The Influence of Amino Acid Sequence on Protein Structure

Guzzo, Anthony V.

doi:10.1016/s0006-3495(65)86753-4

Cited by 151 publications

(68 citation statements)

References 7 publications

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“…The P at position 16 and A at position 21 in the overall sequence logo (Fig. 1B) were not selected for mutagenesis because they were already alanine or are likely to be required for maintenance of protein secondary structure (29).…”

Section: Resultsmentioning

confidence: 99%

Functional Analysis of a Novel Motif Conserved across Geminivirus Rep Proteins

Nash

Dallas

Reyes

et al. 2011

J Virol

109

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Members of the Geminiviridae have single-stranded DNA genomes that replicate in nuclei of infected plant cells. All geminiviruses encode a conserved protein (Rep) that catalyzes initiation of rolling-circle replication. Earlier studies showed that three conserved motifs-motifs I, II, and III-in the N termini of geminivirus Rep proteins are essential for function. In this study, we identified a fourth sequence, designated GRS (geminivirus Rep sequence), in the Rep N terminus that displays high amino acid sequence conservation across all geminivirus genera. Using the Rep protein of Tomato golden mosaic virus (TGMV AL1), we show that GRS mutants are not infectious in plants and do not support viral genome replication in tobacco protoplasts. GRS mutants are competent for protein-protein interactions and for both double-and single-stranded DNA binding, indicating that the mutations did not impair its global conformation. In contrast, GRS mutants are unable to specifically cleave single-stranded DNA, which is required to initiate rolling-circle replication. Interestingly, the Rep proteins of phytoplasmal and algal plasmids also contain GRS-related sequences. Modeling of the TGMV AL1 N terminus suggested that GRS mutations alter the relative positioning of motif II, which coordinates metal ions, and motif III, which contains the tyrosine involved in DNA cleavage. Together, these results established that the GRS is a conserved, essential motif characteristic of an ancient lineage of rolling-circle initiators and support the idea that geminiviruses may have evolved from plasmids associated with phytoplasma or algae.Geminiviruses are plant viruses with small, circular DNA genomes and twin icosahedral capsids (58). They constitute a large family that is divided into the begomovirus, mastrevirus, curtovirus, and topocuvirus genera based on genome arrangement, insect vector, and host range (68). Geminiviruses amplify their single-stranded (ss) genomes in the nuclei of infected cells using a combination of rolling-circle and recombination-mediated replication (reviewed in references 27, 31, and 35). All geminiviruses encode a conserved protein designated Rep (also known as AL1, AC1, C1, L1, or C1:C2), which mediates initiation of viral replication but does not act as a DNA polymerase (33,42,51). Instead, geminiviruses depend on host replication machinery to copy their genomes (28,31,32).Rep is the only geminivirus protein that is essential for viral replication (18, 56). It is a multifunctional protein that mediates virus-specific recognition of its cognate origin (22) and transcriptional repression (17, 69). Rep initiates and terminates viral DNA synthesis (33, 42, 51) and induces the accumulation of host replication factors in infected cells (48). Rep binds specifically to double-stranded DNA (dsDNA) at a repeated sequence in the 5Ј intergenic region of the viral genome (22, 67), cleaves and ligates DNA within an invariant sequence in a hairpin loop of the plus-strand origin (42, 51), and acts as a DNA helicase to unwind vi...

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Section: Resultsmentioning

confidence: 99%

Functional Analysis of a Novel Motif Conserved across Geminivirus Rep Proteins

Nash

Dallas

Reyes

et al. 2011

J Virol

109

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“…As for the function of this asymmetry, we can only speculate that it aids in the proper folding of helical regions of proteins as they come off the ribosome, in the absence of those longrange interactions from residues in the as-yet-unformed C-terminal end of the protein. (4) Chemical Composition of Residues in the Coil Regions near the N-and CTerminal Ends of Helical Segments.-In section 2, we observed a peak in the dipeptide distribution for the coil region near the C-terminal ends of helical segments, whereas there was no clear evidence for a corresponding peak near the N-terminal ends (in section 3). It is interesting now to examine the chemical composition of residue types occurring in these two regions, to see whether their distributions differ from that for the entire coil region of the protein sample.…”

mentioning

confidence: 77%

The Influence of Short-Range Interactions on Protein Conformation, Iii. Dipeptide Distributions in Proteins of Known Sequence and Structure

Kotelchuck

Dygert

Scheraga

1969

Proc. Natl. Acad. Sci. U.S.A.

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Abstract.-A statistical analysis is made of the distribution into a-helical and non-a-helical regions of the various dipeptide types appearing in a sample of seven proteins of known sequence and structure. By considering as a group all dipeptide types occurring at a given location relative to the reported helix-coil boundaries and examining the percentage of cases in which these appear in non-ahelical regions throughout the protein sample, we find a sharp change in the nature of the observed dipeptide types when the helix-coil boundary is crossed. Furthermore, we find that dipeptide types which occur in the coil region near the C-terminal end of helical segments are non-a-helical in almost 90 per cent of the cases in which they appear throughout the sample. No similar effect is found in the coil region near the N-terminal end of helical segments. These results give evidence for the importance of short-range interactions in determining protein conformation. They are also consistent with predictions based on a model for helix formation given in the second paper of this series.1In testing the model for helix formation presented in the second of this series,' we observed that those dipeptide sequences which terminate a-helical segments in four proteins of known sequence and structure recur elsewhere in the proteins, preferentially in non-a-helical regions. These results are not a reliable test of the model, however, since they are based on a small statistical sample and since the boundaries of helical regions are not well defined experimentally. However, the degree to which a small polypeptide segment of an intact protein maintains a conformational identity as a-helical or non-a-helical throughout a protein sample, i.e., independent of its surroundings, is a measure of the validity of the notion that short-range interactions play an important role in determining protein conformation. This measure is independent of the assumptions of any particular short-range-interaction model, e.g., those about the helix-making or helix-breaking properties of certain types of residues. Such a study can and will be used, of course, to check short-range-interaction models, in particular the helix model of paper II.Recently, as the structures of various proteins of known sequence have been determined by X-ray methods, it has become possible to investigate whether certain individual residues or small oligopeptide segments occur preferentially in a-helical or in non-a-helical (coil) regions. As a result, a number of studies have been carried out on the distributions of single residues and indicate that some do occur preferentially in one of these two regions.2-8 Only in the work of Low et al.7 was a study made of the distribution of larger, oligopeptide segments in helix and coil regions. However, only qualitative results were reported and no 615

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“…Most of the early attempts to predict secondary structure (in the 1960s) concerned only a-helices (Guzzo, 1965;Prothero, 1966Prothero, , 1968Low et al, 1968;Schiffer & Edmundson, 1967;Dunhill, 1968;Lewis et al, 1970Lewis et al, , 1971. Since then, statistical methods (Chou & Fasman, 1974b), information theory (Garnier et al, 1978;Gibrat et al, 1987), physical theory (Lim, 1974b) and other computing algorithms such as pattern recognition (Taylor & Thornton, 1983;Cohen et al, 1986;Presnell et al, 1992), neural networks (Qian & Sejnowski, 1988;Holley & Karplus, 1989), machine learning (King & Sternberg, 1990;Muggleton et al, 1992) and the nearest-neighbour methods (Levin et al, 1986;Zhang et al, 1992;Yi & Lander, 1993) have been used.…”

Section: Introductionmentioning

confidence: 99%