XI International Meeting on Paracoccidioidomycosis
Resume:49-1


Oral / Poster
49-1Molecular modeling of two Paracoccidioides lutzii drug targets and virtual screening search to identifying new antifungals
Authors:Ana Karina Rodrigues Abadio (UNB - Universidade de Brasilia) ; Erika Seki Kioshima (UNB - Universidade de Brasilia) ; Bruno Sahium Daher (UNB - Universidade de Brasilia) ; Natalia Florencio Martins (EMBRAPA - Embrapa/Cenargen) ; Bernard Maigret (UHP - NANCY - University Henri Poincaré-Nancy I) ; Maria Sueli Soares Felipe (UNB - Universidade de Brasilia)

Abstract

The development of drugs that act selectively in target pathogenic fungi without producing collateral damage to mammalian cells is a daunting pharmacological challenge. Indeed, many of the toxicities and drug interactions observed with contemporary antifungal therapies can be attributed to ″nonselective″ interactions with homologous enzyme or cell membrane systems found in mammalian host cells. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. In silico analyses and manual mining selected four potential drug targets, in eight human fungal pathogens: trr1, rim8, kre2 and erg6, that encodes for thioredoxin reductase, a protein involved in response to alkaline pH, the α-1,2-mannosyltransferase and Δ-(24)-sterol C-methyltransferase, respectively. Only Trr1 and Kre2 showed a reasonable sequence identity to the templates found in PDB. Consequently, the homology modeling predict 3D protein models, was performed only for these two proteins of Paracoccidioides lutzii. To investigate the stability of Trr1 and Kre2 models, molecular dynamics simulations were performed and revealed that the evolution of the systems is very stable. With the models stable, virtual screening for select the main small molecules that interact with them was performed. Initially, a bank of commercially available compounds from Chimioteque Nationale and Life Chemicals databases was docked with the models by virtual screening simulations. The small molecules that interact with the models were ranked and, among the best hits, 37 and 20 molecules were finally selected as putative inhibitors of Kre2 and Trr1, respectively. These molecules are being synthesized for validation and in vitro activity antifungal assays for Paracoccidioides brasiliensis, P. lutzii, Candida albicans and Cryptococcus neoformans. Therefore, the virtual screening of combinatorial libraries offered new perspectives for new antifungal agents against human pathogens. ਀䘀椀渀愀渀挀椀愀氀 匀甀瀀瀀漀爀琀㨀 䌀一倀焀 愀渀搀 䘀䄀倀䐀䘀㰀⼀昀漀渀琀㸀㰀⼀瀀㸀㰀戀爀㸀㰀戀㸀䬀攀礀眀漀爀搀㨀 㰀⼀戀㸀☀渀戀猀瀀㬀䴀漀氀攀挀甀氀愀爀 洀漀搀攀氀椀渀最Ⰰ 瘀椀爀琀甀愀氀 猀挀爀攀攀渀椀渀最Ⰰ 搀爀甀最 琀愀爀最攀琀猀Ⰰ 㰀椀㸀倀愀爀愀挀漀挀挀椀搀椀漀搀攀猀 氀甀琀稀椀椀㰀⼀椀㸀Ⰰ 愀渀琀椀昀甀渀最愀氀㰀⼀琀搀㸀㰀⼀琀爀㸀㰀⼀琀愀戀氀攀㸀㰀⼀琀爀㸀㰀⼀琀搀㸀㰀⼀琀愀戀氀攀㸀㰀⼀戀漀搀礀㸀㰀⼀栀琀洀氀㸀