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Journal articles on the topic 'Protein families'

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Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Shewry, P., J. Jenkins, S. Griffiths-Jones, F. Beaudoin, and C. Mills. "Plant protein families." Biochemical Society Transactions 30, no. 5 (October 1, 2002): A107. http://dx.doi.org/10.1042/bst030a107b.

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2

Orengo, C. A., T. P. Flores, W. R. Taylor, and J. M. Thornton. "Identification and classification of protein fold families." "Protein Engineering, Design and Selection" 6, no. 5 (1993): 485–500. http://dx.doi.org/10.1093/protein/6.5.485.

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3

Chothia, Cyrus. "Protein families in the metazoan genome." Development 1994, Supplement (January 1, 1994): 27–33. http://dx.doi.org/10.1242/dev.1994.supplement.27.

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The evolution of development involves the development of new proteins. Estimates based on the initial results of the genome projects, and on the data banks of protein sequences and structures, suggest that the large majority of proteins come from no more than one thousand families. Members of a family are descended from a common ancestor. Protein families evolve by gene duplication and mutation. Mutations change the conformation of the peripheral regions of proteins; i.e. the regions that are involved, at least in part, in their function. If mutations proceed until only 20% of the residues in related proteins are identical, it is common for the conformational changes to affect half the structure. Most of the proteins involved in the interactions of cells, and in their assembly to form multicellular organisms, are mosaic proteins. These are large and have a modular structure, in that they are built of sets of homologous domains that are drawn from a relatively small number of protein families. Patthy's model for the evolution of mosaic proteins describes how they arose through the insertion of introns into genes, gene duplications and intronic recombination. The rates of progress in the genome sequencing projects, and in protein structure analyses, means that in a few years we will have a fairly complete outline description of the molecules responsible for the structure and function of organisms at several different levels of developmental complexity. This should make a major contribution to our understanding of the evolution of development.

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4

Altschuh, D., T. Vernet, P. Berti, D. Moras, and K. Nagai. "Coordinated amino acid changes in homologous protein families." "Protein Engineering, Design and Selection" 2, no. 3 (1988): 193–99. http://dx.doi.org/10.1093/protein/2.3.193.

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5

Hassell, J. R., J. H. Kimura, and V. C. Hascall. "Proteoglycan Core Protein Families." Annual Review of Biochemistry 55, no. 1 (June 1986): 539–67. http://dx.doi.org/10.1146/annurev.bi.55.070186.002543.

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6

Holm, Liisa. "Unification of protein families." Current Opinion in Structural Biology 8, no. 3 (June 1998): 372–79. http://dx.doi.org/10.1016/s0959-440x(98)80072-9.

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7

Buljan, Marija, and Alex Bateman. "The evolution of protein domain families." Biochemical Society Transactions 37, no. 4 (July 22, 2009): 751–55. http://dx.doi.org/10.1042/bst0370751.

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Protein domains are the common currency of protein structure and function. Over 10000 such protein families have now been collected in the Pfam database. Using these data along with animal gene phylogenies from TreeFam allowed us to investigate the gain and loss of protein domains. Most gains and losses of domains occur at protein termini. We show that the nature of changes is similar after speciation or duplication events. However, changes in domain architecture happen at a higher frequency after gene duplication. We suggest that the bias towards protein termini is largely because insertion and deletion of domains at most positions in a protein are likely to disrupt the structure of existing domains. We can also use Pfam to trace the evolution of specific families. For example, the immunoglobulin superfamily can be traced over 500 million years during its expansion into one of the largest families in the human genome. It can be shown that this protein family has its origins in basic animals such as the poriferan sponges where it is found in cell-surface-receptor proteins. We can trace how the structure and sequence of this family diverged during vertebrate evolution into constant and variable domains that are found in the antibodies of our immune system as well as in neural and muscle proteins.

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8

Boberg, Jorma, Tapio Salakoski, and Mauno Vihinen. "Representative selection of proteins based on nuclear families." "Protein Engineering, Design and Selection" 8, no. 5 (1995): 501–3. http://dx.doi.org/10.1093/protein/8.5.501.

