Computational Investigations of Biomolecular Motions and Interactions in Genomic Maintenance and Regulation

The most critical biochemistry in an organism supports the central dogma of molecular biology: transcription of DNA to RNA and translation of RNA to peptide sequence. Proteins are then responsible for catalyzing, regulating and ensuring the fidelity of transcription and translation. At the heart of these processes lie selective biomolecular interactions and specific dynamics that are necessary for complex formation and catalytic activity. Through advanced biophysical and computational methods, it has become possible to probe these macromolecular dynamics and interactions at the molecular and atomic levels to tease out their underlying physical bases. To the end of a more thorough understanding of these physical bases, we have performed studies to probe the motions and interactions intrinsic to the function of biomolecular complexes: modeling the dual-base flipping strategy of alkylpurine glycosylase D, dynamically tracing evolution and epistasis in the 3-ketosteroid family of nuclear receptors, discovering the allosteric and conformational aspects of transcription regulation in liver receptor homologue 1, leveraging specific contacts in tyrosyl-DNA phosphodiesterase 2 for the development of novel inhibitor scaffolds, and detailing the experimentally observed connection between solvation and sequence-specific binding affinity in PU.1-DNA complexes at the atomic level. While each study seeks to solve system-specific problems, the collection outlines a general and broadly applicable description of the biophysical motivations of biochemical processes

Targeting The Dimerization Of ERBB Receptor Tyrosine Kinases

The epidermal growth factor receptor: EGFR) is a membrane receptor tyrosine kinase whose over-activation has been implicated to cause many human cancers. Novel strategies to inhibit the activation of EGF receptors other than the conventional antibody-based and tyrosine kinase inhibitors are virtually non-existent but could provide benefits both in the laboratory and clinical settings. In an effort to expand the current approaches, this thesis focused on targeting the homodimerization of the EGF receptors themselves and the heterodimerization of EGF receptors with the related ErbB2 receptor. Three sub-projects were completed in the process. The first project explored the feasibility of inhibiting the EGF receptor by targeting receptor dimerization with small molecules. Two lead compounds were initially predicted by virtual screening the NCI compound library, and were biochemically characterized. The benefit gained from the application of virtual screening in this project initiated another project to enhance the accessibility of virtual screening within the non-computational community. The OpenScreening project utilizes distributed computing resources and provides open-access screening server at: http://omg.phy.umassd.edu/xvhts. A final project identified the structural mechanism that may explain the observed preference of EGFR-ErbB2 heterodimerization over EGFR homodimerization. Key residues were computationally predicted and biochemically tested to reveal critical dimerization interface

In silico studies of the effect of phenolic compounds from grape seed extracts on the activity of phosphoinositide 3-kinase (PI3K) and the farnesoid x receptor (FXR)

