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Insilico drug designing
Dinesh Gupta Structural and Computational Biology Group ICGEB
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Modern drug discovery process
Target identification Target validation Lead identification Lead optimization Preclinical phase Drug discovery 6-9 years 2-5 years Drug discovery is an expensive process involving high R & D cost and extensive clinical testing A typical development time is estimated to be years.
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Drug discovery technologies
Target identification Genomics, gene expression profiling and proteomics Target Validation Gene knock-out, inhibition assay Lead Identification High throughput screening, fragment based screening, combinatorial libraries Lead Optimization Medicinal chemistry driven optimization, X-ray crystallography, QSAR, ADME profiling (bioavailability) Pre Clinical Phase Pharmacodynamics (PD), Pharmacokinetics (PK), ADME, and toxicity testing through animals Clinical Phase Human trials
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Rational Approach to Drug Discovery
Identify and validate target Clone gene encoding target Express target Crystal structures/MM of target and target/inhibitor complexes Identify lead compounds Synthesize modified lead compounds Toxicity & pharmacokinetic studies Preclinical trials
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Bioinformatics tools in DD
Comparison of Sequences: Identify targets Homology modelling: active site prediction Systems Biology: Identify targets Databases: Manage information In silico screening (Ligand based, receptor based): Iterative steps of Molecular docking. Pharmacogenomic databases: assist safety related issues
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Currently used drug targets
J. Drews Science 287, (2000) This information is used by bioinformaticians to narrow the search in the groups Published by AAAS
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Insilico methods in Drug Discovery
Molecular docking Virtual High through put screening. QSAR (Quantitative structure-activity relationship) Pharmacophore mapping Fragment based screening
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Molecular Docking Docking is the computational determination of binding affinity between molecules (protein structure and ligand). Given a protein and a ligand find out the binding free energy of the complex formed by docking them. L R R L
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Molecular Docking: classification
Docking or Computer aided drug designing can be broadly classified Receptor based methods- make use of the structure of the target protein. Ligand based methods- based on the known inhibitors
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Receptor based methods
Uses the 3D structure of the target receptor to search for the potential candidate compounds that can modulate the target function. These involve molecular docking of each compound in the chemical database into the binding site of the target and predicting the electrostatic fit between them. The compounds are ranked using an appropriate scoring function such that the scores correlate with the binding affinity. Receptor based method has been successfully applied in many targets
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Ligand based strategy In the absence of the structural information of the target, ligand based method make use of the information provided by known inhibitors for the target receptor. Structures similar to the known inhibitors are identified from chemical databases by variety of methods, Some of the methods widely used are similarity and substructure searching, pharmacophore matching or 3D shape matching. Numerous successful applications of ligand based methods have been reported
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Search for similar compounds
Ligand based strategy Search for similar compounds database structures found known actives
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Binding free energy Binding free energy is calculated as the sum of the following energies - Electrostatic Energy - Vander waals Energy - Internal Energy change due to flexible deformations - Translational and rotational energy Lesser the binding free energy of a complex the more stable it is
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Basic binding mechanism
Complementarities between the ligand and the binding site: Steric complementarities, i.e. the shape of the ligand is mirrored in the shape of the binding site. Physicochemical complementarities
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Components of molecular docking
A) Search algorithm To find the best conformation of the ligand and the protein system. Rigid and flexible docking B) Scoring function Rank the ligands according to the interaction energy. Based on the energy force-field function.
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Success with vHTS Dihydrofolate reductase inhibitor (1992)
HIV-protease (1992) Phospholypase A2 (1994) Thrombine (1996) Carbonic anhydrase inhibitors(2002)
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Virtual High Throughput Screening
Less expensive than High Throughput Screening Faster than conventional screening Scanning a large number of potential drug like molecules in very less time. HTS itself is a trial and error approach but can be better complemented by virtual screening.
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QSAR QSAR is statistical approach that attempts to relate physical and chemical properties of molecules to their biological activities. Various descriptors like molecular weight, number of rotatable bonds LogP etc. are commonly used. Many QSAR approaches are in practice based on the data dimensions. It ranges from 1D QSAR to 6D QSAR.
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Pharmacophore mapping
It is a 3D description of a pharmacophore, developed by specifying the nature of the key pharmacophoric features and the 3D distance map among all the key features. A Pharmacophore map can be generated by superposition of active compounds to identify their common features. Based on the pharmacophore map either de novo design or 3D database searching can be carried out.
