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ExploreTurns: A web tool for the exploration, analysis, and classification of beta turns and structured loops in proteins; application to beta-bulge and Schellman loops, Asx helix caps, beta hairpins, and other hydrogen-bonded motifs.

Protein science : a publication of the Protein Society·2025

Same author

MapTurns: mapping the structure, H-bonding, and contexts of beta turns in proteins.

Bioinformatics (Oxford, England)·2024

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Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

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Geometric descriptors for beta turns.

Nicholas E Newell¹

¹Independent Researcher, Reading, Massachusetts, USA.

Protein Science : a Publication of the Protein Society

|August 24, 2024

Summary

This summary is machine-generated.

This study introduces a new Euclidean-space method to analyze protein beta turns, enabling better visualization and design. The developed geometric descriptors offer novel insights into turn structure and function.

Keywords:

H‐bonded motif beta turn beta‐bulge loop beta‐hairpin protein design protein structure secondary structure sequence motif structural analysis structural bioinformatics

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Area of Science:

Protein structure analysis
Biophysics
Computational biology

Background:

Beta turns are crucial protein secondary structures with diverse conformations.
Existing classification systems in Ramachandran space limit detailed Euclidean analysis.
Lack of a standardized local coordinate system hinders turn comparison and design.

Purpose of the Study:

To develop a local Euclidean-space coordinate system for protein beta turns.
To create novel geometric descriptors for characterizing beta turn backbone shape and variation.
To enhance the analysis, visualization, and design of beta turns and protein structures.

Main Methods:

Derivation of a turn-local coordinate system for implicit alignment.
Development of geometric descriptors to characterize bulk beta turn shapes.
Analysis of relationships between descriptors, electrostatic energy, side-chain motifs, and structural contexts.

Main Results:

A novel turn-local coordinate system and geometric descriptors were successfully derived.
These descriptors capture meaningful modes of structural variation not found in existing systems.
Clear correlations were established between descriptor values and key biophysical properties (e.g., H-bond energy, side-chain preferences).

Conclusions:

The new geometric descriptors complement existing Ramachandran-space classifications.
These descriptors facilitate the selection of beta turns compatible with specific side-chain interactions and structural contexts.
The developed methods and web tools (ExploreTurns, MapTurns, ProfileTurn) offer improved understanding and performance in protein design and analysis.