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Design Of New Rac1 Inhibitors Through Computational
Approaches
Alessandro Contini, Nicola Ferri, Stefano Stragliotto – CDDD – L’Aquila 2011
What is Rac1?
-signal transducer
- GTPase
-activated by GEFs
-acts through effectors
Rac1 regulates:[1]
a) cellular mobility
b) cellular proliferation
c) superoxide production
[1] Bosco, E.E.; Mulloy, J.C.; Zheng, Y. Cell. Mol. Life Sci. 2009, 66, 370.
[2] Parri, M.; Chiarugi, P. Cell Communication and Signaling 2010, 8, 23.
[3] Sawada N.; Li Y.; Liao, J.K. Curr Opin Pharmacol. 2010, 10, 116.
Implication of Rac1 in cancer and cardiovascular disease![2,3]
Rac1 is activated by GEF
[4] Gao, Y.; Dickerson, J. B.; Guo, F.; Zheng, J.; Zheng, Y. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 7618.
GEF selectivity:
Tiam is the only Rac1 specific GEF (PAK → cytoskeleton reorganization)
NCI
DB
N
NH2 HN
N
NHN
N
NSC23766 [4]
Identification of Rac1 inhibitors: the library[5]
[5] Ferri, N.; Corsini, A.; Bottino, P.; Clerici, F.; Contini, A. J. Med. Chem. 2009, 52, 4087
The "mp3" compression…
1024 molecules with RMSD 0.1-1 Å
FP:BIT_MACCS
tanimoto index=0.8
overlap= 50%
lowest MW
Ph4 filtration
1.5 ≤logP(o/w) ≤4
643 molecules for docking
x: molar refractivity y: HB acceptor
z: logP(o/w)
Identification of Rac1 inhibitors: consensus docking
The receptor:
Rac1-NSC23766
complex
X-ray
coordinates "transcription"
Ph4 Hits
(643)
The docking:
MOE: TM (engine)
Affinity + LdG (scoring)
AD4: 80x80x80, spc 0.175 Ǻ
pop_size 150
ga_num_evals 500000
GA-LS runs 20
106 compounds
affinity < -4.2,
London dG < -7.5,
AD4 < -6.4 kcal/mol
(thresholds from
NSC23766)
Visual inspection Purchased
Hits
(50)
The results:
33 more active than
NSC23766 (11.1%)
5 with > 45% inhibitory
effect
G-LISA bioassay
selectivity specificity
N SO2
NHO
O
NHN
H2N SO2
NH
O
O
ON
4(45.8% inhibition)
5(65.6% inhibition)
MD
Amber ff03
TIP3P box
Current development: hit-to-lead optimization
bioassays
NH
O
YX
R2
R1
scale-up In vivo
Ph4, QSAR synthesis
Ar3
Ar4
docking NH
O
YX
R2
R1
similarity search G-LISA
Ar1
Ar2
Focussed library and method optimization
N SO2
NHO
O
NHN
H2N SO2
NH
O
O
ON
4
5
ZINC
DB
similarity search
170 cmpds
similarity=85%
NH
O
YX
R2
R1
consensus docking
56 cmpds purchased
NH
O
YX
R2
R1
G-LISA bioassay
active
(31)
inactive
(25)
Rate of acceptance:
active compounds (78) : 99%
decoys (374): 16%
New Ph4 model
Placement: Alpha Triangle
Scoring: Affinity dG
Refinement: MMFF94x
(5000 iteration, rmsd 0.001)
Rescoring: Affinity dG
Ph4 filtration
QSAR filter
binary model, r2=0,68
Improved docking protocol
Virtual library generation: COMBIGEN
SCAFFOLD-An + A0-R SCAFFOLD-R
Two different database of –R groups:
1. –R groups taken from molecules with
known activity
2. –R groups taken from most common
functional groups (i.e. hydroxil, alkyl,
halogen…)
An ATTACHMENT POINT
301 MOLECULES
2234 MOLECULES
Virtual library generation: BREED
“crossover” scheme for the generation of new ligands
1. Molecules with known activity are aligned
2. Substituents on superposed bonds are exchanged
Bond 3D superposition
exchange
1153
NEW
MOLECULES
molecules aligned in groups of 5
process reiterated until no new
molecules were generated
Virtual library screening
301
–R groups from
known molecules 2234
–R groups from
common funct. groups
1153 BREED
Eg(MOE)<-6.0 kcal/mol Eg(ADT)<-9.0 kcal/mol
Filtration by property
(Oprea, nonreactive, 2.1<logP<4.2)
5 29
17
COMBIGEN
Filtration by docking + Ph4
Filtration by QSAR
($PRED ≥0.5)
Synthesis of new compounds
+
TEA
CH2Cl2
Pharmacology, preliminary results, SMCs
Pharmacology, preliminary results, cancer lines[6]
[6] Prof. David Williams, Children’s Hospital, Boston MA
Acknoledgements
The modelers, actual The modelers, former
Degree thesis on Rac1:
Paolo Bottino
Stefano Assolari
Chiara Gregorio
Giada Maggioni Synthesis:
Dr. Emanuela Erba
Prof. Francesca Clerici
Fundings:
MIUR: FIRB – progetto giovani 2009
Regione Lombardia: Progetto Astil
Pharmacology:
Prof. Alberto Corsini
Prof. David Williams
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