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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Estimating quality of template-based protein models by alignment stability.

Hao Chen¹, Daisuke Kihara

¹Department of Biological Sciences, College of Science, Purdue University, West Lafayette, Indiana 47907, USA.

|November 29, 2007

Summary

We developed SuboPtimal Alignment Diversity (SPAD) to estimate protein structure prediction errors. SPAD accurately predicts global and local errors, outperforming other measures and aiding experimental biologists.

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Area of Science:

Computational Biology
Structural Bioinformatics
Protein Structure Prediction

Background:

Protein tertiary structure prediction errors are inherent but often unquantified.
Accurate error estimation is vital for experimental biologists using predicted structures for design and interpretation.

Purpose of the Study:

To develop and validate a novel method for estimating errors in protein structure predictions.
To introduce the SuboPtimal Alignment Diversity (SPAD) index for quantifying alignment stability and predicting structural errors.

Main Methods:

Proposed a method to estimate prediction errors based on the stability of optimal versus suboptimal sequence alignments.
Quantified alignment stability using the SuboPtimal Alignment Diversity (SPAD) index.
Implemented SPAD in a profile-based threading algorithm and evaluated its performance on a large benchmark dataset (5232 alignments).

Main Results:

SPAD demonstrated strong correlations with both alignment shift errors and structure-level errors (global RMSD and local residue errors).
SPAD outperformed other quality measures, including sequence identity and DOPE, in predicting global and local structure errors across different homology levels (family, superfamily, fold).
SPAD was used to predict errors for CASP7 targets, and a novel 'sausage' representation was proposed to visualize structures with estimated error ranges.

Conclusions:

The SuboPtimal Alignment Diversity (SPAD) index is a robust and effective measure for estimating errors in protein structure predictions.
SPAD provides crucial information for experimental biologists, improving the utility of predicted protein structures.
The proposed sausage representation offers an intuitive way to visualize predicted structures alongside their estimated error margins.