Neural network system for the evaluation of side-chain packing in protein structures.
An artificial neural network system is used for pattern recognition in protein side-chain-side-chain contact maps. A back-propagation network was trained on a set of patterns which are popular in side-chain contact maps of protein structures. Several neural network architectures and different training parameters were tested to decide on the best combination for the neural network. The resulting network can distinguish between original (from protein structures) and randomized patterns with an accuracy of 84.5% and a Matthews' coefficient of 0.72 for the testing set. Applications of this system for protein structure evaluation and refinement are also proposed. Examples include structures obtained after the application of molecular dynamics to crystal structures, structures obtained from X-ray crystallography at various stages of refinement, structures obtained from a de novo folding algorithm and deliberately misfolded structures.
D000595:Amino Acid Sequence; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D004268:DNA-Binding Proteins; D005069:Evaluation Studies as Topic; D005656:Fungal Proteins / Q000737:chemistry; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017510:Protein Folding; D011494:Protein Kinases / Q000737:chemistry; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D029701:Saccharomyces cerevisiae Proteins
D004268:DNA-Binding Proteins; D005656:Fungal Proteins; D029701:Saccharomyces cerevisiae Proteins; D011494:Protein Kinases