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SAFAN-ISP small molecule & peptides profiling with experimental reliability Luisa Pugliese [email protected]
Transcript of SAFAN-ISPsafan-bioinformatics.it/wp-content/uploads/2019/safanisp... · 2019-10-14 · Virtual...
Luisa [email protected]
Who we are:S.A.F.AN. BIOINFORMATICS (Structural.And.Functional.Analysis) specializes in chemoinformatic and structural bioinformatics research. Today, S.A.F.AN. BIOINFORMATICS offers one of the most innovative and effective in-silico profiling technologies anywhere, supporting initial drug discovery and repositioning projects and greatly reducing costs for our customers.
Our Mission:
Our ambition is to deliver you compound→protein binding predictions as reliable as in vitro tests.
in silico in vitro in vivo
Virtual Screening or Profiling?
Luisa [email protected]
Virtual screening SAFAN-ISP (In Silico Profiling): relies on proprietary ligand based algorithms to forecast protein-compounds affinities, through fragments weight assignment. It works with a refactored bioactivity database derived from CHEMBL25 database.
Luisa [email protected]
SAFAN-ISP & protein-small molecules interaction
S.A.F.AN.BIOINFORMATICS
Quantitative drug-targetBinding prediction
disease database
side effect database
Customer drug
GO STOP
Target profiling with experimental reliability
Drug Repositioning Toxicology Prediction
Luisa [email protected]
SAFAN-ISP & protein-peptide interaction
● Protein-peptide interaction → critical role
● Promising candidatesPrediction methods:
high priority
Structure Based methods challenges:
● Lack of peptide distinct fold
● No knowledge on binding site
ligand based approach to predict protein-peptides binding affinities
Luisa [email protected]
TECHNOLOGY
COMPANY
√ √ √ √ √ √
CHEMOTARGETS √ √ √ X X X
GVK BIOSCIENCE √ X X X X X
THOMPSON REUTER √ √ X X X X
ELSERVIER √ X X X X X
NUMEDICUS Ldt √ X X X X X
EUROFINS BIOPRINT √ X X X X X
CRESSET X √ X X X X
SOM BIOTECH X √ X X X X
DATABASE ANALYSIS:
IMPORTANT INFORMATION
NO NEW PATENTS
COMPUTATIONAL CHEMISTRY
PREDICTIONS: NEW IP & NEW
PATENTS
QUANTITATIVE SCORING:
DIRECT COMPARISON
WITH EXPERIMENTAL
DATA
SPECIFIC FRAGMENTS:
GOOD RESULTS AT LOW
SIMILARITY. NEW PATENTS
FAST CHIRAL ANALYSIS
HIGH SPEED PEPTIDE ANALYSIS
SAFAN BIOINFORMATICS
Luisa [email protected]
How it worksYour
CompoundYour
Compound
GPCR
517 Targets
198354 Compounds
324803 Interactio
ns
Transporters & Auxiliary26 Targets
532 Compounds685 Interactions
Transcription Factors115 Targets
101381 Compounds116803 Interactions
Adhesion Proteins14 Targets
810 Compounds1406 Interactions
En
zym
es
257
7 Ta
rget
s5
6604
2 C
om
po
un
ds
109
284
9 In
tera
cti
on
s
Ion Channels
332 Targets
50931 Compounds
121058 Interactions
Secreted Proteins
38 Targets
2485 Compounds
2558 Interactio
ns
Surface Antigens
15 Targets
755 Compounds
756 Interactions
Stru
ctural P
rote in
s21 Ta
rgets
1412 C
om
po
un
ds
8331
Intera
ction
s
Cyto
solic P
rote in
s53 Ta
rgets
16856
Co
mp
ou
nd
s22
831 In
teractio
ns
Me
mb
ran
e P
rote
ins
12 T
arg
ets
184
1 C
om
po
un
ds
190
7 I
nte
ract
ion
s
Unclassified Proteins534 Targets
164057 Compounds269433 Interactions
Ep
ige
net
ic P
rote
ins
90 T
arg
ets
940
34C
om
po
un
ds
113
067
In
tera
ctio
ns
Nu
clear P
rotein
s8 Targ
ets704
Co
mp
ou
nd
s72
2 Inte
ractio
ns
15 Functional classes4562 Targets
