A New Clustering and Nomenclature for Beta Turns Derived from High-Resolution Protein Structures

Abstract

Protein 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 and k-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 crossplatform software, BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable from dunbrack.fccc.edu/betaturn and github.com/shmaxim/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.

A systematic analysis of the beta hairpin motif in the Protein Data Bank

“…3C). These findings largely agree with existing evidence on the prevalence and importance of prolines in the beginning of turn regions [42][43][44] but the nearly four-fold enrichment for residue two prolines in hairpin structures with five amino acid long turn regions when compared to background levels is particularly surprising. In combination with the fact that over half of all fourth residues in five amino acid long turn regions are glycines, these findings suggest that beta hairpins with longer turn regions may have very specific physiochemical requirements that limit amino acid diversity.…”

“…One major limitation of our approach is that we were only able to establish broad general properties of beta hairpins that might influence overall structure or function. This is in contrast to prior work that has focused on identifying key design factors for specific beta hairpin scaffolds 23,24,42,58 or grouping beta hairpins and related structures into increasingly detailed classifications 20,31,44 . While the PDB dataset that we analyzed could be used to expand upon these highly focused areas of research, the broad applicability of our results would be compromised.…”

Characterization of β-turns by electronic circular dichroism spectroscopy: a coupled molecular dynamics and time-dependent density functional theory computational study

“…1 Over the last 50 years, with the increasing number of solved protein structures, additional types were identified (IV, V, VI, VII and VIII), some subtypes were introduced (IV 1 , IV 2 , IV 3 , IV 4 , VIa1, VIa2, VIb), while some types were eliminated (III, III', V and VII) and incorporated in others categories (for example III into I, III' into I'). 1,2,6,[13][14][15][16][17][18][19] Although a new nomenclature has recently been proposed, 19 we decided, in order to allow direct comparisons with the dense literature on such topic, to keep the classification proposed by De Brevern in 2016. 18 As shown in Table 1, it is composed of 12 types of β-turns.…”

Hiding in plain sight: structure and sequence analysis reveals the importance of the antibody DE loop for antibody-antigen binding

“… 15 This allowed us to find clusters that are not defined by mis-solved residues, such as peptide flips, 55 which is an important step in validating clusters and backbone structures not only for clustering the DE loop, but the other CDRs as well as backbone structures from other proteins. 64 , 65 …”