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9

Liang, Ping, Bernard Labedan, and Monica Riley. "Physiological genomics of Escherichia coli protein families." Physiological Genomics 9, no. 1 (April 10, 2002): 15–26. http://dx.doi.org/10.1152/physiolgenomics.00086.2001.

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The well-researched Escherichia coli genome offers the opportunity to explore the value of using protein families within a single organism to enrich functional annotation procedures and to study mechanisms of protein evolution. Having identified multimodular proteins resulting from gene fusion, and treated each module as a separate protein, nonoverlapping sequence-similar families in E. coli could be assembled. Of 3,902 proteins of length 100 residues or more, 2,415 clustered into 609 protein families. The relatedness of function among members of each family was dissected in detail. Data on paralogous protein families provides valuable information in attributing putative function to unknown genes, supplementing existing function annotation. Enzymes, transporters, and regulators represent the three major types of proteins in E. coli. They are shown to have distinctive patterns in gene duplication and divergence and gene fusion, suggesting that details of protein evolution have been different for genes in these categories. Data for the complete list of paralogous protein families and updated functional annotation for E. coli K-12 are accessible in GenProtEC ( http://genprotec.mbl.edu ).

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10

Kumari, Rakhi, Nivedita Deo, and Pradeep Bhadola. "Random Matrix Analysis of Protein Families." ECS Transactions 107, no. 1 (April 24, 2022): 18877–91. http://dx.doi.org/10.1149/10701.18877ecst.

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Proteins are vital for almost all biochemical and cellular processes. Although there is an enormous growth in the protein sequence data, the statistical characterization, structure, and function of many of these sequences are still unknown. The statistical and spectral analysis of the Pearson correlation matrices between positions based on physiochemical properties of amino acids of seven protein families is performed and compared with the random Wishart matrix model results. A detailed analysis shows that the protein families significantly diverge from the Marcenko-Pastur distribution with many eigenvalues (outliers) outside the Wishart lower and upper bound. It is shown that level spacing distribution of protein families is similar to the Gaussian orthogonal ensemble. Further, the number variance varies as log of the system size indicating the presence of long range correlations within the protein families.

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11

Bateman, A. "The Pfam protein families database." Nucleic Acids Research 32, no. 90001 (January 1, 2004): 138D—141. http://dx.doi.org/10.1093/nar/gkh121.

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12

Finn, Robert D., Alex Bateman, Jody Clements, Penelope Coggill, Ruth Y. Eberhardt, Sean R. Eddy, Andreas Heger, et al. "Pfam: the protein families database." Nucleic Acids Research 42, no. D1 (November 27, 2013): D222—D230. http://dx.doi.org/10.1093/nar/gkt1223.

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13

Bateman, A. "The Pfam Protein Families Database." Nucleic Acids Research 30, no. 1 (January 1, 2002): 276–80. http://dx.doi.org/10.1093/nar/30.1.276.

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14

Finn, R. D., J. Tate, J. Mistry, P. C. Coggill, S. J. Sammut, H. R. Hotz, G. Ceric, et al. "The Pfam protein families database." Nucleic Acids Research 36, Database (December 23, 2007): D281—D288. http://dx.doi.org/10.1093/nar/gkm960.

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15

Bruce, David. "Pollen coat protein gene families." Genome Biology 2 (2001): spotlight—20010703–01. http://dx.doi.org/10.1186/gb-spotlight-20010703-01.

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16

Bateman, A. "The Pfam Protein Families Database." Nucleic Acids Research 28, no. 1 (January 1, 2000): 263–66. http://dx.doi.org/10.1093/nar/28.1.263.

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17

Chen, Yu, and Gabriele Varani. "Protein families and RNA recognition." FEBS Journal 272, no. 9 (April 21, 2005): 2088–97. http://dx.doi.org/10.1111/j.1742-4658.2005.04650.x.