898696Compounds
2106314 Interactions
122265Fragments
Fragments &SAFAN Fingerprints
generation
SAFAN Small Molecule library
SAFAN Fragmentlibrary
Selection of targets interacting with
fragments similar to those of the query molecule
Compound basedpK binding prediction
Fragment basedpK binding prediction
SAFAN-ISP
SMV (weka)
Luisa [email protected]
SAFAN-ISP validation:small molecules & peptides
Compound:target interactions
Validation method Leave-one-out
Number of small molecules 21090 65936
Number of peptides 4382 6079
Pre
dict
ions
(pC
hem
bl)
Experiments (pChembl)
Small Molecules Peptides
pChEMBL is defined as: -Log(molar IC50, XC50, EC50, AC50, Ki, Kd or Potency)
Pearson: 0.91 Pearson: 0.87
Luisa [email protected]
SAFAN-ISP validation: expected predictions with error < 1
0,3 0,4 0,5 0,6 0,7 0,8 0,9 10
10
20
30
40
50
60
70
80
90
100
SMALL MOLECULES
PEPTIDES
Similarity with SAFAN compounds database
% in
tera
ctio
n w
ith e
rror
< 1
% p
red
ictio
ns
with
err
or <
1
Similarity with SAFAN database (Tanimoto)
Luisa [email protected]
SAFAN-ISP validation:similarity measure
CHEMBL315063 CHEMBL311125
Similary (tanimoto)
SAFAN 0.95
openbabel 1
Rdkit topological fingerprints 1
Rdkit MACCS keys 1
Rdkit Atom Pairs and Topological Torsions 1
Rdkit Morgan Fingerprints 1
Pyruvate dehydrogenase Kinase (Pchembl)
4.31 8.35
Luisa [email protected]
SAFAN-ISP VS. EXPERIMENTAL ERROR
Dataset:2102 SMALL MOLECULES2830 SMALL MOLECULES/TARGET INTERACTIONS15 AVERAGE NUMBER OF EXPERIMENT REPLICATION
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9 1
1,1
1,2
1,3
1,4
1,5
1,6
1,7
1,8
1,9 2
2,1
2,2
2,3
2,4
2,5
2,6
2,8
2,9
3,1
3,4
3,7
3,8
0
50
100
150
200
250
300
350
400
Maximum difference between experimental value and their average
Difference between SAFAN-ISP predictions and average of the expe-rimental data
Error (pK unitis)
Num
ber
of c
ompo
und/
arge
t in
tera
ctio
ns
Luisa [email protected]
Drug repurposing of generic compounds
Feb 28th 2019
TOWARDS THE end of 2014 a 66-year-old British man named Alistair had an inoperable brain tumour—a glioblastoma—that was likely to kill him in a few years. Soon afterwards, he read a newspaper article suggesting that a cocktail of cheap, everyday drugs, chosen for their anti-cancer effects, had helped a patient with the same disease. Four years on Alistair is still taking this drug regimen alongside the “standard-of-care” treatment. The drug cocktail is prescribed by Care Oncology, a private clinic in London, which recommends a statin , metformin , doxycycline and mebendazole .
SAFAN-ISP results:
compounds Targets with reliable pk predicted > 6
mebendazole Adenosine receptor A2a, Angiopoietin-1 receptor, Vascular endothelial growth factor receptor 2
metformin Prelamin-A/C (known data)
doxycycline Prelamin-A/C, Estrogen receptor beta
S.A.F.A.N. delivered results in less than a month:Biotech halted the project
• French Biotech want to in-license a potent and selective Phosphodiesterase inhibitor
• SAFAN analyzed the molecule versus related enzyme subtypes
• SAFAN in-silico results: the molecule is not selective
• Results confirmed in lab experiments
CASE STUDY 1
Luisa [email protected]
Euroqsar 2016, Verona, Italy - September 4-8, 2016
● Three compounds from Boehringer Ingelheim
● Experimental data on original target known to Boehringer
Ingelheim
● No info provided to SAFAN
CASE STUDY 2
