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BMCBI
2008

Validation of protein models by a neural network approach

8 years 9 months ago
Validation of protein models by a neural network approach
Background: The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction. Results: In this contribution, we present a computational method (Artificial Intelligence Decoys Evaluator: AIDE) which is able to consistently discriminate between correct and incorrect protein models. In particular, the method is based on neural networks that use as input 15 structural parameters, which include energy, solvent accessible surface, hydrophobic contacts and secondary structure content. The results obtained with AIDE on a set of decoy structures were evaluated using statistical indicators such as Pearson correlation coefficients, Znat, fraction enrichment, as well as ROC plots. It turned out that AIDE performances are comparable and often complementary to a...
Paolo Mereghetti, Maria Luisa Ganadu, Elena Papale
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2008
Where BMCBI
Authors Paolo Mereghetti, Maria Luisa Ganadu, Elena Papaleo, Piercarlo Fantucci, Luca De Gioia
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