vlifemds 4.1 - molecular design suite
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Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
Improving Research Productivity with VLifeMDS 4.1
www.vlifesciences.com
All trademarks, methodologies, product names mentioned in this
presentation are sole property of their respective owners.
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
VLife and your research requirements
Are you computational chemist/biologist?
VLife can help you with it’s innovative computational
platform VLifeMDS
Are you an experimental setup?
VLife can help you with it’s strong decision support system
for efficient results
Already doing computation with one
tool?
VLife can help you build consensus with an Unbiased
perspective
Looking for leads or optimization?
VLife can do a time base service for identifying, screening and
optimizing leads
Looking for new target or multi-target?
VLife can do a time base service on VLife RVHTS platform
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
Multiple scenarios single platform
YesPrimary
lead chemistry
Yes
No
Activity data
No activity
data
I
II
III
Yes
No
Close homolog
Remote homolog
Primary lead
chemistry
Activity data
No activity
data
No
IV
V
VI
VII
Combinatorial library
Protein structure analysis
Pharmacophore identification
Conformer generation
Property visualization
QSAR analysis
Database querying
Virtual screening
Active site analysis
Homology modeling
Docking
Ne
wE
dge
: End
-to-end
capa
bilitie
s
Target structure
Approaches Applications
NewEdge platform: Application summary
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
NewEdge Technologies
Hit Identification Hit Filtration Hit to Lead Library Generation Lead Optimization
Shape similarityLigand StructureChemDBS
Target specificitySATREA
Fragment basedGQSAR
Scaffold basedLeadGrow
Fragment basedGQSAR
PharmacophoreLigand StructureChemDBS – MolSign
QSAR basedVLife QSAR
Scaffold hoppingGQSAR + GLib
Fragment TemplateBasedAdv LeadGrow
Structure Ligand Hybrid methodVLife SCOPE
FingerprintLigand basedChemDBS
3D-QSARVLife QSAR
DockingStructure basedBioPredicata
Target SpecificitySATREA
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
Key elements of SATREA
Where is SATREA useful
Tool to aid in depth understanding of
specificity requirements of a target Provide clues in growth of
molecules in the active site
Identification of regions of active site
to be explored by ligand Identification of regions of active
site to be avoided (in green) by ligand Electrostatic or hydrophobicity mapping on the regions to be explored (in blue and yellow) List of neighboring residues to be explored/avoided Quantification of specificity based
on ratio of overlap volumes
SATREA: For target specificity
- Specificity analysis of target AKT1 wrt AKT3- Dotted region is common for both targets- Property mapped region is necessary for specificity- Unmapped green colored regions must be avoided
SATREA: Specificity Analysis Tool for Region Exploration and Avoidance
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
SATREA: Specific and Non-specific Inhibition
STK6 (PDB id: 3E5A) ABL1 (PDB id: 2F4J)Specificity STK6/ABL1: 1
FAK2 (PDB id: 3FZS) MK14 (PDB id: 1KV2)Specificity MK14/FAK2: >1000
Yellow ligand & white isosurface associated to target A (target of interest). Magenta ligand & green isosurface associated with target B (target against which specificity to achieve). Dotted region is common between both the targets.
