Tutorial: re-docking a 7-mer peptide
These pages describe version 1.0, distributed with ADFRsuite 1.0. For new work use ADCP 1.1.
This tutorial re-docks a known peptide into its native receptor: a pseudophosphorylated Smad1 peptide into the TPR domain of CHIP (PDB 3Q47).
You will learn to:
- generate a target file for docking in a box defined by the known peptide
- run ADCP to re-dock the peptide
- understand the output of an ADCP docking run
Prerequisites: ADFRsuite 1.0 installed with ADFRsuite-1.0/bin on your PATH, and the tutorial data downloaded (see Tutorials).
1. Convert PDB files to PDBQT files
PDBQT is the file format of the AutoDock programs. It adds two columns to the PDB format: the partial charge and the AutoDock atom type.
We start from two separate PDB files for the receptor and the peptide, split from the 3Q47 entry. Both contain only protein atoms.
Unzip the data and go to the directory for this tutorial:
unzip ADCP_tutorial_data.zip
cd ADCP_tutorial_data/3Q47Add hydrogens to both files with reduce (included in ADFRsuite):
reduce 3Q47_rec.pdb > 3Q47_recH.pdb
reduce 3Q47_pep.pdb > 3Q47_pepH.pdbConvert the protonated PDB files into PDBQT files with prepare_receptor and prepare_ligand:
prepare_receptor -r 3Q47_recH.pdb
prepare_ligand -l 3Q47_pepH.pdb2. Generate the target file
A target file is a zipped file with everything needed for docking with ADFR and ADCP. It can be generated from the command line or a GUI.
agfr -r 3Q47_recH.pdbqt -l 3Q47_pepH.pdbqt -asv 1.1 -o 3Q47This puts the docking box around the given peptide with 4 Å padding. If you do not know the peptide binding site, see the agfrgui tutorials.
| Option | Meaning |
|---|---|
-r |
the receptor file (required) |
-l |
the ligand file |
-asv |
AutoSite version, 1.0 or 1.1 (default 1.0 when -asv is not given) |
-o |
name for the target file |
Details. agfr positions and sizes the docking box over the receptor pocket(s) where the ligand will be docked, and computes affinity maps for a list of AutoDock4 atom types. The maps are calculated by AutoGrid4.
- Box. The box can be set in several ways with
-b/--boxMode. Here agfr uses the bounding box of the known ligand (-l/--ligand), with 4 Å of padding added on every side. Change the padding with-P/--padding. - Output. The target file (
.trg) is saved under the name given by-o/--output; without it, a unique descriptive name is created. It contains the affinity maps, translational points for placing the ligand at sensible positions in the box, and metadata about the grid size, position and spacing, the receptor atoms involved and the maps. Inspect it with theaboutcommand. - Pockets. The translational points are computed by AutoSite, which clusters high-affinity grid points into potential binding pockets.
-p/--pocketModesets how multiple clusters are handled; when it is omitted, as here, all clusters are merged into one set of translational points. - Atom types. By default maps are computed for all AutoDock4 atom types. Restrict them with
-m/--mapTypesfor smaller files and faster runs, but the target file then cannot dock ligands with atom types it has no map for.
#################################################################
# If you used AGFR in your work, please cite: #
# #
# P.A. Ravindranath S. Forli, D.S. Goodsell, A.J. Olson and #
# M.F. Sanner #
# AutoDockFR: Advances in Protein-Ligand Docking with #
# Explicitly Specified Binding Site Flexibility #
# PLoS Comput Biol 11(12): e1004586 #
# DOI:10.1371/journal.pcbi.1004586 #
# #
# P. Ananad Ravindranath and M.F. Sanner #
# AutoSite: an automated approach for pseudoligands prediction #
# - From ligand binding sites identification to predicting key #
# ligand atoms #
# Bioinformatics (2016) #
# DOI:10.1093/bioinformatics/btw367 #
# #
# Please see http://adfr.scripps.edu for more information. #
#################################################################
Computing grids on ambae a Linux-4.15.0-47-generic-x86_64-with-debian-buster-sid computer
Date Mon Jun 3 16:04:22 2019
loading receptor: 3Q47_recH.pdbqt
loading ligand: 3Q47_pepH.pdbqt
set box using ligand
Box center: 16.052 9.415 5.617
Box length: 20.250 21.000 21.000
Box size : 54 56 56
padding : 4.000
spacing : 0.375
identifying pockets using AutoSite ....
