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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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Updated: May 2, 2026

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

Context-based features enhance protein secondary structure prediction accuracy.

Ashraf Yaseen¹, Yaohang Li

¹Department of Computer Science, Old Dominion University , Norfolk, Virginia 23529, United States.

Journal of Chemical Information and Modeling

|February 28, 2014

Summary

This summary is machine-generated.

We developed a new method using statistical context-based scores to improve neural network accuracy for protein secondary structure prediction. This approach enhances prediction of beta-sheets and alpha-helices, leading to more confident and accurate results.

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Last Updated: May 2, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

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

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

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

Published on: July 25, 2013

Area of Science:

Computational Biology
Bioinformatics
Structural Biology

Background:

Accurate protein secondary structure prediction is crucial for understanding protein function.
Existing neural network methods struggle to fully account for interdependencies between neighboring residue structures.

Purpose of the Study:

To introduce and evaluate a novel approach for enhancing secondary structure prediction accuracy.
To leverage statistical context-based scores as features for neural network training.

Main Methods:

Developed context-based scores using pseudo-potentials to capture high-order inter-residue interactions.
Encoded these scores as features for training neural networks.
Performed 7-fold cross-validation on over 7987 protein chains.

Main Results:

Achieved 82.74% Q3 accuracy and 86.25% Segment Overlap Accuracy (SOV).
Outperformed popular prediction servers like Psipred and Jpred on benchmark datasets.
Observed a significant >4% improvement in SOV accuracy and >15% increase in high-confidence predictions.

Conclusions:

Context-based scores effectively improve neural network-based secondary structure prediction.
The method shows particular strength in predicting alpha-helices and beta-sheets.
Online prediction servers (SCORPION) implementing these methods are now available.