Raunak Shrestha
29th November 2011
Source:
Durrant JD, Amaro RE, Xie L, Urbaniak MD, Ferguson MA, Haapalainen A, Chen Z, D...
BACKGROUND 2
http://www.alzdiscovery.org/wp-content/uploads/2009/04/pre-discovery.jpg
Drug Discovery Process
3
In a span of ~ 10 – 15 y...
http://graphics.thomsonreuters.com/119/GLB_PHMRD1109.gif
A Bitter Truth !!!
4
Most drug
fail at
Clinical
Trials
5
Therapeutic
outcome
‘‘one gene, one drug, one disease’’
Conventional Drug Design
Therapeutic
Slide adapted from: Irene K...
• A drug that selectively binds to only one target is very rare !!!
• Many drugs interact with multiple target via a compl...
Objective of the paper
• To identify the multiple protein receptors of a given compound
• TbREL1 is a confirmed drug targe...
METHODS 8
9
NCBI blastclust
Identity threshold = 30%
overlap threshold 0.9
Queried Compound 1
in RSCB PDB
Randomly picking single ch...
10
In silico Docking using AutoDock 4.0
Docking cross-validated using:
• SITE data included in the published PDB file
• Ex...
RESULTS 11
Secondary Target Prediction
• Compound 1 docking was performed in each of the 645
potential secondary targets of the PDBr
...
Predicted Human Proteins
(secondary targets)
• 12 were Human Protein (out of 35 predicted secondary targets )
13
Binding
E...
Predicted Bacterial and Parasitic
Pathogens Proteins (secondary targets)
• 23 were bacterial and parasitic pathogens prote...
Conclusion
• A good computational pipeline to predict the off-targets
(secondary targets) of a compound.
• Can give signif...
Critique
• FATCAT : a popular structural alignment tool
for proteins
• SOIPPA algorithm + Docking predicted
secondary targ...
Critique
• Randomly selected the chains from the cluster
• Can a single randomly selected protein from a cluster
be a repr...
Critique
• Missing data in Table 1 and 2 ?? 18
2
Limitations
19Limited structural coverage of the given proteome
This will seriously limit the algorithm ability to predict...
20
Thank you
Have a nice day !!!
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A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology.

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A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology.

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  • SITE records specify residues comprising catalytic, co-factor, anti-codon, regulatory or other essential sites or environments surrounding ligands present in the structure
  • Flexible structure AlignmenT by Chaining AFPs (Aligned Fragment Pairs) with Twists (FATCAT)
  • Flexible structure AlignmenT by Chaining AFPs (Aligned Fragment Pairs) with Twists (FATCAT)
  • Flexible structure AlignmenT by Chaining AFPs (Aligned Fragment Pairs) with Twists (FATCAT)
  • A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology.

