Conformations of (X-L-Pro-Y)2 cyclic hexapeptides. Preferred .beta.-turn conformers and implications for .beta. turns in proteins

Biochemistry ◽  
1981 ◽  
Vol 20 (16) ◽  
pp. 4730-4738 ◽  
Author(s):  
Lila M. Gierasch ◽  
Charles M. Deber ◽  
Vincent Madison ◽  
Chien-Hua Niu ◽  
Elkan R. Blout
2018 ◽  
Author(s):  
Maxim Shapovalov ◽  
Slobodan Vucetic ◽  
Roland L. Dunbrack

AbstractProtein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops. The commonly used classification of beta turns (Type I, I’, II, II’, VIa1, VIa2, VIb, and VIII) was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution, and represents only two thirds of beta turns observed in proteins (with a generic class Type IV representing the rest). We present a new clustering of beta turn conformations from a set of 13,030 turns from 1078 ultra-high resolution protein structures (≤1.2 Å). Our clustering is derived from applying the DBSCAN andk-medoids algorithms to this data set with a metric commonly used in directional statistics applied to the set of dihedral angles from the second and third residues of each turn. We define 18 turn types compared to the 8 classical turn types in common use. We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues (e.g., ‘A’ and ‘D’ for alpha regions on the left side of the Ramachandran map and ‘a’ and ‘d’ for equivalent regions on the right-hand side; classical Type I turns are ‘AD’ turns and Type I’ turns are ‘ad’). We identify 11 new types of beta turn, 5 of which are sub-types of classical beta turn types. Up-to-date statistics, probability densities of conformations, and sequence profiles of beta turns in loops were collected and analyzed. A library of turn types,BetaTurnLib18, and cross-platform software,BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable fromdunbrack.fccc.edu/betaturnandgithub.com/sh-maxim/BetaTurn18. Given the ubiquitous nature of beta turns, this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination, refinement, and prediction programs.


1992 ◽  
Vol 118 (4) ◽  
pp. 831-839 ◽  
Author(s):  
J M Backer ◽  
S E Shoelson ◽  
M A Weiss ◽  
Q X Hua ◽  
R B Cheatham ◽  
...  

We have investigated the role of tyrosine residues in the insulin receptor cytoplasmic juxtamembrane region (Tyr953 and Tyr960) during endocytosis. Analysis of the secondary structure of the juxtamembrane region by the Chou-Fasman algorithms predicts that both the sequences GPLY953 and NPEY960 form tyrosine-containing beta-turns. Similarly, analysis of model peptides by 1-D and 2-D NMR show that these sequences form beta-turns in solution, whereas replacement of the tyrosine residues with alanine destabilizes the beta-turn. CHO cell lines were prepared expressing mutant receptors in which each tyrosine was mutated to phenylalanine or alanine, and an additional mutant contained alanine at both positions. These mutations had no effect on insulin binding or receptor autophosphorylation. Replacements with phenylalanine had no effect on the rate of [125I]insulin endocytosis, whereas single substitutions with alanine reduced [125I]insulin endocytosis by 40-50%. Replacement of both tyrosines with alanine reduced internalization by 70%. These data suggest that the insulin receptor contains two tyrosine/beta-turns which contribute independently and additively to insulin-stimulated endocytosis.


2008 ◽  
Vol 2 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Kevin Campbell ◽  
Lukasz Kurgan

Development of accurate β-turn (beta-turn) type prediction methods would contribute towards the prediction of the tertiary protein structure and would provide useful insights/inputs for the fold recognition and drug design. Only one existing sequence-only method is available for the prediction of beta-turn types (for type I and II) for the entire protein chains, while the proposed method allows for prediction of type I, II, IV, VII, and non-specific (NS) beta-turns, filling in the gap. The proposed predictor, which is based solely on protein sequence, is shown to provide similar performance to other sequence-only methods for prediction of beta-turns and beta-turn types. The main advantage of the proposed method is simplicity and interpretability of the underlying model. We developed novel sequence-based features that allow identifying beta-turns types and differentiating them from non-beta-turns. The features, which are based on tetrapeptides (entire beta-turns) rather than a window centered over the predicted residues as in the case of recent competing methods, provide a more biologically sound model. They include 12 features based on collocation of amino acid pairs, focusing on amino acids (Gly, Asp, and Asn) that are known to be predisposed to form beta-turns. At the same time, our model also includes features that are geared towards exclusion of non-beta-turns, which are based on amino acids known to be strongly detrimental to formation of beta-turns (Met, Ile, Leu, and Val).


Toxins ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 522
Author(s):  
Patrick Romano Monteiro ◽  
Samuel Cavalcante do Amaral ◽  
Andrei Santos Siqueira ◽  
Luciana Pereira Xavier ◽  
Agenor Valadares Santos

Cyanobacteria are microorganisms with photosynthetic mechanisms capable of colonizing several distinct environments worldwide. They can produce a vast spectrum of bioactive compounds with different properties, resulting in an improved adaptative capacity. Their richness in secondary metabolites is related to their unique and diverse metabolic apparatus, such as Non-Ribosomal Peptide Synthetases (NRPSs). One important class of peptides produced by the non-ribosomal pathway is anabaenopeptins. These cyclic hexapeptides demonstrated inhibitory activity towards phosphatases and proteases, which could be related to their toxicity and adaptiveness against zooplankters and crustaceans. Thus, this review aims to identify key features related to anabaenopeptins, including the diversity of their structure, occurrence, the biosynthetic steps for their production, ecological roles, and biotechnological applications.


2017 ◽  
Vol 58 (27) ◽  
pp. 2675-2680 ◽  
Author(s):  
Lissa S. Tsutsumi ◽  
Ghee T. Tan ◽  
Dianqing Sun

ChemInform ◽  
1988 ◽  
Vol 19 (1) ◽  
Author(s):  
T. INABA ◽  
I. UMEZAWA ◽  
M. YUASA ◽  
T. INOUE ◽  
S. MIHASHI ◽  
...  

Biochemistry ◽  
1971 ◽  
Vol 10 (8) ◽  
pp. 1330-1335 ◽  
Author(s):  
C. Allen Bush ◽  
Stanley M. Ziegler

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