• Title/Summary/Keyword: protein structures

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The C-terminal Region of Human Tau Protein with Ability of Filament Formation

  • Chung, Sang-Ho
    • Animal cells and systems
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    • v.1 no.2
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    • pp.317-321
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    • 1997
  • Tau protein is one of the microtubule-associated proteins in the mammalian brain. In Alzheimer's disease, tau protein is immobilized in the somatodendritic compartment of certain nerve cells, where it forms a part of the paired helical filament (PHF). To understand the role of tau protein in the formation of PHF, a recombinant human tau protein expressed in Escherichia coli and five synthetic peptide fragments (peptide 1 to peptide 5), corresponding to the C-terminal region of tau protein, were prepared and their ability in self-assembly to form filamentous structures was examined. The recombinant human tau protein formed short rod-like structures in 0.1M MES buffer containing 1 mM $MgCI_2$, while a synthetic peptide fragment 1 containing 55 amino acid residues could assemble into a lot of long filamentous structures in water and particularly twisted helical structures in 0.1M MES buffer containing 1 mM $MgCI_2$. This suggests that the C-terminal region possesses a filament-forming ability and may be related to the formation of the helical structure by providing a powerful filament-forming driving force.

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A bioinformatics approach to characterize a hypothetical protein Q6S8D9_SARS of SARS-CoV

  • Md Foyzur Rahman;Rubait Hasan;Mohammad Shahangir Biswas;Jamiatul Husna Shathi;Md Faruk Hossain;Aoulia Yeasmin;Mohammad Zakerin Abedin;Md Tofazzal Hossain
    • Genomics & Informatics
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    • v.21 no.1
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    • pp.3.1-3.10
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    • 2023
  • Characterization as well as prediction of the secondary and tertiary structure of hypothetical proteins from their amino acid sequences uploaded in databases by in silico approach are the critical issues in computational biology. Severe acute respiratory syndrome-associated coronavirus (SARS-CoV), which is responsible for pneumonia alike diseases, possesses a wide range of proteins of which many are still uncharacterized. The current study was conducted to reveal the physicochemical characteristics and structures of an uncharacterized protein Q6S8D9_SARS of SARS-CoV. Following the common flowchart of characterizing a hypothetical protein, several sophisticated computerized tools e.g., ExPASy Protparam, CD Search, SOPMA, PSIPRED, HHpred, etc. were employed to discover the functions and structures of Q6S8D9_SARS. After delineating the secondary and tertiary structures of the protein, some quality evaluating tools e.g., PROCHECK, ProSA-web etc. were performed to assess the structures and later the active site was identified also by CASTp v.3.0. The protein contains more negatively charged residues than positively charged residues and a high aliphatic index value which make the protein more stable. The 2D and 3D structures modeled by several bioinformatics tools ensured that the proteins had domain in it which indicated it was functional protein having the ability to trouble host antiviral inflammatory cytokine and interferon production pathways. Moreover, active site was found in the protein where ligand could bind. The study was aimed to unveil the features and structures of an uncharacterized protein of SARS-CoV which can be a therapeutic target for development of vaccines against the virus. Further research are needed to accomplish the task.

Computational Approaches for Structural and Functional Genomics

  • Brenner, Steven-E.
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2000.11a
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    • pp.17-20
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    • 2000
  • Structural genomics aims to provide a good experimental structure or computational model of every tractable protein in a complete genome. Underlying this goal is the immense value of protein structure, especially in permitting recognition of distant evolutionary relationships for proteins whose sequence analysis has failed to find any significant homolog. A considerable fraction of the genes in all sequenced genomes have no known function, and structure determination provides a direct means of revealing homology that may be used to infer their putative molecular function. The solved structures will be similarly useful for elucidating the biochemical or biophysical role of proteins that have been previously ascribed only phenotypic functions. More generally, knowledge of an increasingly complete repertoire of protein structures will aid structure prediction methods, improve understanding of protein structure, and ultimately lend insight into molecular interactions and pathways. We use computational methods to select families whose structures cannot be predicted and which are likely to be amenable to experimental characterization. Methods to be employed included modern sequence analysis and clustering algorithms. A critical component is consultation of the presage database for structural genomics, which records the community's experimental work underway and computational predictions. The protein families are ranked according to several criteria including taxonomic diversity and known functional information. Individual proteins, often homologs from hyperthermophiles, are selected from these families as targets for structure determination. The solved structures are examined for structural similarity to other proteins of known structure. Homologous proteins in sequence databases are computationally modeled, to provide a resource of protein structure models complementing the experimentally solved protein structures.

