖

2. A plot of the explained variance for each PC is shown//每個PC的方差解釋圖顯示

3. The first 10 principal components can be visualized by running vmd -e loadAllPcs.vmd //顯示前10個主成分

運行流程：

(base) $ source runWorkflow.sh ---------------------------------

START

---------------------------------

PDBFILENAME ../1-trajectories/4R3M_every50ns_aligned.pdb

GRIDSPACING 1.0

INCLUSIONSPHERE 38.2 -47.5 63.7 15

CONTIGUOUSPOINTSCRITERIA 3

DISTANCECUTOFF 1.09

CONVEXHULLEXCLUSION none

OUTPUTFILENAMEPREFIX ./4R3M_every50ns/4R3M_every50ns_

COMPRESSOUTPUT false

NUMPROCESSORS 1

If you use POVME in your research, please cite the following reference:? Durrant, J. D., C. A. de Oliveira, et al. (2011). "POVME: An algorithm for measuring binding-pocket volumes." J Mol Graph Model 29(5): 773-776.

Parameters:

OUTPUTFILENAMEPREFIX: ./4R3M_every50ns/4R3M_every50ns_

POINTSINCLUDEREGIONS: sphere at (38.2, -47.5, 63.7), radius = 15.0

NUMFRAMES: -1

OutputFrameFilenamePrefix: ./4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_

MAXGROWITERATIONS: 10000000000.0

GRIDSPACING: 1.0

NUMPROCESSORS: 1

DISTANCECUTOFF: 1.09

COMPRESSOUTPUT: False

PDBFILENAME: ../1-trajectories/4R3M_every50ns_aligned.pdb

CONVEXHULLEXCLUSION: none

SAVEREGIONS: False

CONTIGUOUSPOINTSCRITERIA: 3

OUTPUTBASENAME: 4R3M_every50ns_

---------------------------------

PARAMETERS DEFINED

---------------------------------

---------------------------------

ABOUT TO LOAD RECEPTORS

---------------------------------

Reading frames from ../1-trajectories/4R3M_every50ns_aligned.pdb

Further processing frame 1

Further processing frame 2

Further processing frame 3

Further processing frame 4

Further processing frame 5

---------------------------------

RECEPTORS LOADED

---------------------------------

Generating the pocket-encompassing point field

Saving the point field as a PDB and NPY file

Point field saved to ./4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_inclusion.pdb to permit visualization Point field saved to ./4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_inclusion.npy to optionally load for the volume calculation

Calculating the pocket volume of each frame

---------------------------------

STARTING CALC VOLUME

---------------------------------

Frame 1:? Volume 2399.0 A^3? Surf. A. 1136.0 A^2

---------------------------------

FINISHING CALC VOLUME

---------------------------------

---------------------------------

STARTING CALC VOLUME

---------------------------------

Frame 2:? Volume 2038.0 A^3? Surf. A. 1172.0 A^2

---------------------------------

FINISHING CALC VOLUME

---------------------------------

---------------------------------

STARTING CALC VOLUME

---------------------------------

Frame 3:? Volume 1925.0 A^3? Surf. A. 1153.0 A^2

---------------------------------

FINISHING CALC VOLUME

---------------------------------

---------------------------------

STARTING CALC VOLUME

---------------------------------

Frame 4:? Volume 2121.0 A^3? Surf. A. 1147.0 A^2

---------------------------------

FINISHING CALC VOLUME

---------------------------------

---------------------------------

STARTING CALC VOLUME

---------------------------------

Frame 5:? Volume 2255.0 A^3? Surf. A. 1177.0 A^2

---------------------------------

FINISHING CALC VOLUME

---------------------------------

---------------------------------

VOLUMES CALCULATED

---------------------------------

FRAME? ? ? ? | VOLUME (A^3) | SURF. A. (A^2)

-------------+--------------+----------------

1? ? ? ? ? ? |? 2399.0? ? |? 1136.0

2? ? ? ? ? ? |? 2038.0? ? |? 1172.0

3? ? ? ? ? ? |? 1925.0? ? |? 1153.0

4? ? ? ? ? ? |? 2121.0? ? |? 1147.0

5? ? ? ? ? ? |? 2255.0? ? |? 1177.0

Execution time = 83.1753201485 sec

---------------------------------

ABOUT TO CALCULATE OCCUPANCY AVERAGE

---------------------------------

---------------------------------

CALCULATED OCCUPANCY AVERAGE

---------------------------------

---------------------------------

ABOUT TO CALCULATE COLOR MAPS

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---------------------------------

