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Fragment-based docking with CHARMM

9/1/2014

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Fragment-based docking: Development of the CHARMMing Web user interface as a platform for Computer-Aided Drug Design

Yuri Pevzner , Emilie Frugier , Vinushka Schalk , Amedeo Caflisch , and H. Lee Woodcock
J. Chem. Inf. Model., Just Accepted Manuscript

Abstract
Web-based front end interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser. One such interface, CHARMMing (CHARMM interface and graphics), allows researcher to take advantage of the functionality of the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multi-scale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing's capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly on the CHARMMing Web server or on local HPC resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of "re-docking's" with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing's docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening.

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Virtual Screening Workflow with the ZINC library on the apple cluster

3/6/2014

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SP
Make a directory and include the following files:
  • make_dirs
  • copy_files2
  • glide-dock_SP_0.in
Example of SP.in file:
USECOMPMAE YES
NREPORT 30000
RINGCONFCUT 2.500000
GRIDFILE /home/eva/Zinc_screening/SP_1MV9/1MV9_no_wat_glide-grid.zip
LIGANDFILE /home/ZINC/druglike-Zinc-library-mol2/3_p0.0.mol2
LIGFORMAT mol2
  1. Run the make_dirs script --> creates 88 folders (0-87)
  2. Run the copy_files2 script --> leave the glide-dock_SP_0.in file out of its folder to run the script and when it is finished then cut+paste it to folder_0.
  3. Use the submit_SP_all script to run the SP on apple cluster. In my case I used to put up to 12 jobs in each node ( modify this line of the script: for (( i = 76 ; i<=87; i++   )) ). Due to the fact that the jobs were put randomly to the nodes and in order to have up to 12 jobs on each node, I was using first some "fake" gromacs runs to use 12 cores and left the other 12 free for screening runs (Evi's idea! Thank's Evi!!).
  4. When the run is finished you should check all the 88 directories and see if you have outputs like: e.g. glide-dock_SP_8_pv.maegz. If something goes wrong and the run has stopped then you will see e.g. glide-dock_SP_8_raw.maegz as an output. Then you should copy your glide-dock_SP_8.in to glide-dock_SP_8_b.in and run it from the ligand that it had stopped. Finally, you will have: glide-dock_SP_8_raw.maegz and glide-dock_SP_8_b_pv.maegz. Both are useful! Don't delete the first one!
  5. In order to finish the SP process you have to take the top-scored 40.000 compounds from all the directories and put them to a new file (SP_top40000_1MV9_pv.maegz) to use as an input for the XP process.
You can run the GlideSortScript_ZINC.csh script to acheive this result. The content of the script is the following: (You have to add the possible raw.maegz files,too.)
/opt/schrodinger/utilities/glide_sort -n 40000 -o SP_top40000_1MV9_pv.maegz -r  SP_top40000_1MV9.rept -hbond_cut 0.00 -cvdw_cut 0.00 -metal_cut 10.00 \ db3_p0.0/glide-dock_SP_0_pv.maegz \db3_p0.1/glide-dock_SP_1_pv.maegz \db3_p0.2/glide-dock_SP_2_pv.maegz \db3_p0.3/glide-dock_SP_3_raw.maegz \db3_p0.3/glide-dock_SP_3_b_pv.maegz \ etc.


XP
Each XP directory includes:
  • grid.zip file
  • SP_top40000_1MV9_pv.maegz (output of SP)
  • XP.in files (I split them into 8 files in my case) 

Example of XP.in file:
WRITEREPT YES
WRITE_RES_INTERACTION YES
WRITE_XP_DESC YES
USECOMPMAE YES
POSTDOCK_NPOSE 10
LIGAND_END 10000
LIGAND_START 5001 (this line is not included in the XP_1.in file as it starts from the beginning)
MAXREF 800RINGCONFCUT 2.500000
GRIDFILE /home/eva/XP_Zinc/1MV9_XP_Zinc_2_8/1MV9_no_wat_glide-grid.zip
LIGANDFILE /home/eva/XP_Zinc/1MV9_XP_Zinc_2_8/SP_top40000_1MV9_pv.maegz
PRECISION XP


So, I split it to 8 XP directories:
0 - 5000, 5001  - 10000, 10001 - 15000, ..., 35001 - 40000


Run each one on the cluster or on our PCs manually :
/opt/schrodinger/glide 1MVC_Zinc_XP1.in -HOST xgrid-server
/opt/schrodinger/glide 1MVC_Zinc_XP2.in -HOST xgrid-server etc. 

