Computational Modeling of Protein Kinases: Molecular Basis for Inhibition and Catalysis

Protein kinases catalyze protein phosphorylation reactions, i.e. the transfer of the γ-phosphoryl group of ATP to tyrosine, serine and threonine residues of protein substrates. This phosphorylation plays an important role in regulating various cellular processes. Deregulation of many kinases is directly linked to cancer development and the protein kinase family is one of the most important targets in current cancer therapy regimens. This relevance to disease has stimulated intensive efforts in the biomedical research community to understand their catalytic mechanisms, discern their cellular functions, and discover inhibitors. With the advantage of being able to simultaneously define structural as well as dynamic properties for complex systems, computational studies at the atomic level has been recognized as a powerful complement to experimental studies. In this work, we employed a suite of computational and molecular simulation methods to (1) explore the catalytic mechanism of a particular protein kinase, namely, epidermal growth factor receptor (EGFR); (2) study the interaction between EGFR and one of its inhibitors, namely erlotinib (Tarceva); (3) discern the effects of molecular alterations (somatic mutations) of EGFR to differential downstream signaling response; and (4) model the interactions of a novel class of kinase inhibitors with a common ruthenium based organometallic scaffold with different protein kinases. Our simulations established some important molecular rules in operation in the contexts of inhibitor-binding, substrate-recognition, catalytic landscapes, and signaling in the EGFR tyrosine kinase. Our results also shed insights on the mechanisms of inhibition and phosphorylation commonly employed by many kinases

Analysis of the protein-Ligand and protein-peptide interactions using a combined sequence- and structure-based approach

Proteins participate in most of the important processes in cells, and their ability to perform their function ultimately depends on their three-dimensional structure. They usually act in these processes through interactions with other molecules. Because of the importance of their role, proteins are also the common target for small molecule drugs that inhibit their activity, which may include targeting protein interactions. Understanding protein interactions and how they are affected by mutations is thus crucial for combating drug resistance and aiding drug design. This dissertation combines bioinformatics studies of protein interactions at both primary sequence and structural level. We analyse protein-protein interactions through linear motifs, as well as protein-small molecule interactions, and study how mutations affect them. This is done in the context of two systems. In the first study of drug resistance mutations in the protease of the human immunodeficiency virus type 1, we successfully apply molecular dynamics simulations to estimate the effects of known resistance-associated mutations on the free binding energy, also revealing molecular mechanisms of resistance. In the second study, we analyse consensus profiles of linear motifs that mediate the recognition by the mitogen-activated protein kinases of their target proteins. We thus gain insights into the cellular processes these proteins are involved in.Proteine sind an den meisten wichtigen Prozessen in Zellen beteiligt, und ihre Fähigkeit, ihre Funktion zu erfüllen, hängt letztlich von ihrer dreidimensionalen Struktur ab. In diesen Prozessen wirken sie normalerweise durch Wechselwirkungen mit anderen Molekülen. Aufgrund der Bedeutung ihrer Rolle sind Proteine auch die häufigsten Angriffspunkte für niedermolekulare Wirkstoffe, die ihre Aktivität hemmen. Dies kann das Targeting von Proteinwechselwirkungen umfassen. Um Wechselwirkungen mit Medikamenten zu bekämpfen und das Wirkstoffdesign zu unterstützen, ist es wichtig, die Wechselwirkungen zwischen Proteinen und deren Einfluss auf Mutationen zu verstehen. Diese Dissertation kombiniert bioinformatische Studien zu Proteinwechselwirkungen sowohl auf primärer als auch auf struktureller Ebene. Wir analysieren Protein-Protein-Wechselwirkungen anhand linearer Motive sowie Protein-Kleinmolekül-Wechselwirkungen und untersuchen, wie sich Mutationen auf sie auswirken. Dies wird untersucht im Kontext von zwei Systemen. In der ersten Studie zu Resistenzmutationen in der Protease des humanen Immundefizienzvirus Typ 1 haben wir molekulardynamische Simulationen erfolgreich eingesetzt, um die Auswirkungen bekannter Resistenz-assoziierter Mutationen auf die freie Bindungsenergie abzuschätzen und molekulare Resistenzmechanismen aufzuzeigen. In der zweiten Studie analysieren wir Konsensusprofile von linearen Motiven, die die Erkennung der Zielproteine durch die Mitogen-aktivierten Proteinkinasen vermitteln. So gewinnen wir Einblick in die zellulären Prozesse, an denen diese Proteine beteiligt sind