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18

Finn, Robert D., Jaina Mistry, John Tate, Penny Coggill, Andreas Heger, Joanne E. Pollington, O. Luke Gavin, et al. "The Pfam protein families database." Nucleic Acids Research 38, suppl_1 (November 17, 2009): D211—D222. http://dx.doi.org/10.1093/nar/gkp985.

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19

Punta, M., P. C. Coggill, R. Y. Eberhardt, J. Mistry, J. Tate, C. Boursnell, N. Pang, et al. "The Pfam protein families database." Nucleic Acids Research 40, no. D1 (November 29, 2011): D290—D301. http://dx.doi.org/10.1093/nar/gkr1065.

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20

Copley, Richard R., Jörg Schultz, Chris P. Ponting, and Peer Bork. "Protein families in multicellular organisms." Current Opinion in Structural Biology 9, no. 3 (June 1999): 408–15. http://dx.doi.org/10.1016/s0959-440x(99)80055-4.

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21

van der Vleuten, Gerly M., Anneke Hijmans, Leo A. J. Kluijtmans, Henk J. Blom, Anton F. H. Stalenhoef, and Jacqueline de Graaf. "Thioredoxin interacting protein in Dutch families with familial combined hyperlipidemia." American Journal of Medical Genetics 130A, no. 1 (September 15, 2004): 73–75. http://dx.doi.org/10.1002/ajmg.a.30036.

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22

Zhang, C. T. "Relations of the numbers of protein sequences, families and folds." Protein Engineering Design and Selection 10, no. 7 (July 1, 1997): 757–61. http://dx.doi.org/10.1093/protein/10.7.757.

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23

Karmirantzou, M., and S. J. Hamodrakas. "A Web-based classification system of DNA-binding protein families." Protein Engineering, Design and Selection 14, no. 7 (July 2001): 465–72. http://dx.doi.org/10.1093/protein/14.7.465.

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24

Korkin, Dmitry, Fred P. Davis, and Andrej Sali. "Localization of protein-binding sites within families of proteins." Protein Science 14, no. 9 (September 2005): 2350–60. http://dx.doi.org/10.1110/ps.051571905.

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25

Reguant, Roc, Yevgeniy Antipin, Rob Sheridan, Christian Dallago, Drew Diamantoukos, Augustin Luna, Chris Sander, and Nicholas Paul Gauthier. "AlignmentViewer: Sequence Analysis of Large Protein Families." F1000Research 9 (March 27, 2020): 213. http://dx.doi.org/10.12688/f1000research.22242.1.

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AlignmentViewer is a web-based tool to view and analyze multiple sequence alignments of protein families. The particular strengths of AlignmentViewer include flexible visualization at different scales as well as analysis of conservation patterns and of the distribution of proteins in sequence space. The tool is directly accessible in web browsers without the need for software installation. It can handle protein families with tens of thousands of sequences and is particularly suitable for evolutionary coupling analysis, e.g. via EVcouplings.org.

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26

Reguant, Roc, Yevgeniy Antipin, Rob Sheridan, Christian Dallago, Drew Diamantoukos, Augustin Luna, Chris Sander, and Nicholas Paul Gauthier. "AlignmentViewer: Sequence Analysis of Large Protein Families." F1000Research 9 (October 15, 2020): 213. http://dx.doi.org/10.12688/f1000research.22242.2.

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AlignmentViewer is a web-based tool to view and analyze multiple sequence alignments of protein families. The particular strengths of AlignmentViewer include flexible visualization at different scales as well as analysis of conservation patterns and of the distribution of proteins in sequence space. The tool is directly accessible in web browsers without the need for software installation. It can handle protein families with tens of thousands of sequences and is particularly suitable for evolutionary coupling analysis, e.g. via EVcouplings.org.

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27

Bar-Lavan, Yael, Netta Shemesh, and Anat Ben-Zvi. "Chaperone families and interactions in metazoa." Essays in Biochemistry 60, no. 2 (October 15, 2016): 237–53. http://dx.doi.org/10.1042/ebc20160004.