In silico studies of the effect of phenolic compounds from grape seed extracts on the activity of phosphoinositide 3-kinase (PI3K) and farnesoid X receptor (FXR)Montserrat Vaqué Marquès En aquesta tesis es pretén aplicar metodologies computacionals (generació de farmacòfors i docking proteïna lligand) en l'àmbit de la nutigenòmica (ciència que pretén entendre, a nivell molecular, com els nutrients afecten la salut). S'aplicaran metodologies in silico per entendre a nivell molecular com productes naturals com els compostos fenòlics presents en la nostra dieta, poden modular la funció d'una diana comportant un efect en la salut. Aquest efecte es creu que podria ser degut a la seva interacció directa amb proteïnes de vies de senyalització molecular o bé a la modificació indirecta de l'expressió gènica. Donat que el coneixement de l'estructura del complex lligand-receptor és bàsic per entendre el mecanisme d'acció d'aquests lligands s'aplica la metodologia docking per predir l'estructura tridimensional del complex. En aquest sentit, un dels programes de docking és AutoGrid/AutoDock (un dels més citats). No obstant, l'automatització d'AutoGrid/AutoDock no és trivial tan per (a) la cerca virtual en una llibreria de lligands contra un grup de possibles receptors, (b) l'ús de flexibilitat, i (c) realitzar un docking a cegues utilitzant tota la superfície del receptor. Per aquest motiu, es dissenya una interfície gràfica de fàcil ús per utilitzar AutoGrid/AutoDock. Blind Docking Tester (BDT) és una aplicació gràfica que s'executa sobre quatre programes escrits en Fortran i que controla les condicions de les execucions d'AutoGrid i AutoDock. BDT pot ser utilitzat per equips d'investigadors en el camp de la química i de ciències de la vida interessats en dur a terme aquest tipus d'experiments però que no tenen suficient habilitats en programació. En la modulació del metabolisme de la glucosa, treballs in vivio i in vitro en el nostre grup de recerca s'han atribuït els efectes beneficiosos de l'extracte de pinyol de raïm en induir captació de glucosa (punt crític pel manteniment de l'homeostasis de la glucosa). No obstant alguns compostos fenòlics no tenen efecte en la captació de la glucosa, d'altres l'inhibeixen reversiblement. En alguns casos aquesta inhibició és el resultat de la competició dels compostos fenòlics amb ATP pel lloc d'unió de l'ATP de la subunitat catalítica de la fosfatidil inositol 3-kinasa (PI3K). Estudis recents amb inhibidors específics d'isoforma han identificat la p110α (la subunitat catalítica de PI3Kα) com la isoforma crucial per la captació de glucosa estimulada per insulina en algunes línies cel·lulars. Els programes computacionals han estat aplicats per tal de correlacionar l'activitat biològica dels compostos fenòlics amb informació estructural per obtenir una relació quantitativa estructura-activitat (3D-QSAR) i obtenir informació dels requeriments estructura-lligand per augmentar l'afinitat i/o selectivitat amb la diana (proteïna). Tot hi haver-se demostrat que l'adició d'extractes de compostos fenòlics en l'aliment pot tenir en general un benefici per la salut, s'ha de tenir en compte que l'estudi 3D-QSAR (construït a partir d'inhibidors sintètics de p110α) prediu que algunes d'aquestes molècules poden agreujar la resistència a la insulina en individus susceptibles dificultant la capatació de glucosa en múscul i teixit adipós i, per tant, produir un efecte secundari indesitjat. Resultats en el nostre grup de recerca han demostrat que compostos fenòlics presents en extractes de llavor de raïm incrementen l'activitat del receptor "farnesoid x receptor" (FXR) de manera dosi depenent quan el lligand natural de FXR (CDCA) és present. Les metodologies in silico, docking i 3D-QSAR, han estat aplicades juntament amb dades biològiques d'agonistes no esteroidals de FXR que s'uneixen a un lloc d'unió proper però diferent al lligand esteroidal 6CDCA. Els resultats han mostrat que els compostos fenòlics no són capaços d'activar FXR per ells mateixos però poden afegir noves interaccions que estabilitzarien la conformació activa de FXR en presència del lligand natural CDCA. Els compostos fenòlics podrien induir canvis conformacionals específics que augmentarien