Exploring Structure-Dynamics-Function Relationship in Proteins, Protein: Ligand and Protein: Protein Systems through Computational Methods

The study focuses on understanding the dynamic nature of interactions between molecules and macromolecules. Molecular modeling and simulation technologies are employed to understand how the chemical constitution of the protein, specific interactions and dynamics of its structure provide the basis of its mechanism of function. The structure-dynamics-function relationship is investigated from quantum to macromolecular-assembly level, with applications in the field of rationale drug discovery and in improving efficiency of renewable sources of energy. Results presented include investigating the role of dynamics in the following: 1) In interactions between molecules: analyzing dynamic nature of a specific non-covalent interaction known as “anion-π [pi]” in RmlC protein. 2) In interactions between molecules and macromolecules: defining the structural basis of testosterone activation of GPRC6A. 3) In disrupting the function using specific substrate interactions: incorporating protein dynamics and flexibility in structure-based drug-discovery approach targeting the prothrombinase coagulation complex. 4) In interactions between macromolecules: elucidating the protein-protein binding and dynamics of electron-transport proteins, Ferrodoxin and Cytochrome c6, with Cyanobacterial Photosystem I

Dynamics of protein-drug interactions inferred from structural ensembles and physics-based models

The conformational flexibility of target proteins is a major challenge in understanding and modeling protein-drug interactions. A fundamental issue, yet to be clarified, is whether the observed conformational changes are controlled by the protein, or induced by the inhibitor. While the concept of induced fit has been widely adopted for describing the structural changes that accompany ligand binding, there is growing evidence in support of the dominance of proteins' intrinsic dynamics, which has been evolutionarily optimized to accommodate its functional interactions. The wealth of structural data for target proteins in the presence of different ligands now permits us to make a critical assessment of the balance between these two effects in selecting the bound forms. We focused on three widely studied drug targets, HIV-1 reverse transcriptase, p38 MAP kinase, and cyclin-dependent kinase 2. A total of 292 structures determined for these enzymes in the presence of different inhibitors as well as unbound form permitted us to perform an extensive comparative analysis of the conformational space accessed upon ligand binding, and its relation to the intrinsic dynamics prior to ligand binding as predicted by elastic network model analysis. Further, we analyzed NMR ensembles of ubiquitin and calmodulin representing their microseconds range solution dynamics. Our results show that the ligand selects the conformer that best matches its structural and dynamic properties amongst the conformers intrinsically accessible to the protein in the unliganded form. The results suggest that simple but robust rules encoded in the protein structure play a dominant role in pre-defining the mechanisms of ligand binding, which may be advantageously exploited in designing inhibitors. We apply these lessons to the study of MAP kinase phosphatases (MKPs), which are therapeutically relevant but challenging signaling enzymes. Our study provides insights into the interactions and selectivity of MKP inhibitors and shows how an allosteric inhibition mechanism holds for a recently discovered inhibitor of MKP-3. We also provide evidence for the functional significance of the structure-encoded dynamics of rhodopsin and nicotinic acetylcholine receptor, members of two membrane proteins classes serving as targets for more than 40% of all current FDA approved drugs