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Quality control is an essential aspect of cellular function, with protein folding quality control being carried out by molecular chaperones, a diverse group of highly conserved proteins that specifically identify misfolded conformations. Molecular chaperones are thus required to support proteins affected by expressed polymorphisms, mutations, intrinsic errors in gene expression, chronic insult or the acute effects of the environment, all of which contribute to a flux of metastable proteins. In this article, we review the four main chaperone families in metazoans, namely Hsp60 (where Hsp is heat-shock protein), Hsp70, Hsp90 and sHsps (small heat-shock proteins), as well as their co-chaperones. Specifically, we consider the structural and functional characteristics of each family and discuss current models that attempt to explain how chaperones recognize and act together to protect or recover aberrant proteins.

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28

Hadwiger, Mark Andrew. "Families of proteins with similar total structure identified using statistical geometry." "Protein Engineering, Design and Selection" 7, no. 11 (1994): 1283–93. http://dx.doi.org/10.1093/protein/7.11.1283.

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29

Zhang, Jian, Zhiyuan Zhao, Jennifer Evershed, and Guoying Li. "Monophyletic clustering and characterization of protein families." Journal of Integrative Bioinformatics 4, no. 3 (December 1, 2007): 89–100. http://dx.doi.org/10.1515/jib-2007-67.

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Summary A protein family contains sequences that are evolutionarily related. Generally, this is reflected by sequence similarity. There have been many attempts to organize the set of protein families into evolutionarily homogenous clusters using certain clustering methods. How do we characterize these clusters? How can we cluster protein families using these characterizations? In this work, these questions were addressed by use of a concept called group-wide co-evolution, and was exemplified by some real and simulated protein family data. The results have shown that the trend of a group of monophyletic proteins might be characterized by a normal distribution, while the strength and variability of this trend can be described by the sample mean and variance of the observed correlation coefficients after a suitable transformation. To exploit this property, we have developed a monophyletic clustering method called monophyletic k−medoids clustering. A software package written in R has been made available at http://www.kent.ac.uk/ims/personal/jz .

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30

Sacchi, E., M. Pinotti, G. Marchetti, G. Merati, L. Tagliabue, P. M. Mannucci, and F. Bernardi. "Protein S mRNA in Patients with Protein S Deficiency." Thrombosis and Haemostasis 73, no. 05 (1995): 746–49. http://dx.doi.org/10.1055/s-0038-1653862.

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SummaryA protein S gene polymorphism, detectable by restriction analysis (BstXI) of amplified exonic sequences (exon 15), was studied in seven Italian families with protein S deficiency. In the 17 individuals heterozygous for the polymorphism the study was extended to platelet mRNA through reverse transcription, amplification and densitometric analysis. mRNA produced by the putative defective protein S genes was absent in three families and reduced to a different extent (as expressed by altered allelic ratios) in four families. The allelic ratios helped to distinguish total protein S deficiency (type I) from free protein S deficiency (type IIa) in families with equivocal phenotypes. This study indicates that the study of platelet mRNA, in association with phenotypic analysis based upon protein S assays in plasma, helps to classify patients with protein S deficiency.

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31

Choi, I. G., and S. H. Kim. "Evolution of protein structural classes and protein sequence families." Proceedings of the National Academy of Sciences 103, no. 38 (September 7, 2006): 14056–61. http://dx.doi.org/10.1073/pnas.0606239103.

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32

Holt, Lowenna J., and Roger J. Daly. "Adapter protein connections: The MRL and Grb7 protein families." Growth Factors 23, no. 3 (January 2005): 193–201. http://dx.doi.org/10.1080/08977190500196267.

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33

Jeffryes, Matt, and Alex Bateman. "Rapid identification of novel protein families using similarity searches." F1000Research 7 (December 24, 2018): 1975. http://dx.doi.org/10.12688/f1000research.17315.1.

