Theses supervised by Prof. Dr. Zehra Özlem Keskin Özkaya ; Prof. Dr. Attila Gürsoy
12 theses · Koç University
The investigation of mechanistic differences of Rac1P29S and Rac1A159V activation via molecular dynamics simulations
Rac1 is a small GTPase which plays key roles in actin reorganization, cell motility, cell survival/growth as well as in various cancer types and neurodegenerative diseases. Similar to other Ras superfamily GTPases, Rac1 switches between GTP-bound active and GDP-bound inactive states. When active, Rac1 signals to various downstream effectors including Pak family kinases. The switch I and switch II regions open and close during GDP/GTP exchange. Rac1P29S and Rac1A159V (paralogous to K-RasA146) mutations are the two most common somatic mutations of Rac1. Rac1P29S is a known hotspot for melanoma, where it is the third most occurring mutation after B-RafV600 and N-RasQ61 mutations. Rac1A159V is most commonly observed in head and neck cancer. Both mutations are relatively newly discovered and require better characterization. In this thesis, how the mutations Rac1P29S and Rac1A159V differ the Rac1 dynamics is investigated by using molecular dynamics simulations. A total of five systems are simulated as follows: Rac1WT-GTP, Rac1WT-GDP, Rac1P29S-GTP, Rac1P29S-GDP, and Rac1A159V-GTP. Here, wild-type systems are considered as the control groups. For the analysis of the simulation trajectories, we focused on the conformational changes of switch regions and changes in nucleotide binding residues as these changes are important for GDP/GTP exchange of Rac1. This thesis suggests that P29S and A159V mutations activate Rac1 with different mechanisms. In the Rac1P29S-GTP system, proline to serine substitution changes the flexibility of the switch I region and keeps the switch in an open conformation. We propose that the open conformation of switch I region is one of the underlying reasons for rapid GDP/GTP exchange of Rac1P29S. On the other hand, in Rac1A159V-GTP, some of the contacts of guanosine ring of GTP with Rac1 temporarily lost, enabling guanosine ring to move towards switch I region and subsequently close the switch. The switch II regions of both Rac1P29S-GTP and Rac1A159V-GTP systems are stabilized in a closed conformation with respect to the Rac1WT-GTP system. Rac1A159V-GTP adopts a conformation similar to Ras state 2, where both switch regions are in closed conformations, switch I residue Thr35 forms a hydrogen bond with the nucleotide, and switch II residue Gly60 also interacts with the nucleotide. The fact that all Rac1WT-GTP, Rac1P29S-GTP, and Rac1A159V-GTP are stabilized with different conformations suggests that all three systems would interact with Rac1 regulators and downstream effectors differently.
A data-centric approach for investigation of protein-protein interfaces in Protein Data Bank
Understanding the structural architecture of protein interfaces is one of the key challenges in explaining how proteins interact and function. Protein-protein interfaces can be important targets for drug discovery and repurposing studies; this is only possible if the structural data available can be utilized in a meaningful way. Advancements in experimental techniques make it possible for scientists to determine larger and more complex protein structures. As these tools are becoming more accessible, the number of protein structures deposited in PDB is also increasing rapidly. This increase in the size of the protein complexes and the size of the data is an invaluable tool for understanding protein interactions. Still, it does not come without its computational challenges. This study first focuses on identifying protein-protein interfaces and overcoming some of the difficulties rooting from growing structure sizes in PDB. We evaluated 169,681 available protein structures in PDB and extracted 499,169 protein-protein interfaces. A unique set of protein interfaces can only be created by structural comparison of interfaces. The increasing number of interface structures makes it almost impossible to compare all interface structures in a reasonable time frame. Therefore, computational solutions are needed to reduce the comparison number within a manageable range without losing structural information. The second part of this study explains an approach for filtering highly similar proteins using sequential and structural information. As a result of this process, we identified 331,231 interfaces, unique in terms of sequence and structure. The reduced number of interfaces results in almost a 2.3-fold reduction in the number of comparisons needed, without losing any structural information. Implementing these techniques can make future studies on protein interface comparison possible for large data sets.
