Loops, bulges, turns and hairpins in proteins

Milner‐White, E. James; Poet, Ron

doi:10.1016/0968-0004(87)90091-0

Cited by 87 publications

(40 citation statements)

References 18 publications

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“…The second largest cluster of loops, S.23.6.1.4.32, was made up of 618 peptides from 507 proteins. These loops have been termed as beta hairpin type [1][2][3][4]4:4 1 . The geometry of these beta hairpin octapeptides seems to be more conserved than any of the beta strand 31 between the first and fourth dot, average information content of the cluster between the fourth and fifth dot, C a1 -C a8 distance between the fifth and sixth dot and a unique identification number for each cluster after the sixth dot.…”

Section: Loopsmentioning

confidence: 99%

“…We decided to compare our clusters with some of the broad-based loop classification databases 8,9,32 in addition to the specialized classification of certain categories of loops. 1,4 To that end, each of the peptides in our clusters was scanned against the Loop 8 and Sloop 32 database. A total of 341 of the clusters showed at least one match with the known classes of loops described in the Loop database, while 4077 of the clusters show at least one match with those of the SLoop database.…”

Section: Loopsmentioning

confidence: 99%

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Clustering of Protein Structural Fragments Reveals Modular Building Block Approach of Nature

Tendulkar

Joshi

Sohoni

et al. 2004

Journal of Molecular Biology

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Structures of peptide fragments drawn from a protein can potentially occupy a vast conformational continuum. We co-ordinatize this conformational space with the help of geometric invariants and demonstrate that the peptide conformations of the currently available protein structures are heavily biased in favor of a finite number of conformational types or structural building blocks. This is achieved by representing a peptides' backbone structure with geometric invariants and then clustering peptides based on closeness of the geometric invariants. This results in 12,903 clusters, of which 2207 are made up of peptides drawn from functionally and/or structurally related proteins. These are termed "functional" clusters and provide clues about potential functional sites. The rest of the clusters, including the largest few, are made up of peptides drawn from unrelated proteins and are termed "structural" clusters. The largest clusters are of regular secondary structures such as helices and beta strands as well as of beta hairpins. Several categories of helices and strands are discovered based on geometric differences. In addition to the known classes of loops, we discover several new classes, which will be useful in protein structure modeling. Our algorithm does not require assignment of secondary structure and, therefore, overcomes the limitations in loop classification due to ambiguity in secondary structure assignment at loop boundaries.Keywords: geometric invariants; protein structure comparison; secondary structure; loop IntroductionGlobular proteins are made up of regular secondary structures such as a-helices and b-strands and non-regular secondary structures, which are referred to as loops. The regular secondary structures are defined based on a regular pattern of the backbone torsion angles for consecutive amino acid residues or periodic patterns of hydrogen bonding between the backbone NH and CvO groups.1 Loops are the regions that join the regular secondary structures and lack the regularity of torsion angles for consecutive residues. In fact, loops, polypeptide fragments from proteins, which trace a "loop-shaped" path in three-dimensional space, were considered to be random coils until recently.2 Loops are highly compact substructures and are typically situated at the protein surface. Certain loop geometries have been shown to recur across non-homologous proteins, which forms the basis for classification of loops into structural families. For example, the b-hairpin and a-turn families of loops have been described in detail.1,3 -7 A much wider classification of loops of various categories has been provided by Kwasigroch et al. 8 and by Oliva et al. 9 Loop classification has important implications in protein structure modeling and in fitting the NMR distance constraints or an X-ray crystallography electron density map to a loop geometry. On the contrary, no such fine-grained classification has been attempted for a-helices and b-strands, although detectable variations are reported in these regular secondar...

