bbc 2015
BBC2015_booklet
BBC2015_booklet
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 <strong>2015</strong><br />
Abstract ID: P<br />
Poster<br />
10th Benelux Bioinformatics Conference <strong>bbc</strong> <strong>2015</strong><br />
P43. BINDING SITE SIMILARITY DRUG REPOSITIONING:<br />
A GENERAL AND SYSTEMATIC METHOD FOR DRUG DISCOVERY<br />
AND SIDE EFFECTS DETECTION<br />
Daniele Parisi & Yves Moreau.<br />
I developed a protocol based on prediction of druggable cavities, comparison of these putative binding sites and crossdocking<br />
between bound ligands and the binding site detected to be similar to the one of the complex, in order to study the<br />
cross reactivity of known compounds. It is a general method because it can find applications both in drug repositioning<br />
and in the study of adverse effects, and it is systematic because it consists in several subsequent steps. It would indicate<br />
ligands to screen, reducing the number of candidates and allowing companies or universities to save money and time<br />
from unnecessary tests.<br />
INTRODUCTION<br />
The ability of small molecules to interact with multiple<br />
proteins is referred to as polypharmacology [1] , and the<br />
strategy that aims to exploit the positive aspects of<br />
polypharmacology is drug repositioning, whereby existing<br />
drugs are investigated for efficacy against targets for other<br />
indications. Existing drugs are privileged structures with<br />
verified bioavailability and compatibility. Furthermore,<br />
virtual screening allows to conduct repositioning of<br />
existing drugs against novel disease targets without the<br />
expense of purchasing thousands of compounds [2] . The<br />
combination of structure-based virtual screening (such as<br />
estimation of similarity of protein-ligand binding sites and<br />
consequent cross-docking) and drug repositioning<br />
represents a highly efficient and fast methodology for<br />
predicting cross-reactivity and putative side effects of drug<br />
candidates [3] .<br />
METHODS<br />
Each step of my work is related to a bioinformatics<br />
technique or tool, resulting to be the coupling of different<br />
software.<br />
1. At first there is the choice of the query (a single protein<br />
as PDB file) and the templates (a set of PDB<br />
structures). At least one of the two categories has to<br />
present a ligand bound in a cavity;<br />
2. prediction of druggable cavities in all the protein<br />
structures using a geometry-based or an energy-based<br />
algorithm (Fpocket, geometry-based tool, in my case);<br />
3. comparison of the query binding sites to the binding<br />
sites of the templates for assessing the similarity. It can<br />
be carried out by an alignment or alignment-free<br />
algorithm (I used Apoc, an alignment based tool);<br />
4. cross-docking of the ligand available in the pair of<br />
similar binding sites, into the other cavity, in order to<br />
study the binding with a different target for toxicity or<br />
new therapeutic indications (AutodockVina);<br />
5. Fingerprinting of the new complex ligand-cavity for<br />
scoring the docking poses.<br />
I applied this protocol on two different queries (Thrombin<br />
and Dihydrofolate reductase), using a data set of 1067<br />
druggable proteins as tamplates (Druggable Cavity<br />
Directory).<br />
RESULTS & DISCUSSION<br />
The method works well in repositioning ligands among<br />
proteins of the same family (intraprotein), but is not able<br />
to detect interprotein similarities (among not related<br />
proteins). It happens because of the big size of the<br />
predicted cavities (larger than the mere space occupied by<br />
the ligand) coupled to the alignment-based algorithm used,<br />
which make difficult to have a sufficient similarity rate<br />
and exponentially increase the false negatives. For my<br />
further works I will divide the cavity space in subpockets,<br />
disengage the similarity from the sequence by using<br />
pharmacophoric maps, and couple the structure based<br />
similarity to the ligand based and network based. All the<br />
information will be fused with data integrations algorithms.<br />
REFERENCES<br />
On the origins of drug polypharmacology, Xavier Jalencas and Jordi<br />
Mestres, Med. Chem. Commun., 2013, 4, 80.<br />
Drug repositioning by structure-based virtual screening, Dik-Lung Ma,<br />
Daniel Shiu-Hin Chana and Chung-Hang Leung, Chem. Soc. Rev.,<br />
2013, 42, 2130.<br />
Comparison and Druggability Prediction of Protein−Ligand Binding<br />
Sites from Pharmacophore-Annotated Cavity Shapes, Jérémy<br />
Desaphy, Karima Azdimousa, Esther Kellenberger, and Didier<br />
Rognan, J. Chem. Inf. Model. 2012, 52, 2287−2299.<br />
87