CALCULATED COLOR MAPS

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接下來的4個軌跡略霞溪，每個軌跡5幀

['../../2-POVME_analysis/4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_frame_1.npy'

, '../../2-POVME_analysis/1UYF_every50ns/1UYF_every50ns_frameInfo/1UYF_every50ns_frame_1.npy', '../../2-POVME_analysis/1UYL_every50ns/1UYL_every50ns_frameInfo/1UYL_every50ns_frame_1.npy', '../../2-POVME_analysis/3D0B_every50ns/3D0B_every50ns_frameInfo/3D0B_every50ns_frame_1.npy', '../../2-POVME_analysis/1UYI_every50ns/1UYI_every50ns_frameInfo/1UYI_every50ns_frame_1.npy', '../../2-POVME_analysis/4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_frame_2.npy', '../../2-POVME_analysis/1UYF_every50ns/1UYF_every50ns_frameInfo/1UYF_every50ns_frame_2.npy', '../../2-POVME_analysis/1UYL_every50ns/1UYL_every50ns_frameInfo/1UYL_every50ns_frame_2.npy', '../../2-POVME_analysis/3D0B_every50ns/3D0B_every50ns_frameInfo/3D0B_every50ns_frame_2.npy', '../../2-POVME_analysis/1UYI_every50ns/1UYI_every50ns_frameInfo/1UYI_every50ns_frame_2.npy', '../../2-POVME_analysis/4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_frame_3.npy', '../../2-POVME_analysis/1UYF_every50ns/1UYF_every50ns_frameInfo/1UYF_every50ns_frame_3.npy', '../../2-POVME_analysis/1UYL_every50ns/1UYL_every50ns_frameInfo/1UYL_every50ns_frame_3.npy', '../../2-POVME_analysis/3D0B_every50ns/3D0B_every50ns_frameInfo/3D0B_every50ns_frame_3.npy', '../../2-POVME_analysis/1UYI_every50ns/1UYI_every50ns_frameInfo/1UYI_every50ns_frame_3.npy', '../../2-POVME_analysis/4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_frame_4.npy', '../../2-POVME_analysis/1UYF_every50ns/1UYF_every50ns_frameInfo/1UYF_every50ns_frame_4.npy', '../../2-POVME_analysis/1UYL_every50ns/1UYL_every50ns_frameInfo/1UYL_every50ns_frame_4.npy', '../../2-POVME_analysis/3D0B_every50ns/3D0B_every50ns_frameInfo/3D0B_every50ns_frame_4.npy', '../../2-POVME_analysis/1UYI_every50ns/1UYI_every50ns_frameInfo/1UYI_every50ns_frame_4.npy', '../../2-POVME_analysis/4R3M_every50ns/4R3M_every50ns_frameInfo/4R3M_every50ns_frame_5.npy', '../../2-POVME_analysis/1UYF_every50ns/1UYF_every50ns_frameInfo/1UYF_every50ns_frame_5.npy', '../../2-POVME_analysis/1UYL_every50ns/1UYL_every50ns_frameInfo/1UYL_every50ns_frame_5.npy', '../../2-POVME_analysis/3D0B_every50ns/3D0B_every50ns_frameInfo/3D0B_every50ns_frame_5.npy', '../../2-POVME_analysis/1UYI_every50ns/1UYI_every50ns_frameInfo/1UYI_every50ns_frame_5.npy']The number of frames found was: 25 所有的五條軌跡25幀

Starting Tanimoto calculations

Overlap Matrix for Tanimoto calculation

[[1.? ? ? ? 0.62900448 0.64765217 0.59501348 0.66082691 0.64882943

? 0.62778152 0.50920051 0.64109882 0.74176218 0.64160972 0.66573329

? 0.48633702 0.67280453 0.65585088 0.60796869 0.67383146 0.50098945

? 0.64149636 0.7079489? 0.56489576 0.62705055 0.56037358 0.67741935

? 0.64851826]