Left Panel: Yellow ligand is in common region & overlaps with only white region. Magenta ligand overlaps with white region, which is avoidance region for ligand of target B leading to loss of specificityRight Panel: Both yellow and magenta ligands accommodate in the common region & have no overlap with white or green region. No target specificity
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
Key elements of GQSAR
Where is GQSAR useful
Lead optimization by using site specific clues from GQSAR model Scaffold hopping by choosing groups/fragments satisfying descriptor ranges of actives in the dataset Novel library generation along with predicted activity of ligands
Alignment independent fragment based QSAR modeling Conformer independent method GQSAR models generation for both congeneric and non-congeneric data Provides site specific clues Patented method
GQSAR: For lead optimization
Publication references
• QSAR Combi Science 2009, 28:36–51• J Mol Graph Mod 2010;28:683-694
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
GQSAR: For lead optimization
Actual GQSAR snapshot shows the newly optimized molecule formed on the screen with R1 fragment (red) of Akt225 and R2 fragment (yellow) of Akt126
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
GQSAR: Scaffold Hopping of Akt1 inhibitors
Original Dataset Scaffolds used in GQSAR
New Scaffolds suggested by GQSAR & are in BindingDB
GQSAR model built using 264 molecules from BindingDB and corresponding scaffolds are shown (left panel). Use of GQSAR model to find new scaffolds showed match from revised dataset of Akt1 inhibitors (right panel) from latest BindingDB
This demonstrates that GQSAR is useful in scaffold hopping
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
Key elements of kNN-MFA
Where is kNN-MFA useful
Location of field values and ranges of field values provide clues for lead optimization Automatic selection of groups at a given
site satisfying required field ranges providing optimized lead
Novel 3D QSAR method that inherently captures non-linearity in the relationship of activity with field values Considers steric, electrostatic & hydrophobicity fields Improved predictive ability than conventional 3D-QSAR methods Extensively used method in the
literature (~35 publications)
kNN-MFA: For lead optimization
Publication reference
J Chem Inf Model 2006;46: 24-31
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
H-BondSteric groups
Ile219Val49
Met258
Arg47
Lys120
Key elements of VLifeSCOPE
Where is VLifeSCOPE useful
Identifies key residues for protein-ligand interactions leading to optimization of Ligand
Improved ranking of ligands compared to docking
Allows screening of large databases to predict the activity of new compounds
Active site residues are considered Partitioning of binding energy or
docking score in to residue wise interactions terms & utilized as descriptors, f(Exp. Activity)
Generates QSAR models of docked compounds
VLifeSCOPE: For lead optimization
Publication reference
•Bioorg Medl Chem 2004; 12: 2937-2950•Chem Biol Drug Des 2009; 74: 582–595
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
Key elements of GLib
Where is GLib useful
Exhaustive chemical space exploration by hybrid library provides optimized leads Suggests new molecules to be synthesized in the series
Generates novel molecules by combinatorial principle using fragments of existing dataset Generated library adheres to applicability domain of original dataset Activity prediction for newly generated molecules Intuitive graphical interface
GLib: Library generation by scaffold hopping
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
QSAR benchmarking II: kNN MFA
Activity prediction benchmarking : VLifeSCOPE
Voriconozol ER30346 TAK187 J1_114 Sankyo SCH42427 Itraconozole Fluconozole 0
1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
7
8
1
2
3
4
6
5
8
7
4
5
3
8
7
1
2
6
Rank in the Lab VLife SCOPE Binding Energy
Reference: Modeling and interactions of Aspergillus fumigatus lanosterol 14-α demethylase ‘A’ with azole anti fungals (Bioorganic & Medicinal Chemistry 2004, 12 2937–2950)
Comparison of VLifeSCOPE with force field based docking as a means of predicting likely experimental MIC
Accuracy measure: Rank order comparison of each molecule of the data set with their MIC
With VLife SCOPE predicted rank order for first four compounds exactly matches experimental finding while binding energy based rank order is completely off track.
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
Comparison of patent pending GQSAR with other 2D QSAR and 3D QSAR methods for accuracy of predicted activity
Accuracy measure: Established statistical measures, pred_r2 and q2
Pred_R2 Q20
0.10.20.30.40.50.60.70.80.9
1
Reference: Group-Based QSAR (G-QSAR): Mitigating Interpretation Challenges in QSAR ,Subhash Ajmani, Kamalakar Jadhav, Sudhir A. Kulkarni, QSAR & Combinatorial Science, 28, 1, 2009, 36–51
XXSolution to inverse QSAR problem
XSite specific clues for NCE design
Fast evaluation of descriptors
XMolecule alignment independent
XIndependent of conformations
NewEdge GQSAR
3D QSAR2D QSAR
XXSolution to inverse QSAR problem
XSite specific clues for NCE design
Fast evaluation of descriptors
XMolecule alignment independent
XIndependent of conformations
NewEdge GQSAR
3D QSAR2D QSAR
QSAR benchmarking I: GQSAR
QSAR benchmarking : GQSAR
VLife’s patented GQSAR is more accurate than similar technologies and far more insightful for lead optimization.
