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Journal articles on the topic 'Protein structure analysis'

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Published: 4 June 2021

Last updated: 6 September 2023

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1

Schulze-Kremer, Steffen, and Ross D. King. "IPSA—Inductive Protein Structure Analysis." "Protein Engineering, Design and Selection" 5, no. 5 (1992): 377–90. http://dx.doi.org/10.1093/protein/5.5.377.

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2

Baek, Mihwa, Masakatsu Kamiya, Taichi Nakazumi, Satoshi Tomisawa, Yasuhiro Kumaki, Takashi Kikukawa, Makoto Demura, Keiichi Kawano, and Tomoyasu Aizawa. "3P011 Structural analysis of antimicrobial peptide CP1 with LPS by NMR(01A. Protein: Structure,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S213. http://dx.doi.org/10.2142/biophys.53.s213_5.

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3

Wang, Zhuo, Yasuo Okuma, Daiske Kasuya, Kaoru Mitsuoka, Yasushi Saeki, and Takuo Yasunaga. "2P010 Structural analysis of the 26S proteasome by cryo-electron microscopy and Single-Particle Analysis(01A. Protein: Structure,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S160. http://dx.doi.org/10.2142/biophys.53.s160_4.

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4

Presta, Leonard. "Protein structure analysis and development of databases." "Protein Engineering, Design and Selection" 2, no. 6 (1989): 395–97. http://dx.doi.org/10.1093/protein/2.6.395.

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5

Gray, Peter M. D., Norman W. Paton, Graham J. L. Kemp, and John E. Fothergill. "An object-oriented database for protein structure analysis." "Protein Engineering, Design and Selection" 3, no. 4 (1990): 235–43. http://dx.doi.org/10.1093/protein/3.4.235.

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6

Nanni, L., S. Mazzara, L. Pattini, and A. Lumini. "Protein classification combining surface analysis and primary structure." Protein Engineering Design and Selection 22, no. 4 (January 10, 2009): 267–72. http://dx.doi.org/10.1093/protein/gzn084.

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7

Taylor, William R., Jaap Heringa, Franck Baud, and Tomas P. Flores. "A Fourier analysis of symmetry in protein structure." Protein Engineering, Design and Selection 15, no. 2 (February 2002): 79–89. http://dx.doi.org/10.1093/protein/15.2.79.

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8

Ohta, Emi, Takuya Muto, Yusuke Kishi, Mariko Yamaguchi, takayoshi Watanabe, Yoichi Yamazaki, Hironari Kamikubo, Takahiro Hohsaka, and mikio Kataoka. "3P029 Analysis of unfolded structure of Staphylococcal nuclease mutants by using FRET(01A. Protein: Structure,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S216. http://dx.doi.org/10.2142/biophys.53.s216_5.

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9

Noble, M. E. M., A. Cleasby, L. N. Johnson, M. R. Egmond, and L. G. J. Frenken. "Analysis of the structure of Pseudomonas glumae lipase." "Protein Engineering, Design and Selection" 7, no. 4 (1994): 559–62. http://dx.doi.org/10.1093/protein/7.4.559.

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10

Balamurugan, B., M. N. A. Md Roshan, B. Shaahul Hameed, K. Sumathi, R. Senthilkumar, A. Udayakumar, K. H. Venkatesh Babu, et al. "PSAP: protein structure analysis package." Journal of Applied Crystallography 40, no. 4 (July 13, 2007): 773–77. http://dx.doi.org/10.1107/s0021889807021875.

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A computing engine, theProtein Structure Analysis Package(PSAP), has been developed to calculate and display various hidden structural and functional features of three-dimensional protein structures. The proposed computing engine has several utilities to enable structural biologists to analyze three-dimensional protein molecules and provides an easy-to-use Web interface to compute and visualize the necessary features dynamically on the client machine. Users need to provide the Protein Data Bank (PDB) identification code or upload three-dimensional atomic coordinates from the client machine. For visualization, the free molecular graphics programsRasMolandJmolare deployed in the computing engine. Furthermore, the computing engine is interfaced with an up-to-date local copy of the PDB. The atomic coordinates are updated every week and hence users can access all the structures available in the PDB. The computing engine is free and is accessible online at http://iris.physics.iisc.ernet.in/psap/.

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11

Akagi, K., T. S. Kodama, Y. Tsujimoto, Y. Kyougoku, and H. Akutsu. "Structure analysis of SMN protein." Seibutsu Butsuri 40, supplement (2000): S24. http://dx.doi.org/10.2142/biophys.40.s24_2.

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12

Shively, John E. "Micromethods for Protein Structure Analysis." Methods 6, no. 3 (September 1994): 207–12. http://dx.doi.org/10.1006/meth.1994.1023.

