Browsing by Subject "RNA"

Background

Methods

Findings

Interpretation

Funding

As of late 2009, the Protein Data Bank (PDB) has grown to contain over 70,000 models. This recent increase in the amount of structural data allows for more extensive explication of the governing principles of macromolecular folding and association to complement traditional studies focused on a single molecule or complex. PDB-wide characterization of structural features yields insights that are useful in prediction and validation of the 3D structure of macromolecules and their complexes. Here, these insights lead to a deeper understanding of protein--protein interfaces, full-atom critical assessment of increasingly more accurate structure predictions, a better defined library of RNA backbone conformers for validation and building 3D models, and knowledge-based automatic correction of errors in protein sidechain rotamers.

My study of protein--protein interfaces identifies amino acid pairing preferences in a set of 146 transient interfaces. Using a geometric interface surface definition devoid of arbitrary cutoffs common to previous studies of interface composition, I calculate inter- and intrachain amino acid pairing preferences. As expected, salt-bridges and hydrophobic patches are prevalent, but likelihood correction of observed pairing frequencies reveals some surprising pairing preferences, such as Cys-His interchain pairs and Met-Met intrachain pairs. To complement my statistical observations, I introduce a 2D visualization of the 3D interface surface that can display a variety of interface characteristics, including residue type, atomic distance and backbone/sidechain composition.

My study of protein interiors finds that 3D structure prediction from sequence (as part of the CASP experiment) is very close to full-atom accuracy. Validation of structure prediction should therefore consider all atom positions instead of the traditional Calpha-only evaluation. I introduce six new full-model quality criteria to assess the accuracy of CASP predictions, which demonstrate that groups who use structural knowledge culled from the PDB to inform their prediction protocols produce the most accurate results.

My study of RNA backbone introduces a set of rotamer-like "suite" conformers. Initially hand-identified by the Richardson laboratory, these 7D conformers represent backbone segments that are found to be genuine and favorable. X-ray crystallographers can use backbone conformers for model building in often poor backbone density and in validation after refinement. Increasing amounts of high quality RNA data allow for improved conformer identification, but also complicate hand-curation. I demonstrate that affinity propagation successfully differentiates between two related but distinct suite conformers, and is a useful tool for automated conformer clustering.

My study of protein sidechain rotamers in X-ray structures identifies a class of systematic errors that results in sidechains misfit by approximately 180 degrees. I introduce Autofix, a method for automated detection and correction of such errors. Autofix corrects over 40% of errors for Leu, Thr, and Val residues, and a significant number of Arg residues. On average, Autofix made four corrections per PDB file in 945 X-ray structures. Autofix will be implemented into MolProbity and PHENIX for easy integration into X-ray crystallography workflows.

Post-transcriptional regulation of cytoplasmic mRNAs is an efficient mechanism of regulating the amounts of active protein within a eukaryotic cell. RNA sequence elements located in the untranslated regions of mRNAs can influence transcript degradation or translation through associations with RNA-binding proteins. Tristetraprolin (TTP) is the best known member of a family of CCCH zinc finger proteins that targets adenosine-uridine rich element (ARE) binding sites in the 3’ untranslated regions (UTRs) of mRNAs, promoting transcript deadenylation through the recruitment of deadenylases. More specifically, TTP has been shown to bind AREs located in the 3’-UTRs of transcripts with known roles in the inflammatory response. The mRNA-binding region of the protein is the highly conserved CCCH tandem zinc finger (TZF) domain. The synthetic TTP TZF domain has been shown to bind with high affinity to the 13-mer sequence of UUUUAUUUAUUUU. However, the binding affinities of full-length TTP family members to the same sequence and its variants are unknown. Furthermore, the distance needed between two overlapping or neighboring UUAUUUAUU 9-mers for tandem binding events of a full-length TTP family member to a target transcript has not been explored. To address these questions, we recombinantly expressed and purified the full-length C. albicans TTP family member Zfs1. Using full-length Zfs1, tagged at the N-terminus with maltose binding protein (MBP), we determined the binding affinities of the protein to the optimal TTP binding sequence, UUAUUUAUU. Fluorescence anisotropy experiments determined that the binding affinities of MBP-Zfs1 to non-canonical AREs were influenced by ionic buffer strength, suggesting that transcript selectivity may be affected by intracellular conditions. Furthermore, electrophoretic mobility shift assays (EMSAs) revealed that separation of two core AUUUA sequences by two uridines is sufficient for tandem binding of MBP-Zfs1. Finally, we found evidence for tandem binding of MBP-Zfs1 to a 27-base RNA oligonucleotide containing only a single ARE-binding site, and showed that this was concentration and RNA length dependent; this phenomenon had not been seen previously. These data suggest that the association of the TTP TZF domain and the TZF domains of other species, to ARE-binding sites is highly conserved. Domains outside of the TZF domain may mediate transcript selectivity in changing cellular conditions, and promote protein-RNA interactions not associated with the ARE-binding TZF domain.

