RF-Score is a machine learning approach to predicting protein-ligand binding affinity.
Eight executables, rf-prepare, rf-train, rf-test, rf-stat, rf-extract, rf-predict, rf-score and rf-inspect, will be compiled in the bin folder.
GCC 4.6 or higher is required.
make
Visual Studio 2012 or higher is required.
msbuild RF-Score.sln /t:Build /p:Configuration=Release
The original RF-Score is trained on the PDBbind 2007 refined set minus the core set (N = 1105), and tested on the PDBbind 2007 core set (N = 195). To reproduce the results presented in the original paper DOI: 10.1093/bioinformatics/btq112, two csv files pdbbind-2007-refined-core-yx36i.csv and pdbbind-2007-core-yx36i.csv are provided and they contain the 36 RF-Score features only.
rf-train pdbbind-2007-refined-core-yx36i.csv pdbbind-2007-refined-core-x36.rf
Training 36 random forests of 500 trees with mtry from 1 to 36 and seed 89757 on 1105 samples using 4 threads
mtry = 6 yields the minimum MSE
Mean of squared residuals: 2.296
Var explained: 0.487
Var %incMSE Tgini
0 27.303 716.346
1 24.479 587.571
2 25.544 547.815
3 23.313 207.058
4 22.962 314.036
5 20.463 265.488
6 19.825 249.223
7 17.470 113.768
8 22.534 228.789
9 22.528 224.864
10 23.097 213.574
11 17.303 159.686
12 15.540 122.153
13 16.854 122.409
14 13.681 98.701
15 9.189 32.068
16 15.764 75.275
17 15.809 71.725
18 12.584 65.668
19 6.364 30.108
20 5.030 19.569
21 4.588 21.152
22 4.399 18.572
23 4.183 10.887
24 1.569 18.361
25 4.623 17.018
26 1.503 20.058
27 2.292 13.288
28 5.191 9.563
29 3.085 8.507
30 4.083 8.150
31 3.942 5.680
32 -1.858 3.095
33 -0.243 2.909
34 -0.683 2.624
35 0.046 2.256
rf-test pdbbind-2007-refined-core-x36.rf pdbbind-2007-refined-core-yx36i.csv | tail -n +2 | cut -d, -f2,3 | rf-stat
n,rmse,sdev,pcor,scor,kcor
1105,0.619,0.620,0.956,0.958,0.826
rf-test pdbbind-2007-refined-core-x36.rf pdbbind-2007-core-yx36i.csv | tail -n +2 | cut -d, -f2,3 | rf-stat
n,rmse,sdev,pcor,scor,kcor
195,1.506,1.514,0.773,0.759,0.564
In addition to the original 36 RF-Score features, more can be used. Two csv files pdbbind-2007-refined-core-yx42i.csv and pdbbind-2007-core-yx42i.csv are provided and they contain the 36 RF-Score features, the 5 intermolecular Vina terms, and the flexibility Vina term DOI: 10.1002/jcc.21334.