Targeting protein kinases to manage or prevent Alzheimer’s disease

Due to the pressing need for new disease-modifying drugs for Alzheimer’s disease (AD), new treatment strategies and alternative drug targets are currently being heavily researched. One such strategy is to modulate protein kinases such as cyclin-dependent kinase 1 (CDK1), cyclin-dependent kinase 5 (CDK5), glycogen synthase kinase-3 (GSK-3α and GSK-3β), and the protein kinase RNA-like endoplasmic reticulum kinase (PERK). AD intervention by reduction of amyloid beta (Aβ) levels is also possible through development of protein kinase C-epsilon (PKC-ϵ) activators to recover α-secretase levels and decrease toxic Aβ levels, thereby restoring synaptogenesis and cognitive function. In this way, we aim to develop new AD drugs by targeting kinases that participate in AD pathophysiology. In our studies, comparative modeling was performed to construct 3D models for kinases whose crystal structures have not yet been identified. The information from structurally similar proteins was used to define the amino acid residues in the ATP binding site as well as other important sites and motifs. We searched for the comstructural motifs and domains of GSK-3β, CDK5 and PERK. Further, we identified the conserved water molecules in GSK-3β, CDK5 and PERK through calculation of the degree of water conservation. We investigated the protein-ligand interaction profiles of CDK1, CDK5, GSK-3α, GSK-3β and PERK based on molecular dynamics (MD) simulations, which provided a time-dependent demonstration of the interactions and contacts for each ligand. In addition, we explored the protein-protein interactions between CDK5 and p25. Small molecules which target this interaction may offer a prospective therapeutic benefit for AD. In order to identify new modulators for protein kinase targets in AD, we implemented three virtual screening protocols. The first protocol was a combined ligand- and protein structure-based approach to find new PERK inhibitors. In the second protocol, protein structure-based virtual screening was applied to find multiple-kinase inhibitors through parallel docking simulations into validated models of CDK1, CDK5 and GSK-3 kinases. In the third protocol, we searched for potential activators of PKC-ϵ based on the structure of its C1B domain

Design and Synthesis of Curcumin Analogs for Anticancer Activity and Discovery of Novel Hit Molecules Targeting CXCR4.

Curcumin as a natural compound is made of various components including protein, carbohydrate, and curcuminoid. Curcuminoid is made of curcumin, desmethoxycurcumin and bis desmethoxycurcumin. Curcumin is used for the treatment of cancer and inflammatory disorders, but it has some therapeutic problems like poor bioavailability, poor efficacy, and chemical instability. To overcome these problems, the objective of this study is (1) synthesis of pyrazole curcumin analogs, (2) synthesis of triazole curcumin analogs, and (3) in-vitro study of the anticancer activity of these curcumin analogs on head & neck, breast, pancreatic and glioblastoma cancer cells. During this part of my Ph.D. project, we have synthesized 9 pyrazole and 4 triazole curcumin analogs and studied their anticancer activity against CAL 27 and UM-SCC-74A as head & neck cancer cell lines, MDA-MB-231 as breast cancer cell line, HPAF as pancreatic cancer cell line, and MG118 as glioblastoma cancer cell lines. We have studied the effect of these analogs on head & neck cancer cell lines by using cell proliferation assay and western blotting analysis. Compound 49 was shown the best anticancer activity on these cancer cell lines. Western blotting analysis revealed that compounds 49, 81, and 77, showed anticancer activity. We did MTS assay study on MDA-MB 231 as a human cancer cell line and the study revealed that compounds 6 and 81 had good anticancer activity against these cancer cell lines, while triazole analogs showed weak anticancer activity. We also used MTS assay study to investigate the effect of curcumin analogs. Compounds 6 and 86 showed good anticancer activity against HPAF cell line. Cell Titer Glo-2 assay study on MG118 cell line revealed that compounds 49, 51, and 80 had good anticancer activity against glioblastoma cancer cell lines. The expression of the CXCR4 gene leads to making a CXCR4 protein which is a GPCR protein. Research showed that this protein is involved in different cancer types. Overexpression of CXCR4 leads to cancer metastasis. The objective of this study as the second part of my Ph.D. project is the discovery of novel hit molecules targeting CXCR4. We did the virtual screening of 229358 natural product compounds. Based on the crystallography structure, we generated the receptor file. FRED docking led to the identification of 500 hit compounds out of 229358 compounds. 500 hit compounds were filtered based on several parameters which led to the identification of 4 hit molecules. Root Mean Square Deviation study has shown that two of these hit molecules stabilized the protein Structure. Moreover, based on the Radius of the gyration study, three of these molecules maintain the compactness of protein. The hydrogen bond study of these complexes showed that two complexes made hydrogen bonds with targets. So, molecular dynamic analysis by Gromacs led to the identification of 2 hit molecules for CXCR4 antagonist activity. Keywords: Curcumin, Cancers, In-vitro study

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