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Minimally Complex Problem Set for an Ab initio Protein Structure Prediction Study

  • Kim RyangGug;Choi Cha-Yong
    • Biotechnology and Bioprocess Engineering:BBE
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    • v.9 no.5
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    • pp.414-418
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    • 2004
  • A 'minimally complex problem set' for ab initio protein Structure prediction has been proposed. As well as consisting of non-redundant and crystallographically determined high-resolution protein structures, without disulphide bonds, modified residues, unusual connectivities and heteromolecules, it is more importantly a collection of protein structures. with a high probability of being the same in the crystal form as in solution. To our knowledge, this is the first attempt at this kind of dataset. Considering the lattice constraint in crystals, and the possible flexibility in solution of crystallographically determined protein structures, our dataset is thought to be the safest starting points for an ab initio protein structure prediction study.

Refinement of protein NMR structures using atomistic force field and implicit solvent model: Comparison of the accuracies of NMR structures with Rosetta refinement

  • Jee, Jun-Goo
    • Journal of the Korean Magnetic Resonance Society
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    • v.26 no.1
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    • pp.1-9
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    • 2022
  • There are two distinct approaches to improving the quality of protein NMR structures during refinement: all-atom force fields and accumulated knowledge-assisted methods that include Rosetta. Mao et al. reported that, for 40 proteins, Rosetta increased the accuracies of their NMR-determined structures with respect to the X-ray crystal structures (Mao et al., J. Am. Chem. Soc. 136, 1893 (2014)). In this study, we calculated 32 structures of those studied by Mao et al. using all-atom force field and implicit solvent model, and we compared the results with those obtained from Rosetta. For a single protein, using only the experimental NOE-derived distances and backbone torsion angle restraints, 20 of the lowest energy structures were extracted as an ensemble from 100 generated structures. Restrained simulated annealing by molecular dynamics simulation searched conformational spaces with a total time step of 1-ns. The use of GPU-accelerated AMBER code allowed the calculations to be completed in hours using a single GPU computer-even for proteins larger than 20 kDa. Remarkably, statistical analyses indicated that the structures determined in this way showed overall higher accuracies to their X-ray structures compared to those refined by Rosetta (p-value < 0.01). Our data demonstrate the capability of sophisticated atomistic force fields in refining NMR structures, particularly when they are coupled with the latest GPU-based calculations. The straightforwardness of the protocol allows its use to be extended to all NMR structures.

Regiospecific Protein Perturbation on F NMR Shifts and Photoisomerization of Fluororhodopsins. An Interpretation Based on Recent Crystal Structures of Rhodopsin

  • Colmenares, Letica U.;Liu, Robert S.H.
    • Journal of Photoscience
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    • v.10 no.1
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    • pp.81-87
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    • 2003
  • Based on structural information provided by recently reported crystal structures of rhodopsin, we present rationales for the regiospecific protein perturbation on the previously reported $\^$19/F chemical shifts of the vinyl and trifluoromethylrhodopsins and their photoproducts. The crystal structures also suggest that H-bonding is a likely cause for the earlier reported regiospecific photoisomerization of the 10-fluororhodopsins. Photoisomerization was revealed by chemical shift of the photoproducts. Additionally, possible use of 3-bond F,F coupling constants for following photoisomerization of retinal-binding proteins is discussed.