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Section: Loopsmentioning

confidence: 99%

Section: Loopsmentioning

confidence: 99%

Clustering of Protein Structural Fragments Reveals Modular Building Block Approach of Nature

Tendulkar

Joshi

Sohoni

et al. 2004

Journal of Molecular Biology

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“…MilnerWhite analyzed the relation between G1 b-bulge and the b-hairpins describing a particular G1 b-bulge loop, related to turns. [17][18][19] Thornton and coworker made the first systematic study of b-bulges on a SPECIAL AS Special b-bulges are very similar to the classic type, but with more than two inserted residues in the bulged strand.…”

Section: Introductionmentioning

confidence: 99%

β‐Bulges: Extensive structural analyses of β‐sheets irregularities

et al. 2013

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b-Sheets are quite frequent in protein structures and are stabilized by regular main-chain hydrogen bond patterns. Irregularities in b-sheets, named b-bulges, are distorted regions between two consecutive hydrogen bonds. They disrupt the classical alternation of side chain direction and can alter the directionality of b-strands. They are implicated in protein-protein interactions and are introduced to avoid b-strand aggregation. Five different types of b-bulges are defined. Previous studies on b-bulges were performed on a limited number of protein structures or one specific family. These studies evoked a potential conservation during evolution. In this work, we analyze the bbulge distribution and conservation in terms of local backbone conformations and amino acid composition. Our dataset consists of 66 times more b-bulges than the last systematic study (Chan et al. Protein Science 1993, 2:1574-1590. Novel amino acid preferences are underlined and local structure conformations are highlighted by the use of a structural alphabet. We observed that bbulges are preferably localized at the N-and C-termini of b-strands, but contrary to the earlier studies, no significant conservation of b-bulges was observed among structural homologues. Displacement of b-bulges along the sequence was also investigated by Molecular Dynamics simulations.

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“…Extramembranous segments and loop sequences in seven transmembrane G-protein-coupled and Type I tyrosine kinase-coupled receptors are, in the sense defined above, disordered, and participate in polypeptide signaling and regulation, in contrast to structural and/or transmembrane scaffolding involved segments (Finkelstein and Ptitsyn, 1987;Branden and Tooze, 1999;Bruccoleri et al, 1988;Howl and Wheatley, 1996;Lin et al, 1998;Milner-White and Poet, 1987;Qu et al, 1999). For these reasons, computational analysis of sequential polypeptide patterns as aa i -hb i in kcal/mol continues to have value in characterizing disordered sequences, particularly with respect to targeting them for rational peptide ligand design.…”

Section: Introductionmentioning

confidence: 99%

Cellular and Behavioral Effects of D2 Dopamine Receptor Hydrophobic Eigenmode-Targeted Peptide Ligands

Mandell

Selz

Owens

et al. 2003

Neuropsychopharmacol

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Patterns in G-protein-coupled receptors' hydrophobically transformed amino-acid sequences can be computationally characterized as hierarchies of autocorrelation waves, 'hydrophobic eigenmodes,' using autocovariance matrix decomposition and all poles power spectral and wavelet transformations. L-or D-amino acid (retro-inverso) 12-18 residue peptides targeting these modes can be designed using eigenvector templates derived from these computations. In all, 12 human long-form D 2 dopamine receptor eigenmode-targeted 15 mer peptides were designed, synthesized, and shown to modulate and/or indirectly activate the extracellular acidification response, EAR, in stably receptor-transfected CHO and LtK cells, with an 83% hit rate. Representative L-and D-amino-acid retro-inverso peptides injected bilaterally in the nucleus accumbens demonstrated changes in rat exploratory behavior and prepulse inhibition similar to those observed following parenteral amphetamine. In contrast with geometric models used for ligand design, such as pharmacophores, the hydrophobic eigenmode approach to lead modulatory peptide design targets hydrophobic eigenmode-bearing subsequences, including those not visible from X-ray and NMR studies such as extracellular segments and loops.

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Loops, bulges, turns and hairpins in proteins

Cited by 87 publications

References 18 publications

Clustering of Protein Structural Fragments Reveals Modular Building Block Approach of Nature

Clustering of Protein Structural Fragments Reveals Modular Building Block Approach of Nature

β‐Bulges: Extensive structural analyses of β‐sheets irregularities

Cellular and Behavioral Effects of D2 Dopamine Receptor Hydrophobic Eigenmode-Targeted Peptide Ligands

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