Browsing by Author "Morcos, Faruck"
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Item Characterization of C-Ring Component Assembly in Flagellar Motors from Amino Acid Coevolution(Royal Soc) dos Santos, Ricardo Nascimento; Khan, Shahid; Morcos, Faruck; Morcos, FaruckBacterial flagellar motility, an important virulence factor, is energized by a rotary motor localized within the flagellar basal body. The rotor module consists of a large framework (the C-ring), composed of the FliG, FliM and FliN proteins. FliN and FliM contacts the FliG torque ring to control the direction of flagellar rotation. We report that structure-based models constrained only by residue coevolution can recover the binding interface of atomic X-ray dimer complexes with remarkable accuracy (approx. 1 angstrom RMSD). We propose a model for FliM-FliN heterodimerization, which agrees accurately with homologous interfaces as well as in situ cross-linking experiments, and hence supports a proposed architecture for the lower portion of the C- ring. Furthermore, this approach allowed the identification of two discrete and interchangeable homodimerization interfaces between FliM middle domains that agree with experimental measurements and might be associated with C-ring directional switching dynamics triggered upon binding of CheY signal protein. Our findings provide structural details of complex formation at the C-ring that have been difficult to obtain with previous methodologies and clarify the architectural principle that underpins the ultra-sensitive allostery exhibited by this ring assembly that controls the clockwise or counterclockwise rotation of flagella.Item DCA-MOL: A PyMOL Plugin to Analyze Direct Evolutionary Couplings(Amer Chemical Soc, 2019-01-11) Jarmolinska, Aleksandra I.; Zhou, Qin; Sulkowska, Joanna I.; Morcos, Faruck; Zhou, Qin; Morcos, FaruckDirect coupling analysis (DCA) is a statistical modeling framework designed to uncover relevant molecular evolutionary relationships from biological sequences. Although DCA has been successfully used in several applications, mapping and visualizing of evolutionary couplings and direct information to a particular set of molecules requires multiple steps and could be prone to errors. DCA-MOL extends PyMOL functionality to allow users to interactively analyze and visualize coevolutionary residue-residue interactions between contact maps and structures. True positive rates for the top N pairs can be computed and visualized in real-time to evaluate the quality of residue-residue contact predictions. Different types of interactions in monomeric proteins, RNA, molecular interfaces, and protein conformational dynamics as well as multiple protein complexes can be studied efficiently within one application. DCA-MOL is available for download from http://dca-mol.cent.uw.edu.plItem Dimeric Interactions and Complex Formation Using Direct Coevolutionary Couplings(2015-09-04) dos Santos, Ricardo N.; Morcos, Faruck; Jana, Biman; Andricopulo, Adriano D.; Onuchic, Jose N.; Morcos, FaruckWe develop a procedure to characterize the association of protein structures into homodimers using coevolutionary couplings extracted from Direct Coupling Analysis (DCA) in combination with Structure Based Models (SBM). Identification of dimerization contacts using DCA is more challenging than intradomain contacts since direct couplings are mixed with monomeric contacts. Therefore a systematic way to extract dimerization signals has been elusive. We provide evidence that the prediction of homodimeric complexes is possible with high accuracy for all the cases we studied which have rich sequence information. For the most accurate conformations of the structurally diverse dimeric complexes studied the mean and interfacial RMSDs are 1.95 angstrom and 1.44 angstrom, respectively. This methodology is also able to identify distinct dimerization conformations as for the case of the family of response regulators, which dimerize upon activation. The identification of dimeric complexes can provide interesting molecular insights in the construction of large oligomeric complexes and be useful in the study of aggregation related diseases like Alzheimer's or Parkinson's.Item Engineering Repressors with Coevolutionary Cues Facilitates Toggle Switches with a Master Reset(Oxford University Press, 2019-04-24) Dimas, R. P.; Jiang, Xian-Li; Alberto de la Paz, Jose; Morcos, Faruck; Chan, C. T. Y.; Jiang, Xian-Li; Alberto de la Paz, Jose; Morcos, FaruckEngineering allosteric transcriptional repressors containing an environmental sensing module (ESM) and a DNA recognition module (DRM) has the potential to unlock a combinatorial set of rationally designed biological responses. We demonstrated that constructing hybrid repressors by fusing distinct ESMs and DRMs provides a means to flexibly rewire genetic networks for complex signal processing. We have used coevolutionary traits among LacI homologs to develop a model for predicting compatibility between ESMs and DRMs. Our predictions accurately agree with the performance of 40 engineered repressors. We have harnessed this framework to develop a system of multiple toggle switches with a master OFF signal that produces a unique behavior: each engineered biological activity is switched to a stable ON state by different chemicals and returned to OFF in response to a common signal. One promising application of this design is to develop living diagnostics for monitoring multiple parameters