In silico odabir lekova iz baze DrugBank kao potencijalnih inhibitora M2 proteina virusa gripa i provera njihove aktivnosti in vitro

Abstract

Virus influence tipa A zbog svog značajnog epidemijskog i pandemijskog potencijala predstavlja trajnu globalnu pretnju za zdravlje ljudi. Pored vakcinacije koja predstavlja prvu liniju odbrane protiv gripa, antivirusni lekovi imaju važnu ulogu u prevenciji i terapiji tokom epidemija i pandemija Zbog nedovoljne efikasnosti vakcina protiv sezonskog gripa, kao i neodgovarajuće antivirusne terapije, koji su uslovljeni pojavom rezistencije na postojeće lekove i pojavom novih sojeva virusa, hitno su potrebni novi efikasni lekovi protiv gripa. Protein M2 virusa influence A (M2) je jonski kanal koji je neophodan za virusnu infekciju, i zbog toga je važna terapeutska meta gripa. Adamantani, inhibitori M2 jonskog kanala influence su bili prvi lekovi koje je FDA odobrio protiv gripa, mada je njihova upotreba obustavljena zbog rezistencije virusa na ovu klasu lekova. Osnovni cilj ove teze je identifikacija i predlaganje jednostavnog teorijskog kriterijuma za virtuelno pretraživanje molekulskih biblioteka za potencijalne dualne inhibitore M2 jonskog kanala kod virusa influence tipa A, divljih vrsta (WT) i virusa rezistentnih na amantadin. Rezultati ove studije zasnivaju se na primeni bioinformatičkog EIIP/AQVN kriterijuma za virtuelno pretraživanje molekulskih biblioteka zasnovanog na analizi dugodosežnih međumolekulskih interakcija. Podaci o poznatim M2 inhibitorima su prikupljeni i analizirani kako za inhibitore M2 proteina influence tip A, divljeg tipa, tako i za inhibitore M2 mutanta S31N. Na osnovu ove analize određen je bioinformatički kriterijum zasnovan na EIIP/AQVN parametrima za virtuelno pretraživanje molekulskih biblioteka u cilju identifikacije kandidata za dvostruke inhibitora M2 jonskog kanala WT influence A virusa i virusa sa mutacijom S31N u proteinu M2. Na osnovu primene ovog kriterijuma za virtuelni skrining odabrano je 39 lekova od 2627 lekova iz baze DrugBank. Posle primene hemoinformatičkog kriterijuma za virtuelni skrining (VS) koji se zasniva na međusobnoj strukturnoj sličnosti (ligand zasnovani VS) poznatih inhibitora M2 proteina sa kandidatima za lekove - dvostruke inhibitora M2 jonskog kanala WT influence A virusa i virusa sa mutacijom S31N u proteinu M2 od 39 lekova je predoženo pet najboljih kandidata, a kao najbolji među njima je gvanetidin. Molekuskim dokingom pet kandidata na divlji tip M2 i mutirani M1 S31N kao kandidat sa najmanjom energijom vezivanja i jednakim afinitetom za oba kanala je identifikivan lek cikrimin. Rezultati eksperimenta in vitro su potvrdili predloženu aktivnost cikrimina protiv dva različita podtipa virusa influence A pandemijskog H1N1 2009 i H3N2, a takođe je utvrđena in vitro aktivnost gvanetidina protiv pandemijskog virusa influence H1N1 2009. Predloženi bioinformatički kriterijum predstavlja osnov za selekciju dvostrukih inhibitora M2 jonskih kanala WT influence A virusa i virusa sa mutacijom S31N u proteinu M2 iz bilo koje molekulske biblioteke malih molekula.

The influenza virus is permanent global health threat with epidemic and pandemic potential. In addition to vaccination, which is the first line of defense against influenza, antivirals play an essential role in prevention and therapy during epidemics and pandemics. Due to the suboptimal effectiveness of vaccination and limitations of current antiviral therapies because of drug resistance and the emergence of new circulating viral strains, novel effective anti-influenza drugs are urgently needed. Influenza A virus (IAV) matrix protein 2 (M2), an ion channel, is crucial for virus infection, and therefore, an important anti-influenza drug target. Adamantanes, also known as M2 channel blockers, were the first approved Food and Drug Administration class- anti-influenza drugs, although their use was discontinued due to prevalent drug resistance. This study identified and proposed a simple bioinformatics criterion for virtual screening of molecular libraries for potential dual inhibitors of influenza A M2 ion channel for both wild type (WT) and amantadine resistant viruses. Results of this study are founded on a simple theoretical criterion for virtual screening of molecular libraries based on the long-range interactions characterized by the molecular descriptors — the average quasi valence number (AQVN) and the electron-ion interaction potential (EIIP). The data on known M2 inhibitors were collected and analyzed for influenza A M2 protein inhibitors both for WT influenza A viruses and their amantadine-resistant mutants. Based on the results of this analysis, the EIIP/AQVN criterion for the selection of drugs candidates from the databases that could represent dual inhibitors of both influenza virus M2 WT protein and M2 S31N mutant was established. By applying the EIIP/AQVN-based virtual screening criterion, 39 drugs were selected out of 2,627 approved drugs from the DrugBank as potential dual influenza M2 ion channel inhibitors. Further, the data collected on known M2 protein inhibitors were used to establish a chemoinformatic criterion for M2 protein (VS) based on the mutual structural similarity of molecules (ligand-based VS) and structural compatibility of inter-reactive molecules and corresponding binding energies (molecular docking). After in silico screening of drug space using the EIIP/AQVN filter and further filtering of drugs by ligand-based virtual screening, the best five candidates from DrugBank were proposed. Further, molecular docking of five selected candidates to both the wild-type M2 channel and S31N mutant channel, was performed. The candidate with the lowest binding energy and equal affinity to both the WT channel and S31N mutant channel was the anticholinergic drug cycrimine. The experimental results showed the anti-influenza activity of cycrimine against two different influenzas A subtypes 2009 H1N1 pandemic influenza and H3N2. As the best-ranked drug selected from ligand-based virtual screening, guanethidine showed measurable anti-influenza activity against the 2009 H1N1 pandemic influenza virus in cell culture. Our work has shown that the proposed in silico criterion represents an useful tool for selection of candidate M2 inhibitors of influenza viruses type A by screening of the approved drugs and other molecular libraries.