l'activitat de FXR. In silico studies of the effect of phenolic compounds from grape seed extracts on the activity of phosphoinositide 3-kinase (PI3K) and farnesoid X receptor (FXR)Montserrat Vaqué Marquès This thesis was written with the aim of applying computational methods that have already been developed for molecular design and simulation (i.e. pharmacophore generation and protein-ligand docking) to nutrigenomics. So, in silico tools that are routinely used by the pharmaceutical industry to develop drugs have been used to understand, at the molecular level, how natural products such as phenolic compounds (i.e. molecules that are commonly found in fruits and vegetables) can improve health and prevent diseases. Therefore, we first focused on predicting the structure of protein-ligand complexes. The docking algorithms can use the individual structures from receptor and ligand to predict (1) whether they can form a complex and (2) if so, the structure of the resulting complex. This prediction can be made, for instance, with AutoGrid/AutoDock, the most cited docking software in the literature. The automation of AutoGrid/AutoDock is not trivial for tasks such as (1) the virtual screening of a library of ligands against a set of possible receptors; (2) the use of receptor flexibility and (3) making a blind-docking experiment with the whole receptor surface. Therefore, in order to circumvent these limitations, we have designed BDT (i.e. blind-docking tester; http://www.quimica.urv.cat/~pujadas/BDT), an easy-to-use graphic interface for using AutoGrid/AutoDock. BDT is a Tcl/Tk graphic front-end application that runs on top of four Fortran programs and which controls the conditions of the AutoGrid and AutoDock runs. As far as the modulation of the glucose metabolism is concerned, several in vivo and in vitro results obtained by our group have shown that grape seed procyanidin extracts (GSPE) stimulate glucose uptake in 3T3-L1 adipocytes and thus help to maintain their glucose homeostasis. In contrast, it is also well known that although some phenolic compounds do not affect glucose uptake, others reversibly inhibit it in several cell lines. Moreover, for at least some of these phenolic compounds, this inhibition is the result of their competition with ATP for the ATP-binding site in p110α (i.e. the α isoform of the catalytic subunit of phosphoinositide 3-kinase or PI3Kα). Furthermore, recent studies with isoform-specific inhibitors have identified p110α as the crucial isoform for insulin-stimulated glucose-uptake in some cell lines. Therefore, although it has been proved that the addition of phenolic compound extracts to food can have an overall benefit on health, it should be taken into account that some of these molecules may exacerbate insulin resistance in susceptible individuals via impaired glucose uptake in muscle and adipose tissues and, therefore, produce an undesirable side effect. In this context, we have applied computational approaches (i.e. protein-ligand docking and 3D-QSAR) to predict the IC50 (i.e. the concentration that reduces the p110α activity to 50%). Our results agree with previous experimental results and predict that some compounds are potential inhibitors of this enzyme. Recent results in our research group have demonstrated that the phenolic compounds in GSPE increase the activity of the farnesoid X receptor (i.e. FXR) in a dose-dependent way when the natural ligand of FXR (i.e. CDCA) is also present. The phenolic compounds might induce specific conformational changes that increase FXR activity and then contribute to cardioprotection through mechanisms that are independent of their intrinsic antioxidant capacities but that involve direct interaction with FXR to modulate gene expression. Taking into account this hypothesis a 3D-QSAR analysis was made in an attempt to understand how phenolic compounds activate FXR. So, our results explain why phenolic compounds cannot activate FXR by themselves and how they can add new interactions to stabilize the active conformation of FXR when its natural ligand (i.e. CDCA) is present. Therefore, we proposed a mechanism of FXR activation by dietary phenolic compounds in which they may enhance bile acid-bound FXR activity