Finally you take the top-scored 1000 compounds (as we did before for the SP process where we took the 40.000 top-scored ) using the same script (GlideSortScript_ZINC.csh):

/opt/schrodinger2012/utilities/glide_sort -n 1000 -o  file_XP_1000_pv.maegz -r  file_XP_1000_.rept -hbond_cut 0.00 -cvdw_cut 0.00 -metal_cut 10.00 glide-dock_XP_0_pv.maegz glide-dock_XP_1_pv.maegz glide-dock_XP_2_pv.maegz glide-dock_XP_3_pv.maegz glide-dock_XP_4_pv.maegz glide-dock_XP_5_pv.maegz glide-dock_XP_6_pv.maegz glide-dock_XP_7_pv.maegz glide-dock_XP_8_pv.maegz

Some useful tips:

Run faster with the -LOCAL flag:
/opt/schrodinger/glide -LOCAL file.in

path for ZINC on apple:
/Network/Servers/xgrid-Server.xgrid/Volumes/RAID/NetUsers/pgkeka/PI3K/Screening/ZINC/druglike-Zinc-library-mol2/

Apple cluster run-command:
/opt/schrodinger/glide your_XP_file.in -HOST xgrid-server
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How to identify the target for an active molecule?

10/11/2013

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Drug target identification, which includes many distinct algorithms for finding genes and proteins, is the first step in drug discovery. When 3D structures of the targets are available, the problem of target identification is usually converted to finding the best interaction mode between the potential target candidates and probe small molecules. But what happens when the target is unknown or we are looking for off-target effects of our active molecule? (in other words, to which other proteins does our active molecule bind?)

Three servers have been developed for this purpose:

Pharmmapper
http://59.78.96.61/pharmmapper/
PharmMapper Server is a freely accessed web-server designed to identify potential target candidates for the given probe small molecules (drugs, natural products, or other newly discovered compounds with binding targets unidentified) using pharmacophore mapping approach. Benefited from the highly efficient and robust mapping method, PharmMapper bears high throughput ability and can identify the potential target candidates from the database within a few hours.

Reversescreen3D
http://www.modelling.leeds.ac.uk/ReverseScreen3D/about.html
ReverseScreen3D is a reverse virtual screening tool that searches against a biologically-relevant and automatically-updated subset of ligands extracted from the RCSB Protein Data Bank in order to identify potential target proteins that are likely to bind a given compound.

Similarity ensemble approach (SEA)
http://sea.bkslab.org/
The Similarity ensemble approach relates proteins based on the set-wise chemical similarity among their ligands. It can be used to rapidly search large compound databases and to build cross-target similarity maps.

Read also the relevant paper:
"Target fishing and docking studies of the novel derivatives of aryl-aminopyridines with potential anticancer activity."
Bioorg Med Chem. 2012 Sep 1;20(17):5220-8. doi: 10.1016/j.bmc.2012.06.051. Epub 2012 Jul 11.Erić S, Ke S, Barata T, Solmajer T, Antić Stanković J, Juranić Z, Savić V, Zloh M.

Thanks to Mire Zloh for this information!

If you have more servers in mind, please let us know!

Zoe
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Peptide docking with Glide

10/3/2013

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In this article, Senior Application Scientist Thijs Beuming discusses the development of a new Glide docking protocol designed specifically for peptides.
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CSAR - 2013 Benchmark exercise: Can you determine which protein binds a steroid?

3/27/2013

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CSAR is a community resource for Docking and Scoring Development

In their third benchmark competition, over one dozen proteins were designed to bind a steroid. Can you determine which were successful? (timeframe: 1 month and ends on 26 April 2013). See www.CSARdock.org to download the zip file.