[0.62900448 1.? ? ? ? 0.65356004 0.695747? 0.74812874 0.55058573

? 0.69299182 0.49346926 0.64285714 0.73632742 0.5311263? 0.69069935

? 0.47880381 0.69395924 0.67008124 0.56946403 0.74684001 0.47969627

? 0.67925207 0.70893372 0.54429101 0.70376956 0.5378712? 0.66357474

? 0.66725601]

[0.64765217 0.65356004 1.? ? ? ? 0.62482615 0.6469553? 0.5600713

? 0.65291262 0.56429047 0.63038793 0.69622042 0.5225? ? 0.65073788

? 0.51325948 0.70431287 0.6398892? 0.5645614? 0.68756531 0.51535606

? 0.67670251 0.68152866 0.54075813 0.64857437 0.58823529 0.66642883

? 0.67958748]

[0.59501348 0.695747? 0.62482615 1.? ? ? ? 0.70594673 0.53978873

? 0.6518724? 0.50803342 0.6281407? 0.668637? 0.50583245 0.6364587

? 0.47801837 0.65541741 0.68194741 0.54454073 0.6650619? 0.47211286

? 0.66370107 0.67572336 0.52339861 0.6363016? 0.5421123? 0.63732024

? 0.66021808]

[0.66082691 0.74812874 0.6469553? 0.70594673 1.? ? ? ? 0.58947368

? 0.69208939 0.51531764 0.64684554 0.72531418 0.54733406 0.65937169

? 0.50768717 0.67300108 0.71021292 0.59129815 0.70105263 0.49666889

? 0.67178571 0.70430108 0.57721345 0.65586207 0.55398587 0.67406877

? 0.66819626]

[0.64882943 0.55058573 0.5600713? 0.53978873 0.58947368 1.

? 0.55807464 0.47963649 0.59841629 0.60843158 0.66512605 0.58087707

? 0.48221205 0.62072808 0.57082153 0.6120155? 0.5830721? 0.47951977

? 0.6001462? 0.58773181 0.56564551 0.53864569 0.50365726 0.59525547

? 0.5790789 ]

[0.62778152 0.69299182 0.65291262 0.6518724? 0.69208939 0.55807464

? 1.? ? ? ? 0.49734126 0.64242639 0.71203639 0.53094233 0.74272198

? 0.46046364 0.66456583 0.66735751 0.54761905 0.71091854 0.46638924

? 0.66167247 0.71070293 0.5357377? 0.72730484 0.52411784 0.66445299

? 0.71499822]

[0.50920051 0.49346926 0.56429047 0.50803342 0.51531764 0.47963649

? 0.49734126 1.? ? ? ? 0.4659164? 0.49798074 0.44208754 0.4935106

? 0.67952522 0.50482005 0.46926166 0.49799331 0.50386399 0.64959766

? 0.49713193 0.51492063 0.54539363 0.50265045 0.67279942 0.4849636

? 0.50380469]

[0.64109882 0.64285714 0.63038793 0.6281407? 0.64684554 0.59841629

? 0.64242639 0.4659164? 1.? ? ? ? 0.70007289 0.56882129 0.6453824

? 0.46138415 0.7118003? 0.66148844 0.54910394 0.6953547? 0.4711051

? 0.69124767 0.6898572? 0.52081911 0.6340694? 0.50841751 0.71223565

? 0.70293353]

[0.74176218 0.73632742 0.69622042 0.668637? 0.72531418 0.60843158

? 0.71203639 0.49798074 0.70007289 1.? ? ? ? 0.596? ? ? 0.72039943

? 0.46971139 0.77501853 0.71136924 0.58980583 0.78556034 0.48138639

? 0.72433597 0.76931447 0.54114958 0.69243986 0.54143646 0.74563319

? 0.73240443]

[0.64160972 0.5311263? 0.5225? ? 0.50583245 0.54733406 0.66512605

? 0.53094233 0.44208754 0.56882129 0.596? ? ? 1.? ? ? ? 0.58644946

? 0.421875? 0.59056886 0.54448017 0.58232303 0.56996103 0.42802102

? 0.57206045 0.55297065 0.49874507 0.5276907? 0.46175243 0.59356287

? 0.5565186 ]

[0.66573329 0.69069935 0.65073788 0.6364587? 0.65937169 0.58087707

? 0.74272198 0.4935106? 0.6453824? 0.72039943 0.58644946 1.

