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Related Experiment Video

Updated: Jul 2, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

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Published on: July 14, 2015

Protein structure alignment considering phenotypic plasticity.

Gergely Csaba1, Fabian Birzele, Ralf Zimmer

  • 1Department of Informatics, Practical Informatics and Bioinformatics Group, Ludwig-Maximilians-University, Amalienstr 17, D-80333 Munich, Germany.

Bioinformatics (Oxford, England)
|August 12, 2008
PubMed
Summary
This summary is machine-generated.

A new phenotypic plasticity method (PPM) models evolutionary distance between protein structures by measuring morphing costs. PPM accurately aligns and classifies protein structures, outperforming existing methods on large datasets.

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Evolutionary biology

Background:

  • Protein structure comparison is crucial for understanding protein evolution.
  • Existing methods lack a robust evolutionary distance measure and alignment that models sequence mutation costs on a structural level.

Purpose of the Study:

  • To introduce a novel method for protein structure similarity measurement and alignment.
  • To model evolutionary distance between protein structures by quantifying the cost of structural transformation.

Main Methods:

  • Developed the Phenotypic Plasticity Method (PPM) to measure structural similarity.
  • PPM quantifies the 'morphing' cost between protein structures.
  • Aligns structures while accounting for natural variations ('phenotypic plasticity') and preserving topology.

Main Results:

  • PPM demonstrates comparable or superior performance to TM-Align and Vorolign on large benchmark datasets (SCOP, CATH).
  • The method reliably classifies known protein structures and detects similarities.
  • PPM computes accurate alignments even with significant structural variations.

Conclusions:

  • PPM offers a new, effective approach for protein structure comparison and evolutionary analysis.
  • The method successfully addresses the challenge of structural variations in alignment and similarity scoring.