This exercise is built around designed proteins developed by Tinberg, Khare, and Baker. This is an interesting twist on traditional docking/scoring problems because there is no existing protein data on which to train models (as was possible in the first two exercises). Three phases, with a possible fourth phase are coming up:

Phase 1: Protein design – Over one dozen proteins were designed to bind a steroid. Can you determine which were successful? (timeframe: 1 month, ending 26 April 2013)

Phase 2: Scoring – Given the set-up crystal structures of the proteins and a set of pregenerated docking decoys, can you identify the correct poses for the steroid? (timeframe: start after Phase 1 ends and run 2 weeks)

Phase 3: Selectivity – Given the set-up crystal structures of some protein-steroid complexes, can you predict the binding affinity (or relative ranking) of several steroids binding to the same protein? (timeframe: start after Phase 2 ends and run 1 month)

Phase 4: Predict the effect of protein mutations – There is extensive mutational data available. Given a subset, can you "bin" their effects into weak, moderate, significant changes in binding? (timeframe: longer and not yet determined)

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Computational Drug Discovery and Design volume from @SpringerPub

3/1/2013

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Riccardo Baron also put together a great computational methods volume you should have access to:http://link.springer.com/book/10.1007/978-1-61779-465-0/page/1
Here's the list of contents:

Drug Binding Site Prediction, Design, and Descriptors
A Molecular Dynamics Ensemble-Based Approach for the Mapping of Druggable Binding Sites
  • Anthony Ivetac, J. Andrew McCammon Look Inside Get Access
Analysis of Protein Binding Sites by Computational Solvent Mapping
  • David R. Hall, Dima Kozakov, Sandor Vajda
Evolutionary Trace for Prediction and Redesign of Protein Functional Sites
  • Angela Wilkins,Serkan Erdin,Rhonald Lua,Olivier Lichtarge
Information Entropic Functions for Molecular Descriptor Profiling
  • Anne Mai Wassermann,Britta Nisius,Martin Vogt,Jürgen Bajorath

Virtual Screening of Large Compound Libraries: Including Molecular Flexibility
Expanding the Conformational Selection Paradigm in Protein-Ligand Docking
  • Guray Kuzu,Ozlem Keskin,Attila Gursoy,Ruth Nussinov Look Inside Get Access
Flexibility Analysis of Biomacromolecules with Application to Computer-Aided Drug Design
  • Simone Fulle,Holger Gohlke
On the Use of Molecular Dynamics Receptor Conformations for Virtual Screening
  • Sara E. Nichols,Riccardo Baron,J. Andrew McCammon
Virtual Ligand Screening Against Comparative Protein Structure Models
  • Hao Fan,John J. Irwin,Andrej Sali
AMMOS Software: Method and Application
  • T. Pencheva,D. Lagorce,I. Pajeva,B. O. Villoutreix,M. A. Miteva
Rosetta Ligand Docking with Flexible XML Protocols
  • Gordon Lemmon,Jens Meiler
Normal Mode-Based Approaches in Receptor Ensemble Docking
  • Claudio N. Cavasotto
Application of Conformational Clustering in Protein–Ligand Docking
  • Giovanni Bottegoni,Walter Rocchia,Andrea Cavalli
How to Benchmark Methods for Structure-Based Virtual Screening of Large Compound Libraries
  • Andrew J. Christofferson,Niu Huang

Prediction of Protein-Protein Docking and Interactions

AGGRESCAN: Method, Application, and Perspectives for Drug Design
  • Natalia S. de Groot,Virginia Castillo,Ricardo Graña-Montes,Salvador Ventura
ATTRACT and PTOOLS: Open Source Programs for Protein–Protein Docking
  • Sebastian Schneider,Adrien Saladin,Sébastien Fiorucci,Chantal Prévost
Prediction of Interacting Protein Residues Using Sequence and Structure Data
  • Vedran Franke,Mile Šikić,Kristian Vlahoviček

Rescoring Docking Predictions
MM-GB/SA Rescoring of Docking Poses
  • Cristiano R. W. Guimarães
A Case Study of Scoring and Rescoring in Peptide Docking
  • Zunnan Huang, Chung F. Wong
The Solvated Interaction Energy Method for Scoring Binding Affinities
  • Traian Sulea, Enrico O. Purisima
Linear Interaction Energy: Method and Applications in Drug Design
  • Hugo Gutiérrez-de-Terán, Johan Åqvist 
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Schrodinger's March 2013 webinars: Phase Shape, ensemble docking, and materials applications

2/27/2013

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Schrodinger's March 2013 seminar series begin next Tuesday, March 5. This seminar series will include presentations on Phase Shape for ligand-based shape screening and new ensemble docking practices to account for protein flexibility.

