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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
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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

conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure.

Chris A Kieslich, James Smadbeck¹, George A Khoury¹

¹Department of Chemical and Biological Engineering, Princeton University , Princeton, New Jersey 08544, United States.

Journal of Chemical Information and Modeling

|March 2, 2016

Summary

A new consensus method, conSSert, improves protein secondary structure prediction, especially for beta-strands. This advance in predicting protein structure aids the broader protein-folding problem.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

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Published on: May 31, 2013

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Published on: March 12, 2012

Area of Science:

* Computational biology
* Bioinformatics
* Structural biology

Background:

* Accurate protein secondary structure prediction is vital for understanding protein folding.
* Predicting ordered secondary structures, particularly beta-strands, remains a significant challenge.

Purpose of the Study:

* To develop an enhanced consensus method for protein secondary structure prediction.
* To improve the accuracy of beta-strand prediction.

Main Methods:

* Developed conSSert, a consensus secondary structure prediction method.
* Utilized support vector machines (SVM) as the core algorithm.
* Integrated predictions from four leading methods: PSSpred, PSIPRED, SPINE-X, and RAPTOR.
* Trained and tested on extensive datasets (PDBSelect25, PISCES) and validated with CASP targets.

Main Results:

* conSSert achieved exceptional accuracy in beta-strand prediction (QE > 0.82, Q2-EH = 0.86).
* Findings suggest training bias, not just non-local contacts, contributes to strand prediction difficulties.
* The method demonstrates superior performance compared to existing approaches.

Conclusions:

* conSSert offers a significant advancement in predicting protein secondary structures, particularly beta-strands.
* The developed method addresses limitations in current prediction techniques.
* conSSert is available as a free webservice for non-commercial use.