To run PI-LZerD program for protein-protein docking prediction, the Receptor and Ligand in PDB format, and the predictions results from meta-PPISP server are required.

For example, to run PI-LZerD for protein 1A2K, 1A2K_R.pdb and 1A2K_L.pdb are needed in the first step, (usually larger protein is named as receptor, and the smaller one is named as ligand). The prediction need a simple command as:
 ./PI_LZerD.sh <id> <rec_chn> <lig_chn>
For 1A2K, the command will be:
 ./PI_LZerD.sh 1A2K R L

The output is the 1A2K.i61 file, predictions are listed in the format of:
<score> <interface-RMSD> <rotation-matrix>
For example, 
**** Begin 1A2K.i61 ****
...
-6438.12 1.14679  0.988 -0.09 0.126 0.108 0.984 -0.145 -0.111 0.156 0.981 -9.879 5.268 6.523
...
**** End 1A2K.i61 ****
The physics-based score is -6438.12, the interface-RMSD is 1.14679A, and translation-rotation-matrix is defined in 12-numbers of 0.988 -0.09 0.126 0.108 0.984 -0.145 -0.111 0.156 0.981 -9.879 5.268 6.523, first 9 numbers are the rotation matrix, and the last 3 numbers are the translations in X, Y, Z coordinates.