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13

El Hefnawi, Mahmoud M., Mohamed E. Hasan, Amal Mahmoud, Yehia A. Khidr, Wessam H. El Behaidy, El-sayed A. El-absawy, and Alaa A. Hemeida. "Prediction and Analysis of Three-Dimensional Structure of the p7- Transactivated Protein1 of Hepatitis C Virus." Infectious Disorders - Drug Targets 19, no. 1 (February 4, 2019): 55–66. http://dx.doi.org/10.2174/1871526518666171215123214.

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Background:The p7-transactivated protein1 of Hepatitis C virus is a small integral membrane protein of 127 amino acids, which is crucial for assembly and release of infectious virions. Ab initio or comparative modelling, is an essential tool to solve the problem of protein structure prediction and to comprehend the physicochemical fundamental of how proteins fold in nature.Results:Only one domain (1-127) of p7-transactivated protein1 has been predicted using the systematic in silico approach, ThreaDom. I-TASSER was ranked as the best server for full-length 3-D protein structural predictions of p7-transactivated protein1 where the benchmarked scoring system such as C-score, TM-score, RMSD and Z-score are used to obtain quantitative assessments of the I-TASSER models. Scanning protein motif databases, along with secondary and surface accessibility predictions integrated with post translational modification sites (PTMs) prediction revealed functional and protein binding motifs. Three protein binding motifs (two Asp/Glutamnse, CTNNB1- bd_N) with high sequence conservation and two PTMs prediction: Camp_phospho_site and Myristyl site were predicted using BLOCKS and PROSITE scan. These motifs and PTMs were related to the function of p7-transactivated protein1 protein in inducing ion channel/pore and release of infectious virions. Using SCOP, only one hit matched protein sequence at 71-120 was classified as small proteins and FYVE/PHD zinc finger superfamily.Conclusion:Integrating this information about the p7-transactivated protein1 with SCOP and CATH annotations of the templates facilitates the assignment of structure–function/ evolution relationships to the known and the newly determined protein structures.

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14

Ikeya, Teppei, Peter Güntert, and Yutaka Ito. "Protein Structure Determination in Living Cells." International Journal of Molecular Sciences 20, no. 10 (May 17, 2019): 2442. http://dx.doi.org/10.3390/ijms20102442.

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To date, in-cell NMR has elucidated various aspects of protein behaviour by associating structures in physiological conditions. Meanwhile, current studies of this method mostly have deduced protein states in cells exclusively based on ‘indirect’ structural information from peak patterns and chemical shift changes but not ‘direct’ data explicitly including interatomic distances and angles. To fully understand the functions and physical properties of proteins inside cells, it is indispensable to obtain explicit structural data or determine three-dimensional (3D) structures of proteins in cells. Whilst the short lifetime of cells in a sample tube, low sample concentrations, and massive background signals make it difficult to observe NMR signals from proteins inside cells, several methodological advances help to overcome the problems. Paramagnetic effects have an outstanding potential for in-cell structural analysis. The combination of a limited amount of experimental in-cell data with software for ab initio protein structure prediction opens an avenue to visualise 3D protein structures inside cells. Conventional nuclear Overhauser effect spectroscopy (NOESY)-based structure determination is advantageous to elucidate the conformations of side-chain atoms of proteins as well as global structures. In this article, we review current progress for the structure analysis of proteins in living systems and discuss the feasibility of its future works.

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15

Halaby, D. M., A. Poupon, and J. P. Mornon. "The immunoglobulin fold family: sequence analysis and 3D structure comparisons." Protein Engineering, Design and Selection 12, no. 7 (July 1999): 563–71. http://dx.doi.org/10.1093/protein/12.7.563.

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16

Woodcock, Steve, Jean-Paul Mornon, and Bernard Henrissat. "Detection of secondary structure elements in proteins by hydrophobic cluster analysis." "Protein Engineering, Design and Selection" 5, no. 7 (1992): 629–35. http://dx.doi.org/10.1093/protein/5.7.629.

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17

Milik, M., S. Szalma, and K. A. Olszewski. "Common Structural Cliques: a tool for protein structure and function analysis." Protein Engineering Design and Selection 16, no. 8 (August 1, 2003): 543–52. http://dx.doi.org/10.1093/protein/gzg080.

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18

Kriwacki, Richard, Nichole Reisdorph, and Gary Siuzdak. "Protein structure characterization with mass spectrometry." Spectroscopy 18, no. 1 (2004): 37–47. http://dx.doi.org/10.1155/2004/407960.

