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Pcons: a neural-network-based consensus predictor that improves fold recognition.

J Lundström¹, L Rychlewski, J Bujnicki

¹Stockholm Bioinformatics Center, Stockholm University, SE 10691 Stockholm, Sweden.

Protein Science : a Publication of the Protein Society

|October 18, 2001

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Pcons, a neural-network consensus predictor, improves protein fold recognition by combining predictions from multiple servers. This method enhances accuracy and specificity compared to individual servers, aiding protein structure prediction.

Area of Science:

Computational biology
Structural bioinformatics
Machine learning in bioinformatics

Background:

Numerous protein fold recognition methods exist, utilizing diverse algorithms and data.
Evaluating these methods through benchmarks and real-world data reveals that combining diverse approaches can enhance prediction accuracy.

Purpose of the Study:

To develop and evaluate Pcons, a neural-network-based consensus predictor for protein fold recognition.
To improve the accuracy and specificity of protein structure prediction by intelligently combining predictions from multiple servers.

Main Methods:

Pcons integrates predictions from six different protein structure prediction servers.
It uses a neural network to translate server confidence scores into uniform accuracy values.

Related Experiment Videos

Pcons analyzes the similarity between models generated by different servers for final selection.

Main Results:

Pcons outperforms individual prediction servers, achieving 8-10% more correct predictions on newly solved proteins.
The specificity of Pcons is significantly higher than that of any single server.
Analysis indicates that model similarity measurement is key to Pcons's improved performance.

Conclusions:

Pcons effectively enhances protein fold recognition accuracy and specificity by consensus prediction.
The method demonstrates the power of combining diverse prediction strategies.
Pcons is available to the academic community via a web server for protein structure prediction.