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Modeling and informatics in drug design
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Increased application of structure based drug designing is facilitated by:
Growth of targets number Growth of 3D structures determination (PDB database) Growth of computing power Growth of prediction quality of protein-compound interactions
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Summary: role of Bioinformatics?
Identification of homologs of functional proteins (motif, protein families, domains) Identification of targets by cross species examination Visualization of molecular models Docking, vHTS QSAR, Pharmacophore mapping
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Example: use of Bioinformatics in Drug discovery Identification of novel drug targets against human malaria
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Malaria – A global problem!
Malaria causes at least 500 million clinical cases and more than one million deaths each year. A child dies of malaria every 30 seconds. Out of four Plasmodium species causing human malaria, P.falciparum poses most serious threat: because of its virulence, prevalence and drug resistance. Malaria takes an economic toll - cutting economic growth rates by as much as 1.3% in countries with high disease rates. There are four types of human malaria: Plasmodium falciparum Plasmodium vivax Plasmodium malariae Plasmodium ovale.
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Approximately half of the world's population is at risk of malaria, particularly those living in lower-income countries. Today, there are 109 malaria affected countries in 4 regions
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Chemical structures of drugs in widely used for treatment of Malaria
a) Chloroquine b) Quinine c) Artemether d) Sodium artesunate e) Dihydroartemisinin f) Pyrimethamine g) Sulfadoxine h) Mefloquine i) Halofantrine j) Primaquine k) Tafenoquine l) Chlorproguanil m) Dapsone
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Problems with the existing drugs
Drug resistance is most common problem Adverse effects (Shock and cardiac arrhythmias caused by Chloroquine) Poor patient compliance (Quinine tastes very unpleasant, causes dizziness, nausea etc.) High cost of production for some effective drugs (Atovaquine). Urgent need for identification of novel drug targets which are effective and affordable.
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Strategies for drug target identification in P. falciparum
Parasite culture for functional assays are difficult and expensive. Making computational approaches more relevant. Malaria remains a neglected disease- very few stake holders! Availability of the genomic data of P.falciparum and H.sapiens has facilitated the effective application of comparative genomics. Comparative genomics helps in the identification and exploitation of different characteristic features in host and the parasite. Identification of specific metabolic pathways in P. falciparum and targeting the crucial proteins is an attractive approach of target based drug discovery.
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Comparison of proteomes helps in identifying important indispensible parasite proteins
Out of 5334 predicted proteins in P. falciparum, 60% didn’t show any similarity to known proteins. Hence assigning a physiological functional role to these hypothetical proteins using bioinformatics approach still remains a challenge. A. gambiae P. falciparum H. sapiens Predicted proteome
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Novel drug target identification in P.falciparum
Comparative genomics studies ~40% identity threshold for three-dimensional modeling Relational Database of homology models 476 P.falciparum proteins BlastP Human proteome Large set of proteins with no/low similarity Literature search for all these proteins Check for physiological and biochemical functions; etc .. Putative drug targets in P.falciparum Proteasome machinery (ClpQY and ClpAP) in P.falciparum
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Targets identified by comparison of proteins models
Identification of two proteasomal proteins of prokaryotic origin, not present in hosts. The protein degradation is an important process in parasite development inside host RBCs.
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Eukaryotic and prokaryotic proteasome machinery
26S proteasome: eukaryotic type 19S regulatory + 20S proteolytic particle Present only in Eukaryotes and archae Degrades ubiquitinated proteins > 20 different proteins involved 20S proteasome ClpQY system: prokaryotic type ClpY cap + ClpQ core particle Present only in prokaryotes No ubiquitination in prokaryote Substrate specificity is not known Only two proteins ClpQ & ClpY Substrate protein ClpY ClpQ ClpY Peptides
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ATP Dependent Protease Machinery ClpQY (PfHslUV system)
The HslUV complex in prokaryotes is composed of an HslV threonine protease and HslU ATP-dependent protease, a chaperone of Clp/Hsp100 family. HslV (ClpQ) subunits are arranged in form of two-stacked hexameric rings and are capped by two HslU (ClpY) hexamers at both ends. HslU (ClpY) hexamer recognizes and unfold peptide substrates with an ATP dependent process, and translocates them into HslV for degradation.