Scanning at: -0.36 -0.792 -0.6
0 #: 0
Scanning at: -0.315 -0.693 -0.525
clust.| Energy| # of |Rad. of | energy | bns |score |
number| |points|gyration|per vol.|buriedness|v*b^2/rg|
------+-------+------+--------+--------+----------+--------|
1 -33.11 59 2.50 -0.56 0.729 12.54
Scanning at: -0.27 -0.594 -0.45
clust.| Energy| # of |Rad. of | energy | bns |score |
number| |points|gyration|per vol.|buriedness|v*b^2/rg|
------+-------+------+--------+--------+----------+--------|
1 -46.72 89 2.94 -0.52 0.773 18.09
2 -34.13 68 2.32 -0.50 0.756 16.79
3 -46.20 84 2.74 -0.55 0.715 15.65
Scanning at: -0.225 -0.495 -0.375
clust.| Energy| # of |Rad. of | energy | bns |score |
number| |points|gyration|per vol.|buriedness|v*b^2/rg|
------+-------+------+--------+--------+----------+--------|
1 -84.90 165 3.52 -0.51 0.747 26.14
2 -66.18 140 3.26 -0.47 0.664 18.92
3 -73.12 162 4.53 -0.45 0.689 16.97
found 3 pocket(s)
pocket| energy | # of |Rad. of | energy | bns | score
number| |points|gyration|per vol.|buriedness|v*b^2/rg
------+---------+------+--------+--------+----------+---------
1 -100.51 251 4.08 -0.40 0.82 41.49
2 -84.03 236 4.02 -0.36 0.77 34.84
3 -92.62 265 4.92 -0.35 0.78 32.82
merging clusters ...
done. got 106 fill Points, in 5.07 (sec)
setting map types using: all to ['HS', 'Mg', 'HD', 'NA', 'Fe', 'Br', 'NS', 'A', 'C', 'Mn', 'G', 'F', 'I', 'H', 'J', 'N', 'Q', 'P', 'S', 'GA', 'Z', 'Zn', 'Cl', 'Ca', 'OA', 'SA', 'OS']
computing maps for center=(16.052 9.415 5.617) size=(20.250 21.000 21.000) dims=(54 56 56) ...
104 points inside the box
maps computed in 3.02 (sec)
making target file 3Q47.trg ...done.
done. 6.42 (sec)3. Dock the peptide from its sequence
adcp -t 3Q47.trg -s npisdvd -N 20 -n 1000000 -o 3Q47_redocking -ref 3Q47_pepH.pdbadcp detects the available cores and uses all of them by default. Here it runs 20 independent searches (-N, --nbRuns 20), each with up to 1,000,000 evaluations of the scoring function (-n, --numSteps). By default adcp runs 50 searches of 2.5 million evaluations each. Harder docking problems need more searches to raise the chance of finding the best pose (the global minimum of the scoring function).
The run writes:
| File | Content |
|---|---|
3Q47_redocking_ranked_*.pdb |
final solutions, ranked after clustering |
3Q47_redocking_*.pdb |
the Monte Carlo trajectory of each replica |
3Q47_redocking_*.out |
detailed output of each replica |
- The search is stochastic: docking the same peptide into the same target twice can give different results with different random seeds. The energy landscape is the same in both runs; if both find its global minimum they give the same solution, whatever path the search took. Searches trapped in local minima give different poses. Setting the seed (
--seed) makes a run reproducible for a given version of the code. - In general, run 100 replicas with 1 million steps per amino acid, starting from a mixture of extended and helical conformations.
Example output (abridged; the full run lists 100 modes):
Detected 8 cores, using 8 cores
using ramaprob.data in current folder
Performing search (20 ADCP runs with 1000000 steps each) ...
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
Docking performed in 148.19 seconds, i.e. 0 hours 02 minutes 28.191412 seconds
mode | affinity | clust. | ref. | clust. | rmsd | best | energy | best |
| (kcal/mol) | rmsd | rmsd | size | avg. | rmsd | avg. | run |
-----+------------+--------+------+--------+------+------+--------+------+
1 -17.2 0.0 0.9 245 1.0 0.5 -13.5 923
2 -14.7 2.2 2.5 82 2.7 2.4 -11.9 1834
3 -13.1 6.3 6.7 46 7.0 6.2 -10.3 1174
4 -12.9 9.3 9.3 24 9.3 9.2 -11.5 2505
5 -12.5 7.6 7.5 22 7.8 7.5 -11.6 776
6 -12.2 5.1 5.4 73 5.5 5.3 -9.6 1050
7 -12.0 4.5 4.8 6 4.8 4.8 -11.8 2093
8 -11.9 6.6 7.1 31 7.3 7.0 -11.0 2259
9 -11.8 6.7 6.8 26 6.9 6.7 -10.9 517
10 -11.7 4.2 4.3 22 3.5 3.1 -9.4 2088
11 -11.6 6.8 7.2 2 6.7 6.2 -11.3 1179
12 -11.5 2.0 2.1 1 NA NA 382
13 -11.5 9.5 9.4 28 9.7 9.4 -8.4 767
14 -11.4 9.6 9.4 44 9.5 9.3 -9.8 2364
15 -11.4 8.6 8.5 17 8.6 8.1 -10.0 1548
16 -11.1 2.5 2.6 41 2.4 1.9 -9.6 227
17 -11.0 3.0 3.2 2 3.2 3.2 -11.0 2084
18 -10.8 5.6 5.9 1 NA NA 1060
...