    1. 1. Raunak Shrestha 29th November 2011 Source: Durrant JD, Amaro RE, Xie L, Urbaniak MD, Ferguson MA, Haapalainen A, Chen Z, Di Guilmi AM, Wunder F, Bourne PE, McCammon JA. A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology. PLoS Comput Biol. 2010 Jan 22;6(1)
    2. 2. BACKGROUND 2
    3. 3. http://www.alzdiscovery.org/wp-content/uploads/2009/04/pre-discovery.jpg Drug Discovery Process 3 In a span of ~ 10 – 15 years, ~ $ 5 – $ 10 Million
    4. 4. http://graphics.thomsonreuters.com/119/GLB_PHMRD1109.gif A Bitter Truth !!! 4 Most drug fail at Clinical Trials
    5. 5. 5 Therapeutic outcome ‘‘one gene, one drug, one disease’’ Conventional Drug Design Therapeutic Slide adapted from: Irene Kouskoumvekaki, From Chemoinformatics to Systems Chemical Biology Side Effects Therapeutic outcome Polypharmacology :: multi-target drugs Emerging Concept in Drug Design Important Reason for the Drug Failure Potential Solution
    6. 6. • A drug that selectively binds to only one target is very rare !!! • Many drugs interact with multiple target via a complex network pathway • Emergence of Drug Resistant strains of pathogens • Resistance against a single target can be easy • but resistance against multiple targets may be hard to achieve • Many adverse affect of a drug is due to its interaction with multiple target • Some drugs may have alternative therapeutic applications (drug repurposing) 6
    7. 7. Objective of the paper • To identify the multiple protein receptors of a given compound • TbREL1 is a confirmed drug target in Trypanosoma brucei (causative agent of human African trypanosomiasis) 7 T. brucei RNA editing ligase I (TbREL1) NCS45208 (Compound 1) Primary Target Secondary Targets ???
    8. 8. METHODS 8
    9. 9. 9 NCBI blastclust Identity threshold = 30% overlap threshold 0.9 Queried Compound 1 in RSCB PDB Randomly picking single chain from each cluster as a representative of the cluster Select the chains having similar active sites to the primary target TbREL1 “…. sequence order-independent profile–profile alignments (SOIPPA) is able to detect distant evolutionary relationships in cases where both a global sequence and structure relationship remains obscure …. ” (Xie and Bourne, PNAS, 2008 Apr 8;105(14):5441-6) Also included the chains having similar active sites to that of TbREL1 Filtered only the proteins from human or known human-pathogen species
    10. 10. 10 In silico Docking using AutoDock 4.0 Docking cross-validated using: • SITE data included in the published PDB file • Examination of co-crystallized ligands bound in native active sites • Homology modeling : to determine the locations of active sites for the remaining protein chains if Compound 1 had a high predicted energy of binding Hit If Compound 1 bind in an identified active site of known biochemical or pharmacological activity
    11. 11. RESULTS 11
    12. 12. Secondary Target Prediction • Compound 1 docking was performed in each of the 645 potential secondary targets of the PDBr • both protein chains of unknown function and redundant chains were omitted • 87 non-redundant secondary targets were predicted • 35 chains: known active sitses contained docked ligands • 35 chains: alternate sites contained docked ligands • 17 chains: could not be classified 12 Also some of the predicted secondary targets were experimentally verified in wet-lab
    13. 13. Predicted Human Proteins (secondary targets) • 12 were Human Protein (out of 35 predicted secondary targets ) 13 Binding Energy Active site similarity Structural similarity Neither FATCAT nor CLUSTALW2 could predict any similarity between most of the primary target and secondary target but SOIPPA algorithm along with Docking confirmed as a potential secondary targets
    14. 14. Predicted Bacterial and Parasitic Pathogens Proteins (secondary targets) • 23 were bacterial and parasitic pathogens protein (out of 35 predicted secondary targets ) 14 Binding Energy Active site similarity Structural similarity Neither FATCAT nor CLUSTALW2 could predict any similarity between most of the primary target and secondary target but SOIPPA algorithm along with Docking confirmed as a potential secondary targets
    15. 15. Conclusion • A good computational pipeline to predict the off-targets (secondary targets) of a compound. • Can give significant insight over the system-biology of druggable genome • Also give valuable insight over the possible side-effects of a drug • Even in the absence of sequence homology, the pipeline can predict the off-targets. 15
    16. 16. Critique • FATCAT : a popular structural alignment tool for proteins • SOIPPA algorithm + Docking predicted secondary targets when even FATCAT and CLUSTALW2 could not !!! • The pipeline seems to be very efficient to detect secondary targets 16 1 • Performed Experimental (wet-lab) validation of the secondary targets • Most of the bioinformatics papers does not seem to do so !!! 2
    17. 17. Critique • Randomly selected the chains from the cluster • Can a single randomly selected protein from a cluster be a representative of the cluster ? • Can be accepted if there is conversation within the active site residues (but this information is not mentioned in the paper) • Taking a consensus sequence could be an alternative • If so generating the structural information would be very difficult for a consensus sequence 17 1
    18. 18. Critique • Missing data in Table 1 and 2 ?? 18 2
    19. 19. Limitations 19Limited structural coverage of the given proteome This will seriously limit the algorithm ability to predict secondary-targets Number of structures in the PDB from 1972 - 2010. Image courtesy of the RCSB Protein Data Bank.
    20. 20. 20 Thank you Have a nice day !!!

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