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Protein family databases are an important tool for biologists trying to dissect the function of proteins. Comparing potential new families to the thousands of existing entries is an important task when operating a protein family database. This comparison helps to understand whether a collection of protein regions forms a novel family or has overlaps with existing families of proteins. In this paper, we describe a method for performing this analysis with an adjustable level of accuracy, depending on the desired speed, enabling interactive comparisons. This method is based upon the MinHash algorithm, which we have further extended to calculate the Jaccard containment rather than the Jaccard index of the original MinHash technique. Testing this method with the Pfam protein family database, we are able to compare potential new families to the over 17,000 existing families in Pfam in less than a second, with little loss in accuracy.

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34

Shinohara, Akira, and Tomoko Ogawa. "Rad51/RecA protein families and the associated proteins in eukaryotes." Mutation Research/DNA Repair 435, no. 1 (September 1999): 13–21. http://dx.doi.org/10.1016/s0921-8777(99)00033-6.

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35

Haft, D. H. "The TIGRFAMs database of protein families." Nucleic Acids Research 31, no. 1 (January 1, 2003): 371–73. http://dx.doi.org/10.1093/nar/gkg128.

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36

Striegel, D. A., D. Wojtowicz, T. M. Przytycka, and V. Periwal. "Correlated rigid modes in protein families." Physical Biology 13, no. 2 (April 11, 2016): 025003. http://dx.doi.org/10.1088/1478-3975/13/2/025003.

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37

Veeramachaneni, V. "Visualizing Sequence Similarity of Protein Families." Genome Research 14, no. 6 (May 12, 2004): 1160–69. http://dx.doi.org/10.1101/gr.2079204.

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38

Tatusov, R. L. "A Genomic Perspective on Protein Families." Science 278, no. 5338 (October 24, 1997): 631–37. http://dx.doi.org/10.1126/science.278.5338.631.

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39

Heger, Andreas, and Liisa Holm. "Exhaustive Enumeration of Protein Domain Families." Journal of Molecular Biology 328, no. 3 (May 2003): 749–67. http://dx.doi.org/10.1016/s0022-2836(03)00269-9.

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40

Kriventseva, Evgenia V., Margaret Biswas, and Rolf Apweiler. "Clustering and analysis of protein families." Current Opinion in Structural Biology 11, no. 3 (June 2001): 334–39. http://dx.doi.org/10.1016/s0959-440x(00)00211-6.

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41

Benedek, Kálmán, Andrea Várkonyi, Betsy Hughes, Karen Zabel, and Lawrence M. Kauvar. "“Paralogs”, sorbent families for protein separations." Journal of Chromatography A 627, no. 1-2 (December 1992): 51–61. http://dx.doi.org/10.1016/0021-9673(92)87186-c.

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42

Barton, John P., Arup K. Chakraborty, Simona Cocco, Hugo Jacquin, and Rémi Monasson. "On the Entropy of Protein Families." Journal of Statistical Physics 162, no. 5 (January 13, 2016): 1267–93. http://dx.doi.org/10.1007/s10955-015-1441-4.

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43

Ouzounis, Christos, Nikos Kyrpides, and Chris Sander. "Novel protein families in archaean genomes." Nucleic Acids Research 23, no. 4 (1995): 565–70. http://dx.doi.org/10.1093/nar/23.4.565.

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44

Downes, G. B., and N. Gautam. "The G Protein Subunit Gene Families." Genomics 62, no. 3 (December 1999): 544–52. http://dx.doi.org/10.1006/geno.1999.5992.

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45

Gradowski, Marcin, Bartosz Baranowski, and Krzysztof Pawłowski. "The expanding world of protein kinase-like families in bacteria: forty families and counting." Biochemical Society Transactions 48, no. 4 (July 17, 2020): 1337–52. http://dx.doi.org/10.1042/bst20190712.