Schrodinger is also debuting a Materials Science Suite this year - Dr. Vyacheslav S. Bryantsev of Liox Power will also be presenting on computational modeling of rechargeable Li-air battery materials on March 7. Please feel free to invite your colleagues in the materials industries to join us for this materials-focused presentation!

Here is the full webinar list with dates:

Accounting for protein flexibility in virtual screening using ensemble docking

Dr. Woody Sherman
Schrödinger's Vice President, Applications Science
Abstract March 5, 2013
10 am ET Register
1pm ET Register

Computational Modeling of Rechargeable Li-Air Battery Materials
Dr. Vyacheslav S. Bryantsev
Liox Power, Senior Scientist
Abstract March 7, 2013
10 am ET Register

Using Phase Shape for rapid ligand alignment and virtual screening
Dr. Woody Sherman
Schrödinger's Vice President, Applications Science
Abstract March 12, 2013
10 am ET Register
1pm ET Register
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1-Click Docking

12/19/2012

6 Comments

 
The authors of this app made it possible to draw a molecule online and dock it with simply one click!

http://blog.mcule.com/2012/12/1-click-docking.html


The app uses the Autodock Vina software for docking.

Check out also the mcule.com site for more drug discovery tools and a compound database.

Zoe

6 Comments

Visualize Glide XP scoring function terms with XP Visualizer

12/10/2012

1 Comment

 
Glide XP Visualizer, visualizes Glide XP scoring function terms in Maestro. Its main  functions are:
  • To display the Glide XP results from a pose viewer file (jobname_pv.mae) in a table of XP terms for each ligand.
  • To provide 3D visualizations for XP terms. Information for these visualizations is read from the pose viewer file. The descriptor file (jobname.xpdes), which is also generated by Glide XP, can be used instead; it must be in the same directory as the pose viewer file.
  • To allow selective evaluation of ligands (and groups of ligands) within the table. This helps you analyze ligands separately during the screening process.
Before you can use the Glide XP Visualizer, you must generate the descriptor information. This information is not included in a normal XP run. To generate it, select Write XP descriptor information in the Settings tab of the Ligand Docking panel. You should also select Write pose viewer file in the Output tab of the Ligand Docking panel to write the required pose viewer file.

If you forgot to check the 'Write XP descriptor information' option and have results of a long XP job and don't want to rerun it in order to generate the descriptors, you could run an XP job on your previous results with the "Ligand sampling" option set to "None (score in place only)". This will re-score the ligands (and add XP descriptors if you've selected that option), which is much faster than re-docking the ligands.

Eva


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Contact Module in Maestro - contact cutoff ratio

11/30/2012

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In Maestro when visualizing a structure file, you can  select Tools -> Measurements -> Contacts panel in order to see Good contacts and Bad&Ugly contacts. When contacts are displayed, good contacts are green by default, bad ones are orange, and ugly ones are red. These criteria whether a contact is defined as good, bad or ugly are based on the following formula:

C = D12 / ( R1 + R2 )

where D12 is the distance between atomic centers 1 and 2, and R1 and R2 are the radii of atomic centers 1 and 2. C is defined as the "contact cutoff ratio" in Maestro and has default values of 0.85 for "Bad" contacts and 0.70 for "Ugly" contacts. C must be monotonically increasing for each of the contact types, that is C(ugly) < C(bad) < C(good). These three values then provide 4 ranges. Distances greater than C(good) are not marked, less than C(good) but greater than C(bad) are marked as "good", etc.

This answer was provided by the Schrodinger help team.

Zoe

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