Understanding the pleckstrin homology (PH) domain peculiar mechanism in akt translocation, phosphorylation, and activation
The protein kinase B (PKB, also designated Akt), is unarguably a crucial player in cell proliferation, survival, metabolism, angiogenesis, and apoptosis. Akt plays a pivotal role in the Ras-PI3K-Akt-mTOR signaling pathway. Akt recruitment to the plasma membrane is enabled by its Pleckstrin homology (PH) domain, which interacts with signaling membrane lipid, PIP3, or PIP2. The interplay between the PH domain and PIP3 results in conformational changes that facilitate phosphorylations of Thr308 in the kinase domain, and Ser473 in the C-terminal regulatory domain by PDK1 and mTORC2 complex, respectively. Enormous Akt activation mechanisms have been proposed by various studies, which all seem to result in PH domain peculiar role in translocating Akt to the plasma membrane, and to implication of Calmodulin (CaM) intermolecularly interacting with the PH domain in breast cancer as established by previous NMR studies. However, the exact mechanism of how CaM interacts with the PH domain at the atomic level remains unclear. Also, in Akt autoinhibition state or a "PH-in" conformer, the PH domain intramolecularly interact with the kinase domain to prevent phosphorylation of a functional residue in the kinase domain. Several residues in the PH-kinase allosteric interface maintain the PH-kinase domain autoinhibition, and mutations of critical residues in the PH-kinase domain interface have the proclivity to disrupt the interface, resulting in a "PH-out" conformer state. A "PH-out" conformer of Akt is required for the PH domain to interact with membrane lipids, an event critical for Akt activation. Furthermore, phosphorylation of Ser473 in the C-terminal tail that forms electrostatic interaction with Arg 144 in the PH-kinase linker leads to conformational rearrangements in the PH domain activating Akt. However, the structure of full-length Akt autoinhibited state is yet to be crystalized to understand the PH and kinase domain autoinhibition. Here, this dissertation purpose is two-fold: using modeling and molecular dynamics (MD) simulations to figure out how CaM interacts with the PH domain to recruit Akt to the plasma membrane and how the PH domain intramolecularly interact with the kinase domain at the atomic level with emphasis on the interfacial residues that play a crucial role in the PH-kinase domain autoinhibition. In the dissertation's first part, CaM-PH domain complexes were modeled and subjected to all atoms MD simulations. The simulation results show that CaM-PH domain interactions are thermodynamically stable and involve a 𝝱-strand, rather than an 𝛂-helix interaction, both agreeing with the NMR data and that electrostatic and hydrophobic interactions are critical to maintaining CaM-PH complex. The PH domain interacts with CaM lobes; however, multiple modes are possible, and the involvement of IP4, polar head of PIP3 attenuates CaM-PH domain interaction, implicating the release mechanism at the plasma membrane. In the second part of the dissertation, iv we modeled full-length Akt (480 residues) in the inactive state to explore the intramolecular interaction between the PH and kinase domains and identify crucial interfacial residues that maintain the PH-kinase intact interface. Further, Asp 323, an important interface residue was mutated to His to discern its effect on PH-kinase allosteric interface. The results show that the mutation led to substantial displacement of ATP from the ATP binding pocket in Akt and led to kinase domain adopting a more open conformation. Additionally, the RMSD and RMSF profiles depict that D323H leads to an increase in conformational changes, and we deduce that although the mutation is approximately 21Å away from ATP binding site, perhaps there is an allosteric communication between these two functional sites in Akt. The modeled structures of full-length autoinhibited Akt state are best representations that would guide pharmaceutical chemists to develop Akt allosteric inhibitors, which require an intact PH-kinase interface, and ATP-competitive inhibitors that do not require such intact PH-kinase interface but might rely on intact kinase domain N- and C- lobes interface for their cellular activity. The development of allosteric and ATP-competitive inhibitors is crucial for targeting Akt since it is frequently activated in tumor cells, necessitating its regulation. The dissertation results have functional implications in developing inhibitors that would target CaM-PH domain complex and allosteric and ATP-competitive inhibitors that bind to the PH-kinase domain interface and ATP binding pocket in Akt, respectively. Finally, these results add to the already existing incredible wealth of information on the Akt kinase activation mechanism, which all seem to result from loosening the PH-kinase domain autoinhibition.