Design and Synthesis of New Modulators of Liver Receptor Homologue – 1

Nuclear receptor (NR) liver receptor homologue-1 (LRH-1) is a potential target for the treatment of breast cancers as a consequence of its regulation of aromatase and the estrogen receptor (ER). Development of modulators of NRs historically focuses on production of high affinity small molecules that compete for binding with natural ligands at the ligand-binding pocket (LBP) of the NR ligandbinding domain (LBD). Ligand-bound NRs induce transcriptional activity by subsequent recruitment of coactivator proteins. The majority of this thesis describes approaches towards the development of the first antagonists of LRH-1. In the first instance, small molecules that bind at the LBP of the receptor were developed through use of ligand-based virtual screening (VS) programs Cresset and SHop. Secondly, small molecules that directly disrupt the binding of cofactor proteins to the NR activation function-2 (AF-2) were developed through use of rational design and further Cresset VS

DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction

Protein (receptor)--ligand interaction prediction is a critical component in computer-aided drug design, significantly influencing molecular docking and virtual screening processes. Despite the development of numerous scoring functions in recent years, particularly those employing machine learning, accurately and efficiently predicting binding affinities for protein--ligand complexes remains a formidable challenge. Most contemporary methods are tailored for specific tasks, such as binding affinity prediction, binding pose prediction, or virtual screening, often failing to encompass all aspects. In this study, we put forward DeepRLI, a novel protein--ligand interaction prediction architecture. It encodes each protein--ligand complex into a fully connected graph, retaining the integrity of the topological and spatial structure, and leverages the improved graph transformer layers with cosine envelope as the central module of the neural network, thus exhibiting superior scoring power. In order to equip the model to generalize to conformations beyond the confines of crystal structures and to adapt to molecular docking and virtual screening tasks, we propose a multi-objective strategy, that is, the model outputs three scores for scoring and ranking, docking, and screening, and the training process optimizes these three objectives simultaneously. For the latter two objectives, we augment the dataset through a docking procedure, incorporate suitable physics-informed blocks and employ an effective contrastive learning approach. Eventually, our model manifests a balanced performance across scoring, ranking, docking, and screening, thereby demonstrating its ability to handle a range of tasks. Overall, this research contributes a multi-objective framework for universal protein--ligand interaction prediction, augmenting the landscape of structure-based drug design

Sirtuin 1 inhibiting thiocyanates (S1th)-a new class of isotype selective inhibitors of NAD(+) dependent lysine deacetylases

Sirtuin 1 (Sirt1) is a NAD(+) dependent lysine deacetylase associated with the pathogenesis of various diseases including cancer. In many cancer types Sirt1 expression is increased and higher levels have been associated with metastasis and poor prognosis. However, it was also shown, that Sirt1 can have tumor suppressing properties and in some instances even a dual role for the same cancer type has been reported. Increased Sirt1 activity has been linked to extension of the life span of cells, respectively, organisms by promoting DNA repair processes and downregulation of tumor suppressor proteins. This may have the downside of enhancing tumor growth and metastasis. In mice embryonic fibroblasts depletion of Sirt1 was shown to decrease levels of the DNA damage sensor histone H2AX. Impairment of DNA repair mechanisms by Sirt1 can promote tumorigenesis but also lower chemoresistance toward DNA targeting therapies. Despite many biological studies, there is currently just one small molecule Sirt1 inhibitor in clinical trials. Selisistat (EX-527) reached phase III clinical trials for treatment of Huntington's Disease. New small molecule Sirt1 modulators are crucial for further investigation of the contradicting roles of Sirt1 in cancer. We tested a small library of commercially available compounds that were proposed by virtual screening and docking studies against Sirt1, 2 and 3. A thienopyrimidone featuring a phenyl thiocyanate moiety was found to selectively inhibit Sirt1 with an IC50 of 13 mu M. Structural analogs lacking the thiocyanate function did not show inhibition of Sirt1 revealing this group as key for the selectivity and affinity toward Sirt1. Further analogs with higher solubility were identified through iterative docking studies and in vitro testing. The most active compounds (down to 5 mu M IC50) were further studied in cells. The ratio of phosphorylated gamma H2AX to unmodified H2AX is lower when Sirt1 is depleted or inhibited. Our new Sirtuin 1 inhibiting thiocyanates (S1th) lead to similarly lowered gamma H2AX/H2AX ratios in mouse embryonic fibroblasts as Sirt1 knockout and treatment with the reference inhibitor EX-527. In addition to that we were able to show antiproliferative activity, inhibition of migration and colony forming as well as hyperacetylation of Sirt1 targets p53 and H3 by the S1th in cervical cancer cells (HeLa). These results reveal thiocyanates as a promising new class of selective Sirt1 inhibitors.Chemical Immunolog

Mind the Gap - Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs

New techniques of molecular modelling and structural chemistry for the development of bioactive compounds

