Building protein-protein interaction networks for Leishmania species through protein structural information

Crhisllane Rafaele Dos Santos Vasconcelos,Túlio De Lima Campos,Antonio Mauro Rezende

doi:10.1186/s12859-018-2105-6

Crhisllane Rafaele Dos Santos Vasconcelos, Túlio De Lima Campos + Show 1 more

Open Access

https://doi.org/10.1186/s12859-018-2105-6

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Abstract

BackgroundSystematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs.ResultsThe predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability.ConclusionsThe present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.

Highlights

Systematic analysis of a parasite interactome is a key approach to understand different biological processes
Observing the number of reported cases of leishmaniasis and the difficulties in the treatment and prevention, it is clear the need for approaches that allow a wider understanding of the mechanisms of the diseases, and we will be able to accelerate the steps toward the development of new drugs
Prediction of protein structures The sequences of the predict proteomes of L. braziliensis and L. infantum version 8.0 were obtained from the TriTrypDB database [45]

Summary

Introduction

Systematic analysis of a parasite interactome is a key approach to understand different biological processes. Several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. This study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. Because the problems involving experimental methods, such as cost, laboriousness and susceptibility to systemic errors, over the years, several computational methods have been developed and used to predict protein interaction networks (PIN) [18, 19]

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