Literature DB >> 19205025

Description and recognition of regular and distorted secondary structures in proteins using the automated protein structure analysis method.

Sushilee Ranganathan1, Dmitry Izotov, Elfi Kraka, Dieter Cremer.   

Abstract

The Automated Protein Structure Analysis (APSA) method, which describes the protein backbone as a smooth line in three-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 three-dimensional structure information to two-dimensional representations that are easy to interpret and understand. 2008 Wiley-Liss, Inc.

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Year:  2009        PMID: 19205025     DOI: 10.1002/prot.22357

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  4 in total

1.  Description of local and global shape properties of protein helices.

Authors:  Zhanyong Guo; Elfi Kraka; Dieter Cremer
Journal:  J Mol Model       Date:  2013-03-27       Impact factor: 1.810

2.  Polyphony: superposition independent methods for ensemble-based drug discovery.

Authors:  William R Pitt; Rinaldo W Montalvão; Tom L Blundell
Journal:  BMC Bioinformatics       Date:  2014-09-30       Impact factor: 3.169

3.  SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

Authors:  Niraj Verma; Xingming Qu; Francesco Trozzi; Mohamed Elsaied; Nischal Karki; Yunwen Tao; Brian Zoltowski; Eric C Larson; Elfi Kraka
Journal:  Int J Mol Sci       Date:  2021-01-30       Impact factor: 5.923

4.  Differential geometric analysis of alterations in MH α-helices.

Authors:  Birgit Hischenhuber; Hans Havlicek; Jelena Todoric; Sonja Höllrigl-Binder; Wolfgang Schreiner; Bernhard Knapp
Journal:  J Comput Chem       Date:  2013-05-24       Impact factor: 3.376

  4 in total

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