Drug repurposing in Ras/Raf/Mek/ERK signaling pathway
Ras/Raf/MEK/ERK signaling pathway regulates cell growth, division, and differentiation. In this work, we focus on drug repurposing in the Ras/Raf/MEK/ERK signaling pathway, considering structural similarities of protein-protein interfaces. The protein-protein complexes in this pathway are extracted from literature and the interfaces formed by physically interacting proteins are found via PRISM (a template-based protein-protein docking tool) if not available in Protein Data Bank. As a result, the structural coverage of these interactions has been increased from 21% to 92% using PRISM. Multiple conformations of each protein are used to include protein dynamics. Then, the Food and Drug Administration (FDA) approved drugs bound to the interfaces are proposed for the other protein-protein interfaces that are structurally similar. The results suggest that HIV protease inhibitors tipranavir, indinavir and saquinavir bind to Epidermal Growth Factor Receptor (EGFR) and Receptor Tyrosine-Protein Kinase ErbB-3 (ERBB3/HER3) interface. Tipranavir and indinavir also bind to EGFR and Receptor Tyrosine- Protein Kinase ErbB-2 (ERBB2/HER2) interface. Additionally, a drug used in Alzheimer's disease (galantamine) and an antinauseant for cancer chemotherapy patients (granisetron) can bind to RAF proto-oncogene serine/threonine-protein kinase (RAF1) and Serine/threonine-protein kinase B-raf (BRAF) interface. Hence, these drugs can be used for anti-tumor activities in cancer with future experimental validation.
Yapısal özellikler ve makine öğrenme metodaları kullanarak PDZ domain etkileşimlerini ve sınıfını tahmin etme
PDZ domains (PSD-95/Discs-large/ZO-1 homology) are one of the most abundant and evolutionary conserved domain families through uni- and multi-cellular organisms. As its abundance and high evolutinary conservation indicates, PDZ domains mediate a number of distinct functions in the cell including vesicular sorting, neuronal synaptic plasticity, development and neural guidance. Therefore, malfunction of the PDZ domain causes several crucial diseases such as Usher's syndrome, epilepsy, schizophrenia and types of cancer. PDZ domains are 80-100 residues long and consist of two α-helices (αA- αB) and six β-sheets (βA to βF). The canonical interaction is the most common interaction type of PDZ domain where the PDZ domain binds the C-terminal of the target protein via the binding cavity. PDZ domains are categorized into three classes according to the motif of their binding partners as Class I, Class II and Class III. Altough, PDZ domains prefer to bind a particular class of peptides there are cases where the PDZ domain can interact with both Class I and Class II peptides, classified as Class I-II. This study focuses on building prediction models for PDZ domain mediated interactions and PDZ domain classification by using their structural features. By utilizing the properties of the PDZ domains and their ligands, those have the available interaction experimental data, an interaction prediction and classification model was built via machine learning approaches. One of the most robust machine learning approach, support vector machine (SVM) algorithm, was selected to train the models. The interaction prediction and the classification models performances were evaluated by cross-fold validation test and validation of human proteome scanning results on experimentally known interactions data. The interaction prediction model and the classification model have area under ROC curve with a number of 0.99 and 0.91, respectively. Moreover, the human proteome scanning results showed that the interaction prediction model was able to predict the known PDZ domain mediated interactions correctly with a TP rate of 17 %. Additionally, the general knowledge of classification of PDZ domains were supported by our results. These models could be utilized by future experimental studies to narrow the search space of the novel binding partners of PDZ domains as well as drug discovery studies that target PDZ domain containing proteins.