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The protein kinase-like clan/superfamily is a large group of regulatory, signaling and biosynthetic enzymes that were historically regarded as typically eukaryotic proteins, although bacterial members have also been known for a long time. In this review, we explore the diversity of bacterial protein kinase like families, and discuss functional versatility of these enzymes, both the ones acting within the bacterial cell, and those acting within eukaryotic cells as effectors during infection. We focus on novel bacterial kinase-like families discovered in the last five years. A bioinformatics perspective is held here, hence sequence and structure comparison overview is presented, and also a comparison of genomic neighbourhoods of the families. We perform a phylum-level census of the families. Also, we discuss apparent pseudokinases that turned out to perform alternative catalytic functions by repurposing their atypical kinase-like active sites. We also highlight some ‘unpopular' kinase-like families that await characterisation.

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46

Dénes, Judit, Márta Korbonits, Erika Hubina, Gábor László Kovács, László Kovács, Zoltán Görömbey, Sándor Czirják, and Miklós Góth. "Familial isolated pituitary adenoma syndrome." Orvosi Hetilap 152, no. 18 (May 2011): 722–30. http://dx.doi.org/10.1556/oh.2011.29093.

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Familial pituitary adenomas occur in multiple endocrine neoplasia type 1, Carney complex, as well as in familial isolated pituitary adenoma syndrome. Familial isolated pituitary adenoma syndrome is an autosomal dominant disease with incomplete penetrance. Pituitary adenomas occur in familial setting but without any other specific tumors. In 20-40% of families with this syndrome, mutations have been identified in the aryl hydrocarbon receptor interacting protein gene while in the rest of the families the causative gene or genes have not been identified. Families carrying aryl hydrocarbon receptor interacting protein gene mutations have a distinct phenotype with younger age at diagnosis and a predominance of somatotroph and lactotroph adenomas. Germline mutations of the aryl hydrocarbon receptor interacting protein gene can be occasionally identified in usually young-onset seemingly sporadic cases. Genetic and clinical testing of relatives of patients with aryl hydrocarbon receptor interacting protein gene mutations can lead to earlier diagnosis and treatment at an earlier stage of the pituitary tumor. Orv. Hetil., 2011, 152, 722–730.

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47

Somers, Daryl J., Randal W. Giroux, and W. Gary Filion. "The expression of temperature-stress proteins in a desert cactus (Opuntia ficus indica)." Genome 34, no. 6 (December 1, 1991): 940–43. http://dx.doi.org/10.1139/g91-145.

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Opuntia ficus indica roots grown hydroponically at 20 or 30 °C were subjected to a range of heat-shock temperatures as high as 50 °C for 2 h. Roots grown at 30 °C sustained a greater level of total protein synthesis than did 20 °C-grown roots following heat-shock treatments ≥ 45 °C. The 30 °C-grown roots synthesized 31 families of heat-shock proteins between 38 and 47 °C in comparison with 20 °C-grown roots, which synthesized 19 families of heat-shock proteins at 45 °C. In both groups of roots, the heat-shock response was dominated equally by the 71–75 and a 62 kDa heat-shock protein families. In addition, the 20 °C-grown roots expressed 11 families of cold-shock proteins following 2 h at 4 °C, five of which had similar relative molecular masses to heat-shock protein families. There were numerous qualitative differences in the heat shock protein profiles between the roots grown at 20 and 30 °C; the 30 °C-grown roots expressed several unique heat shock protein families.Key words: heat-shock protein(s), cactus, thermal stress, acclimation.

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48

Marsden, Russell L., Juan A. G. Ranea, Antonio Sillero, Oliver Redfern, Corin Yeats, Michael Maibaum, David Lee, et al. "Exploiting protein structure data to explore the evolution of protein function and biological complexity." Philosophical Transactions of the Royal Society B: Biological Sciences 361, no. 1467 (February 2006): 425–40. http://dx.doi.org/10.1098/rstb.2005.1801.

