1. Docking and Virtual Screening Using the BMI cluster
Jacek Biesiada
Cincinnati, 2008

2. The Docking Problem
Docking is the process by which two molecules fit together in 3D space.
Finding the geometry and strength of ligand binding to a receptor.

3. High Throuput Docking and Virtual Screening for Drug Design
Definition of Virtual Screening
Use of high-performance computing to analyze large database of chemical compounds in order to indetify possible drug candidates.
W.P. Walters, M.T. Stahl and M.A. Murcko, „Virtual Screening-An Overview”, Drug Discovery Today, 3, (1998)
Virtual Screening is also known as:
High-Throughput Docking
High-Throughput Virtual Screening
For introducing the the problem in first step I would like difine the VC. In paper of Waltes and colaborator , the VS is defined as: USe .., Another sinonims for VS are .. HTD ... Main task for VS is reduce the subset of compound for experimentalis.

4. Why Use Virtual Screening ?
VS is a computational filter:
Reduces the size of a chemical library to be screened experimentaly ~ 106 to 103 – Saves time & money
ZINC library version 2007, only „drug-like” compounds ~ 2.2 * 106
Expected ZINC version 2008 about 4 * 106 compounds
May improve likelihood of finding interesting compounds
As oppoesed to random screening
Enhance „hit rates”
VS can:
Evaluate virtual combinatorial libraries before synthesized
VS can be usefull tool for discovering new targets
in „post-genomic” era
Examples of aplications: Design of inhibitors for Norovirus and Glycoprotein IV
(collaboration with Jason Jiang, CCHMC and
Andrew B. Herr – UC College of Medicine )
Which allow the save time and money, Free ZINC library include

5. Autodock
AutoDock has been widely-used and there are many examples of its successful application in the literature:
(First clinicaly-approved HIV Integrase Inhibitor – Autodock used during the research – prof. Andrew McCammon)
Citation Index showed more than 1100 publications have cited the primary AutoDock methods papers.
It is very fast, provides high quality predictions of ligand conformations, and good correlations between predicted inhibition constants and experimental ones.
Very well calibrated force fieled (188 known protein)
Autodock is free software and version 4 is distributed under the GNU General Public License

7. Screening Pipeline: Scripts to Run Autodock on BMI Cluster
Adscr.pl – input: Receptor.cfg adscr.cfg Ligand_info[1-N_CPU]
– output: *_ki, *_cl. *_bestlig, *_currentlig, *_errors
Que.pl - input: Number_of_CPU Receptor.cfg Ligand_info_all
Best.pl - input: File with results (*_ki)
Restart.pl – building new Ligand_info_all file in case of restart of
calculation
Ligand_info_all (Ligand_info) – example:
ZINC _p0.0.pdbqt
ZINC _p0.0.pdbqt
ZINC _p0.0.pdbqt
line
In our lab we (was) prepared several scripts for automation the multi-docking process. The first script called „adsc.pl” is responsible for processing all step conected with docking, storing the results in appropriate directory, making the backup of results, log file and linking apropriete files from library to aproppriate node in claster and cleaning after the finishing the multi-docking process. Log file include the information conected with acutally processed ligand, errors.
Script „Que.pl” js responsible for dividing jobs and submitnig jobs into que (PBS server).
Best.pl scripts create ranking obtained results. Last „Restart.pl” script read logs file (*_current) and create new Ligand_info_file for resubmiting the process of multi-docking. Receptor.cfg include all information about our Rescptor like: name of Receptor, whers is stored the file with Receptor, where is stored the file with maps, format results, which depend from ad version. Adscr.cfg include main information about system, like path to ligand library, path to binaries (autodock, autogrid, python scripts conected with preparation of dpf file for autodock and several other parameters).

8. Preparing Receptor and Ligand for Docking Simulations
Prepare_receptor.pl – input: Receptor.pdb 3(4)
Prepare_gpf.pl - – input: Receptor.pdb Ligand.pdb 3(4)
binding site, grid box
Autogrid.pl – input: Receptor.pdb Ligand.pdb 3(4)
Configure: Receptor.cfg
adscr.cfg
- Read the „Tutorial” connected to prepared scripts – all details connected with Virtual Screening

9. Our Library of Compounds
Library in directory
ZINC offer four formats:
sdf, mol2, Smail and flexibase
/database/Zinc/ (version from 2007)
/mol2
/sdf /sdf_index
/pdb /pdb_index
/pdbq /pdbq_index (Autodock v.3)
/pdbqt /pdbqt_index (Autodock v.4)
Our library is stored in directory /database/ZINC/, this directory include several subdirectories conected with format of ligand, like mol2, sdf and pdb. And pdbq and pdbqt with ligand in Autodock format. Pdbq for ad3 and pdbqt ad4 respectively. Directories with suffixes _index include the file with information about start, stop, name ligand and name of file which include the ligand. This solution
Allow users in easy way add new ligands to library.

10. Virtual Screening is CPU Intensive: Some Estimates Using BMI Cluster
2.2 mln compounds
On average 1 min for one docking (depends of grid map and several search parameter – with default value)
50 CPU
(( /50)/ 60min)/ 24h ~ 30 day
About 30 days – 1th screeaning
Because we have bigg library of compound, difficult is not mention about the time conected with screaning of ligand.
Assume that in library is 2.2 mln compound and one docking takaes about 1 minutes on average (this depend from grid map and parameters of search method), and that we submit the we use 50 CPU the Estimated time for screaning all elements in library. take about 30 days.

11. Validation and Analysis of Docking Results
Similarity compounds
molprint2d – (free)
Method based on MOLecular fingerPRINT
ADME (absorption, distribution, metabolism, excretation)
Paramaters like: Molecular weight, LogP, LogD, pKa , number of H-bond donor/acceptor, PSA, ,... (15-16 parameters)
ALOGS – (free)
JChem (MARVIN, JCLUSTOR)
– (free for academic research)

12. Future Work Testing scripts Increase library of compounds
Writing good „Tutorial” for users
Gold standard – similarity and ADME ?
Improve scripts for finall-automatic analisys of docking, similarity and ADME.
Our feature work include several task like, finishing test of scripts, increasing the number of ligand in library, and prepare the scripts for automated analisys of resulst. Hammer out – wypracowc !

13. THANK YOU