Proteomdaki protein tümleşiklerini modellemek
Most (if not all) proteins function when associated in multimolecular assemblies. An important aim of structural biology is to attain the structures of protein assemblies at the atomic scale. Experimentally, structures are increasingly available but many are still missing or incomplete. Some experimental methods provide high resolution data for small proteins and some provide low resolution data for large proteins. Computational approaches can help bridge this resolution gap. They are needed to determine structural data of multi-molecular protein assemblies at atomic scale. Existing computational methods have made substantial progress toward this aim; however, current approaches are still limited. Some involve manual adjustment of experimental data; some are automated docking methods, which are computationally expensive and not applicable to large-scale proteome studies; still others exploit the symmetry of the complexes, thus they are not applicable to non-symmetrical complexes. Our study aims to take steps toward overcoming these limitations. We have developed a strategy to construct protein assemblies computationally based on binary interactions predicted by a motif-based protein interaction prediction tool, PRISM (PRotein Interactions by Structural Matching). PRISM predicts pair-wise interactions; here we take a step toward multimolecular assemblies, which reflects the more prevalent cellular scenarios. This method is able to construct homo-/hetero-complexes and symmetric/asymmetric complexes without a limitation on the number of components, considers conformational changes and is applicable to large-scale studies. We modeled a benchmark set of various protein assemblies starting from the unbound forms and obtained successful predictions (0.5 - 5.6 Å). Moreover, we exploit different conformations of the proteins available in the Protein Data Bank (PDB) to consider protein flexibility in modeling protein assemblies. We could increase the success in prediction of binary interactions from 27% to 67% and obtained higher accuracy in modeling protein assemblies by exploiting alternative conformations. Furthermore, we modified our method to exploit electron microscopy (EM) density maps to eliminate improper structures. Filtering structures through EM data prevents assembly construction based on wrong structures and saves computational time. We successfully modeled protein assemblies using EM data, most of them with RMSD less than 5 Å and correlation in density maps are close to or higher than 0.8. Comparing our results with other methods' showed higher accuracy in our interface predictions. We present the methods, illustrate their results, and highlight the current limitations.
Toll-benzeri reseptörlerin yapısal yolaklarını oluşturarak kanser ile enflamasyon arasındaki ilişkiyi anlamak
Toll-like receptor (TLR) pathway is one of the major pathways that give rise to inflammation, which is the first line of defense against pathogens. Although it is essential for host defense, inflammation also contributes to almost all phases of tumor development. TLR pathway plays a key role in inflammation-cancer crosstalk, but the exact mechanisms how they contribute to this remain elusive. Construction of structural TLR pathway provides insights into its roles in this crosstalk. The classical node-and-edge representation of pathways provides the big picture in a simple way, but it is incomplete. Structural pathways can help complete missing parts of such diagrams: they can help in understanding how an upstream signal is transduced to downstream; how higher-order oligomerization modes of proteins can influence their function; how mutations, inhibitors or antagonists affect the signaling and change cellular outcome; and which alternative parallel pathways can be activated simultaneously. Constructing structural pathways is a challenging task. In this dissertation, I constructed structural network of TLR pathway by employing the powerful PRISM (PRotein Interactions by Structural Matching) algorithm and available mutational/biochemical data. I built the structural network of TLR pathway; its regulatory pathways; and also other pathways that have important roles in inflammation-cancer relation, such as different cytokine pathways, and CASP8, and TRAF3 centered pathways. The architectures that I obtained provide the structural basis for TLR clustering upon stimulation and assembly of key signaling complexes, such as "TIR domain signalosome". They also demonstrate that almost all downstream parallel pathways of TLRs are competitive and clarify decisions at pathway branching points. I showed that several negative regulators restrict TLR signaling by interfering with the formation of the key interactions and signalosomes. I also performed in silico mutagenesis analysis to characterize the effects of oncogenic mutations and found that some mutations that fall on the interfaces disrupt the interactions with their targets and enable constitutive activation of NF-κB, which might lead to chronic inflammation, which promotes oncogenesis. Our results help to understand the crosstalk between cancer and inflammation from a structural perspective.
PRISM'in geçmiş CAPRI turları ile değerlendirilmesi
Proteins are key elements of a cell to perform the wide range of molecular and cellular activity. Proteins perform their function through binding to other proteins, DNA, RNA, and small molecules. Therefore, predicting how a protein interacts with its binding partners is one of the most important objectives of structural biology. As a result of the improvements in experimental structure determination methods, the number of individual protein structures in PDB has increased vastly. However, the number of complex structures does not increase as fast as the individual proteins which creates demand for new approaches to predict complexes from the individual protein structures. Currently, there are a lot of computational approaches to predict the complex structures. Critical Assessment of Prediction of Interactions (CAPRI) is a well-known community-wide experiment with the purpose of establishing a routine which allows testing the performance of several different docking algorithms created. The success rate of predicted structures is verified by the several evaluation criteria determined by the CAPRI association. Those criteria are interface and ligand rmsds (I-rmsd, L-rmsd), native residue contacts and number of clashes. In this study, I assessed the performance of PRISM (Protein Interaction by Structural Matching) using CAPRI evaluation criteria. The main objectives were to determine how much PRISM is successful in predicting the complex structures of available CAPRI targets and to force the limits of PRISM by stretching the parameters of PRISM. PRISM could not predict the correct complex structures for 33% of targets majority of which correspond to homodimers and enzyme/inhibitor complexes. The results also indicate that considering just the structures with the negative energy score results in the loss of 36% of successful predictions implying a problem in the scoring function. For further testing, RosettaDock was used as an alternative scoring function. Both scoring approaches yielded a correlation. To increase the success rate, some parameters of PRISM were changed however, no significant improvement has been achieved.
