title: Systematic analysis of non-structural protein features for the prediction of PTM function potential by artificial neural networks.
abstract: Post-translational modifications (PTMs) provide an extensible framework for regulation of protein behavior beyond the diversity represented within the genome alone. While the rate of identification of PTMs has rapidly increased in recent years, our knowledge of PTM functionality encompasses less than 5% of this data. We previously developed SAPH-ire (Structural Analysis of PTM Hotspots) for the prioritization of eukaryotic PTMs based on function potential of discrete modified alignment positions (MAPs) in a set of 8 protein families. A proteome-wide expansion of the dataset to all families of PTM-bearing, eukaryotic proteins with a representational crystal structure and the application of artificial neural network (ANN) models demonstrated the broader applicability of this approach. Although structural features of proteins have been repeatedly demonstrated to be predictive of PTM functionality, the availability of adequately resolved 3D structures in the Protein Data Bank (PDB) limits the scope of these methods. In order to bridge this gap and capture the larger set of PTM-bearing proteins without an available, homologous structure, we explored all available MAP features as ANN inputs to identify predictive models that do not rely on 3D protein structural data. This systematic, algorithmic approach explores 8 available input features in exhaustive combinations (247 models; size 2-8). To control for potential bias in random sampling for holdback in training sets, we iterated each model across 100 randomized, sample training and testing sets-yielding 24,700 individual ANNs. The size of the analyzed dataset and iterative generation of ANNs represents the largest and most thorough investigation of predictive models for PTM functionality to date. Comparison of input layer combinations allows us to quantify ANN performance with a high degree of confidence and subsequently select a top-ranked, robust fit model which highlights 3,687 MAPs, including 10,933 PTMs with a high probability of biological impact but without a currently known functional role.
authors.affiliation: School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
authors.forename: Henry M
authors.identifier: http://orcid.org/0000-0001-6838-8628
authors.initials: HM
authors.lastname: Dewhurst
authors.affiliation: School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
authors.forename: Matthew P
authors.identifier: http://orcid.org/0000-0001-7296-4536
authors.initials: MP
authors.lastname: Torres
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D013058:Mass Spectrometry; D016571:Neural Networks, Computer; D011499:Protein Processing, Post-Translational; D020543:Proteome / Q000378:metabolism*
chemical_list: D020543:Proteome