rf-train pdbbind-2007-refined-core-yx42i.csv pdbbind-2007-refined-core-x42.rf
Training 42 random forests of 500 trees with mtry from 1 to 42 and seed 89757 on 1105 samples using 4 threads
mtry = 15 yields the minimum MSE
Mean of squared residuals: 2.121
Var explained: 0.526
Var %incMSE Tgini
0 24.295 680.104
1 17.708 476.658
2 19.967 383.655
3 20.625 125.506
4 21.196 192.999
5 21.457 158.961
6 18.652 134.777
7 12.953 63.346
8 16.747 136.037
9 17.223 139.852
10 17.035 135.004
11 13.543 104.498
12 16.359 132.054
13 17.537 137.725
14 12.171 78.885
15 7.719 14.182
16 9.275 40.193
17 9.948 38.737
18 9.894 39.744
19 3.045 15.081
20 1.631 10.026
21 2.310 11.981
22 2.030 8.801
23 1.645 6.201
24 0.354 9.723
25 1.214 10.148
26 2.283 12.112
27 0.580 7.202
28 2.462 5.651
29 3.616 5.523
30 3.315 6.037
31 3.597 5.817
32 -0.237 1.187
33 0.406 1.056
34 -0.572 0.763
35 1.971 1.218
36 20.846 308.316
37 17.127 351.996
38 13.887 159.452
39 22.729 274.117
40 18.177 198.633
41 17.129 172.959
rf-test pdbbind-2007-refined-core-x42.rf pdbbind-2007-refined-core-yx42i.csv | tail -n +2 | cut -d, -f2,3 | rf-stat
n,rmse,sdev,pcor,scor,kcor
1105,0.518,0.518,0.970,0.971,0.857
rf-test pdbbind-2007-refined-core-x42.rf pdbbind-2007-core-yx42i.csv | tail -n +2 | cut -d, -f2,3 | rf-stat
n,rmse,sdev,pcor,scor,kcor
195,1.424,1.431,0.800,0.791,0.596
Another two csv files pdbbind-2007-refined-core-yx47i.csv and pdbbind-2007-core-yx47i.csv are provided and they contain the 36 RF-Score features, the 5 intermolecular Vina terms, the 5 intramolecular Vina terms and the flexibility Vina term.
rf-train pdbbind-2007-refined-core-yx47i.csv pdbbind-2007-refined-core-x47.rf
Training 47 random forests of 500 trees with mtry from 1 to 47 and seed 89757 on 1105 samples using 4 threads
mtry = 12 yields the minimum MSE
Mean of squared residuals: 2.106
Var explained: 0.529
Var %incMSE Tgini
0 22.459 600.050
1 17.400 400.325
2 17.287 341.899
3 15.637 103.776
4 20.844 168.332
5 17.555 135.993
6 17.291 130.322
7 13.460 57.575
8 16.694 114.634
9 16.527 122.487
10 15.981 115.503
11 12.078 95.173
12 16.323 118.737
13 17.416 125.161
14 12.160 79.593
15 7.455 14.617
16 9.834 40.672
17 9.562 35.798
18 7.812 32.743
19 2.286 14.311
20 2.910 11.542
21 2.243 10.193
22 3.169 8.642
23 1.459 5.807
24 2.384 10.107
25 2.225 9.172
26 2.675 9.644
27 2.251 6.462
28 2.997 6.780
29 2.281 5.879
30 2.492 5.066
31 3.801 3.441
32 1.722 0.880
33 -1.891 1.243
34 -1.200 1.072
35 1.120 1.174
36 18.326 274.812
37 16.230 377.279
38 14.376 131.867
39 20.477 221.890
40 18.660 163.409
41 16.514 122.612
42 14.727 149.597
43 12.839 105.897
44 14.866 146.066
45 8.189 39.389
46 15.070 119.821
rf-test pdbbind-2007-refined-core-x47.rf pdbbind-2007-refined-core-yx47i.csv | tail -n +2 | cut -d, -f2,3 | rf-stat
n,rmse,sdev,pcor,scor,kcor
1105,0.517,0.517,0.970,0.971,0.857
rf-test pdbbind-2007-refined-core-x47.rf pdbbind-2007-core-yx47i.csv | tail -n +2 | cut -d, -f2,3 | rf-stat
n,rmse,sdev,pcor,scor,kcor
195,1.425,1.432,0.800,0.795,0.601
Another two csv files pdbbind-2007-refined-core-yx51i.csv and pdbbind-2007-core-yx51i.csv are provided and they contain the 36 RF-Score features, the 11 Vina features, and the 4 Cyscore features DOI: 10.1093/bioinformatics/btu104.