Understanding Rho GTPase interactions with scaffolding proteins and Ras protein shuttling mechanisms
Rho GTPaz ailesi ve Ras GTPaz ailesi, Ras GTPaz superailesinin alt aileleridir. GTPazlar yapısal ve işlevsel olarak farklılıklar gösteren efektörlerle seçici olarak etkileşirler. IQ motif-containing GTPase-activating protein (IQGAP) ve phosphodiesterase-δ (PDEδ) bu efektörler arasındadır. Rho GTPazlar diğer GTPazlardan, yüklü amino asitlerce zengin bir heliks motifi olan "insert loop"un varlığıyla ayrılır. Rho GTPazlar, Cdc42 ve Rac1 GTP bağlı aktif formlarındayken üç insan IQGAP proteiniyle de etkileşim kurarlar. IQGAP–Cdc42 etkileşimi aktin polimerizasyonunu arttırarak metaztaza katkıda bulunurlar. Ancak, yüksek sekans benzerliğine ragmen Cdc42 ve Rac'ın IQGAP ile olan etkileşimi farklılık gösterir. Cdc42 ve Rac1'ın IQGAP'e bağlanma mekanizması kesin olarak bilinmemektedir. Moleküler dinamik simulasyonlarını kullanarak Cdc42 ve Rac1'ın IQGAP2 ile olan etkileşimlerinin detaylı mekanizması çalışıldı. Cdc42'nin "insert loop"u ilk Cdc42'nin bağlanmasında önem taşımakta ve ikinci Cdc42'nin bağlanmasına yardımcı olmaktadır. Aksine, Rac1'ın "insert loop" sekansındaki ve yapısındaki farklılılar onun IQGAP2 ile etkileşimini engellemiştir ve sadece bir Rac1 IQGAP2'ye bağlanabilmektedir. Ras GTPaz alailesi proteinlerinde Ras, insan kanserlerinde yüksek oranda mutasyona uğramış halde bulunmaktadır. Ras proteinlerinin plasma membranında (PM) uygun şekilde konumlanması, Ras fonksiyonu için kritik önem taşımaktadır. Ras proteinlerinin konumlandırılmasında PDEδ görev almaktadır. GTP-bağlı Arf-like protein 2 (Arl2), farnesilli proteinlerin PDEδ-aracılı taşınmasında bir regülatör görevi almaktadır, ancak farneillli proteinlerin PDE'dan Arl2-yardımlı salınımının kesin mekanizması bilinmemektedir. MD simülasyonlarını kullanarak Arl2-aracılı en çok rastlanan onkojenik Ras izoformu olan KRas4B'nin PDEδ'dan salınımının detaylı mekanizmasını araştırıldı. Detaylı analizler, PDEδ'nın β6 bölgesi ve bazı amino asitlerini kapsayan alosterik değişikliklerin hidrofobik PDEδ cebini sıkıştırarak KRas4B'yi dışarı ittiğini gösterdi. Ayrıca, tekli amino asit varyasyonlarının (SAV) protein-protein etkileşim (PPI) ara yüzlerine dağılımını da incelendi. Ara yüzler, hot spot adı verilen ve bağlanma enerjisine önemli katkıda bulunan az sayıda amino asit içerir, bu hot spotlar kümelenerek hot regionları oluşturur. Tekli hot spotlar ise hot regionlar dışında kalan hot spotlara denmektedir. Literatürden toplanmış deneysel thermodinamik dataya sahip SAVlarla, istatistiksel ve yapısal analizler gerçekleştirildi. Bunun sonucunda, PPIı destabilize eden ve hastalığa neden olan SAVların, hot regionlar ya da energy bakımında daha az önemli bölgelerde bulunmaktansa, tekli hot spotlarda bulunmayı tercih ettiğini gösterildi. Bu analizler tekli hot spotlara denk gelen SAVların, proteinin stabilitesine ve fonksiyonuna önemli etkisi olduğunu gösteriyor. Bu tez çalışması TUBITAK tarafından desteklenmiştir (Araştırma Projesi No: 114M196 ve Burs: 2211-E).