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
Comparison of kNN MFA method with other QSAR methods for accuracy of prediction in case of non-linear relationships
Accuracy measure: Established statistical measures, pred_r2 and q2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Pred_r2 q2
-1
-0.5
0
0.5
1
1.5
Pred_r2 q2 Pred_r2 q2
Steroids Anti-Inflammatory Cancer
Reference: Three-Dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation by Subhash Ajmani, Kamalakar Jadhav, Sudhir A. Kulkarni , Journal of Chemical Information and Modeling, 2006, 46, 24-31
QSAR benchmarking II: kNN MFA
QSAR benchmarking : kNN-MFA
VLife’s kNN-MFA method is consistently more accurate than similar technologies across widely varying chemistries.
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
Docking tool0
20
40
60
80
100
Num
ber
of p
rote
in li
gand
co
mpl
exes
with
RM
SD
< 1
RMSD1 <1.0 RMSD1 <1.50
50
100
150
200
250
Pro
tein
lig
and c
om
ple
xes
Accuracy
Reference: Standard data for comparison taken from ‘Deciphering common failures in molecular docking of ligand-protein complexes’ by G.M. Verkhivker, D. Bouzida, D.K. Gehlhaar, P.A. Rejto, S. Arthurs, A.B. Colson, S.T. Freer, V. Larson, B. A. Luty, T. Marronne, P.W. Rose, J. Comp. Aid. Mol. Des., 2000, 14, 731-751
Comparison with multiple other technologies for accuracyAccuracy measure: Difference of < 1A0 between predicted and laboratory determined result
Docking benchmarking I: GRIP
Docking benchmarking – I: GRIP
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Comparison with multiple other technologies for speed and ability to handle complex molecules
Speed measure: Minutes taken per docking
Molecular complexity measure: Number of rotatable bonds within molecule
0
0.5
1
1.5
2
2.5
3
3.5
4
Docking tool
Avera
ge t
ime p
er
dock
ing
Speed
> 1 > 5 > 10 > 150
20
40
60
80
100
Number of rotatable bonds
Perc
enta
ge s
truct
ure
s belo
w
1.0
A
Complexity
Docking benchmarking II: GRIP
Docking benchmarking – II: GRIP
VLife’s GRIP docking is faster, more accurate and is better able to handle complex molecules vis-a-vis wide spectrum of competing technologies.
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
VLife software and research has been cited in more than two hundred peer reviewed publications in the last 3 years
Docking benchmarking II: GRIP
Peer Reviewed Publication Citations
•Biosensors and Bioelectronics (5.143)•Current Medicinal Chemistry (4.994)•Journal of Medicinal Chemistry (4.898)•Protein Science (4.856)•International Journal of Cancer (4.734)•Molecular BioSystems (4.23)•BMC Bioinformatics (3.78)•Journal of Computer-Aided Molecular Design
(3.62)•Journal of Molecular Modeling (2.018)
•Bioorganic & Medicinal Chemistry (3.075)• Mutation Research - Fundamental and Molecular
Mechanisms of Mutagenesis (3.198)•Journal of Chemical Information and Modeling
(2.986)•European Journal of Medicinal Chemistry (2.882)•Molecular Diversity (2.708)•QSAR & Combinatorial Science (2.594)•Journal of Molecular Graphics and Modeling (2.347)
VLife Component No. of Citations
BioPredicta 50
ChemDBS 4GQSAR 4
LeadGrow 4MolSign 2Proviz 7
QSARPlus 80
VLife SCOPE 2
VLife Research work 107
>3 2.0 to 2.9 <20
1020304050607080
Product citations in Peer Reviews Journals for the last 3 years
Citations
Impact Factor
No. of Citations
Representative list of Journals (Impact Factor)
Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com
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Email : yogeshw@vlifesciences.com
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