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- Creators: Yang, Yezhou
- Member of: ASU Electronic Theses and Dissertations
Description
Proteins are a fundamental unit in biology. Although proteins have been extensively studied, there is still much to investigate. The mechanism by which proteins fold into their native state, how evolution shapes structural dynamics, and the dynamic mechanisms of many diseases are not well understood. In this thesis, protein folding is explored using a multi-scale modeling method including (i) geometric constraint based simulations that efficiently search for native like topologies and (ii) reservoir replica exchange molecular dynamics, which identify the low free energy structures and refines these structures toward the native conformation. A test set of eight proteins and three ancestral steroid receptor proteins are folded to 2.7Å all-atom RMSD from their experimental crystal structures. Protein evolution and disease associated mutations (DAMs) are most commonly studied by in silico multiple sequence alignment methods. Here, however, the structural dynamics are incorporated to give insight into the evolution of three ancestral proteins and the mechanism of several diseases in human ferritin protein. The differences in conformational dynamics of these evolutionary related, functionally diverged ancestral steroid receptor proteins are investigated by obtaining the most collective motion through essential dynamics. Strikingly, this analysis shows that evolutionary diverged proteins of the same family do not share the same dynamic subspace. Rather, those sharing the same function are simultaneously clustered together and distant from those functionally diverged homologs. This dynamics analysis also identifies 77% of mutations (functional and permissive) necessary to evolve new function. In silico methods for prediction of DAMs rely on differences in evolution rate due to purifying selection and therefore the accuracy of DAM prediction decreases at fast and slow evolvable sites. Here, we investigate structural dynamics through computing the contribution of each residue to the biologically relevant fluctuations and from this define a metric: the dynamic stability index (DSI). Using DSI we study the mechanism for three diseases observed in the human ferritin protein. The T30I and R40G DAMs show a loss of dynamic stability at the C-terminus helix and nearby regulatory loop, agreeing with experimental results implicating the same regulatory loop as a cause in cataracts syndrome.
ContributorsGlembo, Tyler J (Author) / Ozkan, Sefika B (Thesis advisor) / Thorpe, Michael F (Committee member) / Ros, Robert (Committee member) / Kumar, Sudhir (Committee member) / Shumway, John (Committee member) / Arizona State University (Publisher)
Created2011
Interactions driving the collapse of islet amyloid polypeptide: implications for amyloid aggregation
Description
Human islet amyloid polypeptide (hIAPP), also known as amylin, is a 37-residue intrinsically disordered hormone involved in glucose regulation and gastric emptying. The aggregation of hIAPP into amyloid fibrils is believed to play a causal role in type 2 diabetes. To date, not much is known about the monomeric state of hIAPP or how it undergoes an irreversible transformation from disordered peptide to insoluble aggregate. IAPP contains a highly conserved disulfide bond that restricts hIAPP(1-8) into a short ring-like structure: N_loop. Removal or chemical reduction of N_loop not only prevents cell response upon binding to the CGRP receptor, but also alters the mass per length distribution of hIAPP fibers and the kinetics of fibril formation. The mechanism by which N_loop affects hIAPP aggregation is not yet understood, but is important for rationalizing kinetics and developing potential inhibitors. By measuring end-to-end contact formation rates, Vaiana et al. showed that N_loop induces collapsed states in IAPP monomers, implying attractive interactions between N_loop and other regions of the disordered polypeptide chain . We show that in addition to being involved in intra-protein interactions, the N_loop is involved in inter-protein interactions, which lead to the formation of extremely long and stable β-turn fibers. These non-amyloid fibers are present in the 10 μM concentration range, under the same solution conditions in which hIAPP forms amyloid fibers. We discuss the effect of peptide cyclization on both intra- and inter-protein interactions, and its possible implications for aggregation. Our findings indicate a potential role of N_loop-N_loop interactions in hIAPP aggregation, which has not previously been explored. Though our findings suggest that N_loop plays an important role in the pathway of amyloid formation, other naturally occurring IAPP variants that contain this structural feature are incapable of forming amyloids. For example, hIAPP readily forms amyloid fibrils in vitro, whereas the rat variant (rIAPP), differing by six amino acids, does not. In addition to being highly soluble, rIAPP is an effective inhibitor of hIAPP fibril formation . Both of these properties have been attributed to rIAPP's three proline residues: A25P, S28P and S29P. Single proline mutants of hIAPP have also been shown to kinetically inhibit hIAPP fibril formation. Because of