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Simulation Methods for Prediction of Membrane Protein Structure

  • Son, Hyeon-S.
    • Proceedings of the Korean Biophysical Society Conference
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    • 1998.06a
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    • pp.10-10
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    • 1998
  • IMPs are important to cells in functions such as transport, energy transduction and signalling. Three dimensional molecular structures of such proteins at atomic level are needed to understand such processes. Prediction of such structures (and functions) is necessary especially because there are only a small number of membrane protein structures determined in atomic resolution.(omitted)

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Sport impact on the strength of the nanoscale protein tissues under the thermal condition

  • Xin, Fang;Mengqian, Hou
    • Advances in nano research
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    • v.13 no.6
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    • pp.561-574
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    • 2022
  • The stability of protein tissues and protein fibers in the human muscle is investigated in the presented paper. The protein fibers are modeled via tube structures embedded in others proteins fibers like the elastic substrate. Physical sport and physical exercise play an important role in the stability of synthesis and strength of the protein tissues. In physical exercise, the temperature of the body increases, and this temperature change impacts the stability of the protein tissues, which is the aim of the current study. The mathematical simulation of the protein tissues is done based on the mechanical sciences, and the protein fibers are modeled via wire structures according to the high-order theory beams. The thermal stress due to the conditions of the sport is applied to the nanoscale protein fibers, then the stability regarding the frequency analysis is investigated. Finally, the impact of temperature change, physical exercise, and small-scale parameters on the stability of the protein tissues are examined in detail.

A Protein Structure Comparison System based on PSAML (PSAML을 이용한 단백질 구조 비고 시스템)

  • Kim Jin-Hong;Ahn Geon-Tae;Byun Sang-Hee;Lee Su-Hyun;Lee Myung-Joon
    • Journal of KIISE:Computing Practices and Letters
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    • v.11 no.2
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    • pp.133-148
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    • 2005
  • Since understanding of similarities and differences among protein structures is very important for the study of the relationship between structure and function, many protein structure comparison systems have been developed. Hut, unfortunately, these systems introduce their own protein data derived from the PDB(Protein Data Bank), which are needed in their algorithms for comparing protein structures. In addition, according to the rapid increase in the size of PDB, these systems require much more computation to search for common substructures in their databases. In this paper, we introduce a protein structure comparison system named WS4E(A Web-Based Searching Substructures of Secondary Structure Elements) based on a PSAML database which stores PSAML documents using the eXist open XML DBMS. PSAML(Protein Structure Abstraction Markup Language) is an XML representation of protein data, describing a protein structure as the secondary structures of the protein and their relationships. Using the PSAML database, the WS4E provides web services searching for common substructures among proteins represented in PSAML. In addition, to reduce the number of candidate protein structures to be compared in the PSAML database, we used topology strings which contain the spatial information of secondary structures in a protein.

Structural Features of β2 Adrenergic Receptor: Crystal Structures and Beyond

  • Bang, Injin;Choi, Hee-Jung
    • Molecules and Cells
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    • v.38 no.2
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    • pp.105-111
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    • 2015
  • The beta2-adrenergic receptor (${\beta}2AR$) belongs to the G protein coupled receptor (GPCR) family, which is the largest family of cell surface receptors in humans. Extra attention has been focused on the human GPCRs because they have been studied as important protein targets for pharmaceutical drug development. In fact, approximately 40% of marketed drugs directly work on GPCRs. GPCRs respond to various extracellular stimuli, such as sensory signals, neurotransmitters, chemokines, and hormones, to induce structural changes at the cytoplasmic surface, activating downstream signaling pathways, primarily through interactions with heterotrimeric G proteins or through G-protein independent pathways, such as arrestin. Most GPCRs, except for rhodhopsin, which contains covalently linked 11 cis-retinal, bind to diffusible ligands, having various conformational states between inactive and active structures. The first human GPCR structure was determined using an inverse agonist bound ${\beta}2AR$ in 2007 and since then, more than 20 distinct GPCR structures have been solved. However, most GPCR structures were solved as inactive forms, and an agonist bound fully active structure is still hard to obtain. In a structural point of view, ${\beta}2AR$ is relatively well studied since its fully active structure as a complex with G protein as well as several inactive structures are available. The structural comparison of inactive and active states gives an important clue in understanding the activation mechanism of ${\beta}2AR$. In this review, structural features of inactive and active states of ${\beta}2AR$, the interaction of ${\beta}2AR$ with heterotrimeric G protein, and the comparison with ${\beta}1AR$ will be discussed.