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Mass spectrometry is now commonly being used to determine both the primary and higher order structures of proteins. The basis for these investigations lies in the ability of mass analysis techniques to detect changes in protein conformation under differing conditions. These experiments can be conducted on proteins alone (with no modifying substance present) or in combination with proteolytic digestion or chemical modification. In addition to primary structure determination, proteases and chemical modification have long been used as probes of higher order structure, an approach that has been recently rejuvenated with the emergence of highly sensitive and accurate mass analysis techniques. Here, we review the application of proteases as probes of native structure and illustrate key concepts in the combined use of proteolysis, chemical modification, and mass spectrometry. For example, protein mass maps have been used to probe the structure of a protein/protein complex in solution (cell cycle regulatory proteins, p21 and Cdk2). This approach was also used to study the protein/protein complexes that comprise viral capsids, including those of the common cold virus where, in addition to structural information, protein mass mapping revealed mobile features of the viral proteins. Protein mass mapping clearly has broad utility in protein identification and profiling, yet its accuracy and sensitivity is also allowing for further exploration of protein structure and even structural dynamics.

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19

Jeltsch, Albert, Tatjana Sobotta, and Alfred Pingoud. "Structure prediction of the EcoRV DNA methyltransferase based on mutant profiling, secondary structure analysis, comparison with known structures of methyltransferases and isolation of catalytically inactive single mutants." "Protein Engineering, Design and Selection" 9, no. 5 (1996): 413–23. http://dx.doi.org/10.1093/protein/9.5.413.

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20

Shimbo, Itsuki, Rie Nakajima, Shigeyuki Yokoyama, and Koichi Sumikura. "Patent protection for protein structure analysis." Nature Biotechnology 22, no. 1 (January 2004): 109–12. http://dx.doi.org/10.1038/nbt0104-109.

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21

Kim, Jaegil, and Thomas Keyes. "Inherent Structure Analysis of Protein Folding." Journal of Physical Chemistry B 111, no. 10 (March 2007): 2647–57. http://dx.doi.org/10.1021/jp0665776.

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22

Kobayashi, Junji, Deborah Applebaum-Bowden, Klaus A. Dugi, David R. Brown, Vikram S. Kashyap, Catherine Parrott, Cornelio Duarte, Nobuyo Maeda, and Silvia Santamarina-Fojo. "Analysis of Protein Structure-Functionin Vivo." Journal of Biological Chemistry 271, no. 42 (October 18, 1996): 26296–301. http://dx.doi.org/10.1074/jbc.271.42.26296.

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23

Man, Orna, Tal Atarot, Avital Sadot, Tsviya Olender, and Doron Lancet. "From subgenome analysis to protein structure." Current Opinion in Structural Biology 13, no. 3 (June 2003): 353–58. http://dx.doi.org/10.1016/s0959-440x(03)00071-x.

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24

Mayr, Gabriele, Francisco S. Domingues, and Peter Lackner. "Comparative Analysis of Protein Structure Alignments." BMC Structural Biology 7, no. 1 (2007): 50. http://dx.doi.org/10.1186/1472-6807-7-50.

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25

Geula, Shay, Hammad Naveed, Jie Liang, and Varda Shoshan-Barmatz. "Structure-based Analysis of VDAC1 Protein." Journal of Biological Chemistry 287, no. 3 (November 23, 2011): 2179–90. http://dx.doi.org/10.1074/jbc.m111.268920.

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26

kumari, Preeti, Brijesh Tripathi, Rashmi Rashmi, Ajay Narayan Gangopadhyay, Dr S. Shamal Dr. S. Shamal, Tribhuvan Mohan Mohapatra, and Dr Royana Singh. "In-silico modeling of EDNRB Protein Structure and its mutational analysis in Hirschsprung Disease." Paripex - Indian Journal Of Research 3, no. 4 (January 15, 2012): 182–86. http://dx.doi.org/10.15373/22501991/apr2014/59.

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27

Krüttgen, A., J. Grötzinger, G. Kurapkat, J. Weis, R. Simon, M. Thier, M. Schröder, et al. "Human ciliary neurotrophic factor: a structure-function analysis." Biochemical Journal 309, no. 1 (July 1, 1995): 215–20. http://dx.doi.org/10.1042/bj3090215.