2011 - 2012Computational chemistry represents today a valid and fast tool for the research of new compounds with potential biological activity. The analysis of ligand-macromolecule interactions and the evaluation of possible “binding modes” have a crucial role for the design and the development of new and more powerful drugs. In silico Virtual Screening campaigns of large libraries compounds (fragments or drug-like) on a specific target allow the selection of promising compounds, leading the identification of new scaffolds. The accurate analysis and the comparison of different bioactive compounds clarify the molecular basis of their interaction and the construction of pharmacoforic models. In parallel, another crucial aspect of pharmacological research is the identification of targets of interaction of bioactive molecules, and this is particularly true for compounds from natural sources. In fact, a wide range of drug tests on a large number of biological targets can represent a useful approach for the study of natural products, but often one of the main problems is their limited availability. Starting from these assumptions, a new computational method named Inverse Virtual Screening is described in details in this thesis. The different works based on this approach were performed considering panels of targets involved in the cancer events, determining the identification of the specific antitumor activity of the natural compounds investigated. Inverse Virtual Screening studies were performed by means of molecular docking experiments on different natural compounds, organized in small libraries or as single compounds. Firstly, a mathematical method for the exclusion of false positive and false negative results was proposed applying a normalization of the predicted binding energies (expressed in kcal/mol) obtained from the docking calculations. Then this approach was applied on a library of 10 compounds extracted from natural sources, obtaining a good validation through in vitro biological tests. Afterwards, another study was performed on the cyclopeptide namalide. Its biological inhibitory activity and selectivity on Carboxipeptidase A target was in accordance with Inverse Virtual Screening results. Virtual Screening topic was also inspected analyzing the efficacy of Molecular Dynamics-based methods for the accurate calculations of the binding affinities. This work was conducted on a library of 1588 compounds (44 ligands + 1544 decoys) extracted from the DUD database on trypsin target, using the Linear Interaction Energy (LIE) method by means of extensive Molecular Dynamics simulations. Four different LIE results obtained combining different scaling factors were compared with docking results, evaluating and comparing ROC and enrichment curves for each of the considered methods. Poor results were obtained with LIE, and further analysis with MM-GBSA and MM-PBSA approaches are under investigation. Moreover, in silico screenings were performed for the detailed study of natural compounds whose activities are known a priori. With this procedure, several binding modes were reported for a library of compounds on PXR target, whose activity or inactivity were rationalized comparing their binding poses with that of Solomonsterol A, used as a reference compound on this receptor. The presence/absence of biological activity of another library of compounds extracted from the marine sponge Plakinastrella Mamillaris on PPAR-γ and for the diterpene oridonin on HSP70 1A are described at a molecular level with molecular docking and Molecular Dynamics simulations. The putative binding modes for the reported molecules was described offering a complete rationalization of docking results, evaluating how ligand target specific interactions (e.g. hydrophobic, hydrophilic, electrostatic contacts) can influence their biological activity. [edited by author]La chimica computazionale rappresenata un valido e rapido strumento per l’identificazione di nuovi potenziali composti bioattivi. L’analisi delle interazioni ligando-target macromolecolare e la valutazione di un possibile “binding mode” sono cruciali per il design e lo sviluppo di nuovi potenziali farmaci. Il Virtual Screening di grandi librerie di composti (fragments o drug-like) condotto in silico su uno specifico recettore può permettere la selezione di composti dalla promettente attività, e parallelamente l’identificazione di nuovi scaffolds molecolari. L’analisi accurata dei modelli di interazione ligando-recettore e il confronto di tali modelli con quelli di composti dalla già nota attività permette la costruzione di un modello farmacoforico, punto di partenza per successivi studi di potenziamento dell’attività farmacologica. Parallelamente, un altro aspetto fondamentale della ricerca farmacologica è rappresentato dall’identificazione dei targets di interazione per composti dalla nota bioattività, e questo risulta particolarmente interessante per i composti di origine naturale. Infatti, per tale classe di molecole sarebbe molto utile effettuare tests biologici su un elevato numero di recettori, ma ciò risulta spesso proibitivo a causa della scarsa quantità di composto disponibile. Partendo da tali presupposti, nella presente tesi è descritto approfonditamente un nuovo metodo computazionale definito Inverse Virtual Screening. I vari lavori basati su questo nuovo tipo di approccio sono stati effettuati considerando pannelli composti da diversi targets coinvolti nello sviluppo del cancro, portando all’identificazione della specifica attività antitumorale dei vari composti naturali investigati. Gli studi basati sull’Inverse Virtual Screening sono stati effettuati attraverso calcoli di Molecular Docking utilizzando diversi composti naturali, raggruppati in piccole librerie o studiati singolarmente. In primo luogo, è stato proposto un metodo matematico con l’obiettivo di escludere i falsi positivi e i falsi negativi applicando una normalizzazione delle affinità di legame predette (espresse in kcal/mol). Successivamente, tale approccio è stato applicato su una libreria di 10 composti di origine naturale, validando l’applicabilità di tale metodo attraverso tests biologici in vitro. Successivamente, un ulteriore studio è stato incentrato su un ciclopeptide definito namalide, la cui attività biologica su Carbossipeptidasi A era in totale accordo con i dati provenienti dallo studio di Inverse Virtual Screening condotto. Il Virtual Screening è stato inoltre studiato anche analizzando l’efficacia dei metodi per il calcolo accurato delle affinità di legame basati su simulazioni di Dinamica Molecolare. Tale studio è stato condotto su una libreria di 1588 composti (44 ligandi + 1544 decoys, estratti dal DUD database) sul target tripsina, utilizzando il metodo LIE (Linear Interaction Energy) attraverso un elevato numero di simulazioni di Dinamica Molecolare. Sono stati ottenuti quattro differenti scale di affinità predetta (attraverso quattro combinazioni di differenti scaling factors) e sono stati confrontati con i risultati derivanti dai calcoli di Molecular Docking, valutando e confrontando curve ROC e di enrichment. Attraverso il metodo LIE sono stati ottenuti risultati non incoraggianti, e quindi ulteriori analisi attraverso metodi MM-GBSA e MM-PBSA sono in corso di studio. Inoltre, screenings in silico sono stati effettuati anche per lo studio dettagliato di altri composti naturali la cui attività era nota a priori. Attraverso questa procedura, sono stati proposti diversi modelli di binding di una libreria di composti sul target PXR, e per tali composti è stata razionalizzata l’attività/inattività confrontando il loro binding mode con quello del Solomonsterol A, utilizzato come composto di riferimento su tale target. La presenza/assenza di attività biologica è stata è stata descritto a livello molecolare per un’altra classe di composti estratti dalla spugna Plakinastrella Mamillaris sul target PPAR-γ e sul diterpene oridonina sul target HSP70 1A attraverso esperimenti combinati di Molecular Docking e Molecular Dynamics. Sono stati proposti e descritti approfonditamente modelli di binding di tali composti, valutando come specifiche interazioni ligando-target macromolecolare (di natura idrofobica, elettrostatica o caratterizzata dalla presenza di specifici legami ad idrogeno) possano influenzare l’attività biologica. [a cura dell'autore]XI n.s