In summary, the evidence presented here suggests that Zfs1-mediated decay of mRNA targets may require additional interactions, in addition to ARE-TZF domain associations, to promote transcript destabilization and degradation. These studies further our understanding of post-transcriptional steps in gene regulation.

Codon usage bias is a fundamental aspect of the genetic code. For many years synonymous mutations to a coding sequence were considered to be functionally “silent.” We now appreciate that is not the case and that synonymous codon choice can have drastic implications for gene expression and protein production. A major debate in the field remains whether codon usage bias is evolutionarily selected for to drive efficient translation in a tissue-specific manner. Here we perform an organism wide screen in Drosophila using codon modified reporters to reveal tissue-specific responses to codon usage bias. We uncover a strict limit on rare codon usage for protein expression, and this limit coincides with the rareness limit of endogenous genes in Drosophila. We find that rare codon usage near the edge of this limit is sufficient to impart tissue-specific gene expression, notably in the testis and brain. We define a new codon usage metric, the tissue-apparent Codon Adaptation Index (taCAI) to reveal a conserved enrichment in rare codons in endogenous testis genes of both flies and humans. We further demonstrate that rare codons in the evolutionarily young gene, RpL10Aa, are required for female fertility.

RNA recognition frequently results in conformational changes that optimize

intermolecular binding. As a consequence, the overall binding affinity of RNA

to its binding partners depends not only on the intermolecular interactions

formed in the bound state, but also on the energy cost associated with changing

the RNA conformational distribution. Measuring these conformational penalties

is however challenging because bound RNA conformations tend to have equilibrium

populations in the absence of the binding partner that fall outside detection by

conventional biophysical methods.

In this work we employ as a model system HIV-1 TAR RNA and its interaction with

the ligand argininamide (ARG), a mimic of TAR’s cognate protein binding partner,

the transactivator Tat. We use NMR chemical shift perturbations (CSP) and NMR

relaxation dispersion (RD) in combination with Bayesian inference to develop a

detailed thermodynamic model of coupled conformational change and ligand

binding. Starting from a comprehensive 12-state model of the equilibrium, we

estimate the energies of six distinct detectable thermodynamic states that are

not accessible by currently available methods.

Our approach identifies a minimum of four RNA intermediates that differ in terms

of the TAR conformation and ARG-occupancy. The dominant bound TAR conformation

features two bound ARG ligands and has an equilibrium population in the absence

of ARG that is below detection limit. Consequently, even though ARG binds to TAR

with an apparent overall weak affinity ($\Kdapp \approx \SI{0.2}{\milli

\Molar}$), it binds the prefolded conformation with a $K_{\ch{d}}$ in the nM

range. Our results show that conformational penalties can be major determinants

of RNA-ligand binding affinity as well as a source of binding cooperativity,

with important implications for a predictive understanding of how RNA is

recognized and for RNA-targeted drug discovery.

Additionally, we describe in detail the development of our approach for fitting

complex ligand binding data to mathematical models using Bayesian

inference. We provide crucial benchmarks and demonstrate the

robustness of our fitting approach with the goal of application

to other systems. This thesis aims to provide new insight into

the dynamics of RNA-ligand recognition as well as provide new

methods that can be applied to achieve this goal.

Stroke is the leading cause of morbidity and the third leading cause of death in the United States. Over 80% of strokes are ischemic in nature, produced by a thrombus occluding the cerebral circulation. Currently, there is only one pharmacologic treatment FDA approved for ischemic stroke; recombinant tissue-type plasminogen activator (rtPA). Unfortunately, thrombolysis with rtPA is underutilized, as it must be administered within three hours of symptom onset and it is not uncommon for treatment to result in intracranial hemorrhage. For these reasons, safe and effective treatments of stroke are a medical necessity.