? 0.45285439 0.6780086? 0.63786575 0.57559775 0.73573466 0.4778906

? 0.66430595 0.705? ? ? 0.53833333 0.72548318 0.53355372 0.6665483

? 0.69828203]

[0.48633702 0.47880381 0.51325948 0.47801837 0.50768717 0.48221205

? 0.46046364 0.67952522 0.46138415 0.46971139 0.421875? 0.45285439

? 1.? ? ? ? 0.47039581 0.45989134 0.49044807 0.46247655 0.72191235

? 0.47049234 0.4763285? 0.56914894 0.46643445 0.67067217 0.46011673

? 0.46793426]

[0.67280453 0.69395924 0.70431287 0.65541741 0.67300108 0.62072808

? 0.66456583 0.50482005 0.7118003? 0.77501853 0.59056886 0.6780086

? 0.47039581 1.? ? ? ? 0.66302817 0.58502674 0.75453885 0.48441645

? 0.75573956 0.72327273 0.53126045 0.66527488 0.53825593 0.75906344

? 0.71335031]

[0.65585088 0.67008124 0.6398892? 0.68194741 0.71021292 0.57082153

? 0.66735751 0.46926166 0.66148844 0.71136924 0.54448017 0.63786575

? 0.45989134 0.66302817 1.? ? ? ? 0.59231853 0.68267581 0.4729686

? 0.65780266 0.67899512 0.5376735? 0.63218005 0.51506317 0.67706487

? 0.67120894]

[0.60796869 0.56946403 0.5645614? 0.54454073 0.59129815 0.6120155

? 0.54761905 0.49799331 0.54910394 0.58980583 0.58232303 0.57559775

? 0.49044807 0.58502674 0.59231853 1.? ? ? ? 0.58384668 0.4998245

? 0.55924502 0.57137907 0.65694813 0.55111713 0.50736554 0.55897795

? 0.54632807]

[0.67383146 0.74684001 0.68756531 0.6650619? 0.70105263 0.5830721

? 0.71091854 0.50386399 0.6953547? 0.78556034 0.56996103 0.73573466

? 0.46247655 0.75453885 0.68267581 0.58384668 1.? ? ? ? 0.47717185

? 0.73107143 0.74689386 0.5461039? 0.7037037? 0.54287556 0.72058301

? 0.73598297]

[0.50098945 0.47969627 0.51535606 0.47211286 0.49666889 0.47951977

? 0.46638924 0.64959766 0.4711051? 0.48138639 0.42802102 0.4778906

? 0.72191235 0.48441645 0.4729686? 0.4998245? 0.47717185 1.

? 0.46941446 0.49557812 0.58810811 0.49870045 0.72211426 0.4748438

? 0.48074413]

[0.64149636 0.67925207 0.67670251 0.66370107 0.67178571 0.6001462

? 0.66167247 0.49713193 0.69124767 0.72433597 0.57206045 0.66430595

? 0.47049234 0.75573956 0.65780266 0.55924502 0.73107143 0.46941446

? 1.? ? ? ? 0.7113031? 0.5105194? 0.65894955 0.53710483 0.72945966

? 0.71630948]

[0.7079489? 0.70893372 0.68152866 0.67572336 0.70430108 0.58773181

? 0.71070293 0.51492063 0.6898572? 0.76931447 0.55297065 0.705

? 0.4763285? 0.72327273 0.67899512 0.57137907 0.74689386 0.49557812

? 0.7113031? 1.? ? ? ? 0.55322688 0.6764099? 0.55140496 0.72869629

? 0.72192321]

[0.56489576 0.54429101 0.54075813 0.52339861 0.57721345 0.56564551

? 0.5357377? 0.54539363 0.52081911 0.54114958 0.49874507 0.53833333

? 0.56914894 0.53126045 0.5376735? 0.65694813 0.5461039? 0.58810811

? 0.5105194? 0.55322688 1.? ? ? ? 0.53866232 0.56885813 0.51020408

? 0.52481104]

[0.62705055 0.70376956 0.64857437 0.6363016? 0.65586207 0.53864569

? 0.72730484 0.50265045 0.6340694? 0.69243986 0.5276907? 0.72548318

? 0.46643445 0.66527488 0.63218005 0.55111713 0.7037037? 0.49870045

? 0.65894955 0.6764099? 0.53866232 1.? ? ? ? 0.55409836 0.65424431

? 0.66815835]

