Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder.

Ahmet Sinan Yavuz,Osman Ugur Sezerman

doi:10.1186/1471-2164-15-s9-s18

Abstract

BackgroundSumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism.ResultsIn this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner.ConclusionsBy using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction.

Highlights

Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell
The possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction
Small ubiquitin-like modifier (SUMO) proteins are small proteins with an approximate size of 10 kD. They have a high structural similarity with ubiquitin, while sharing up to only 20% sequence identity. In spite of this structural similarity, SUMO proteins have an unstructured stretch of 10-25 amino acids at their N termini, which is not seen in ubiquitin proteins [1]

Summary

Introduction

Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Small ubiquitin-like modifier (SUMO) proteins are small proteins with an approximate size of 10 kD They have a high structural similarity with ubiquitin, while sharing up to only 20% sequence identity. SUMO proteins are inactive when they are synthesized and they become activated after the cleavage of a C terminal peptide (2-11 amino acids long) by sentrin-specific proteases, exposing an invariant Gly-Gly motif [1]. After this cleavage, SUMO proteins become ready for the sumoylation process and attachment to a target protein

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Conclusion