VLifeMDS 4.1 - Molecular Design Suite

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.


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


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


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


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


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


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


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


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


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


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


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


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.


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.


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.


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


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.


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)


Thank you- Reconnect us

VLife Sciences Technologies Pvt. Ltd.

101, Pride Purple Coronet,

Pune , MH 411 045

India

Yogesh Wagh

Manager Scientific Solution & Services

Email : [email protected]

Phone: +91 202 729 1590

mailto:[email protected]

Date post:	07-May-2015
Category:	Technology
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VLifeMDS 4.1 - Molecular Design Suite

Technology