A Balanced Secondary Structure Predictor

Secondary structure (SS) refers to the local spatial organization of the polypeptide backbone atoms of a protein. Accurate prediction of SS is a vital clue to resolve the 3D structure of protein. SS has three different components- helix (H), beta (E) and coil (C). Most SS predictors are imbalanced as their accuracy in predicting helix and coil are high, however significantly low in the beta. The objective of this thesis is to develop a balanced SS predictor which achieves good accuracies in all three SS components. We proposed a novel approach to solve this problem by combining a genetic algorithm (GA) with a support vector machine. We prepared two test datasets (CB471 and N295) to compare the performance of our predictors with SPINE X. Overall accuracy of our predictor was 76.4% and 77.2% respectively on CB471 and N295 datasets, while SPINE X gave 76.5% overall accuracy on both test datasets

A Balanced Secondary Structure Predictor

Secondary structure (SS) refers to the local spatial organization of the polypeptide backbone atoms of a protein. Accurate prediction of SS is a vital clue to resolve the 3D structure of protein. SS has three different components- helix (H), beta (E) and coil (C). Most SS predictors are imbalanced as their accuracy in predicting helix and coil are high, however significantly low in the beta. The objective of this thesis is to develop a balanced SS predictor which achieves good accuracies in all three SS components. We proposed a novel approach to solve this problem by combining a genetic algorithm (GA) with a support vector machine. We prepared two test datasets (CB471 and N295) to compare the performance of our predictors with SPINE X. Overall accuracy of our predictor was 76.4% and 77.2% respectively on CB471 and N295 datasets, while SPINE X gave 76.5% overall accuracy on both test datasets

Hsp70 and Hsp40 inhibit an inter-domain interaction necessary for transcriptional activity in the androgen receptor.

Molecular chaperones such as Hsp40 and Hsp70 hold the androgen receptor (AR) in an inactive conformation. They are released in the presence of androgens, enabling transactivation and causing the receptor to become aggregation-prone. Here we show that these molecular chaperones recognize a region of the AR N-terminal domain (NTD), including a FQNLF motif, that interacts with the AR ligand-binding domain (LBD) upon activation. This suggests that competition between molecular chaperones and the LBD for the FQNLF motif regulates AR activation. We also show that, while the free NTD oligomerizes, binding to Hsp70 increases its solubility. Stabilizing the NTD-Hsp70 interaction with small molecules reduces AR aggregation and promotes its degradation in cellular and mouse models of the neuromuscular disorder spinal bulbar muscular atrophy. These results help resolve the mechanisms by which molecular chaperones regulate the balance between AR aggregation, activation and quality control

Robust, Integrated Computational Control of NMR Experiments to Achieve Optimal Assignment by ADAPT-NMR

ADAPT-NMR (Assignment-directed Data collection Algorithm utilizing a Probabilistic Toolkit in NMR) represents a groundbreaking prototype for automated protein structure determination by nuclear magnetic resonance (NMR) spectroscopy. With a [13C,15N]-labeled protein sample loaded into the NMR spectrometer, ADAPT-NMR delivers complete backbone resonance assignments and secondary structure in an optimal fashion without human intervention. ADAPT-NMR achieves this by implementing a strategy in which the goal of optimal assignment in each step determines the subsequent step by analyzing the current sum of available data. ADAPT-NMR is the first iterative and fully automated approach designed specifically for the optimal assignment of proteins with fast data collection as a byproduct of this goal. ADAPT-NMR evaluates the current spectral information, and uses a goal-directed objective function to select the optimal next data collection step(s) and then directs the NMR spectrometer to collect the selected data set. ADAPT-NMR extracts peak positions from the newly collected data and uses this information in updating the analysis resonance assignments and secondary structure. The goal-directed objective function then defines the next data collection step. The procedure continues until the collected data support comprehensive peak identification, resonance assignments at the desired level of completeness, and protein secondary structure. We present test cases in which ADAPT-NMR achieved results in two days or less that would have taken two months or more by manual approaches

A switch element in the autophagy E2 Atg3 mediates allosteric regulation across the lipidation cascade

Autophagy depends on the E2 enzyme, Atg3, functioning in a conserved E1-E2-E3 trienzyme cascade that catalyzes lipidation of Atg8-family ubiquitin-like proteins (UBLs). Molecular mechanisms underlying Atg8 lipidation remain poorly understood despite association of Atg3, the E1 Atg7, and the composite E3 Atg12-Atg5-Atg16 with pathologies including cancers, infections and neurodegeneration. Here, studying yeast enzymes, we report that an Atg3 element we term E123IR (E1, E2, and E3-interacting region) is an allosteric switch. NMR, biochemical, crystallographic and genetic data collectively indicate that in the absence of the enzymatic cascade, the Atg3(E123IR) makes intramolecular interactions restraining Atg3's catalytic loop, while E1 and E3 enzymes directly remove this brace to conformationally activate Atg3 and elicit Atg8 lipidation in vitro and in vivo. We propose that Atg3's E123IR protects the E2 similar to UBL thioester bond from wayward reactivity toward errant nucleophiles, while Atg8 lipidation cascade enzymes induce E2 active site remodeling through an unprecedented mechanism to drive autophagy

Identification of functionally related enzymes by learning-to-rank methods

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes

RNA systems for NMR studies in vitro and in vivo

NMR spectroscopy is an excellent tool to study the structure-function relationship of RNA. Such measurements are usually performed in vitro, which requires large amounts of isotope- labeled sample in high purity and can give access to individual atoms, structure of the molecule and conformational dynamics. This is contrasted by measurements in living cells, where researchers struggle with low signal intensity, line-broadening and rapid sample degradation. In this work, we developed sample preparation methods for NMR studies to expand the range of RNA constructs that are accessible for NMR studies in vitro and in cells. Firstly, we improved yield and purity of in vitro transcription of short RNA constructs by transcribing several repeating target sequences from a tandem template, and cleaving them to the target length with RNase H. This abolishes issues with suboptimal initiation sequences and creates higher purity due to the high sequence-specificity of RNase H guided by a chimeric oligo. We demonstrated the high yield and purity of several such RNA molecules and incorporated the protocol into a workflow for studies of conformational dynamics with relaxation dispersion NMR. Secondly, we demonstrated the site-specific incorporation of a 13 C/15 N-labeled adenosine into a 46 nt RNA molecule with the use of purely enzymatic methods. Such site-specific labeling is an effective approach to overcome resonance overlap in larger RNAs, which can preclude further structural and dynamics studies. We showed the facile production of such a sample and reported on a second conformation which would in a uniformly labeled sample be hidden by overlapping resonances. Lastly, we furthered method development for in-cell NMR methods by exploring transfection strategies, cell culture methods and RNA systems. We adapted a protocol for the production of circular RNA at high concentration in HEK293T cells to generate the first in-cell NMR spectra of intracellular expressed RNAs. Furthermore, we produced the same circular RNAs by in vitro transcription and ligation to assess their improved stability against cellular exonucleases. As circular RNA model systems, we used the fluorescent aptamer Broccoli and a small hairpin RNA, called GUG, which proved useful for relaxation dispersion NMR measurement previously. The expression of both circular constructs at was possible at micromolar concentration in HEK283T cells and both constructs could be transcribed and circularized in vitro. In-cell NMR of the expressed circular RNA did however not yield detectable signals, indicating that either the intracellular concentration is too low, or the location of the expressed RNA precludes free tumbling