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data. Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and several substrates. These experiments were combined with lead optimization techniques to create a potent irreversible inhibitor which protects 95% of cells in vitro. Dynamics studies on a TcdB cysteine protease domain were performed to an allosteric communication pathway. Comparative analysis of the static and dynamic properties of the TcdA and TcdB glucosyltransferase domains were carried out to determine the basis for the differential lethality of these toxins. Large scale biological data is readily available in the post-genomic era, but it can be difficult to effectively use that data. Two bioinformatics methods were developed to process whole-genome data. Software was developed to return all genes containing a motif in single genome. This provides a list of genes which may be within the same regulatory network or targeted by a specific DNA binding factor. A second bioinformatic method was created to link the data from genome-wide association studies (GWAS) to specific genes. GWAS studies are frequently subjected to statistical analysis, but mutations are rarely investigated structurally. HyDn-SNP-S allows a researcher to find mutations in a gene that correlate to a GWAS studied phenotype. Across human DNA polymerases, this resulted in strongly predictive haplotypes for breast and prostate cancer. Molecular dynamics applied to DNA Polymerase Lambda suggested a structural explanation for the decrease in polymerase fidelity with that mutant. When applied to Histone Deacetylases, mutations were found that alter substrate binding, and post-translational modification

Combinatorial in-silico modeling and bioinformatics analysis of immune proteins and small-molecular weight inhibitors: a potential for cancer chemotherapy.