rf-train pdbbind-2007-refined-core-yx51i.csv pdbbind-2007-refined-core-x51.rf
Training 51 random forests of 500 trees with mtry from 1 to 51 and seed 89757 on 1105 samples using 4 threads
mtry = 7 yields the minimum MSE
Mean of squared residuals: 2.070
Var explained: 0.537
Var %incMSE Tgini
0 18.932 341.590
1 15.108 282.158
2 16.561 255.213
3 15.023 82.837
4 19.728 150.789
5 17.298 124.116
6 16.026 117.908
7 9.794 51.846
8 15.018 108.394
9 15.085 115.264
10 14.749 101.802
11 11.681 80.289
12 12.588 88.716
13 14.717 91.971
14 11.152 62.988
15 5.998 15.725
16 10.125 38.881
17 8.952 36.383
18 8.992 34.560
19 2.648 16.044
20 2.636 11.648
21 3.018 11.376
22 2.702 12.244
23 1.642 8.514
24 1.428 8.114
25 3.254 9.284
26 2.159 8.799
27 2.608 6.798
28 3.584 2.991
29 2.180 3.838
30 1.826 3.701
31 0.926 3.652
32 -1.213 1.218
33 1.008 1.123
34 0.143 0.925
35 0.175 0.971
36 16.119 201.329
37 13.666 243.712
38 12.096 106.192
39 18.551 208.111
40 13.370 116.859
41 13.126 106.952
42 12.974 151.465
43 12.025 87.249
44 13.410 128.483
45 4.659 38.031
46 12.968 97.050
47 22.278 401.279
48 16.898 410.172
49 14.915 90.904
50 8.681 82.472
rf-test pdbbind-2007-refined-core-x51.rf pdbbind-2007-refined-core-yx51i.csv | tail -n +2 | cut -d, -f2,3 | rf-stat
n,rmse,sdev,pcor,scor,kcor
1105,0.524,0.525,0.969,0.969,0.854
rf-test pdbbind-2007-refined-core-x51.rf pdbbind-2007-core-yx51i.csv | tail -n +2 | cut -d, -f2,3 | rf-stat
n,rmse,sdev,pcor,scor,kcor
195,1.415,1.423,0.803,0.800,0.606
Here is a comparison of prediction performance of the RF-Score variants.
| 36 features | 42 features | 47 features | 51 features | |
|---|---|---|---|---|
| Evaluation on OOB (out-of-bag) data | ||||
| mse | 2.296 | 2.121 | 2.106 | 2.070 |
| rsq | 0.487 | 0.526 | 0.529 | 0.537 |
| Evaluation on training samples (N = 1105) | ||||
| rmse | 0.708 | 0.608 | 0.608 | 0.619 |
| sdev | 0.620 | 0.518 | 0.517 | 0.525 |
| pcor | 0.956 | 0.970 | 0.970 | 0.969 |
| scor | 0.958 | 0.971 | 0.971 | 0.969 |
| kcor | 0.826 | 0.857 | 0.857 | 0.854 |
| Evaluation on testing samples (N = 195) | ||||
| rmse | 1.578 | 1.512 | 1.520 | 1.513 |
| sdev | 1.514 | 1.431 | 1.432 | 1.423 |
| pcor | 0.773 | 0.800 | 0.800 | 0.803 |
| scor | 0.759 | 0.791 | 0.795 | 0.800 |
| kcor | 0.564 | 0.596 | 0.601 | 0.606 |
RF-Score can be easily re-trained and re-tested on newer version of PDBbind (e.g. v2012) for prospective prediction purpose.
It traverses the PDBbind folder to load proteins and ligands in pdbqt format, and extracts 36 RF-Score features and 6 Vina terms to a csv file.