The details of the process are listed as following, noted that for each step, a script named job.sh will be called to read the results from previous results and generate the outputs for the next step, the program can be accessed here: Pre-LZerD process: A. Data preparation: Step 1. Mark surface residues, rename the receptor and ligand to the format of <id>_<chn>.pdb, where id is the ID of length 4, chn is the chain id, receptor and ligand have the same id. (LZerD/01.BASE/[rec/lig]) Input: Receptor and Ligand proteins Method: Find the surface residues using the mark_sur program Output: Receptor and Ligand proteins with surface residue information Example: 1A2K_R.pdb, 1A2K_L.pdb Step 2. Critical points generation from PDB file using GETPOINTS program (LZerD/01.BASE/CP) Input: Receptor and Ligand proteins with surface residue information Method: Run GETPOINTS_32 to create gts and cp(critical points) files, gts files are used for visualization, and cp files are extracted from gts file for protein docking prediction. Output: gts and cp files Example: 1A2K_R.gts, 1A2K_R.cp, 1A2K_L.gts, 1A2K_L.cp Step 3. Compute Zernike Descritors base on each critical points (LZerD/01.BASE/INV) Input: gts and cp files Method: Compute the zernike descriptors for each critical points Output: Zernike descriptors in inv format. Example:1A2K_R.inv, 1A2K_L.inv B. Pre-LZerD: Step 4. Protein docking prediction using LZerD program (LZerD/02.MPI/01.LZerD_MPI) Input: Receptor and Liand, critical points information, zernike descriptors from Data preparation Method: LZerD program Output: Protein-protein docking predicitons using LZerD Example: 1A2K.out.gz Step 5. Extract rotation matrics information to preparate for physics based scoring and IRMSD computation (LZerD/02.MPI/02.LZerD_MAT) Input: Output of LZerD Method: Extract the rotation/translation matrics Output: Extracted rotation/translation matrics information Example: 1A2K.mat Step 6. Compute physics based scoring and IRMSD for each prediction (LZerD/02.MPI/03.SRB_MPI & LZerD/02.MPI/04.IRMSD_MPI) Input: Rotation/translation matrics and receptor/ligand protein Method: Compute the physics based scoring and IRMSD (Interface RMSD) in parallel Output: Physics based scorings and IRMSDs. Example: 1A2K.srb, 1A2K.irmsd Step 7. Compute the critical points dependencies (LZerD/03.Votes) Input: Protein-protein docking predicitons Method: For each per prediction, extract the critical points dependencies Output: The critical points dependencies information Example: 1A2K.Votes Step 8. Sort predictions by physical based scores (LZerD/04.ORD_SRB) Input: Protein-protein docking predicitons Method: Sort the predictions by physical based scores Output: Sorted predictions Example output: 1A2K.LZerD Step 9. Compute pairwise Common Interface RMSD (CI_RMSD) on top 1000 predictions (LZerD/05.CI_RMSD) Input: Protein-protein docking predicitons Method: Compute Common Interface RMSD (CI_RMSD) for top 1000 predictions Output: Common Interface RMSD (CI_RMSD) for top 1000 predictions Example output: 1A2K.cirmsd Step 10. Cluster predictions using CI_RMSD (LZerD/06.LZerD_CIRMSD) Input: Top 1000 predictions Method: CI_RMSD clustering Output: Clustered top 1000 predictions Example output: 1A2K.t1k C. PI-LZerD Program: Step 11. Add the prediction results from meta-PPISP server in PDB format (PI_LZerD/01.Pred_PPI/01.Pred_PPI_pdb) Input: Receptors and Ligands Method: meta-PPISP server Output: Predicted protein interface Example: 1A2K_R.rec.pdb, 1A2K_L.lig.pdb Step 12. Compute the critical points belonging to predicted interface residues (PI_LZerD/01.Pred_PPI/01.Pred_PPI_pdb) Input: Receptors/Ligands and predicted protein interfaces Method: Compute the critical points belonging to predicted interface residues Output: Critical points on predicted interface residues Example: 1A2K_R.rec.pdb, 1A2K_L.lig.pdb Step 13. Compute the predicted interface residue numbers (PI_LZerD/01.Pred_PPI/03.PPI_RES) Input: Predicted protein interface Method: Extract predicted interface residues Output: List of predicted interface residues Example output: res.txt Step 14. First LZerD iteration using predicted interface residues (PI_LZerD/02.Sim_LZerD) Input: Receptor/Ligand + predicted interface residues Method: Fast LZerD iteration using predicted interface residues Output: First iteration LZerD prediction Example output: 1A2K.sim Step 15. Compute pair-wise CI_RMSD distances on top 1000 predictions (PI_LZerD/03.LZerD_CIRMSD) Input: First iteration LZerD predictions Method: Compute pair-wise CI_RMSD distances on top 1000 predictions Output: CI_RMSD distances of top 1000 predictions Example output: 1A2K.cirmsd Step 16. Cluster on top 1000 predictions base on CI_RMSD distances (PI_LZerD/04.CIRMSD_CLUST) Input: Top 1000 predictions from first iteration LZerD program Method: CI_RMSD clustering Output: Clusterings from top 1000 predictions Example output: 1A2K.t1k Step 17. Select top 60 clustered predictions (PI_LZerD/05.CLUST_T60) Input: Clustered top 1000 predictions Method: Select top 60 clustered predictions Output: Selected top 60 clustered predictions Example output: 1A2K.40A.t60, 1A2K.25A.t60 Step 18. Use simple residue filtering method on predicted interface residues (PI_LZerD/06.SRF) Input: Top 1000 predictions from first iteration LZerD program Method: Sort by the consensus by the percentage of agreement with the predicted interface residues Output: Top 1000 predictions sorted by the percentage of agreement with the predicted interface residues Example output: 1A2K.rcf Step 19. Second LZerD iteration using 60 clustered prediction (PI_LZerD/07.T60_Iter) Input: Selected top 60 clustered predictions Method: Second LZerD iteration Output: Second LZerD iteration using 60 clustered prediction Example output: 1A2K.iter.gz, 1A2K.k60 Step 20. 60 x Pair-wise CI_RMSD distances on top 1000 predictions (PI_LZerD/08.K60_CIRMSD) Input: Selected top 60 clustered predictions Method: Pair-wise CI_RMSD distances Output: 60 x Pair-wise CI_RMSD distances Example output: 1A2K.cirmsd, 1A2K.trans Step 21. Re_rank using 61 prediction lists (PI_LZerD/09.T61) Input: Selected top 60 clustered predictions Method: Re rank from 61 predicted lists Output: Re-ranked predictions Example output: 1A2K.i61