K-Ras4B ve c-Raf etkileşimlerinin moleküler dinamik simülasyonları ve hesaplamalı analizi
Ras proteins are activated through their upstream regulators. Upon activation, Ras proteins recruit their downstream effectors to the plasma membrane and activate them. Activated downstream effectors, then activate other proteins and this signaling cascades lead several responses within the cell. Although Ras proteins are activated through guanine exchange factor protein, specific mutations result with constitutively activate Ras. Those mutations are found in many cancer types and several developmental diseases. Therefore, targeting the mutations on Ras has been the main focus in the treatment of Ras-related diseases. However, Ras proteins are still classified as "undruggable protein". Effects of the mutations on Ras proteins and the interaction dynamics between their effectors are unclear. In this thesis, K-Ras4B and c-Raf interaction studied within two subsystems through molecular dynamics simulations. Computationally, K-Ras4B and c-Raf interaction were predicted by using PRISM (PRotein Interactions by Structural Matching). Residues on Ras and Raf interaction complex were identified using HotRegion. The results gave an idea about the specific amino acids involved in K-Ras4B and c-Raf interaction. To confirm the predictions, R41E/K42D double charge reversal mutations, called B3 mutation, were introduced to the control (G12D). MD simulations were performed on control and B3 mutated subsystems. In the system, which contains K-Ras4B1-166, the mutation slightly changed the binding free energy levels. According to the result, B3 mutation decreased the binding free energy levels. However, the difference in the energy levels was not significant enough. The unexpected energy levels can be studied further by introducing a membrane and a cysteine rich domain (CRD) to the system.
RalGDS-Ral sinyal yolağının Moleküler Dinamik Simulasyon metodu ile incelenmesi
Ras family is composed of at least 35 proteins that are known as GTP-GDP mediated molecular switches that regulate most of the cellular processes through physical interactions with their wide variety of effector proteins. In response to the diverse extracellular stimuli, they become activated, and then recruit their effector proteins (Raf, PI3K, and RalGDS) to the plasma membrane and transmit the signal. Raf cascade and PI3K pathways are well-established effector pathways; RalGDS lately become the research scrutiny. There are four prominent members of the family: H-Ras, N-Ras, K-Ras4A and K-Ras4B proteins that are found as the most frequently mutated proteins in human cancer types. The other well-known Ras members are Rap1 and Ral proteins. Whereas prominent members are well-characterized; the cellular functions of Rap1 and Ral are yet to be understood. In this study, the main aim is to elaborate the RalGDS-Ral signalling pathway in complex with Ras family members (K-Ras, Rap1) and Ral effector proteins using molecular dynamics simulations. The five Ras systems and two Ral systems were generated to elaborate upstream and downstream of RalGDS signaling cascade. The five Ras systems are comprised of K-Ras4Bwt-RalGDS-RBD as a wild-type system, K-Ras4BG12D-RalGDS-RBD and K-Ras4BG12V-RalGDS-RBD as control systems, K-Ras4BG12V+E37G-RalGDS-RBD as a mutational system, and Rap1wt-RalGDS-RBD as a Rap1 system; the two Ral sytems are composed of Ral-Sec5 and Ral-Exo84 effector interactions. The systems were designed to give insight into details of mechanism behind Ras-induced RalGDS-Ral pathway. The frequently involved residues that are responsible for stable complex formations and strong bond formations were investigated via MD simulations. We proposed the residues, Ser39 on Ras; Lys31 in Rap1; Tyr31, Asn54 and Asp56 on RalGDS as significant residues that amplify binding affinity between Ras family members and Ras Binding Domain of RalGDS. It is known that Sec5 and Exo84 have partially overlapping residues on RalA. We discovered most of the common involved residues on RalA in both interactions, which are Glu44, Ala48, Asp49, Ser50, Arg52, Tyr75. The comparison of two Ral systems allowed us to identify important residues that amplify binding affinity of RalA to its effector proteins. These residues are Asp49, Glu73, Asp74, and Asn81. We further demonstrate the frequently involves important residues on Sec5 and Exo84. Ras proteins have been scrutinizing for decades; however, they are still undruggable. The long-term purpose of this intense research is to develop small molecules that target Ras dependent RalGDS signaling particularly.