It requires PDBbind and MGLTools as prerequisites.
curl -O http://pdbbind.org.cn/download/pdbbind_v2012.tar.gz
tar zxf pdbbind_v2012.tar.gz
curl -O http://mgltools.scripps.edu/downloads/downloads/tars/releases/REL1.5.6/mgltools_x86_64Linux2_1.5.6.tar.gz
tar zxf mgltools_x86_64Linux2_1.5.6.tar.gz
cd mgltools_x86_64Linux2_1.5.6
./install.sh -c 1
It requires pdbqt as input format in order to extract the 6 Vina terms.
tail -n +6 ../v2012/INDEX_refined_data.2012 | while read -r line; do
code=${line:0:4}
bin/pythonsh MGLToolsPckgs/AutoDockTools/Utilities24/prepare_receptor4.py -r ../v2012/${code}/${code}_protein.pdb -U nphs_lps_waters_deleteAltB
bin/pythonsh MGLToolsPckgs/AutoDockTools/Utilities24/prepare_ligand4.py -l ../v2012/${code}/${code}_ligand.mol2
done
Here shows how to prepare testing samples from the PDBbind 2012 core set.
cd ../v2012
tail -n +6 INDEX_core_data.2012 | while read -r line; do
echo $line | cut -d' ' -f1,4 --output-delimiter=, >> pdbbind-2012-core-iy.csv
done
rf-prepare pdbbind-2012-core-iy.csv > pdbbind-2012-core-yx42i.csv
Here shows how to prepare training samples from the PDBbind 2012 refined set minus the core set.
core=$(tail -n +6 INDEX_core_data.2012 | cut -d' ' -f1);\
tail -n +6 INDEX_refined_data.2012 | while read -r line; do
if [[ 1 = $(echo ${core} | grep ${line:0:4} | wc -l) ]]; then continue; fi
echo $line | cut -d' ' -f1,4 --output-delimiter=, >> pdbbind-2012-refined-core-iy.csv
done
rf-prepare pdbbind-2012-refined-core-iy.csv > pdbbind-2012-refined-core-yx42i.csv
It trains multiple random forests of different mtry values in parallel, selects the best one with the minimum MSE, outputs its statistics, and saves it to a binary file.
rf-train pdbbind-2012-refined-core-yx42i.csv pdbbind-2012-refined-core-x42.rf
It loads a random forest from a binary file, predicts the RF-Score values of testing samples.
rf-test pdbbind-2012-refined-core-x42.rf pdbbind-2012-refined-core-yx42i.csv > pdbbind-2012-refined-core-iyp.csv
rf-test pdbbind-2012-refined-core-x42.rf pdbbind-2012-core-yx42i.csv > pdbbind-2012-core-iyp.csv
It loads two vectors of values from standard input and computes their n, rmse, sdev, pcor, scor and kcor.
tail -n +2 pdbbind-2012-refined-core-iyp.csv | cut -d, -f2,3 | rf-stat
tail -n +2 pdbbind-2012-core-iyp.csv | cut -d, -f2,3 | rf-stat
It parses a receptor and multiple conformations of a ligand, and extracts their RF-Score features and Vina terms.
rf-extract receptor.pdbqt ligand.pdbqt > x.csv
It loads a random forest from a binary file, reads features from standard input, and score them.
tail -n +2 x.csv | rf-predict pdbbind-2012-refined-core-x42.rf
It loads a random forest from a binary file, parses a receptor and multiple conformations of a ligand, extracts their RF-Score features and Vina terms, and score them.
rf-score pdbbind-2012-refined-core-x42.rf receptor.pdbqt ligand.pdbqt
rf-score is a streamlined combination of rf-extract and rf-predict. The above command is equivalent to
rf-extract receptor.pdbqt ligand.pdbqt | tail -n +2 | rf-predict pdbbind-2012-refined-core-x42.rf
It inspects the node values of a built random forest.
rf-inspect pdbbind-2012-refined-core-x42.rf | sort -t, -k1,1n
Pedro J. Ballester and John B. O. Mitchell. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics, 26(9):1169-1175, 2010. DOI: 10.1093/bioinformatics/btq112
Oleg Trott and Arthur J. Olson. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31:455-461, 2010. DOI: 10.1002/jcc.21334
Yang Cao and Lei Li. Improved protein-ligand binding affinity prediction by using a curvature dependent surface area model. Bioinformatics, 2014. DOI: 10.1093/bioinformatics/btu104
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