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Crystal structure of HslUV complex
in H. influenzae PfClpQY complex model in P. falciparum
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ATP Dependent Protease machineries ClpQY (PfHslUV system)
The HslUV complex in prokaryotes is composed of an HslV threonine protease and ATP-dependent protease HslU, a chaperone of clp/Hsp100 family. HslV subunits are arranged in the form of two-stacked hexameric rings and are capped by two HslU hexamers at both ends. In an ATP dependent process, HslU hexamer recognizes and unfold peptide substrates and translocate them into HslV for degradation.
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MFIRNFVNIIGSQKSITKTIARNYFSDNSKLIIPRHGTTILCVRKNN
PfClpQ component MFIRNFVNIIGSQKSITKTIARNYFSDNSKLIIPRHGTTILCVRKNN EVCLIGDGMVSQGTMIVKGNAKKIRRLKDNILMGFAGATADCFTLLDKFETKIDEYPNQL LRSCVELAKLWRTDRYLRHLEAVLIVADKDILLEVTGNGDVLEPSGNVLGTGSGGPYAMA AARALYDVENLSAKDIAYKAMNIAADMCCHTNNNFICETL For full length & matured active protein Length : 207 aa (170) Pro domain : 37aa Important motifs found: TT at N terminal in mature protein GSGG common chymotrypsin protease signal. Lys(28) and Arg(35) are two conserved amino acids play some role in the activity.
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Homologs of PfClpQ protein in other Plasmodium spp
PK_ClpQ TTILCVRKNNEVCLIGDGMVSQGTMIVKGNAKKIRRLKDNILMGFAGATADCFTLLDKFE PV_ClpQ TTILCVRKNNEVCLIGDGMVSQGTMIVKGNAKKIRRLKDNILMGFAGATADCFTLLDKFE PF_ClpQ TTILCVRKNNEVCLIGDGMVSQGTMIVKGNAKKIRRLKDNILMGFAGATADCFTLLDKFE PY_ClpQ TTILCVRKNNEVCLIGDGMVSQGTMIVKGNAKKIRRLKDNILMGFAGATADCFTLLDKFE PB_ClpQ TTILCVRKNNEVCLIGDGMVSQGTMIVKGNAKKIRRLKDNILMGFAGATADCFTLLDKFE ************************************************************ PK_ClpQ TKIDEYPDQLLRSCVELAKLWRTDRYLRHLEAVLIVADKDVLLEVTGNGDVLEPSGNVLG PV_ClpQ TKIDEYPDQLLRSCVELAKLWRTDRYLRHLEAVLIVADKDVLLEVTGNGDVLEPSGNVLG PF_ClpQ TKIDEYPNQLLRSCVELAKLWRTDRYLRHLEAVLIVADKDILLEVTGNGDVLEPSGNVLG PY_ClpQ TKIDEYPDQLLRSCVELAKLWRTDRYLRHLEAVLIVADKDTLLEVTGNGDVLEPSGNVLG PB_ClpQ TKIDEYPDQLLRSCVELAKLWRTDRYLRHLEAVLIVADKDTLLEVTGNGDVLEPSGNVLG *******:******************************** ******************* PK_ClpQ TGSGGPYAIAAARALYDVENLSAKDIAYKAMNIAADMCCHTNNNFICETL PV_ClpQ TGSGGPYAIAAARALYDVENLSAKDIAYKAMNIAADMCCHTNNNFICETL PF_ClpQ TGSGGPYAMAAARALYDVENLSAKDIAYKAMNIAADMCCHTNNNFICETL PY_ClpQ TGSGGPYAMAAARALYDIENLSAKDIAYKAMNIAADMCCHTNHNFICETL PB_ClpQ TGSGGPYAIAAARALYDIENLSAKDIAYKAMNIAADMCCHTNHNFICETL ********:********:************************:******* PfClpQ is almost identical in all P.falciparum species
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Homology modeling of PfClpQ
1kyi Conservation of catalytic residues S125-G45-T1-K33 Structural alignment of PfClpQ and HslV (H.influenzae)
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Homology Modeling of PfClpQ
E. coli S. enterica H. influenzae X. campestris W. pipientis P. falciparum T. brucei T. cruzi L. infantum E. coli S. enterica H. influenzae X. campestris W. pipientis P. falciparum T. brucei T. cruzi L. infantum E. coli S. enterica H. influenzae X. campestris W. pipientis P. falciparum T. brucei T. cruzi L. infantum Most of the conserved residues in different bacterial species were either identical or similar in PfClpQ
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Biochemical characterization of PfClpQ protein
Activity assay for PfClpQ protein Fluorogenic peptide substrate Fluorescence Protease Threonine protease like Substrate: Inhibitor: Chymotrypsin like Suc-LLVY-AMC chymostatin Peptidyl glutamyl hydrolase Z-LLE-AMC MG132 Cbz-GGL-AMC Lactacystin 50 100 150 1h 2h 3h 4h 5h 6h 100 200 300 400 500 30 60 90 120 150 180 Time in minutes 50 100 150 1h 2h 3h 4h 5h 6h Time AMC released (m moles) AMC released (m moles) AMC released (m moles) Time Substrate conc (mM) Substrate conc (mM) Substrate conc (mM) Km =19.18 mM Km = mM Km =37.79 mM