[0.56037358 0.5378712? 0.58823529 0.5421123? 0.55398587 0.50365726

? 0.52411784 0.67279942 0.50841751 0.54143646 0.46175243 0.53355372

? 0.67067217 0.53825593 0.51506317 0.50736554 0.54287556 0.72211426

? 0.53710483 0.55140496 0.56885813 0.55409836 1.? ? ? ? 0.53528628

? 0.53522054]

[0.67741935 0.66357474 0.66642883 0.63732024 0.67406877 0.59525547

? 0.66445299 0.4849636? 0.71223565 0.74563319 0.59356287 0.6665483

? 0.46011673 0.75906344 0.67706487 0.55897795 0.72058301 0.4748438

? 0.72945966 0.72869629 0.51020408 0.65424431 0.53528628 1.

? 0.697733? ]

[0.64851826 0.66725601 0.67958748 0.66021808 0.66819626 0.5790789

? 0.71499822 0.50380469 0.70293353 0.73240443 0.5565186? 0.69828203

? 0.46793426 0.71335031 0.67120894 0.54632807 0.73598297 0.48074413

? 0.71630948 0.72192321 0.52481104 0.66815835 0.53522054 0.697733

? 1.? ? ? ? ]]

Starting Tversky calculations

Overlap Matrix for Tversky calculation

[[1.? ? ? ? 0.76115048 0.77615673 0.73614006 0.7861609? 0.72780325

? 0.77615673 0.66902876 0.74906211 0.86327637 0.70446019 0.79283035

? 0.62317632 0.79199667 0.79199667 0.71238016 0.82325969 0.63318049

? 0.77198833 0.8315965? 0.70029179 0.78074198 0.70029179 0.79658191

? 0.78449354]

[0.78369099 1.? ? ? ? 0.79184549 0.82145923 0.85793991 0.66566524

? 0.8360515? 0.66480687 0.76094421 0.87253219 0.63347639 0.8223176

? 0.62532189 0.81845494 0.81416309 0.69313305 0.88755365 0.62360515

? 0.81072961 0.84463519 0.69356223 0.84935622 0.69184549 0.79828326

? 0.80987124]

[0.79640719 0.78913601 1.? ? ? ? 0.76860565 0.786142? 0.67194183

? 0.80538922 0.7245509? 0.75064157 0.84302823 0.6257485? 0.79213003

? 0.65397776 0.82420873 0.79041916 0.68819504 0.84431138 0.65312233

? 0.8075278? 0.82378101 0.68947819 0.8075278? 0.73139435 0.79897348

? 0.81736527]

[0.75631692 0.81970021 0.76959315 1.? ? ? ? 0.82869379 0.65653105

? 0.80513919 0.67708779 0.74946467 0.8235546? 0.61284797 0.78201285

? 0.62398287 0.79014989 0.82184154 0.67280514 0.82826552 0.61627409

? 0.7987152? 0.82012848 0.67537473 0.7987152? 0.69464668 0.77815846

? 0.80385439]

[0.80563862 0.85390859 0.78513456 0.82656984 1.? ? ? ? 0.69372063

? 0.83340453 0.68261427 0.76206749 0.86287911 0.64459633 0.79794959

? 0.64886801 0.80179411 0.84066638 0.70824434 0.85348142 0.6369073

? 0.80350278 0.83938488 0.71849637 0.8124733? 0.70354549 0.80392994

? 0.80862879]

[0.85672228 0.76104024 0.77085378 0.75220805 0.79685967 1.

? 0.78508342 0.69921492 0.77870461 0.83562316 0.7767419? 0.79293425

? 0.671737? 0.81992149 0.79097154 0.7747792? 0.82139352 0.66633955

? 0.80569185 0.80863592 0.76104024 0.77281649 0.70951914 0.80029441

? 0.79587831]

[0.76657061 0.80197612 0.77521614 0.77398106 0.8032112? 0.65870729

? 1.? ? ? ? 0.65459037 0.74557431 0.83779333 0.621655? 0.84026348

? 0.59695348 0.78139152 0.79538905 0.66282421 0.8443804? 0.59983532

? 0.78180321 0.82832441 0.67270482 0.84767394 0.66652944 0.7826266

? 0.82626595]