Aptamers are an attractive emerging class of therapeutic agents that offer additional safety because their activity can be reversed with administration of a complimentary oligonucleotide. Accordingly, I hypothesized that aptamers could be used to treat acute ischemic stroke. First, an antithrombotic aptamer previously generated against coagulation factor IXa was used in a murine model of middle cerebral artery occlusion. Upon factor IXa aptamer administration following stroke, neurological function and inflammatory profiles were improved. Moreover, mice previously treated with the aptamer, followed by induction of subarachnoid hemorrhage, had severe mortality levels and hemorrhage grades that were mitigated by administration of the aptamer's matched antidote.

Second, I generated aptamers against the antifibrinolytic protein plasminogen activator inhibitor-1 (PAI-1), under the hypothesis that aptamer inhibition of PAI-1 would result in a reversible thrombolytic agent. However, after further testing, the aptamers were not found to disrupt the interaction between PAI-1 and its target proteases. Instead, the aptamers were shown to prevent PAI-1 binding to vitronectin, which translated to restoration of breast cancer cell adhesion in an environment of PAI-1 mediated detachment.

Therefore, aptamer inhibition of factor IXa has demonstrated efficacy in improving outcome following stroke, and should life-threatening hemorrhage arise, an antidote specific to the interventional agent is able to decrease not only hemorrhage grade, but also mortality. This may result in a safer stroke therapy, while a novel aptamer generated against PAI-1 may have application as an antimetastatic agent, which could be used as adjuvant therapy to traditional breast cancer treatment.

RNA is a critical macromolecule in many biological processes by encoding both structural and genetic information. It can serve as the physical template for ribosome read-through during protein synthesis and the intermediary interfering gene expression. For example, messenger RNA encodes specific gene sequence, microRNA regulates expression level of the gene, riboswitch controls translation level and RNA splicing, non-coding RNA provides molecular scaffolding for protein recruitment. Undoubtedly, malfunction of cellular RNAs lead to multiple diseases and targeting disease related RNAs has emerged as the new strategy in many drug development campaigns. Indeed, ribosomal RNA has been utilized as the drug target for a long history and fruitful studies on naturally occurred or synthetic ligands were brought to elucidate the mechanism of translation inhibition. It was the past two decades that witnessed growing research on using small-molecule probes to interrogate non-ribosomal RNAs in various disease pathways.RNA molecules bear distinct chemical properties from proteins that make the design of selective and potent chemical probes challenging. The poor chemical diversity of four building units, immensely charged phosphate backbone, shallow and highly hydrophilic binding pocket, dynamic conformations, all combined render a mysterious ligand space to RNA-targeted small molecules that needs further exploration. A deep understanding of privileged chemotypes or physicochemical properties of RNA-targeting ligands will definitely benefit a broad-scope developing novel chemical entities with desired RNA-interfering outcome. In my thesis work, I first applied the computational approach by building the quantitative structure-activity relationship (QSAR) model to predict the binding profiles of a set of biased ligands scaffolding an amiloride core structure against HIV viral RNA elements. The well-performed model predicted the binding parameters of a set of untested molecules and selected the top-ranked one during lead optimization. The study showed the potential of this computational tool in decision-making during synthesis of RNA-targeted ligands. In the following study, we extended the scope of the QSAR study and leveraged the workflow to cater for the context with diverse structures as substrates. We applied explicit algorithms to build the baseline models to allow easy interpretation of binding behaviors of structurally distinct ligands to HIV-1 TAR. The model first time demonstrated molecular factors that contribute to RNA: small molecule recognition, both kinetically and thermodynamically. The general workflow we described will serve as a powerful computational tool to effectively assess underexplored chemical space and guide decision-making for synthesizing RNA-targeted chemical probes. We then bridged our QSAR approach with the generative deep learning model to pursue de novo ligand design to target SARS-CoV-2 frameshifting pseudoknot. The QSAR model that built on the experimentally validated data provided label annotation of the large training sample for deep learning model. A tree graph-based variational auto-encoder was trained to learn the molecular generation process. Annotated label of each training sample was encoded into the continuous latent space where molecules were reduced their dimensionality and projected. Conditions were applied when sampling new entities from the latent space, leading to the new compounds with desired binding properties. The method mentioned here constitutes the first deep learning practice for automatic chemical design against an RNA target and the first-time application of conditional molecular generation via a junction tree-based variational auto-encoder. Overall, the work presented in this thesis explored possibility of data-driven methods such as QSAR studies and deep learning in accelerating ligand discovery for RNA targets. It is anticipated that these workflows will benefit a wide-range studies in understanding and pursuing RNA-centric drug development, yet slight modifications might be needed for tuning into larger data size.

Abstract