Doctoral Degree. University of KwaZulu-Natal, Durban.The immune system carries out pivotal functions in the protection of the body from damaging substances, microbes, and cellular alteration that could affect the health of an individual. The component of the immune system comprises of proteins, cells, and diverse organs. Individuals remain in a good state of health and wholeness if the immune system is working optimally, but if it becomes incapacitated toward fighting off germs or other harmful foreign substances, a diseased state set in. The innate and adaptive systems are the two sub-categories of the immune system. They work synergistically in the defence of the body and fighting off germs that triggered an immune response. Several proteins have been discovered to play pivotal roles in immune evasion and have therefore become attractive targets. Three of these proteins form the core of this thesis. Programmed death-ligand 1 (PD-L1), is an immune checkpoint protein which upon binding with another inhibitory checkpoint protein programmed cell death protein 1 (PD-1), elicit a cascade of reaction that leads to the reduction of proliferating antigen-specific T cells. The upregulation of PD-L1 can therefore, lead to evasion of the immune system by cancer cells. Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) is made up of three parts (a transmembrane part, extracellular part, and a cytoplasmic part). CTLA-4 has been implicated in the downregulation of immune response and blocking CTLA-4 activity results in a surge in immune functions. It has also been found that CTLA-4 negatively controls the T-cells. Natural killer group 2, member D (NKG2D) is located on the surface of immune cells where it acts as an activating receptor and regulator of the adaptive and innate immune system upon binding to its constitutive ligands such as UL16-binding protein (ULBP6). ULBP6 is a highly polymorphic protein, hence, the interaction between NKGD2 and ULBP6 is often altered. This effect has a great impact on the function of NKGD2 as a regulator of the immune system. Various humanized antibodies have been developed to specifically target PD-L1 and CTLA4. Some have been approved while others are at different phases of clinical trial. Ipilimumab and tremelimumab are designed as CTLA-4 inhibitors. Durvalumab and atezolimab are designed as anti-PD-L1 inhibitor. However, due to the limitations that have characterized the use of humanized antibodies inhibitors such as production cost, instability, and low tumour penetration, etc, small molecule inhibitors have been considered as a better alternative to humanized antibodies inhibitors. This thesis explored the mechanism of inhibition of newly synthesised PD-L1 inhibitors (BMS- 1166 and BMS-1001). Also, per-residue based virtual screening was employed to predict potential CTLA-4 inhibitors. Computational methods such as molecular docking, molecular dynamic simulation, virtual screening, and SNPinformatics were employed. These computational techniques revealed that BMS-1166 and BMS-1001 caused a motional movement in the monomers of PD-L1 to form a dimer, thereby preventing PD-L1-PD-1 interaction. Although the PD-L1 monomers have the same residues, their affinity for the BMS compounds differ. Two compounds ZINC04515726 and ZINC08985213 were identified as possible targets of CTLA-4. These two compounds elicited favourable interaction with CTLA- 4 facilitated by some crucial residues. Furthermore, the non-synonymous Single Nucleotide Polymorphism (nsSNPs) associated with ULBP6 were identified, and the effect of these nsSNPs on the interaction between NKGD2 and ULBP6 was also investigated. The first study (Chapter 4) investigates the structural dynamics and also provides insights into the mechanism of inhibition of BMS-1166 and BMS-1001 on PD-L1. The second study (Chapter 5) determines the binding site landscape of CTLA-4 and also employs binding site similarities between unrelated proteins to repurpose an inhibitor to target CTLA-4. The third study (Chapter 6) identifies deleterious polymorphisms associated with ULBP6. The effect of these polymorphisms on NKGD2-ULBP6 binding as a consequent on immune response is also explored in this chapter. Chapter 7 gives a detailed report on how the use of bioinformatics tools and strategies have aided and advanced the field of cancer immunotherapy. This study provides a thorough insight into the in-silico design, development and mechanism of action of small molecule inhibitors of PD-L1 and CTLA-4. Furthermore, this study gives insight into the polymorphic nature of ULBP6. Thence, the work presented in this study would serve a s a platform towards the design of small molecule inhibitors of CTLA-4 and PD-L1 with high therapeutic and less toxicity