in complex physiological environments and it represents one of many circuit topologies that can be explored with modular repressors designed with coevolutionary information. © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.Item Genotypic and Phenotypic Factors Influencing Drug Response in Mexican Patients with Type 2 Diabetes Mellitus(Frontiers Media SA) Sanchez-Ibarra, Hector E.; Reyes-Cortes, Luisa M.; Jiang, Xian-Li; Luna-Aguirre, Claudia M.; Aguirre-Trevino, Dionicio; Morales-Alvarado, Ivan A.; Leon-Cachon, Rafael B.; Lavalle-Gonzalez, Fernando; Morcos, Faruck; Barrera-Saldana, Hugo A.; Jiang, Xian-Li; Morcos, FaruckThe treatment of Type 2 Diabetes Mellitus (T2DM) consists primarily of oral antidiabetic drugs (OADs) that stimulate insulin secretion, such as sulfonylureas (SUs) and reduce hepatic glucose production (e.g., biguanides), among others. The marked inter-individual differences among T2DM patients' response to these drugs have become an issue on prescribing and dosing efficiently. In this study, fourteen polymorphisms selected from Genome-wide association studies (GWAS) were screened in 495 T2DM Mexican patients previously treated with OADs to find the relationship between the presence of these polymorphisms and response to the OADs. Then, a novel association screening method, based on global probabilities, was used to globally characterize important relationships between the drug response to OADs and genetic and clinical parameters, including polymorphisms, patient information, and type of treatment. Two polymorphisms, ABCC8-Ala1369Ser and KCNJ11-Glu23Lys, showed a significant impact on response to SUs. Heterozygous ABCC8-Ala1369Ser variant (A/C) carriers exhibited a higher response to SUs compared to homozygous ABCC8-Ala1369Ser variant (A/A) carriers (p-value = 0.029) and to homozygous wild-type genotypes (C/C) (p- value = 0.012). The homozygous KCNJ11-Glu23Lys variant (C/C) and wild-type (T/T) genotypes had a lower response to SUs compared to heterozygous (C/T) carriers (p-value = 0.039). The screening of OADs response related genetic and clinical factors could help improve the prescribing and dosing of OADs for T2DM patients and thus contribute to the design of personalized treatments.Item Global Pairwise RNA Interaction Landscapes Reveal Core Features of Protein Recognition(Nature Publishing Group) Zhou, Qin; Kunder, Nikesh; De La Paz, Jose Alberto; Lasley, Alexandra E.; Bhat, Vandita D.; Morcos, Faruck; Campbell, Zachary T.; 0000-0002-3768-6996 (Campbell, ZT); Zhou, Qin; Kunder, Nikesh; De La Paz, Jose Alberto; Lasley, Alexandra E.; Bhat, Vandita D.; Morcos, Faruck; Campbell, Zachary T.RNA-protein interactions permeate biology. Transcription, translation, and splicing all hinge on the recognition of structured RNA elements by RNA-binding proteins. Models of RNA-protein interactions are generally limited to short linear motifs and structures because of the vast sequence sampling required to access longer elements. Here, we develop an integrated approach that calculates global pairwise interaction scores from in vitro selection and high-throughput sequencing. We examine four RNA-binding proteins of phage, viral, and human origin. Our approach reveals regulatory motifs, discriminates between regulated and non-regulated RNAs within their native genomic context, and correctly predicts the consequence of mutational events on binding activity. We design binding elements that improve binding activity in cells and infer mutational pathways that reveal permissive versus disruptive evolutionary trajectories between regulated motifs. These coupling landscapes are broadly applicable for the discovery and characterization of protein-RNA recognition at single nucleotide resolution.Item Molecular Beam Epitaxy of La2-xSrxCuO4 Films and Heterostructures(2022-08-01T05:00:00.000Z) Xu, Xiaotao; Shi, Xiaoyan; Morcos, Faruck; Lv, Bing; Lumata, Lloyd; Zakhidov, Anvar A.; Zhang, FanSince 1986, the study of high-temperature superconductivity (HTS) in cuprates has revealed a massive amount of discoveries, such as pseudogap, charge density wave, d-wave superconductivity, etc. These novel states of matter trigger even more unknowns in fundamental science and inspire enormous emergent applications. This dissertation presents our research on the archetypical La2-xSrxCuO4 (LSCO) thin films and heterostructures. Specifically, the research has been driven by several fundamental questions. For example, can we create c-axis Josephson junctions for scientific research and superconductor-based quantum computation? What controls the fundamental behaviors of interface superconductivity? To answer those questions, high-quality crystals are required. Here we utilized and improved the oxide atomic-layer-by-layer molecular beam epitaxy (ALL-MBE) technique to grow atomically smooth cuprate films and heterostructures to answer the proposed research questions. The main results are presented as follows. First, we improved the ALL-MBE growth in several ways to enhance the film quality significantly. Specifically, we studied the thermal annealing of oxide substrates and developed treatment methods for LaSrAlO4(LSAO) and SrTiO3(STO) substrates. Ramp-up rate and annealing temperature are found to be the most critical parameters. We then studied the synthesis of LSCO thin films via the ALL-MBE system. A detailed recipe for the growth of LSCO thin films on LSAO substrates is presented. Unique reflection high energy electron