Strategies for the Development of Small Molecule Inhibitors of Ebola Viral Infection

The recent outbreak of Ebola viral disease (EVD) in West Africa reminded us that an effective anti-viral treatment still does not exist, despite the significant progress that has recently been made in understanding biology and pathology of this lethal disease. Currently, there are no approved vaccine and/or prophylactic medication for the treatment of EVD in the market. However, the serious pandemic potential of EVD mobilized research teams in the academy and the pharmaceutical industry in the effort to find an Ebola cure as fast as possible. In this chapter, we are giving the condensed review of different approaches and strategies in search of a drug against Ebola. We have been focusing on the review of the targets that could be used for in silico, in vitro, and/or in vivo drug design of compounds that interact with the targets in different phases of the Ebola virus life cycle

Finding ligands for biological targets using algorithms and the use of citizen science

V magistrskem delu sem preizkusil uporabo genetskega algoritma in koncepta skupnostne znanosti pri in silico molekulskem sidranju malih molekul v proteinske tarče. Preizkušeni so bili različni pristopi k problemu iskanja najboljših ligandov. Za najboljšega se je izkazal pristop kombiniranja genetskega algoritma (z uporabo ustreznih SMARTS filtrov) s konceptom igrificirane skupnostne znanosti. Pri tem pristopu so posamezniki, predvsem dijaki, lahko predlagali popolnoma nove molekule ali izboljšali molekulo drugega dijaka in hkrati tekmovali med seboj za čim boljši rezultat. Vzporedno pa je genetski algoritem najboljše predlagane molekule razvijal oz. mutiral naprej. Mutirane molekule in molekule, ki so jih predlagali posamezniki, in niso ustrezale enostavnemu filtru za biološko uporabnost, določene z uporabo enostavnih kemijskih deskriptorjev (Veberjev filter), je algoritem avtomatsko odstranil. Odstranil je tudi molekule, katerim je algoritem s pomočjo SMARTS filtrov določil, da vsebujejo znane kemijsko nestabilne, reaktivne ali toksične skupine. Na enak način je algoritem odstranil še molekule, ki so vsebovale t.i. PAINS skupine, ki napovedujejo nezaželeno splošno neselektivno vezavo molekule na različne tarče. Ta sinergija pristopov je dala daleč boljše rezultate, gledano z vidika napovedane vezavne energije sidranja, saj je našla kar 100 ligandov z napovedano boljšo vezavo, od drugega najbolje uvrščenega liganda, pridobljenega s klasičnimi strategijami s knjižnicami spojin. Kljub odličnim rezultatom pristopa pa smo med pregledovanjem literature odkrili potencialen problem uporabe tega pristopa v praksi, ki bi se znal pokazati v tem, da najdene spojine ne bi dosegale dobrih vezav v praksi oz. in vitro. Omejitev se je pokazala predvsem v tem, da so hitri računski modeli sidranja zaenkrat še neprimerni za napoved slabe vezave molekul, zaradi česar se dobljeni rezultati lahko ne bi skladali s tistimi, dobljenimi v praksi. Slednjih trditev v tem magistrskem delu nismo preverili v praksi oz. in vitro.In the master\u27s thesis we\u27ve tested the use of a genetic algorithm in conjuction with the concept of citizen science in the in silico molecular docking of small molecules in protein targets. Out of the tested approaches for finding the best ligands, we\u27ve found out that the combination with using a genetic algorithm (with the right SMARTS filters) in conjuction of using a gamified approach of citizen science gave the best results. In this approach, individuals, who were mostly high school students, had the possibility of proposing completely new molecules or improve molecules created by other students and at the same time compete among each other in finding the molecule with the best score. In parallel to the input from indidividuals, the genetic algorithm developed (mutated) further the best molecule proposals. The algorithm automatically removed mutated molecules and the proposed molecules by individuals, that haven\u27t passed a simple filter for biological availability that used simple chemical descriptors (Veber filter). Molecules which contained groups, determined via SMARTS filters, that are chemically unstable, reactive, toxic or were classified as PAINS molecules, which predict an unwanted unselective binding of molecules to various targets, were also removed. This sinergy of approaches gave much better results in the sense of predicted binding energy of docking, as it has resulted in 100 ligands with a better predicted binding energy, than the 2nd best ligand that has been found using with the classical strategy of using libraries of compounds. Despite excellent results of this approach, it has been found out, using thorough literature research, that there\u27s a potential problem of using this approach in practice, as we suspected that the best found molecules using this approach, wouldn\u27t achieve such good binding energies in practice in vitro. The limitation is in the quick computational models for molecular docking, which are for now unsuited for prediction of bad binding of molecules. Consequentially our results might not match practical measurments. In this thesis, the later claims weren\u27t tested in practice i.e. in vitro

Perspective Chapter: Bioinformatics Study of the Evolution of SARS-CoV-2 Spike Protein

SARS-CoV-2 belongs to the family of coronaviruses, which are characterized by spikes that sit densely on the surface of the virus. The spike protein (Spro) is responsible for the attachment of the virus to the host cell via the ACE2 receptor on the surface of the host cell. The strength of the interaction between the receptor-binding domain (RBD) of the highly glycosylated spike protein of the virus and the host cell ACE2 receptor represents the key determinant of the infectivity of the virus. The SARS-CoV-2 virus has mutated since the beginning of the outbreak, and the vast majority of mutations has been detected in the spike protein or its RBD. Since specific mutations significantly affect the ability of the virus to transmit and to evade immune response, studies of these mutations are critical. We investigate GISAID data to show how viral spike protein mutations evolved during the pandemic. We further present the interactions of the viral Spro RBD with the host ACE2 receptor. We have performed a large-scale mutagenesis study of the Spro RBD-ACE2 interface by performing point mutations in silico and identifying the ambiguous interface stabilization by the most common point mutations in the viral variants of interest (beta, gamma, delta, omicron)

Prioritisation of compounds for 3CLpro^{pro} inhibitor development on SARS-CoV-2 variants

COVID-19 represents a new potentially life-threatening illness caused by severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 pathogen. In 2021, new variants of the virus with multiple key mutations have emerged, such as B.1.1.7, B.1.351, P.1 and B.1.617, and are threatening to render available vaccines or potential drugs ineffective. In this regard, we highlight 3CL$^{pro}$, the main viral protease, as a valuable therapeutic target that possesses no mutations in the described pandemically relevant variants. 3CL$^{pro}$ could therefore provide trans-variant effectiveness that is supported by structural studies and possesses readily available biological evaluation experiments. With this in mind, we performed a high throughput virtual screening experiment using CmDock and the “In-Stock” chemical library to prepare prioritisation lists of compounds for further studies. We coupled the virtual screening experiment to a machine learning-supported classification and activity regression study to bring maximal enrichment and available structural data on known 3CL$^{pro}$ inhibitors to the prepared focused libraries. All virtual screening hits are classified according to 3CL$^{pro}$ inhibitor, viral cysteine protease or remaining chemical space based on the calculated set of 208 chemical descriptors. Last but not least, we analysed if the current set of 3CL$^{pro}$ inhibitors could be used in activity prediction and observed that the field of 3CL$^{pro}$ inhibitors is drastically underrepresented compared to the chemical space of viral cysteine protease inhibitors. We postulate that this methodology of 3CL$^{pro}$ inhibitor library preparation and compound prioritisation far surpass the selection of compounds from available commercial “corona focused libraries”

COVID.si - Citizen Science and the fight against the coronavirus

Projekt “SKUPNOSTNA ZNANOST IN BOJ PROTI KORONA VIRUSU” je odprt projekt skupnosti dobromislečih raziskovalcev in prostovoljcev, ki želijo s svojim delom, znanjem in izkušnjami, prispevati kamenček v mozaiku poznavanja novega korona virusa in bolezni COVID-19, ki jo ta virus povzroča. Skupino vodita strokovnjaka dr. Črtomir Podlipnik in dr. Marko Jukić, z dolgoletnimi izkušnjami s področja računalniško podprtega načrtovanja molekul, farmacevtske kemije in kemo/bioinformatike. S somišljeniki ter prostovoljci so v zelo kratkem času razvili lastno tehnologijo, ki omogoča porazdeljeno računanje molekulskega sidranja. Tehnologija je osnovana na programčku, ki omogoča povezavo uporabnikovega računalnika s strežnikom, ki porazdeli del knjižnice molekul pripravljenih za molekulsko sidranje (trenutno 10 000 000 spojin). Po končanem sidranju (s programom RxDock), programček pošlje izračunane podatke nazaj na zbirno mesto na strežniku. Po analizi sidranih spojin je končni rezultat seznam molekul s potencialno vezavo na terapevtsko tarčo (v našem primeru so to virusni proteini). Nato lahko preverimo ali so te spojine dobavljive in organiziramo biološko testiranje v sodelovanju z ostalimi raziskovalnimi skupinami. Podatke o nedobavljivih spojinah lahko v nasprotnem primeru posredujemo raziskovalnim skupinam za izvedbo sinteze. V optimalnem scenariju bi lahko identificirali spojine zanimive za farmacevtsko industrijo. Rešitev, ki temelji na porazdeljenem računanju je osnovana na odprtokodnih rešitvah.In our project, we are looking for ligands – small molecules that can successfully bind to protein targets and modulate a specific process that is crucial for the virus biochemistry. Together we have developed software that can be easily installed on your computer to help participants help find the cure for today’s invisible enemy. Based on molecular docking, the ideal ligand should be complementary in shape and properties to the binding site of the target biomolecule. Although the complementarity of small molecules is only one prerequisite for the use of a molecule as a drug. The search for the right molecule is best illustrated by finding a small needle in a huge haystack. It is a much more difficult problem because of the number of possible molecular structures. The amount of all possible molecular structures is also called the chemical universe – the size of this abstract space can be estimated to be 10 to the power of 80. There are many galaxies in this chemical universe, and one of the galaxies is a galaxy with potentially pharmacologically active molecules. The method for this purpose is called virtual screening. In principle, the larger the part of the chemical universe that we study, the higher the likelihood of finding a potential cure for the coronavirus