BIOVIA DISCOVERY STUDIO® 2018COMPREHENSIVE MODELING AND SIMULATIONS

FOR LIFE SCIENCES

ACCURATELY PREDICT LIGAND

BINDING ENERGIES

Drug discovery is a multi-objective optimization. Drug discovery is a multi-objective optimization. Scientists have to optimize both biochemical potency and characteristics such as ADME and toxicity. The latest release of BIOVIA’s predictive science applica-tion, Discovery Studio®, continues the evolution of new free energy prediction simulations. Built on BIOVIA Pipeline Pilot™, Discovery Studio® is uniquely positioned as the most comprehensive, collaborative modeling and simulation application for Life Sciences discovery research.

DISCOVERY STUDIO 2018Part of the 2018 BIOVIA product release series, Discovery Studio 2018 continues to deliver key new Small Molecule simulations and Antibody humanization enhancements.

NEW AND ENHANCED SCIENCE• New! Accurately predict relative ligand binding energy for a congeneric ligand series using the new free energy perturbation (FEP) method

- Pair Ligands for FEP: A new command to pair two ligands for a relative FEP calculation - Generate Ligand Pairs for FEP: A new protocol to plan a set of relative FEP calculations to span a dataset of congeneric ligands

- Set Up Relative FEP Calculations: A new protocol to gen-erate multiple simulation systems for performing FEP calculations - Free Energy Perturbation (FEP): A new protocol to run multiple FEP simulations in serial, parallel, or on grids using NAMD. This replaces the Free Energy Perturbation (NAMD™1) protocol in 2017 R2 that could only run a single system - Collate FEP Simulation Results: A new protocol to integrate and analyze results from multiple relative FEP simulations and calculate absolute free energy values

• New! Type molecules using CGenFF and charmm36 for CHARMm™ or NAMD1-based simulations with the MATCH™ typing engine from the Brooks Lab

• New! Predict stabilizing humanized residue mutations: Predict mutations to make an antibody more human-compatible with-out losing specificity of antigen binding

- Calculate Mutation Energy (Stability): An option has been added to allow mutations to be specified using a sequence alignment. This facilitates trialing large numbers of muta-tions, and is particularly useful in conjunction with the suggested mutation alignments created by the new Predict Humanizing Mutations protocol - A new Create Humanization Database example protocol has been created to process data for use in the Predict Human-izing Mutations protocol - The Prepare Germline Sources example protocol has been enhanced to optionally save each annotated gene sequence to a separate file. This allows a specific gene to be selected for use in the Predict Humanizing Mutations protocol

• New! CHARMM PBEQ ,4,5 Poisson-Boltzmann-based electro-static solver: The DelPhi electrostatics component has been replaced with a CHARMm PBEQ Solver, based on the CHARMm PBEQ module to solve the Poisson-Boltzmann equation. It includes a number of new features and improvements:

- A better definition of the Poisson-Boltzmann grid, based on the grid spacing and distance from boundary - An orthorhombic grid box to calculate the potential of mol-ecules more efficiently - The electrostatic potential values and energies are reported in kcal/mol.e and kcal/mol - The focusing center can be set up automatically as the center of a selected group of atoms, instead of the carte-sian coordinates - Calculations can now be performed for membrane environ-ments, either as a planar slab (uniform dielectric constant) or as multiple regions (cf. low dielectric hydrophobic region and lipid head group regions)

• Enhanced! Several of the pharmacophore protocols have been redesigned to improve performance and scalability for parallel computing, including:

- Ligand Pharmacophore Mapping - Screen Library - Receptor Ligand Pharmacophore Generation - Ligand Profiler

Figure 1: Network map showing the relative similarities in a congeneric set of ligands.

Figure 2: Sequence hotspot map showing humanization hotspots according to germline analysis. At each position, detailed amino acid options are presented via ‘logo plots’.

hsp90_5

hsp90_4

hsp90_9

hsp90_3

hsp90_1

hsp90_2

hsp90_34

hsp90_40

hsp90_30hsp90_29

hsp90_18

hsp90_48

hsp90_51hsp90_12

hsp90_7

0.67

0.67

0.67

0.82

0.82

0.82

0.74

0.670.67

0.61

0.61

0.610.9

0.9

• New! A Post-Process Screen Library Results example protocol has been added to assist with post-processing large Screen Library results

• Update! TopKat® QMRF Reports6: All remaining extensible TopKat Toxicity Models QSAR Model Report Format (QMRF) reports have been accepted and published on the European Commission Joint Research Center (JRC)

QMRF # Title

Q17-33-0048 BIOVIA toxicity prediction model-acute fish toxicity

Q17-31-0047 BIOVIA toxicity prediction model-acute toxicity to Daphnia

Q17-410-0037 BIOVIA toxicity prediction model-Ames Mutagenicity

Q17-49-0046 BIOVIA toxicity prediction model-eye irritant vs non-irritant

Q17-49-0044 BIOVIA toxicity prediction model-mild vs moderate eye irritant

Q17-49-0045 BIOVIA toxicity prediction model-moderate vs severe eye irritant

Q17-412-0052BIOVIA toxicity prediction model-NTP carcinogenicity call (female mouse)

Q17-412-0051 BIOVIA toxicity prediction model-NTP carcinogenicity call (female rat)

Q17-412-0050BIOVIA toxicity prediction model-NTP carcinogenicity call (male mouse)

Q17-412-0039 BIOVIA toxicity prediction model-NTP carcinogenicity call (male rat)

