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Employment No conflict of interest to disclose

Research support No conflict of interest to disclose

Scientific advisory board No conflict of interest to disclose

Consultancy No conflict of interest to disclose

Speakers bureau No conflict of interest to disclose

Major stockholder No conflict of interest to disclose

Patents No conflict of interest to disclose

Honoraria No conflict of interest to disclose

Travel support No conflict of interest to disclose

Other No conflict of interest to disclose

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MAIN "TARGETS:"!!Enzymes "!!GPCRs "

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Often: Proteins !

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"!Volume (smaller for allosteric): ~500 Å3!

"!Depth: !7-11 Å!

"!Surface area: 300-600Å2!"!Hydrophobicity: 60 to 80 %!

•!Gao & Skolnick, Plos Comput Biol, Oct 2013 !

•! Perot et al. DDT 2010*

•!Li et al, J Mol Graphics & Model, 2013!

Dihydrofolate reductase"methotrexate" 1TFT: Xiap-caspase 9"

“PPI pockets”"

"!In general (although some pockets at the interface can look like regular pockets)!"!3 to 5 subpockets, each ~ 50 Å3!

"!Interface regions more likely to be ligandable are more predisposed to surface pocket formation (pockets form with little energetic cost)! •!Fuller et al., Jackson, DDT 2009!

•!Eyrisch & Helms, 2007!

•!Karanicolas et al, Plos Comput Biol, 2013!

1) Target “validation” & “ligandability”: large-scale analysis "?).f0#*./053.'9*"'#8&0,1*g*1/'-0*`5'#./01a*./0531#&I*g*:3.04:0&1'*

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Binding pocket detection (combine with hotspot tools) "

Geometry-based:"

PocketDepth, Screen2, PASS, Caver,

Fpocket…"

Energy-based:"

Interaction affinity maps: QsiteFinder,

Surflex, SiteHound…"

Docking fragments & ligands:"

FTMAP, FTFlex, FindBindSite…"

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•!Some hotspot residues are pre-organized in the unbound state prior to protein complexation (rigidity of hotspot residues ! same conformation in the bound and unbound states) !•!Next to these “rigid regions” are flexible areas!

•!Keskin et al Chem Rev 2008!

•!Cukuroglu et al Progress Biophys Mol Biol 2014!

•!Kuenemann et al., Prog Biophys Mol Biol 2015!

IL-2 (apo)" IL2-ligand X-ray (holo)"

•!Arkin et al., PNAS, 2003!

IL-Receptor!Simulation tools can be used to capture this type of flexibility, "

In 2016, the initial screening of 6 million compounds can take ~15 min "on a small “GPU” computer "

•!Dr. Miteva’s team, Eur Biophys J. 2010!

•!Rueda (Dr. Abagyan’s lab) J Chem inf Model 2010!

•!Eyrisch & Helms, 2007!

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Lipinski, Lombardo Dominy, Feeney

1997!

2) Cmpd collection design: ADMETox & related"

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ADMET-physchem

friendly space!

Explore new regions of the chemical space: more LMW molecules than stars in the universe"

(1023 stars gathered into 1011 galaxies)"

At present with 17 atoms and simple chemistry rules"#!Virtual library: ~166 billion cmpds, thus screening even with a collection of 5M cmpds, we are still far away… “BIG DATA”"

•!Ruddigkeit, van Deursen Ruud, Blum; Reymond. J. Chem. Inf. Model., 2012!

Can we find a “iPPI chemical space”?"

2) Cmpd collection design"

•!Pagliaro et al, Current Opinion in Chemical Biology 2004, 8:442–449!•!Villoutreix et al, Molecular Informatics 2014!

19 iPPIs on 12 targets with only 8 molecules surviving the rule of 5 (RO5)"

Chemical diversity database Maybridge Asinex!

119475 cmpds! 59223 cmpds! 321867 cmpds!

2) Cmpd collection design"

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•!Reynes et al. DDT 2010!•!Sperandio et al. Plos Comput Biol 2010 !

•!Villoutreix et al. Curr Pharm Des 2012!•!Labbe et al. NAR 2015!

–! 66 diverses iPPIs & 557 regular oral drugs. Decision tree: Specific molecular shape (RDF above 13) and a critical number of 15 multiple bonds "

2) Cmpd collection design"

•!Lagorce et al, NAR 2015!•!Lagorce D, et al. Bioinformatics. 2011; 27:2018-20 !

•!Miteva et al, NAR 2006!

Dr M Miteva"

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