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Ciliary neurotrophic factor (CNTF) promotes survival in vitro and in vivo of several neuronal cell types including sensory and motor neurons. The primary structure of CNTF suggests it to be a cytosolic protein with strong similarity to the alpha-helical cytokine family which is characterized by a bundle of four anti-parallel helices. CNTF exerts its activity via complexation with CNTF receptor (CNTF-R). This complex consists of a CNTF-binding protein (CNTF-R) and two proteins important for signal transduction [gp130 and leukaemia inhibitory factor receptor (LIF-R)]. We have shortened the cDNA coding for CNTF at both the 5′ and the 3′ end and expressed the truncated proteins in bacteria. Biological activities of the protein preparations were determined by their ability to induce proliferation of BAF/3 cells that were stably transfected with CNTF-R, gp130 and LIF-R cDNAs. CNTF proteins with 14 amino acid residues removed from the N-terminus were biologically active whereas the removal of 23 amino acids resulted in an inactive protein. In addition, 18 amino acid residues could be removed from the C-terminus of the CNTF protein without apparent loss of bioactivity, but further truncation at the C-terminus yielded biologically inactive proteins. The introduction of two point mutations into the CNTF protein at a site that presumably interacts with one of the two signal-transducing proteins resulted in a CNTF mutant with no measurable bioactivity. In addition, a model of the three-dimensional structure of human CNTF was constructed using the recently established structural co-ordinates of the related cytokine, granulocyte colony-stimulating factor. CD spectra of CNTF together with our mutational analysis and our three-dimensional model fully support the view that CNTF belongs to the family of alpha-helical cytokines. It is expected that our results will facilitate the rational design of CNTF mutants with agonistic or antagonistic properties.

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28

Widjaja, Vianney, Albert Lim, Benedicta Aini, Gabrielle Audrey Gandasasmita, Jeremie Theddy Darmawan, and Arli Aditya Parikesit. "Identification of Uncharacterized Plasmodium falciparum Proteins via In-silico Analysis." BIOEDUSCIENCE 6, no. 2 (August 31, 2022): 198–210. http://dx.doi.org/10.22236/j.bes/628770.

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Background: Numerous types of research on malaria were done over a long period of time but there are still some unknowns. However, it is globally known that malaria is caused by the Plasmodium parasite, mainly and most lethally by Plasmodium falciparum. The purpose of this research is to understand the structure and function of three uncharacterized P. falciparum proteins (PF3D7_1468000, PF3D7_1147400, PF3D7_1351100) using bioinformatic methods in hopes to learn more about malaria. Methods: The three uncharacterized P. falciparum proteins were inserted into Phyre2 for knowing the protein homology, InterPro, and SUPERFAMILY hidden Markov models for understanding the domain annotation, scanprosite for knowing the post-translational modification, Ramachandran plot for protein validation, and Yasara for visualizing the protein. Results: According to the Phyre2 results, the third protein showed the highest confidence and coverage level of 100%, followed by the second protein, and the lowest was the first protein. Interpro and SUPERFAMILY results identified the first protein as WD40 repeat superfamily, the second protein as Cytochrome C subunit II-like, and the third protein as CXXC motif. Scanprosite revealed all sequences possessing protein domains in which the first protein has three protein domains, the second protein has one protein domain, and the third protein has two protein domains. According to the Ramachandran plot, the first and second protein generally has an Î±-helix structure while the third protein has an overall Î²-sheet structure, which differs to some extent from the protein structure visualization. The three protein visualizations exhibited secondary structures and more than 50 amino acid residues for each protein. Conclusion: This research concluded that the second and third uncharacterized proteins (PF3D7_1147400, PF3D7_1351100) could be promising antimalarial drug targets leading to the P. falciparum parasite death.

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29

Zhang, X. J., and B. W. Matthews. "EDPDB: a multifunctional tool for protein structure analysis." Journal of Applied Crystallography 28, no. 5 (October 1, 1995): 624–30. http://dx.doi.org/10.1107/s0021889895001063.

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EDPDB is a Fortran program that simplifies the analysis of protein structure and makes it easy to extract various types of geometrical and biologically relevant information for the molecule both in isolation as well as in its crystallographic context. EDPDB offers a large set of functions by which the user can evaluate, select and manipulate the coordinates of protein structures. Types of calculation available include the determination of solvent accessibility, bond lengths and torsion angles, determination of the van der Waals volume of a group of atoms, determination of the best-fit plane through a set of points, evaluation of crystal contacts between a molecule in a crystal and all symmetry-related molecules, and the determination of `hinge-bending' motion between protein domains. It is also possible to compare different structures, to perform coordinate manipulations and to edit coordinate files. The program augments the graphic analysis of protein structure by allowing the user to construct a simple set of commands that will rapidly screen an entire structure. It may also make special purpose analyses feasible without complicated programming.

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30

Roznowski, Aaron P., and Bentley A. Fane. "Structure-Function Analysis of the ϕX174 DNA-Piloting Protein Using Length-Altering Mutations." Journal of Virology 90, no. 17 (June 29, 2016): 7956–66. http://dx.doi.org/10.1128/jvi.00914-16.

