Description and Recognition of Regular and Distorted Secondary Structures in Proteins Using the Automated Protein Structure Analysis Method

TL;DR

The paper introduces APSA, an automated method that describes and recognizes protein secondary structures using curvature and torsion, providing accurate, quantifiable, and interpretable backbone analysis for structural comparison.

Contribution

APSA offers a novel automated approach to identify and analyze secondary structures in proteins through curvature and torsion patterns, improving structural similarity assessment.

Findings

APSA recognized 533 alpha-helices and 644 beta-strands in 77 proteins.

APSA's similarity measures align well with known structural classifications.

The method effectively reduces complex 3D structures to interpretable 2D representations.

Abstract

The Automated Protein Structure Analysis (APSA) method, which describes the protein backbone as a smooth line in 3-dimensional space and characterizes it by curvature kappa and torsion tau as a function of arc length s, was applied on 77 proteins to determine all secondary structural units via specific kappa(s) and tau(s) patterns. A total of 533 alpha-helices and 644 beta-strands were recognized by APSA, whereas DSSP gives 536 and 651 units, respectively. Kinks and distortions were quantified and the boundaries (entry and exit) of secondary structures were classified. Similarity between proteins can be easily quantified using APSA, as was demonstrated for the roll architecture of proteins ubiquitin and spinach ferridoxin. A twenty-by-twenty comparison of all-alpha domains showed that the curvature-torsion patterns generated by APSA provide an accurate and meaningful similarity measurement for secondary, super-secondary, and tertiary protein structure. APSA is shown to accurately reflect the conformation of the backbone effectively reducing 3-dimensional structure information to 2-dimensional representations that are easy to interpret and understand.