their intrinsic dihedral angle preferences, prolines are expected to affect conformational ensembles of intrinsically disordered proteins. The specific effect of proline substitutions on IAPP structure and dynamics has not yet been explored, as the detection of such properties is experimentally challenging due to the low molecular weight, fast reconfiguration times, and very low solubility of IAPP peptides. High-resolution techniques able to measure tertiary contact formations are needed to address this issue. We employ a nanosecond laser spectroscopy technique to measure end-to-end contact formation rates in IAPP mutants. We explore the proline substitutions in IAPP and quantify their effects in terms of intrinsic chain stiffness. We find that the three proline mutations found in rIAPP increase chain stiffness. Interestingly, we also find that residue R18 plays an important role in rIAPP's unique chain stiffness and, together with the proline residues, is a determinant for its non-amyloidogenic properties. We discuss the implications of our findings on the role of prolines in IDPs.
ContributorsCope, Stephanie M (Author) / Vaiana, Sara M (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Ros, Robert (Committee member) / Lindsay, Stuart M (Committee member) / Ozkan, Sefika B (Committee member) / Arizona State University (Publisher)
Created2013
Description
Single molecule DNA Sequencing technology has been a hot research topic in the recent decades because it holds the promise to sequence a human genome in a fast and affordable way, which will eventually make personalized medicine possible. Single molecule differentiation and DNA translocation control are the two main challenges in all single molecule DNA sequencing methods. In this thesis, I will first introduce DNA sequencing technology development and its application, and then explain the performance and limitation of prior art in detail. Following that, I will show a single molecule DNA base differentiation result obtained in recognition tunneling experiments. Furthermore, I will explain the assembly of a nanofluidic platform for single strand DNA translocation, which holds the promised to be integrated into a single molecule DNA sequencing instrument for DNA translocation control. Taken together, my dissertation research demonstrated the potential of using recognition tunneling techniques to serve as a general readout system for single molecule DNA sequencing application.
ContributorsLiu, Hao (Author) / Lindsay, Stuart M (Committee member) / Yan, Hao (Committee member) / Levitus, Marcia (Committee member) / Arizona State University (Publisher)
Created2013
Description
The photosynthetic reaction center is a type of pigment-protein complex found widely in photosynthetic bacteria, algae and higher plants. Its function is to convert the energy of sunlight into a chemical form that can be used to support other life processes. The high efficiency and structural simplicity make the bacterial reaction center a paradigm for studying electron transfer in biomolecules. This thesis starts with a comparison of the primary electron transfer process in the reaction centers from the Rhodobacter shperoides bacterium and those from its thermophilic homolog, Chloroflexus aurantiacus. Different temperature dependences in the primary electron transfer were found in these two type of reaction centers. Analyses of the structural differences between these two proteins suggested that the excess surface charged amino acids as well as a larger solvent exposure area in the Chloroflexus aurantiacus reaction center could explain the different temperature depenence. The conclusion from this work is that the electrostatic interaction potentially has a major effect on the electron transfer. Inspired by these results, a single point mutant was designed for Rhodobacter shperoides reaction centers by placing an ionizable amino acid in the protein interior to perturb the dielectrics. The ionizable group in the mutation site largely deprotonated in the ground state judging from the cofactor absorption spectra as a function of pH. By contrast, a fast charge recombination assoicated with protein dielectric relaxation was observed in this mutant, suggesting the possibility that dynamic protonation/deprotonation may be taking place during the electron transfer. The fast protein dielectric relaxation occuring in this mutant complicates the electron transfer pathway and reduces the yield of electron transfer to QA. Considering the importance of the protein dielectric environment, efforts have been made in quantifying variations of the internal field during charge separation. An analysis protocol based on the Stark effect of reaction center cofactor spectra during charge separation has been developed to characterize the charge-separated radical field acting on probe chromophores. The field change, monitored by the dynamic Stark shift, correlates with, but is not identical to, the electron transfer kinetics. The dynamic Stark shift results have lead to a dynamic model for the time-dependent dielectric that is complementary to the static dielectric asymmetry observed in past steady state experiments. Taken together, the work in this thesis emphasizes the importance of protein electrostatics and its dielectric response to electron transfer.