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ABSTRACTAlthough the ϕX174 H protein is monomeric during procapsid morphogenesis, 10 proteins oligomerize to form a DNA translocating conduit (H-tube) for penetration. However, the timing and location of H-tube formation are unknown. The H-tube's highly repetitive primary and quaternary structures made it amenable to a genetic analysis using in-frame insertions and deletions. Length-altered proteins were characterized for the ability to perform the protein's three known functions: participation in particle assembly, genome translocation, and stimulation of viral protein synthesis. Insertion mutants were viable. Theoretically, these proteins would produce an assembled tube exceeding the capsid's internal diameter, suggesting that virions do not contain a fully assembled tube. Lengthened proteins were also used to test the biological significance of the crystal structure. Particles containing H proteins of two different lengths were significantly less infectious than both parents, indicating an inability to pilot DNA. Shortened H proteins were not fully functional. Although they could still stimulate viral protein synthesis, they either were not incorporated into virions or, if incorporated, failed to pilot the genome. Mutant proteins that failed to incorporate contained deletions within an 85-amino-acid segment, suggesting the existence of an incorporation domain. The revertants of shortened H protein mutants fell into two classes. The first class duplicated sequences neighboring the deletion, restoring wild-type length but not wild-type sequence. The second class suppressed an incorporation defect, allowing the use of the shortened protein.IMPORTANCEThe H-tube crystal structure represents the first high-resolution structure of a virally encoded DNA-translocating conduit. It has similarities with other viral proteins through which DNA must travel, such as the α-helical barrel domains of P22 portal proteins and T7 proteins that form tail tube extensions during infection. Thus, the H protein serves as a paradigm for the assembly and function of long α-helical supramolecular structures and nanotubes. Highly repetitive in primary and quaternary structure, they are amenable to structure-function analyses using in-frame insertions and deletions as presented herein.

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31

Yamagishi, Jun-ichi, Hitoshi Kawashima, Noriyuki Matsuo, Mayumi Ohue, Michiko Yamayoshi, Toshikazu Fukui, Hirotada Kotani, Ryuji Furuta, Katsuji Nakano, and Masaaki Yamada. "Mutational analysis of structure—activity relationships in human tumor necrosis factor-alpha." "Protein Engineering, Design and Selection" 3, no. 8 (1990): 713–19. http://dx.doi.org/10.1093/protein/3.8.713.

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32

He, Zhiquan, Chao Zhang, Yang Xu, Shuai Zeng, Jingfen Zhang, and Dong Xu. "MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis." BMC Genomics 15, Suppl 11 (2014): S2. http://dx.doi.org/10.1186/1471-2164-15-s11-s2.

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33

WANG, LU-YONG. "COVARIATION ANALYSIS OF LOCAL AMINO ACID SEQUENCES IN RECURRENT PROTEIN LOCAL STRUCTURES." Journal of Bioinformatics and Computational Biology 03, no. 06 (December 2005): 1391–409. http://dx.doi.org/10.1142/s0219720005001648.

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Local structural information is supposed to be frequently encoded in local amino acid sequences. Previous research only indicated that some local structure positions have specific residue preferences in some particular local structures. However, correlated pairwise replacements for interacting residues in recurrent local structural motifs from unrelated proteins have not been studied systematically. We introduced a new method fusing statistical covariation analysis and local structure-based alignment. Systematic analysis of structure-based multiple alignments of recurrent local structures from unrelated proteins in representative subset of Protein Databank indicates that covarying residue pairs with statistical significance exist in local structural motifs, in particular β-turns and helix caps. These residue pairs are mostly linked through polar functional groups with direct or indirect hydrogen bonding. Hydrophobic interaction is also a major factor in constraining pairwise amino acid residue replacement in recurrent local structures. We also found correlated residue pairs that are not clearly linked with through-space interactions. The physical constrains underlying these covariations are less clear. Overall, covarying residue pairs with statistical significance exist in local structures from unrelated proteins. The existence of sequence covariations in local structural motifs from unrelated proteins indicates that many relics of local relations are still retained in the tertiary structures after protein folding. It supports the notion that some local structural information is encoded in local sequences and the local structural codes could play important roles in determining native state protein folding topology.

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34

PAI, TUN-WEN, RUEI-HSIANG CHANG, CHIEN-MING CHEN, PO-HAN SU, LEE-JYI WANG, KUEN-TSAIR LAY, and KUO-TORNG LAN. "MULTIPLE STRUCTURE ALIGNMENT BASED ON GEOMETRICAL CORRELATION OF SECONDARY STRUCTURE ELEMENTS." New Mathematics and Natural Computation 06, no. 01 (March 2010): 77–95. http://dx.doi.org/10.1142/s1793005710001621.