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Ligand docked into protein’s active site
Insilico identification of novel inhibitors against PfClpQ , a novel drug target of P.falciparum by high throughput docking Drug-like compound library (1,000,00) PfclpQ Molecular docking Ligand docked into protein’s active site Top 100 solutions Out of top 40 only 10 compounds available for purchase
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Phe46 Arg36 Val21 Gly49 Gly48 Ser22 Thr2 Thr50 ClpQ interaction with ligand identified by virtual screening Crystal structure of HslV complexed with a vinyl sulfone inhibitor
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Compound Gold Score Flexx score Chemical Structure 1 52.54 -25.14 2 54.76 -17.37 3 54.66 -24.43 4 52.84 -24.47
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Identification of P. falciparum ClpY (PfClpY) gene
A regulatory component of ClpQY system Recognizes the substrate; unfolds the substrate; feeds it into the degradation machine (ClpQ) Belongs to AAA+ family of proteins ClpY ClpQ ClpY ATPase domain PfClpY Walker A Walker B DOMAINS N I N C N-Domain C-Domain I-Domain ~1.3 kb Contain all the three ClpY domains- N, I and C
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plays role in recognition of different substrate
Homology of PfClpY protein with homologs in other organisms Variation in I domain: plays role in recognition of different substrate
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Targeting the ClpQY interaction
Crystal structure of HslUV in H. influenzae Modeled ClpQY interaction in P.falciparum J Biomol Struct Dyn Feb;26(4):473-9
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IDENTIFICATION OF DRUG TARGETS USING INTERACTION NETWORKS
FINDING DIFFERENT HUB GENES AND MODULES WHICH CAN BE USED AS DRUG TARGET BY REFERING TO THESE NETWORKS EXTRACTING THE MICROARRAY DATA FROM NCBI GEO NORMALIZATION IF NECESSARY OTHERWISE PREPARING EXCEL FILES FOR WGCNA ANALYSIS VISUALIZATION OF NETWORKS BY DIFFERENT GRAPHS AND SOFTWARE IN R PACKAGE EXCEL SHEET OF NORMALIZED DATA AND GENE SIGNIFICANCE PRINCIPLE BEHIND CONSTRUCTING NETWORK IS THAT THE GENES WHICH ARE CO-EXPRESSED, RELATED AND CAN BE CONNECTED TO MAKE A NETWORK , USING PEARSON CORRELATION COEFFICIENT ANALYSING THESE FILES IN R LANGUAGE AND RUNNING THEM IN ANOTHER R PACKAGE –”WGCNA”
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THESE NETWORKS CAN BE USED FOR FINDING THE DRUG TARGETS
THESE CAN ALSO BE USED FOR ANNOTATION OF PROTEINS AND GENES BY COMPARING THEM BY INTERACTOME STUDIES THESE NETWORKS CAN BE USED FOR PATHWAY ANNOTATION BETTER THAN OTHER STUDIES AS THEY ARE BASED ON THE MICROARRAY DATA
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Tools used: Sequence analysis: Pairwise and multiple sequence alignments, Pfam. Molecular modelling: Modeller Docking: Tripos FlexX, GOLD, Arguslab PP network: R package and Visant
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Molecular docking hands on
Download and install Arguslab in windows Load a PDB file, practice Arguslab tools Follow the tutorial at
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Molecular Docking using Argus lab:
Ex : Benzamidine inhibitor docked into Beta Trypsin 57
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Create a binding site from bound ligand
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Setting docking parameters
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Analyzing docking results
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Polypeptide builder. 63
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