[0.68066158 0.65691264 0.71840543 0.67048346 0.67769296 0.6043257

? 0.67430025 1.? ? ? ? 0.61450382 0.6798134? 0.55682782 0.66115352

? 0.7769296? 0.66624258 0.64418999 0.63146735 0.69126378 0.75318066

? 0.66157761 0.68787108 0.69041561 0.6836302? 0.79092451 0.64970314

? 0.67387617]

[0.81644707 0.80554294 0.79736483 0.79509314 0.81054066 0.72103589

? 0.82280781 0.65833712 1.? ? ? ? 0.8727851? 0.67969105 0.81281236

? 0.62698773 0.85506588 0.83189459 0.69604725 0.87732849 0.63334848

? 0.84325307 0.85597456 0.69332122 0.82189914 0.68605179 0.85688323

? 0.86006361]

[0.84050325 0.82508117 0.79991883 0.78043831 0.81980519 0.6911526

? 0.82589286 0.65056818 0.77962662 1.? ? ? ? 0.66517857 0.81980519

? 0.60105519 0.84862013 0.82021104 0.69034091 0.88758117 0.60876623

? 0.81899351 0.86079545 0.67248377 0.81777597 0.67613636 0.83157468

? 0.83198052]

[0.87792208 0.76675325 0.76? ? ? 0.74337662 0.7838961? 0.82233766

? 0.78441558 0.68207792 0.77714286 0.85142857 1.? ? ? ? 0.82285714

? 0.63116883 0.81974026 0.79168831 0.77350649 0.83584416 0.63480519

? 0.80623377 0.8025974? 0.7225974? 0.78701299 0.68987013 0.8238961

? 0.80051948]

[0.8059322? 0.81186441 0.78474576 0.77372881 0.79152542 0.68474576

? 0.86483051 0.66059322 0.75805085 0.8559322? 0.67118644 1.

? 0.59830508 0.80211864 0.78516949 0.69364407 0.87415254 0.61822034

? 0.79491525 0.83644068 0.68432203 0.85889831 0.68389831 0.79533898

? 0.82669492]

[0.68894009 0.67142857 0.70460829 0.67142857 0.7? ? ? ? 0.63087558

? 0.66820276 0.84423963 0.6359447? 0.68248848 0.55990783 0.65069124

? 1.? ? ? ? 0.66267281 0.66313364 0.65069124 0.68156682 0.83502304

? 0.66497696 0.68156682 0.73963134 0.67880184 0.82304147 0.65391705

? 0.66912442]

[0.8172043? 0.82021505 0.8288172? 0.79354839 0.80731183 0.71870968

? 0.81634409 0.67569892 0.80946237 0.89935484 0.67870968 0.81419355

? 0.61849462 1.? ? ? ? 0.80989247 0.70580645 0.89376344 0.6283871

? 0.86365591 0.85548387 0.68344086 0.82236559 0.69290323 0.86451613

? 0.84344086]

[0.79232694 0.7910759? 0.7706422? 0.80025021 0.8206839? 0.67222686

? 0.80567139 0.63344454 0.76355296 0.84278565 0.63552961 0.77272727

? 0.6000834? 0.7852377? 1.? ? ? ? 0.70100083 0.82985822 0.60925771

? 0.78398666 0.81150959 0.67848207 0.78482068 0.66305254 0.79649708

? 0.80108424]

[0.805752? 0.76143329 0.75860443 0.74068835 0.78170674 0.74446016

? 0.75907591 0.70202735 0.7223008? 0.8019802? 0.70202735 0.77180575

? 0.66572372 0.77369165 0.79255068 1.? ? ? ? 0.80433758 0.67138142

? 0.75436115 0.77746346 0.81801037 0.76756247 0.69825554 0.75294672

? 0.75058934]

[0.78779418 0.82489031 0.78739529 0.77143997 0.79696849 0.66773036

? 0.81810929 0.6501795? 0.77024332 0.8723574? 0.64180295 0.82289589

? 0.58994815 0.82887914 0.79377742 0.68049462 1.? ? ? ? 0.60031911

? 0.81651376 0.8392501? 0.67092142 0.81850818 0.67171919 0.8085361

? 0.82728361]

[0.70585502 0.67518587 0.70957249 0.6686803? 0.69284387 0.63104089

? 0.67704461 0.82527881 0.64776952 0.69702602 0.56784387 0.67797398

? 0.84200743 0.67890335 0.67890335 0.66171004 0.69934944 1.