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Protein structure alignment facilitates the analysis of protein functionality. Through superimposed structures and the comparison of variant components, common or specific features of proteins can be identified. Several known protein families exhibit analogous tertiary structures but divergent primary sequences. These proteins in the same structural class are unable to be aligned by sequence-based methods. The main objective of the present study was to develop an efficient and effective algorithm for multiple structure alignment based on geometrical correlation of secondary structures, which are conserved in evolutionary heritage. The method utilizes mutual correlation analysis of secondary structure elements (SSEs) and selects representative segments as the key anchors for structural alignment. The system exploits a fast vector transformation technique to represent SSEs in vector format, and the mutual geometrical relationship among vectors is projected onto an angle-distance map. Through a scoring function and filtering mechanisms, the best candidates of vectors are selected, and an effective constrained multiple structural alignment module is performed. The correctness of the algorithm was verified by the multiple structure alignment of proteins in the SCOP database. Several protein sets with low sequence identities were aligned, and the results were compared with those obtained by three well-known structural alignment approaches. The results show that the proposed method is able to perform multiple structural alignments effectively and to obtain satisfactory results, especially for proteins possessing low sequence identity.

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35

Nekrasov, Alexei N., Yuri P. Kozmin, Sergey V. Kozyrev, Rustam H. Ziganshin, Alexandre G. de Brevern, and Anastasia A. Anashkina. "Hierarchical Structure of Protein Sequence." International Journal of Molecular Sciences 22, no. 15 (August 3, 2021): 8339. http://dx.doi.org/10.3390/ijms22158339.

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Most non-communicable diseases are associated with dysfunction of proteins or protein complexes. The relationship between sequence and structure has been analyzed for a long time, and the analysis of the sequences organization in domains and motifs remains an actual research area. Here, we propose a mathematical method for revealing the hierarchical organization of protein sequences. The method is based on the pentapeptide as a unit of protein sequences. Employing the frequency of occurrence of pentapeptides in sequences of natural proteins and a special mathematical approach, this method revealed a hierarchical structure in the protein sequence. The method was applied to 24,647 non-homologous protein sequences with sizes ranging from 50 to 400 residues from the NRDB90 database. Statistical analysis of the branching points of the graphs revealed 11 characteristic values of y (the width of the inscribed function), showing the relationship of these multiple fragments of the sequences. Several examples illustrate how fragments of the protein spatial structure correspond to the elements of the hierarchical structure of the protein sequence. This methodology provides a promising basis for a mathematically-based classification of the elements of the spatial organization of proteins. Elements of the hierarchical structure of different levels of the hierarchy can be used to solve biotechnological and medical problems.

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36

A. Rahaman, Siti Nurulnabila, Jastina Mat Yusop, Zeti-Azura Mohamed-Hussein, Wan Mohd Aizat, Kok Lian Ho, Aik-Hong Teh, Jitka Waterman, et al. "Crystal structure and functional analysis of human C1ORF123." PeerJ 6 (September 28, 2018): e5377. http://dx.doi.org/10.7717/peerj.5377.

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Proteins of the DUF866 superfamily are exclusively found in eukaryotic cells. A member of the DUF866 superfamily, C1ORF123, is a human protein found in the open reading frame 123 of chromosome 1. The physiological role of C1ORF123 is yet to be determined. The only available protein structure of the DUF866 family shares just 26% sequence similarity and does not contain a zinc binding motif. Here, we present the crystal structure of the recombinant human C1ORF123 protein (rC1ORF123). The structure has a 2-fold internal symmetry dividing the monomeric protein into two mirrored halves that comprise of distinct electrostatic potential. The N-terminal half of rC1ORF123 includes a zinc-binding domain interacting with a zinc ion near to a potential ligand binding cavity. Functional studies of human C1ORF123 and its homologue in the fission yeast Schizosaccharomyces pombe (SpEss1) point to a role of DUF866 protein in mitochondrial oxidative phosphorylation.

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37

van Staalduinen, Laura, Stefanie Novakowski, and Zongchao Jia. "Structure and Functional Analysis of a Ribosomal Oxygenase." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1408. http://dx.doi.org/10.1107/s205327331408591x.