Transkripsiyon faktörü aktivitesi ve gen ifadesinde RNA ve protein etkenleri üzerine bir çalışma
Transcription factors can activate or repress expression of their target genes. Given this critical role, their activity is decisive in health and disease. Transcription factor function is affected by numerous factors that include co-activators, enhancer RNA, and protein oligomerization. Combining functional genomic and proteomic techniques with computational methods, the research presented here investigated how these factors influence transcription factors and modify gene regulation, and the role of a transcriptional repressor in cancer. Using the androgen receptor (AR) as a model transcription factor, we characterized how the enhancer long non-coding RNA KLK3e affects expression of AR regulated genes. Combining in vitro RNA pulldown and mass spectrometry, we identified proteins that interact with KLK3e. These included known AR regulators such as DDX5, DDX17 and HSPA8 that were known to affect prostate cancer pathogenesis. Our results and computational modeling of the RNA-protein complex suggest that KLK3e RNA may influence AR mediated transcription through acting as a scaffold to gather AR co-activators near the transcription site. Such RNA scaffolding effects may be paralleled in other nuclear receptors and transcription factors, as DDX5 has been implicated to interact with other steroid receptors such as the estrogen and vitamin D receptors. Next we explored how co-activator proteins influence gene transcription. We used four novel small molecule inhibitors that target different sites on the AR and disrupt co-activator binding. To identify the disrupted interactions, we conducted rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME) and incorporated RNA sequencing to understand the effects on gene expression. Inhibition of either a co-activator binding site, dimerization site, DNA binding site, or ligand binding site appeared to have a similar negative impact on the number of protein interactors. Also, all of the inhibitors that target different sites affected the gene expression profile similarly and reduced transcription by AR. To understand how transcription factor oligomerization occurs, we conducted in silico modeling with the glucocorticoid receptor (GR). Experimental observations suggested that GR interacts with the mineralocorticoid receptor (MR) and can also form homo-tetramers upon DNA binding. Our computational models explained the variation in experimental observations and demonstrated that GR and MR could interact through several alternative interfaces shared by close relatives of the nuclear receptor family. Predictions demonstrated that ligand or antagonist binding did not prevent interaction but rather changed the interface preference. This can leave different sites available for binding of co-activators, explaining why different ligands can produce different cellular outcomes. We also proposed a mechanism of action for GR tetramerization through the ligand binding domain, and the chromosomal looping model of interaction through the DNA binding domains. Finally, circadian clock regulation also relies on transcriptional factors. Cryptochrome is a circadian transcriptional repressor, and its deletion was observed to delay cancer and extend the lifespan of p53 mutant mice. With RNA sequencing analyses we investigated the transcriptomic changes following UV damage response upon cryptochrome deletion in a p53 mutant background. Gene Set Enrichment Analysis of differentially expressed genes demonstrated enrichment in IFN-γ immune surveillance and TNFα signaling via NF-κB. Protein network analysis pinpointed p21, Sirt1 and Jun as key players. Differentially expressed genes also contained a high ratio of non-coding RNAs. In short, we show that the KLK3e enhancer RNA interacts with AR co-activators, and separately we observed that inhibition of co-activator binding can have the same effect on AR transcription factor activity as inhibition of ligand activation, DNA binding, or dimerization. Our structural models showed how GR might oligomerize with MR through alternative interfaces, and that ligand or antagonist binding can affect interface preference. We also demonstrated how cryptochrome deletion in p53 mutants enhance apoptotic and anti-tumorigenic responses to UV damage at the transcriptome level.