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.
Highlights
IntroductionNeural network models for predicting Post-translational modifications (PTMs) functionality their function
As improved techniques in mass spectrometry have quickened the identification of Post-translational modifications (PTMs), the rate of experimental observation of these modifications has far outpaced our ability to qualifyPLOS ONE | DOI:10.1371/journal.pone.0172572 February 22, 2017Neural network models for predicting PTM functionality their function
Prediction of PTM function potential becomes a classification problem whereby each PTM is ranked by the combination of its Modified Alignment Positions (MAPs) features and its similarity from MAP feature combinations that have been linked with biological function a priori
Summary
Neural network models for predicting PTM functionality their function. It has been demonstrated that biological function is neither necessary nor equivalent amongst the observed PTMs [3,4,5]. Predictive models would directly diagnose the functional significance of a PTM site. This is unfeasible without an initial framework that establishes a relationship between biological features of PTM sites, or rather Modified Alignment Positions (MAPs) (e.g. sequence conservation, protein interface residence, observation frequency, neighbors, etc.) and PTM function status (functional or non functional). The classification problem becomes one of discerning PTMs with reported evidence (or high suspicion) of function from those for which a functional role for the modification has not yet been reported. Prediction of PTM function potential becomes a classification problem whereby each PTM is ranked by the combination of its MAP features and its similarity (or difference) from MAP feature combinations that have been linked with biological function a priori
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