Q17-416-0041 BIOVIA toxicity prediction model-prenatal developmental toxicity

Q17-42-0038 BIOVIA toxicity prediction model-rat oral LD50

Q17-46-0042 BIOVIA toxicity prediction model-skin sensitiser vs non sensitiser

Q17-46-0043 BIOVIA toxicity prediction model-weak vs strong sensitiser

QMRF # Title

Q17-412-0049BIOVIA toxicity prediction model-weight of evidence rodent carcinogenicity

Q17-412-0008 BIOVIA toxicity prediction model-mouse carcinogenic potency TD50

Q17-23a-0009 BIOVIA toxicity prediction model-aerobic biodegradability

Q17-412-0007 BIOVIA toxicity prediction model-rat carcinogenic potency TD50

Q17-414-0006BIOVIA toxicity prediction model-rat chronic lowest observed adverse effect level (LOAEL)

Q17-41-0005 BIOVIA toxicity prediction model-rat inhalational LC50

Q17-44-0004BIOVIA toxicity prediction model-skin irritancy (mild vs moderate/severe)

Q17-44-0003 BIOVIA toxicity prediction model-skin irritancy (moderate vs severe)

Q17-44-0002 BIOVIA toxicity prediction model-skin irritancy (none vs irritant)

• Update! The TopKat Detailed Report now contains additional information to improve traceability and reproducibility

• Update! The Detailed Report in the Toxicity Prediction (Exten-sible) protocol has been modified to improve readability and interpretability

PARTNER SCIENCE• CHARMm: Incorporates the latest release of the academic

CHARMM, version c42b12

• NAMD: Distributed with the CPU edition, version 2.12• MODELER: Incorporates the latest release of the academic

MODELLER, version 9.197 • BLAST+: The BLAST+ version in Discovery Studio has been

updated to 2.7

DATABASES• ANTIBODY has been updated to include the latest antibody

template structures from the PDB (based on PDB release August 2017)

• BLAST databases updated for PDB_nr95, PDB, SwissProt, UniRef90, NR based on corresponding public databases as of March 2017

• The RCSB ligand database was updated for the RCSB Structure Search protocol (November 2017, 25,392 entries)

COMPATIBILITYDiscovery Studio 2018 is built on and supports the latest release of BIOVIA Pipeline Pilot, version 2018• 64-bit Support: Discovery Studio executables are now all

native 64-bit on both Linux and Windows

Figure 3: Comparison in CATALYST pharmacophore screening performance between DS2017 R2 and DS2018, of a 5-feature pharmacophore against a 50000-compound dataset.

Pharmacophore Mapping

Our 3DEXPERIENCE® platform powers our brand applications, serving 12 industries, and provides a rich portfolio of industry solution experiences. Dassault Systèmes, the 3DEXPERIENCE® Company, provides business and people with virtual universes to imagine sustainable innovations. Its world-leading solutions transform the way products are designed, produced, and supported. Dassault Systèmes’ collaborative solutions foster social innovation, expanding possibilities for the virtual world to improve the real world. The group brings value to over 190,000 customers of all sizes in all industries in more than 140 countries. For more information, visit www.3ds.com.

©20

17 D

assa

ult S

ystè

mes

. All

righ

ts re

serv

ed. 3

DEX

PER

IEN

CE®

, the

Com

pass

icon

and

the

3DS

logo

, CA

TIA

, SO

LID

WO

RKS

, EN

OVI

A, D

ELM

IA, S

IMU

LIA

, GEO

VIA

, EXA

LEA

D, 3

D V

IA, 3

DSW

YM, B

IOVI

A, N

ETVI

BES

, and

3D

EXCI

TE a

re c

omm

erci

al tr

adem

arks

or

regi

ster

ed tr

adem

arks

of D

assa

ult S

ystè

mes

or i

ts s

ubsi

diar

ies

in th

e U

.S. a

nd/o

r oth

er c

ount

ries

. All

othe

r tra

dem

arks

are

ow

ned

by th

eir r

espe

ctiv

e ow

ners

. Use

of a

ny D

assa

ult S

ystè

mes

or i

ts s

ubsi

diar

ies

trad

emar

ks is

sub

ject

to th

eir e

xpre

ss w

ritt

en a

ppro

val.

Dassault Systèmes CorporateDassault Systèmes175 Wyman StreetWaltham, Massachusetts02451-1223USA

BIOVIA Corporate EuropeBIOVIA 334 Cambridge Science Park,Cambridge CB4 0WNEngland

BIOVIA Corporate AmericasBIOVIA 5005 Wateridge Vista Drive,San Diego, CA 92121USA

DS-3054-0118

REFERENCES1. Phillips J.C., Braun R., Wang W., Gumbart J., Tajkhorshid E., Villa E., Chipot C,

Skeel C.D., Kale L., and Schulten K., J. Comp. Chem., 2005, 26, 1781-1802.

2. Brooks B. R., Brooks III C. L., Mackerell A. D., Karplus M., et al, J. Comp. Chem., 2009, 30, 1545-1615.

3. Yesselman J.D., Price D.J., Knight J.L., Brooks III C.L., J. Comput. Chem., 2011, 33(2), 189-202.

4. Roux B., Biophys. J., 1997, 73, 2980–2989.

5. Im W., Beglov D., Roux B., Comput. Phys. Commun., 1998, 111, 59–75.

6. http://qsardb.jrc.it/qmrf/

7. Eswar N., Marti-Renom M. A. Webb B., Madhusudhan M. S., Eramian D., Shen M., Pieper U., Sali A., Current Protocols in Bioinformatics, John Wiley & Sons, Inc., 2006, Supplement 15, 5.6.1-5.6.30.