title: Neural network pairwise interaction fields for protein model quality assessment and ab initio protein folding.
abstract: In order to use a predicted protein structure one needs to know how good it is, as the utility of a model depends on its quality. To this aim, many Model Quality Assessment Programs (MQAP) have been developed over the last decade, with MQAP also being assessed at the CASP competition. We present a new knowledge-based MQAP which evaluates single protein structure models. We use a tree representation of the Cα trace to train a novel Neural Network Pairwise Interaction Field (NN-PIF) to predict the global quality of a model. NN-PIF allows fast evaluation of multiple structure models for a single sequence. In our tests on a large set of structures, our networks outperform most other methods based on different and more complex protein structure representations in global model quality prediction. Moreover, given NN-PIF can evaluate protein conformations very fast, we train a separate version of the model to gauge its ability to fold protein structures ab initio. We show that the resulting system, which relies only on basic information about the sequence and the Cα trace of a conformation, generally improves the quality of the structures it is presented with and may yield promising predictions in the absence of structural templates, although more research is required to harness the full potential of the model.
authors.affiliation: School of Computer Science and Informatics and Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin 4, Ireland.
authors.forename: Alberto J M
authors.initials: AJ
authors.lastname: Martin
authors.forename: Claudio
authors.initials: C
authors.lastname: Mirabello
authors.forename: Gianluca
authors.initials: G
authors.lastname: Pollastri
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins