Razvoj in uporaba metod računalniške kemije pri načrtovanju terapevtskih sredstev proti COVID-19 : doktorska disertacija

Kralj, Sebastjan

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Title:	Razvoj in uporaba metod računalniške kemije pri načrtovanju terapevtskih sredstev proti COVID-19 : doktorska disertacija
Authors:	ID Kralj, Sebastjan (Author) ID Jukič, Marko (Mentor) More about this mentor... ID Bren, Urban (Comentor)
Files:	DOK_Kralj_Sebastjan_2026.pdf (42,97 MB) MD5: B32A2091CDD6B7AA9B4BB8872FE7ECFF
Language:	Slovenian
Work type:	Doctoral dissertation
Typology:	2.08 - Doctoral Dissertation
Organization:	FKKT - Faculty of Chemistry and Chemical Engineering
Abstract:	Tekom doktorske disertacije smo se osredotočili na razvoj in uporabo metod računalniške kemije za optimizacijo delovnega toka razvoja zdravilnih učinkovin. V nalogi smo na aplikacijski tematiki SARS-CoV-2 optimizirali načrtovanje knjižnic virtualnih spojin, uporabo molekularnih filtrov za usmeritev kemijskega prostora, uporabo in primerjavo različnih metod izračuna proste vezavne energije ter razvili metodo za kombinatorično in silico mutagenezo in ovrednotenje vezavnega mesta proteinov. Pridobljeno znanje in razvite metode smo aplikativno uporabili za odkritje in vitro zaviralca SARS-CoV-2 proteaze. V prvem delu smo se poglobili v kritično vrednotenje komercialnih molekularnih knjižnic, s poudarkom na tarčnih knjižnicah usmerjenih proti SARS-CoV-2 ter knjižnicah osredotočenih na zaviralce proteaz in zaviralce protein-proteinskih interakcij. Prišli smo do zaključka, da so tarčne knjižnice v veliki večini primerov pripravljene neustrezno, saj ne navajajo primarne literature, nimajo opisanega protokola sidranja kadar je ta uporabljen, niti programske opreme za sidranje ter imajo kljub uporabi molekularih filtrov prisotne spojine, ki niso zaželene. Optimizirali smo tudi protokol za ustrezno pripravo tarčne molekularne knjižnice. V drugem delu doktorske disertacije smo se osredotočili na temeljit popis in odprtokodno implementacijo obstoječih molekularnih filtrov v farmacevtski kemiji. Ugotovili smo, da s pomočjo molekularnih filtrov enostavno prilagodimo kemijski prostor, manjše končno število filtrirane knjižnice pripravljenih spojin, pa vodi v učinkovito uporabo računskega časa ob aplikaciji nadaljnjih bolj zahtevnih računalniških metod. V tretjem delu smo s pomočjo simulacij molekularne dinamike (MD) in izračuna proste energije z metodama MM/GBSA ter BAR, raziskali interakcije ter vpliv mutacij protitelesa tipa IgG na vezavo z različnimi receptorji Fcγ (FcγR). Pridobljeni rezultati vezavne afinitete MM/GBSA in BAR med različnimi FcγR ob vezavi se dobro ujemajo z literaturnimi in vitro podatki, kar kaže na visoko uporabnost razvitih metod. Dodano vrednost pa nosijo na novo odkrite interakcije Fab regije z FcγR, ki so bile kasneje neodvisno dokazane v in vitro poskusih. V četrtem delu doktorske disertacije smo dopolnili obstoječe delo na prosto dostopnih molekularnih filtrov z implementacijo dodatnih molekularnih filtrov in proučevanjem vpliva vseh implementiranih na reprezentativni nabor 100.000 molekul. Ocenili smo vpliv na kemijski prostor in stopnjo retencije molekul za posamezen filter. Pridobljeni rezultati in posodobitev prosto-dostopnega modela dobro doplonjuje obstoječe znanje. V petem delu doktorske disertacije smo izvedli bioinformacijsko analizo skrb vzbujajočih virusnih variant SARS-CoV-2. Da bi razumeli mehanizem večje infektivnosti virusnih variant smo izvedli obsežno in silico mutagenezno študijo vezavne površine RBD-ACE2 ter ocenili spremembo vezavne energije s programom FoldX. Za identificirano varianto z večjo vezavo energijo N501Y smo s pomočjo molekularne dinamike potrdili večjo stabilnost kompleksa RBD-ACE2 hkrati pa dobili vpogled v mutirano interakcijsko površino. V šestem delu smo s pomočjo celovitega pristopa, ki je zajemal pripravo tarčne knjižnice, molekularno sidranje, in vitro biološke teste in simulacije molekularne dinamike identificirali potencialne zaviralce SARS-CoV-2 papainu podobne proteaze (PLpro). Identificirana spojina 372 je pokazala obetavne inhibitorne lastnosti proti PLpro z vrednostjo IC50 82 ± 34 μM. Izvedli smo tudi izračune proste vezavne energije z metodama TI ter LIE, ki sta potrdili in vitro teste in kažeta na uporabnost uporabljenih metod pri ovrednotenju potencialnih učinkovin. V sedmem delu doktorske disertacije smo uporabo metod računalniške kemije razširili na optimizacijo čiščenja protiteles. S pomočjo molekulskega sidranja smo kombinatorično knjižnico tetrapeptidov sidrali na Fc regijo protiteles in iskali vezavne motive, ki bi lahko posnemali protein A. Najdeni tetrape
Keywords:	računalniška kemija, biofizika, teoretska kemija, računalniško podprt razvoj zdravil, molekularna dinamika, prosta vezavna energija, in silico, farmacevtska kemija
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	S. Kralj
Year of publishing:	2026
Number of pages:	1 spletni vir (1 datoteka PDF (X, 196 str. str.))
PID:	20.500.12556/DKUM-94806
UDC:	577.32:004.9(043.3)
COBISS.SI-ID:	265622019
Publication date in DKUM:	21.01.2026
Views:	207
Downloads:	82
Metadata:
Categories:	KTFMB - FKKT
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Licences

License:	CC BY 4.0, Creative Commons Attribution 4.0 International

Link:	http://creativecommons.org/licenses/by/4.0/
Description:	This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:	28.08.2025

Secondary language

Language:	English
Title:	Development and application of computational chemistry methods for designing COVID-19 therapeutics
Abstract:	In this doctoral dissertation, we focused on the development and application of computational chemistry methods for optimizing the drug discovery workflow. Using the application topic of SARS-CoV-2, we optimized the design of virtual compound libraries, the use of molecular filters to direct chemical space, compared various methods for calculating binding free energy, and developed a method for combinatorial in silico mutagenesis and evaluation of protein binding sites. The acquired knowledge and developed methods were applied to discover an in vitro inhibitor of the SARS-CoV-2 protease. In the first part, we critically evaluated commercial molecular libraries, with a focus on targeted libraries directed against SARS-CoV-2 and libraries focused on protease inhibitors and inhibitors of protein-protein interactions. We concluded that most targeted libraries are poorly prepared—they often lack references to primary literature, fail to describe the docking protocol (when used), omit the software used, and contain undesirable compounds despite the use of molecular filters. We also optimized a protocol for properly preparing targeted molecular libraries. In the second part of the dissertation, we focused on a thorough review and open-source implementation of existing molecular filters in pharmaceutical chemistry. We found that molecular filters can easily tailor chemical space, and a smaller final number of filtered, prepared compounds leads to more efficient computational time usage when applying more advanced computational methods. In the third part, we used molecular dynamics (MD) simulations and binding free energy calculations via MM/GBSA and BAR methods to investigate the interactions and effects of IgG-type antibody mutations on binding to various Fcγ receptors (FcγR). The obtained MM/GBSA and BAR binding affinity results between different FcγR complexes align well with in vitro literature data, demonstrating the high utility of the developed methods. An added value is the newly discovered Fab-region interactions with FcγR, which were later independently validated in in vitro experiments. In the fourth part of the doctoral dissertation, we supplemented the existing work on open-access molecular filters by implementing additional filters and studying the impact of all implemented ones on a representative set of 100,000 molecules. We evaluated the effect on the chemical space and the retention rate of molecules for each individual filter. The obtained results and the update of the open-access model significantly complement existing knowledge. In the fifth part, we conducted a bioinformatic analysis of concerning SARS-CoV-2 viral variants. To understand the mechanism of increased infectivity, we performed an extensive in silico mutagenesis study of the RBD-ACE2 binding surface and estimated binding energy changes using FoldX. For the identified N501Y variant, which showed increased binding energy, molecular dynamics confirmed greater RBD-ACE2 complex stability and provided insight into the mutated interaction surface. In the sixth part, we used a comprehensive approach—targeted library preparation, molecular docking, in vitro biological testing, and molecular dynamics simulations—to identify potential inhibitors of the SARS-CoV-2 papain-like protease (PLpro). The identified compound 372 showed promising inhibitory activity against PLpro with an IC50 value of 82 ± 34 μM. We also performed free binding energy calculations using TI and LIE methods, which confirmed the in vitro results and demonstrated the applicability of the employed methods for evaluating potential active compounds. In the seventh part, we extended the application of computational chemistry methods to the optimization of antibody purification. Using molecular docking, we docked a combinatorial library of tetrapeptides to the Fc region of antibodies, searching for binding motifs that could mimic protein A. The identified tetrapeptide motif GSVW showed the best bin
Keywords:	computational chemistry, biophysics, theoretical chemistry, computer aided drug discovery, molecular dynamics, free binding energy, in silico, medicinal chemistry

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