? 0.66682156 0.70306691 0.75836431 0.71328996 0.8633829? 0.67100372

? 0.68447955]

[0.79145299 0.80726496 0.80683761 0.79700855 0.80384615 0.7017094

? 0.81153846 0.66666667 0.79316239 0.86239316 0.66324786 0.8017094

? 0.61666667 0.85811966 0.8034188? 0.68376068 0.87478632 0.61324786

? 1.? ? ? ? 0.84444444 0.66367521 0.81495726 0.68974359 0.84230769

? 0.84273504]

[0.82642916 0.81524441 0.7978459? 0.79328915 0.81400166 0.68268434

? 0.83347142 0.67191384 0.78044739 0.87862469 0.64001657 0.81772991

? 0.61267606 0.82394366 0.8061309? 0.68309859 0.87158244 0.62676056

? 0.81855841 1.? ? ? ? 0.68889809 0.81483016 0.69096935 0.82891466

? 0.83347142]

[0.74501109 0.71662971 0.71485588 0.69933481 0.745898? 0.68780488

? 0.72461197 0.72195122 0.6767184? 0.73481153 0.61685144 0.71618625

? 0.71175166 0.70465632 0.72150776 0.76940133 0.745898? 0.72372506

? 0.6886918? 0.73747228 1.? ? ? ? 0.73215078 0.72904656 0.68736142

? 0.70820399]

[0.76107273 0.80414466 0.76716782 0.75782202 0.77285656 0.63998375

? 0.83665177 0.65501829 0.73506705 0.81877286 0.61560341 0.82364892

? 0.59853718 0.77691995 0.76472978 0.66151971 0.8338074? 0.62373019

? 0.77488826 0.79926859 0.6708655? 1.? ? ? ? 0.68671272 0.77041853

? 0.78870378]

[0.73716542 0.70732778 0.75032909 0.71171566 0.72268539 0.63448881

? 0.7103993? 0.81834138 0.6625713? 0.73102238 0.58271172 0.70820535

? 0.78367705 0.70688899 0.69767442 0.64984642 0.73892058 0.81526986

? 0.70820535 0.73189996 0.72136902 0.74155331 1.? ? ? ? 0.70557262

? 0.71347082]

[0.81911702 0.79725675 0.80068581 0.77882555 0.80668667 0.69909987

? 0.81483069 0.65666524 0.8084012? 0.87826832 0.6798114? 0.80454351

? 0.60822975 0.86155165 0.81868838 0.68452636 0.86883841 0.61894556

? 0.84483498 0.85769396 0.66438063 0.81268753 0.68924132 1.

? 0.83111873]

[0.78909853 0.79119497 0.80125786 0.7870021? 0.79371069 0.68008386

? 0.84150943 0.66624738 0.79371069 0.85953878 0.64612159 0.81802935

? 0.60880503 0.82222222 0.80545073 0.66750524 0.86960168 0.61761006

? 0.82683438 0.84360587 0.66960168 0.81383648 0.68176101 0.8129979

? 1.? ? ? ? ]]

Given index file (-i) and prefix-to-trajectory mapping (-t or -T). Clustering will return prefix and frame numbers of cluster members, and will extract representatives./home/amax/Downloads/miniconda2/lib/python2.7/site-packages/scipy/cluster/hierarchy.py:490:

ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix? return linkage(y, method='average', metric='euclidean')

Matrix-index-to-trajectory-frame mapping given. Writing out trajectory frames to cluster_reps.csv and cluster_members.csv.Extracting trajectory frames

Generating difference maps

[16, 4, 2, 2, 1]

5 2 3

[4]+ Stopped python ../analyzeClusterMembershipInGroups.py

Traceback (most recent call last):

? File "/home/amax/Downloads/miniconda2/bin/pocketPointsPca.py", line 9, in <module>

? ? from sklearn.preprocessing import StandardScaler

ImportError: No module named sklearn.preprocessing

[('1UYI_every50ns', 5), ('3D0B_every50ns', 5), ('1UYL_every50ns', 5), ('4R3M_every50ns', 5),

('1UYF_every50ns', 5)]