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New directions in biology are being driven by the complete sequencing of genomes, which has given us the protein repertoires of diverse organisms from all kingdoms of life. In tandem with this accumulation of sequence data, worldwide structural genomics initiatives, advanced by the development of improved technologies in X-ray crystallography and NMR, are expanding our knowledge of structural families and increasing our fold libraries. Methods for detecting remote sequence similarities have also been made more sensitive and this means that we can map domains from these structural families onto genome sequences to understand how these families are distributed throughout the genomes and reveal how they might influence the functional repertoires and biological complexities of the organisms. We have used robust protocols to assign sequences from completed genomes to domain structures in the CATH database, allowing up to 60% of domain sequences in these genomes, depending on the organism, to be assigned to a domain family of known structure. Analysis of the distribution of these families throughout bacterial genomes identified more than 300 universal families, some of which had expanded significantly in proportion to genome size. These highly expanded families are primarily involved in metabolism and regulation and appear to make major contributions to the functional repertoire and complexity of bacterial organisms. When comparisons are made across all kingdoms of life, we find a smaller set of universal domain families (approx. 140), of which families involved in protein biosynthesis are the largest conserved component. Analysis of the behaviour of other families reveals that some (e.g. those involved in metabolism, regulation) have remained highly innovative during evolution, making it harder to trace their evolutionary ancestry. Structural analyses of metabolic families provide some insights into the mechanisms of functional innovation, which include changes in domain partnerships and significant structural embellishments leading to modulation of active sites and protein interactions.

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49

Schläpfer, Pascal, Devang Mehta, Cameron Ridderikhoff, and R. Glen Uhrig. "DomainViz: intuitive visualization of consensus domain distributions across groups of proteins." Nucleic Acids Research 49, W1 (May 22, 2021): W169—W173. http://dx.doi.org/10.1093/nar/gkab391.

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Abstract The prediction of functional domains is typically among the first steps towards understanding the function of new proteins and protein families. There are numerous databases of annotated protein domains that permit researchers to identify domains on individual proteins of interest. However, it is necessary to perform high-throughput domain searches to gain evolutionary insight into the functions of proteins and protein families. Unfortunately, at present, it is difficult to search for, and visualize domain conservation across multiple proteins and/or multiple groups of proteins in an intuitive manner. Here we present DomainViz, a new web-server that streamlines the identification and visualization of domains across multiple protein sequences. Currently, DomainViz uses the well-established PFAM and Prosite databases for domain searching and assembles intuitive, publication-ready ‘monument valley’ plots (mv-plots) that display the extent of domain conservation along two dimensions: positionality and frequency of occurrence in the input protein sequences. In addition, DomainViz produces a conventional domain-ordering figure. DomainViz can be used to explore the conservation of domains within a single protein family, across multiple families, and across families from different species to support studies into protein function and evolution. The web-server is publicly available at: https://uhrigprotools.biology.ualberta.ca/domainviz.

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FLORES, Ana I., and José M. CUEZVA. "Identification of sequence similarity between 60 kDa and 70 kDa molecular chaperones: evidence for a common evolutionary background?" Biochemical Journal 322, no. 2 (March 1, 1997): 641–47. http://dx.doi.org/10.1042/bj3220641.

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Abstract:

Recent findings support the premise that chaperonins (60 kDa stress-proteins) and α-subunits of F-type ATPases (α-ATPase) are evolutionary related protein families. Two-dimensional gel patterns of synthesized proteins in unstressed and heat-shocked embryonic Drosophila melanogaster SL2 cells revealed that antibodies raised against the α-subunit of the F1-ATPase complex from rat liver recognize an inducible p71 member of the 70 kDa stress-responsive protein family. Molecular recognition of this stress-responsive 70 kDa protein by antibodies raised against the F1-ATPase α-subunit suggests the possibility of partial sequence similarity within these ATP-binding protein families. A multiple sequence alignment between α-ATPases and 60 kDa and 70 kDa molecular chaperones is presented. Statistical evaluation of sequence similarity reveals a significant degree of sequence conservation within the three protein families. The finding suggests a common evolutionary origin for the ATPases and molecular chaperone protein families of 60 kDa and 70 kDa, despite the lack of obvious structural resemblance between them.

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