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The 2-oxoglutarate/Fe(II)-dependent oxygenases (2OG oxygenases) are a large family of proteins that share a similar overall three-dimensional structure and catalyze a diverse array of oxidation reactions. The Jumonji C (JmjC)-domain containing proteins represent an important subclass of the 2OG oxygenase family that typically catalyze protein hydroxylation; however, recently other reactions have been identified, such as tRNA modification. The E. coli gene, ycfD, was predicted to be a JmjC-domain containing protein of unknown function based on primary sequence. Recently YcfD was determined to act as a ribosomal oxygenase, hydroxylating an arginine residue on the 50S ribosomal protein L-16 (RL-16). We have determined the crystal structure of YcfD at 2.7 Å resolution, revealing that YcfD is structurally similar to known JmjC proteins and possesses the characteristic double stranded β-helix fold or cupin domain. Separate from the cupin domain, an additional globular module termed -helical arm mediates dimerization of YcfD. We further have shown that 2-oxoglutarate binds to YcfD using isothermal titration calorimetry and identified R140 and S116 as key 2OG binding residues using mutagenesis which, together with the iron location and structural similarity with other cupin family members, allowed identification of the active site. Structural homology to ribosomal assembly proteins combined with GST-YcfD pull-down of a ribosomal protein and docking of RL-16 to the YcfD active site support the role of YcfD in regulation of bacterial ribosome assembly. Furthermore, overexpression of YcfD is shown to inhibit cell growth signifying a toxic effect on ribosome assembly.

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Nakai, Kenta, Akinori Kidera, and Minoru Kanehisa. "Cluster analysis of amino acid indices for prediction of protein structure and function." "Protein Engineering, Design and Selection" 2, no. 2 (1988): 93–100. http://dx.doi.org/10.1093/protein/2.2.93.

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Gaasterland, Teri. "Strategies for Structural Genomics Target Selection." Scientific World JOURNAL 2 (2002): 67. http://dx.doi.org/10.1100/tsw.2002.33.

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We have developed a protein sequence analysis pipeline that ranks proteins as targets for high throughput structure determination. The ranking is designed to maximize both the biological and informational impact of new 3D protein structures solved through the structural genomics initiative. The analysis system accepts proteins from multiple genomes as input, builds sequence families based on remote homology, identifies families with one or more solved structures, and ranks the remaining families according to criteria designed to maximize structure determination efficacy, increase the likelihood of a novel fold, and maximize the number of new protein structure models that can be built from a solved structure.

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Sakurada, Yoshie, Koichi Matsuo, Shin-ichi Tate, and Kunihiko Gekko. "2P007 Secondary-Structure Analysis of Denatured Proteins by Vacuum-Ultraviolet Circular Dichroism Spectroscopy(29. Protein structure and dynamics (II),Poster Session,Abstract,Meeting Program of EABS & BSJ 2006)." Seibutsu Butsuri 46, supplement2 (2006): S297. http://dx.doi.org/10.2142/biophys.46.s297_3.

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Rainey, Jan K., Larry Fliegel, and Brian D. Sykes. "Strategies for dealing with conformational sampling in structural calculations of flexible or kinked transmembrane peptidesThis paper is one of a selection of papers published in this Special Issue, entitled CSBMCB — Membrane Proteins in Health and Disease." Biochemistry and Cell Biology 84, no. 6 (December 2006): 918–29. http://dx.doi.org/10.1139/o06-178.

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Peptides corresponding to transmembrane (TM) segments from membrane proteins provide a potential route for the determination of membrane protein structure. We have determined that 2 functionally critical TM segments from the mammalian Na+/H+ exchanger display well converged structure in regions separated by break points. The flexibility of these break points results in conformational sampling in solution. A brief review of available NMR structures of helical membrane proteins demonstrates that there are a number of published structures showing similar properties. Such flexibility is likely indicative of kinks in the full-length protein. This minireview focuses on methods and protocols for NMR structure calculation and analysis of peptide structures under conditions of conformational sampling. The methods outlined allow the identification and analysis of structured peptides containing break points owing to conformational sampling and the differentiation between oligomerization and ensemble-averaged observation of multiple peptide conformations.

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Stites, Wesley E. "Protein−Protein Interactions: Interface Structure, Binding Thermodynamics, and Mutational Analysis." Chemical Reviews 97, no. 5 (August 1997): 1233–50. http://dx.doi.org/10.1021/cr960387h.

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Fanelli, Francesca, Angelo Felline, Francesco Raimondi, and Michele Seeber. "Structure network analysis to gain insights into GPCR function." Biochemical Society Transactions 44, no. 2 (April 11, 2016): 613–18. http://dx.doi.org/10.1042/bst20150283.

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G protein coupled receptors (GPCRs) are allosteric proteins whose functioning fundamentals are the communication between the two poles of the helix bundle. Protein structure network (PSN) analysis is one of the graph theory-based approaches currently used to investigate the structural communication in biomolecular systems. Information on system's dynamics can be provided by atomistic molecular dynamics (MD) simulations or coarse grained elastic network models paired with normal mode analysis (ENM–NMA). The present review article describes the application of PSN analysis to uncover the structural communication in G protein coupled receptors (GPCRs). Strategies to highlight changes in structural communication upon misfolding, dimerization and activation are described. Focus is put on the ENM–NMA-based strategy applied to the crystallographic structures of rhodopsin in its inactive (dark) and signalling active (meta II (MII)) states, highlighting changes in structure network and centrality of the retinal chromophore in differentiating the inactive and active states of the receptor.

