Validation of protein models by a neural network approach

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.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.The very large and continuously increasing amount of data obtained by genome sequencing makes the development of reliable computational methods capable to infer protein structures from sequences a crucial step for functional annotation of proteins. In fact, functional annotation is often strictly dependent on the availability of structural data, which in turn are still difficult to obtain experimentally. As a consequence, efforts and progresses in high throughput X-ray and NMR methods need to be accompanied by computational techniques suitable for three-dimensional structure predictions, such as homology modeling, fold recognition or ab-initio methods [1-7], which are intrinsically characterized by different levels of accuracy.In parallel to the development and improvement of prediction methods, reliable and accurate evaluation tools are necessary to check the quality of computational protein models [8,9]. Moreover, in the context of prot