diffraction (RHEED) pattern features are observed in LSCO films. A strategy to monitor the film growth and maintain the correct stoichiometry is developed based on the real-time RHEED feedback. We also investigated the power and stability of ozone oxidation and compiled empirical post-annealing procedures suitable for various doping levels. Substrates and LSCO films were evaluated using atomic force microscopy (AFM) and RHEED. The results indicate that they are atomically perfect with high crystallinity. Mutual inductance (MI) tests reveal that the LSCO films are uniform over the whole sample area with a sharp superconducting transition. Second, LSCO heterostructures and superlattices have been synthesized to study the HTS c-axis Josephson junction and interfacial superconductivity. The method to probe the superconducting dead layer number near the interface is introduced using a series of superlattices. At the LSCO-LSAO interface, MI and transport measurements imply that the first two LSCO layers that are near the LSAO exhibit a substantial suppression of superconductivity, resulting in a barrier that is five layers thick in total. And an overdoped LSCO protective layer is found to be effective against carrier depletion in superconducting layers. Within LSAO barriers, a thickness of 2 unit-cells of LSCO interface superconductor is synthesized. The superconducting transition of the sample is tunable with doping and demonstrates the highest transition temperature of 34 K.Item Post-transcriptional Controls in Nociceptive Signaling(2022-12-01T06:00:00.000Z) Kunder, Nikesh Prakash 1991-; Delk, Nikki; Campbell, Zachary; Dodani, Sheel; Delk, Nikki; Dodani, Sheel; Dodani, Sheel; Palmer, Kelli; Morcos, Faruck; Misra, Jyoti; Palmer, KelliChronic pain is a condition wherein pain continues beyond the completion of the healing process. It is a debilitating condition that diminishes quality of life and is highly prevalent. Persistent pain is characterized by nociceptor plasticity. Dorsal root ganglion (DRG) neurons are responsible for generating nociceptive signals and undergo plasticity changes following injury. These changes are intimately linked to persistent pain. Translational regulation of mRNA permeates pain plasticity. Yet, the identity of translationally regulated mRNA that mediates plasticity is unknown. In this study, we used ribosome profiling to determine the protein landscape of sensory neurons after a brief exposure to the inflammatory mediators, NGF and IL-6. We observed preferential translation of a variety of transcripts. We focused on two immediate early genes, Arc and c-Fos that play a role in neuronal plasticity. These proteins have two very distinct functions: Arc regulates neuroinflammation while c-Fos regulates neuronal excitability. We also observed ribosomal occupancy on long non- coding RNAs as well as uORF utilization in certain mRNA transcripts. Among the various uORF containing transcripts, we identified a novel peptide generated from a uORF present in the 5ˊUTR of Calca mRNA. This short peptide generated from Calca is responsible for nociceptor sensitization via Gq signaling. Finally, we also identified that the 3’UTRs of preferentially translated mRNA contained a U-rich element for a RNA- binding protein called HuR. This protein contributes to nociceptor firing and mechanical hypersensitivity in mice. Our work provides insights into new key players that govern neuronal functions.Item Revealing Protein Networks and Gene-Drug Connectivity in Cancer from Direct Information(Nature Publishing Group, 2018-08-20) Jiang, Xian-Li; Martinez-Ledesma, Emmanuel; Morcos, Faruck; 0000-0003-1697-8575 (Jiang, X-L); Jiang, Xian-Li; Morcos, FaruckThe connection between genetic variation and drug response has long been explored to facilitate the optimization and personalization of cancer therapy. Crucial to the identification of drug response related genetic features is the ability to separate indirect correlations from direct correlations across abundant datasets with large number of variables. Here we analyzed proteomic and pharmacogenomic data in cancer tissues and cell lines using a global statistical model connecting protein pairs, genes and anti-cancer drugs. We estimated this model using direct coupling analysis (DCA), a powerful statistical inference method that has been successfully applied to protein sequence data to extract evolutionary signals that provide insights on protein structure, folding and interactions. We used Direct Information (DI) as a metric of connectivity between proteins as well as gene-drug pairs. We were able to infer important interactions observed in cancer-related pathways from proteomic data and predict potential connectivities in cancer networks. We also identified known and potential connections for anti-cancer drugs and gene mutations using DI in pharmacogenomic data. Our findings suggest that gene-drug connections predicted with direct couplings can be used as a reliable guide to cancer therapy and expand our understanding of the effects of gene alterations on drug efficacies.Item Sequence Co-Evolutionary Information is a Natural Partner to Minimally-Frustrated Models of Biomolecular Dynamics(F1000 Research Ltd, 2016-01-26) Noel, Jeffrey K.; Morcos, Faruck; Onuchic, Jose N.; 16031594600 (Morcos, F); Morcos, FaruckExperimentally derived structural constraints have been crucial to the implementation of computational models of biomolecular dynamics. For example, not