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
available state-of-the-art learning-based methods.
Conclusion: In light of the results obtained with AIDE, as well as its comparison with available
learning-based methods, it can be concluded that AIDE can be successfully used to evaluate the
quality of protein structures. The use of AIDE in combination with other evaluation tools is
expected to further enhance protein refinement efforts.