ContributorsGuo, Zhi (Author) / Woodbury, Neal W (Thesis advisor) / Lindsay, Stuart M (Committee member) / Ross, Robert (Committee member) / Ozkan, Banu S (Committee member) / Moore, Thomas A. (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation describes work on three projects concerning the design and implementation of instrumentation used to study potential organic electronic devices. The first section describes the conducting atomic force microscope (CAFM) in the study of the mechanical and electronic interactions between DNA bases and nucleosides. Previous STM data suggested that an STM tip could recognize single base pairs through an electronic interaction after a functionalized tip made contact with a self assembled monolayer then was retracted. The conducting AFM was employed in order to understand the mechanical interactions of such a system and how they were affecting electrical responses. The results from the conducting AFM showed that the scanning probe system was measuring multiple base-pair interactions, and thus did not have single base resolution. Further, results showed that the conductance between a single base-nucleoside pair is below the detection limit of a potential commercial sequencing device. The second section describes the modifications of a scanning probe microscope in order to study the conductance of single organic molecules under illumination. Modifications to the scanning probe microscope are described as are the control and data analysis software for an experiment testing the single molecule conductance of an organic molecule under illumination. This instrument was then tested using a novel charge-separation molecule, which is being considered for its potential photovoltaic properties. The experiments showed that the instrumentation is capable of detecting differences in conductance upon laser illumination of the molecule on a transparent conductive surface. The third section describes measurements using the illuminated CAFM, as well as the design and construction of an illuminated mercury drop electrode apparatus. Both instruments were tested by attempting to observe photovoltaic behavior in a novel self-organized film of the charge-separation molecules mentioned in the previous paragraph. Results and calculations show that the conducting AFM is not a useful tool in the examination of these organic photovoltaics, while the mercury drop apparatus measured photovoltaic effects in the film. Although photovoltaic effects were measurable with the mercury drop electrode, it was found that the film exhibited very low photon-to-electron conversion efficiency (IPCE).