only does crystallography provide essential starting points for molecular simulations but also high-resolution structures permit for parameterization of simplified models. Since the energy landscapes for proteins and other biomolecules have been shown to be minimally frustrated and therefore funneled, these structure-based models have played a major role in understanding the mechanisms governing folding and many functions of these systems. Structural information, however, may be limited in many interesting cases. Recently, the statistical analysis of residue co-evolution in families of protein sequences has provided a complementary method of discovering residue-residue contact interactions involved in functional configurations. These functional configurations are often transient and difficult to capture experimentally. Thus, co-evolutionary information can be merged with that available for experimentally characterized low free-energy structures, in order to more fully capture the true underlying biomolecular energy landscape.;Item Synthetic Gene Circuits as Benchmarks for Understanding Biological Networks(December 2023) Kang, Taek; Gogate, Vibhav; Bleris, Leonidas; Prasad, Shalini; Morcos, Faruck; Schmidtke, DavidThe expression of genes is controlled by regulatory networks, which perform fundamental information processing and control mechanisms in a cell. Unraveling and modelling these networks will be indispensable to gain a systems-level understanding of biological organisms and genetically related diseases. With their ability to emulate and interface with naturally occurring networks, synthetic gene networks are powerful tools in this process. Recent advancements in genetic engineering technologies have expanded the possibilities in design and implementation of synthetic networks, offering unprecedented opportunities to examine and perturb their activity in cellular milieu. In this thesis, we present development and characterization of synthetic gene circuits constructed specifically for the purpose of mimicking and monitoring the regulatory strategies in human cells. First, we introduce a reverse engineering pipeline using synthetic gene circuit as a benchmark for biological reverse engineering. We discuss the advantages of this method and how one can engineer the circuits amenable to reverse engineering. Using several synthetic gene circuits, we show that network reconstruction results not only reproduce the benchmark network topologies, but also identifies a novel feature that can be critical towards solving a commonly misidentified topology. Furthermore, we consider the application of using synthetic circuits to characterize a rare and complex gene regulatory motif on its output expression. Specifically, we demonstrate that an intragenic miRNA-mediated output regulation operates as a filter with respect to promoter strength and reduces expression noise. Lastly, we present novel microRNA sensing systems based on CRISPR/Cas systems that offer new opportunities for engineering systems.Item Unraveling and Designing Biomolecular Interactions Using Direct Couplings From Global Probabilistic Models(2019-11-20) Jiang, Xianli; Morcos, FaruckCoevolution plays a fundamental role in determining folding, structure, interactions and functionality of proteins. Structural or functional related residues coevolve during the evolutionary history to maintain similar structures, interactions and functional properties among the same protein families. Direct coupling models for coevolutionary analysis have demonstrated outstanding performances in predicting contacting residues of proteins and thereby have commonly used to predict protein structures and interactions. In this dissertation, a global statistical inference framework, direct coupling analysis (DCA) was used to infer coevolutionary couplings in datasets including protein-protein interactions and protein modular-modular compatibility. The couplings were then used to build different computational models, i.e. a Hamiltonian energy function H(S) and compatibility score C(S). The Hamiltonian energy function successfully predicts the specificity strength of protein-protein interactions in two component systems and proposed a novel cross-talk model between different sets of twocomponent system (TCS), VanRS and CroRS, to explain strain specific antibiotic phenotypes in Enterococcus faecalis. On the other hand, the C(S) score predicts the compatibility between two protein subdomains in terms of allosteric communication and function between DNA-binding and ligand-binding modules originated from different proteins in the LacI protein family. This model facilitates screening out functional hybrids from different LacI homologs used to engineer and rewire the connection between signal sensing and genetic output. The compatibility score is also able to predict the mutational effect for hybrid proteins, aiming to improve the functionality of a hybrid protein. Moreover, the application of DCA framework was extended into nonsequence datasets, including pharmacogenomics and clinicopathological data, for the first time. The study has demonstrated that direct coupling approach could capture important connectivities between gene mutations and drug responses, as well as between different clinicopathological features. The direct coupling approach provides new means in pharmacogenomics and clinicopathological data analysis and thereby offers new insights in personalized medicine.