Scripts for processing and analyzing AMBER .xtc trajectory files in the Functional Regulation of Aquaporin Dynamics by Lipid Bilayer Composition manuscript.
Contains scripts to perform distance feature selection from trajectory information, then use the final feature set to construct and validate Markov State Models.
Contains scripts to select 1000 frames from minima of each macrostate in the energy landscape and scripts to perform analyses on them (pore plug diheral, hydrophobic mismatch).
Contains scripts for time-dependent analyses, such as water count, water residence time, and lipid order parameter on a 10000 frame (100 ns) continous trajectory in a energy landscape minima. Also contains a script for HOLE analysis.
All parameter files and trajectories are organized by protein-structure/lipid and are being uploaded on a Box folder. Smaller files related to each analysis section above are on Box with the following tree
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└── SoPIP2-lipid-GitHub/
├── 00-starting-coordinates-and-params/
│ ├── open-structure/
│ │ ├── *.rst/ncrst
│ │ └── *.parm7/prmtop
│ └── closed-structure
├── 01-features-and-MSM/
│ ├── features-per-system/
│ │ ├── POPC-features.tar.bz2: calculated features for all frames and all trajectories
│ │ └── ... (other bilayer systems files are named similarly: $lipid-*)
│ └── MSM-related-objs/
│ ├── POPC/
│ │ ├── *features.pkl: indices of protein residue pairs
│ │ ├── *tica_obj.pkl: PyEMMA tICA object
│ │ ├── *tica_trajs.pkl: PyEMMA tICs for each traj and frame
│ │ ├── *cluster_obj.pkl: PyEMMA k-means clustering object
│ │ ├── *msm_obj.pkl: PyEMMA MSM object
│ │ ├── *ITS-error.pkl: PyEMMA implied timescale object
│ │ ├── *weights.pkl: PyEMMA stationary distribution of MSM
│ │ └── *probabilities.npy: msm and raw probability of each cluster
│ └── ...
├── 02-discrete-frames-and-analysis/
│ ├── minima-box (*.csv files)
│ ├── trajectories.tar.bz2 (*.xtc files with water stripped along with *gro parameters)/
│ │ ├── POPC-frames-stripped-wat.tar.bz2
│ │ └── ...
│ ├── dihedral.tar.bz2/
│ │ ├── POPC-dihedral.tar.bz2 (all *npy containing dihedral data of POPC system)
│ │ └── ...
│ └── mismatch.tar.bz2/
│ ├── prot-bulk-mismatch.tar.bz2 (all *pkl containing prot-bulk data)
│ └── shell-bulk-mismatch.tar.bz2 (all *pkl containing shell-bulk data)
└── 03-continuous-trajs-and-analysis/
├── cont-traj-data.csv (all cont trajs information and calculated results)
├── trajectories-stripped-lipid.tar.bz2/
│ ├── POPC-traj-stripped-lipid.tar.bz2/
│ │ ├── *stripped*xtc of 3 trajs (lipids stripped) per macrostate
│ │ └── *pdb of coordinate files for analyses
│ └── ...
├── trajectories-stripped-water.tar.bz2/
│ ├── POPC-traj-stripped-water.tar.bz2/
│ │ ├── *wat.xtc of 3 trajs (waters stripped) per macrostate
│ │ └── *pdb of coordinate files for analyses
│ └── ...
├── water.tar.bz2/
│ ├── POPC-wat-transport.tar.bz2/
│ │ ├── *passagetime*: time it takes for each water to transport
│ │ ├── *transportedres*: residue ID of the waters that was transported
│ │ ├── *transport*: 3d array recording whether a water was transported at each frame
│ │ └── *wat-in-pore*: number of water occupying the pore at each frame
│ ├── POPC-wat-restime.tar.bz2/
│ │ ├── *AVG-RES-TIME*: average time water continuously spends in slice, per water
│ │ ├── *STD-RES-TIME*: standard deviation of average residence time
│ │ ├── *FRAMES-PER-WAT*: per water, record frames during which water occupy slice
│ │ ├── *WAT-PER-FRAME*: per frame, record waters in slice
│ │ ├── *ALL-WAT-COUNT*: number of water continuously in slice, per water
│ │ └── *ALL-WAT*: record all water that has been in slice
│ └── ...
└── lipid-order.tar.bz2/
├── POPC-lipid-ord.tar.bz2/
│ ├── *protein-x.pkl: protein x positions across traj
│ ├── *protein-y.pkl: protein y positions across traj
│ ├── *lipid-x.pkl: lipid x positions of each lipid
│ ├── *lipid-y.pkl: lipid y positions of each lipid
│ ├── *scc_sn1.pkl: order param of each lipid's tail sn1
│ ├── *scc_sn2.pkl: order param of each lipid's tail sn2
│ ├── *avg_scc.pkl: average order param of each lipid
│ └── *std_scc.pkl: standard deviation of order param
└── ...