Raunak Shrestha

29th November 2011

Source:Durrant JD, Amaro RE, Xie L, Urbaniak MD, Ferguson MA, Haapalainen A, Chen Z, Di Guilmi AM, WunderF, 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)

BACKGROUND 2

http://www.alzdiscovery.org/wp-content/uploads/2009/04/pre-discovery.jpg

Drug Discovery Process

3In a span of ~ 10 – 15 years, ~ $ 5 – $ 10 Million

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 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

• 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)

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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)

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T. brucei RNA editing ligase I

(TbREL1)

NCS45208

(Compound 1)

Primary Target

Secondary Targets ???

METHODS 8

9

NCBI blastclustIdentity 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

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

RESULTS 11

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

12Also some of the predicted secondary targets were experimentally verified in wet-lab

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

Predicted Bacterial and Parasitic Pathogens Proteins (secondary targets)• 23 were bacterial and parasitic pathogens protein (out of 35 predicted

secondary targets )

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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

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.

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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

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1

• Performed Experimental (wet-lab) validation of the secondary targets

• Most of the bioinformatics papers does not seem to do so !!!

2

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

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 secondary-targets

Number of structures in the PDB from 1972 - 2010. Image courtesy of the RCSB Protein Data Bank.

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Thank you

Have a nice day !!!