ContributorsKibel, Ashley Ann (Author) / Lindsay, Stuart M (Thesis advisor) / Chamberlin, Ralph (Committee member) / Moore, Thomas (Committee member) / Ozkan, Sefika (Committee member) / Sankey, Otto (Committee member) / Arizona State University (Publisher)
Created2010
Description
In eukaryotes, DNA is packed in a highly condensed and hierarchically organized structure called chromatin, in which DNA tightly wraps around the histone octamer consisting of one histone 3-histone 4 (H3-H4) tetramer and two histone 2A- histone 2B (H2A-H2B) dimers with 147 base pairs in an almost two left handed turns. Almost all DNA dependent cellular processes, such as DNA duplication, transcription, DNA repair and recombination, take place in the chromatin form. Based on the critical importance of appropriate chromatin condensation, this thesis focused on the folding behavior of the nucleosome array reconstituted using different templates with various controllable factors such as histone tail modification, linker DNA length, and DNA binding proteins. Firstly, the folding behaviors of wild type (WT) and nucleosome arrays reconstituted with acetylation on the histone H4 at lysine 16 (H4K16 (Ac)) were studied. In contrast to the sedimentation result, atomic force microscopy (AFM) measurements revealed no apparent difference in the compact nucleosome arrays between WT and H4K16 (Ac) and WT. Instead, an optimal loading of nucleosome along the template was found necessary for the Mg2+ induced nucleosome array compaction. This finding leads to the further study on the role of linker DNA in the nucleosome compaction. A method of constructing DNA templates with varied linker DNA lengths was developed, and uniformly and randomly spaced nucleosome arrays with average linker DNA lengths of 30 bp and 60 bp were constructed. After comprehensive analyses of the nucleosome arrays' structure in mica surface, the lengths of the linker DNA were found playing an important role in controlling the structural geometries of nucleosome arrays in both their extended and compact forms. In addition, higher concentration of the DNA binding domain of the telomere repeat factor 2 (TRF2) was found to stimulate the compaction of the telomeric nucleosome array. Finally, AFM was successfully applied to investigate the nucleosome positioning behaviors on the Mouse Mammary Tumor Virus (MMTV) promoter region, and two highly positioned region corresponded to nucleosome A and B were identified by this method.
ContributorsFu, Qiang (Author) / Lindsay, Stuart M (Thesis advisor) / Yan, Hao (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2010
Description
Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently. Inspired by these language models, this work focuses on learning embedding spaces for two visual computing tasks, 1. Image Hashing 2. Zero Shot Learning. The training set was used to learn embedding spaces over which similarity/dissimilarity is measured using several distance metrics like hamming / euclidean / cosine distances. While the above-mentioned language models learn generic word embeddings, in this work task specific embeddings were learnt which can be used for Image Retrieval and Classification separately.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
ContributorsGattupalli, Jaya Vijetha (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2019
Description
With the substantial development of intelligent robots, human-robot interaction (HRI) has become ubiquitous in applications such as collaborative manufacturing, surgical robotic operations, and autonomous driving. In all these applications, a human behavior model, which can provide predictions of human actions, is a helpful reference that helps robots to achieve intelligent interaction with humans. The requirement elicits an essential problem of how to properly model human behavior, especially when individuals are interacting or cooperating with each other. The major objective of this thesis is to utilize the human intention decoding method to help robots enhance their performance while interacting with humans. Preliminary work on integrating human intention estimation with an HRI scenario is shown to demonstrate the benefit. In order to achieve this goal, the research topic is divided into three phases. First, a novel method of an online measure of the human's reliance on the robot, which can be estimated through the intention decoding process from human actions，is described. An experiment that requires human participants to complete an object-moving task with a robot manipulator was conducted under different conditions of distractions. A relationship is discovered between human intention and trust while participants performed a familiar task with no distraction. This finding suggests a relationship between the psychological construct of trust and joint physical coordination, which bridges the human's action to its mental states. Then, a novel human collaborative dynamic model is introduced based on game theory and bounded rationality, which is a novel method to describe human dyadic behavior with the aforementioned theories. The mutual intention decoding process was also considered to inform this model. Through this model, the connection between the mental states of the individuals to their cooperative actions is indicated. A haptic interface is developed with a virtual environment and the experiments are conducted with 30 human subjects. The result suggests the existence of mutual intention decoding during the human dyadic cooperative behaviors. Last, the empirical results show that allowing agents to have empathy in inference, which lets the agents understand that others might have a false understanding of their intentions, can help to achieve correct intention inference. It has been verified that knowledge about vehicle dynamics was also important to correctly infer intentions. A new courteous policy is proposed that bounded the courteous motion using its inferred set of equilibrium motions. A simulation, which is set to reproduce an intersection passing case between an autonomous car and a human driving car, is conducted to demonstrate the benefit of the novel courteous control policy.
ContributorsWang, Yiwei (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Ren, Yi (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021