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Mancini, Tiziana, Rosanna Mosetti, Augusto Marcelli, Massimo Petrarca, Stefano Lupi, and Annalisa D’Arco. "Terahertz Spectroscopic Analysis in Protein Dynamics: Current Status." Radiation 2, no. 1 (February 7, 2022): 100–123. http://dx.doi.org/10.3390/radiation2010008.

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Proteins play a key role in living organisms. The study of proteins and their dynamics provides information about their functionality, catalysis and potential alterations towards pathological diseases. Several techniques are used for studying protein dynamics, e.g., magnetic resonance, fluorescence imaging techniques, mid-infrared spectroscopy and biochemical assays. Spectroscopic analysis, based on the use of terahertz (THz) radiation with frequencies between 0.1 and 15 THz (3–500 cm−1), was underestimated by the biochemical community. In recent years, however, the potential of THz spectroscopy in the analysis of both simple structures, such as polypeptide molecules, and complex structures, such as protein complexes, has been demonstrated. The THz absorption spectrum provides some information on proteins: for small molecules the THz spectrum is dominated by individual modes related to the presence of hydrogen bonds. For peptides, the spectral information concerns their secondary structure, while for complex proteins such as globular proteins and viral glycoproteins, spectra also provide information on collective modes. In this short review, we discuss the results obtained by THz spectroscopy in the protein dynamics investigations. In particular, we will illustrate advantages and applications of THz spectroscopy, pointing out the complementary information it may provide.

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Álvarez, Óscar, Juan Luis Fernández-Martínez, Celia Fernández-Brillet, Ana Cernea, Zulima Fernández-Muñiz, and Andrzej Kloczkowski. "Principal component analysis in protein tertiary structure prediction." Journal of Bioinformatics and Computational Biology 16, no. 02 (April 2018): 1850005. http://dx.doi.org/10.1142/s0219720018500051.

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We discuss applicability of principal component analysis (PCA) for protein tertiary structure prediction from amino acid sequence. The algorithm presented in this paper belongs to the category of protein refinement models and involves establishing a low-dimensional space where the sampling (and optimization) is carried out via particle swarm optimizer (PSO). The reduced space is found via PCA performed for a set of low-energy protein models previously found using different optimization techniques. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. This term is aimed at providing high frequency details in the energy optimization. The goal of this research is to analyze how the dimensionality reduction affects the prediction capability of the PSO procedure. For that purpose, different proteins from the Critical Assessment of Techniques for Protein Structure Prediction experiments were modeled. In all the cases, both the energy of the best decoy and the distance to the native structure have decreased. Our analysis also shows how the predicted backbone structure of native conformation and of alternative low energy states varies with respect to the PCA dimensionality. Generally speaking, the reconstruction can be successfully achieved with 10 principal components and the high frequency term. We also provide a computational analysis of protein energy landscape for the inverse problem of reconstructing structure from the reduced number of principal components, showing that the dimensionality reduction alleviates the ill-posed character of this high-dimensional energy optimization problem. The procedure explained in this paper is very fast and allows testing different PCA expansions. Our results show that PSO improves the energy of the best decoy used in the PCA when the adequate number of PCA terms is considered.

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MAEKI, Masatoshi, Hiroshi YAMAGUCHI, Manabu TOKESHI, and Masaya MIYAZAKI. "Microfluidic Approaches for Protein Crystal Structure Analysis." Analytical Sciences 32, no. 1 (2016): 3–9. http://dx.doi.org/10.2116/analsci.32.3.

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Rao, F., and M. Karplus. "Protein dynamics investigated by inherent structure analysis." Proceedings of the National Academy of Sciences 107, no. 20 (April 30, 2010): 9152–57. http://dx.doi.org/10.1073/pnas.0915087107.

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Hendrickson, Wayne A. "Atomic-level analysis of membrane-protein structure." Nature Structural & Molecular Biology 23, no. 6 (June 2016): 464–67. http://dx.doi.org/10.1038/nsmb.3215.

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Mizuguchi, K., C. M. Deane, T. L. Blundell, M. S. Johnson, and J. P. Overington. "JOY: protein sequence-structure representation and analysis." Bioinformatics 14, no. 7 (August 1, 1998): 617–23. http://dx.doi.org/10.1093/bioinformatics/14.7.617.

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Wilcox, George L., Marius Poliac, and Michael N. Liebman. "Neural network analysis of protein tertiary structure." Tetrahedron Computer Methodology 3, no. 3-4 (January 1990): 191–211. http://dx.doi.org/10.1016/0898-5529(90)90052-a.

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