RT Journal Article
T1 Computational Drug Target Screening through Protein Interaction Profiles
A1 Vilar Varela, Santiago
A1 Quezada González, Elías Neftalí
A1 Uriarte Villares, Eugenio
A1 Costanzi, Stefano
A1 Borges, Fernanda
A1 Viña Castelao, María Dolores
A1 Hripcsak, George
K1 Computational models
K1 Target identification
AB The development of computational methods to discover novel drug-target interactions on a large scale is of great interest. We propose a new method for virtual screening based on protein interaction profile similarity to discover new targets for molecules, including existing drugs. We calculated Target Interaction Profile Fingerprints (TIPFs) based on ChEMBL database to evaluate drug similarity and generated new putative compound-target candidates from the non-intersecting targets in each pair of compounds. A set of drugs was further studied in monoamine oxidase B (MAO-B) and cyclooxygenase-1 (COX-1) enzyme through molecular docking and experimental assays. The drug ethoxzolamide and the natural compound piperlongumine, present in Piper longum L, showed hMAO-B activity with IC50 values of 25 and 65μM respectively. Five candidates, including lapatinib, SB-202190, RO-316233, GW786460X and indirubin-3′-monoxime were tested against human COX-1. Compounds SB-202190 and RO-316233 showed a IC50 in hCOX-1 of 24 and 25μM respectively (similar range as potent inhibitors such as diclofenac and indomethacin in the same experimental conditions). Lapatinib and indirubin3′-monoxime showed moderate hCOX-1 activity (19.5% and 28% of enzyme inhibition at 25μM respectively). Our modeling constitutes a multi-target predictor for large scale virtual screening with potential in lead discovery, repositioning and drug safety.
PB Nature Publishing Group
SN 2045-2322
YR 2016
FD 2016-11-15
LK http://hdl.handle.net/10347/16311
UL http://hdl.handle.net/10347/16311
LA eng
NO Vilar, S. et al. Computational Drug Target Screening through Protein Interaction Profiles. Sci. Rep. 6, 36969; doi: 10.1038/srep36969 (2016)
NO This study was supported by grant R01 LM006910 (GH) “Discovering and Applying Knowledge in Clinical Databases” from the U.S. National Library of Medicine, “Angeles Alvariño, Plan Galego de Investigación, Innovación e Crecemento 2011–2015 (I2C)” and European Social Fund (ESF)
DS Minerva
RD 24 abr 2026