A Review and Comparative Assessment of Machine Learning Approaches for Interaction Site Prediction in Membrane Proteins

Title:A Review and Comparative Assessment of Machine Learning Approaches for Interaction Site Prediction in Membrane Proteins

Volume: 10 Issue: 3

Author(s): Ebrahim Barzegari Asadabadi and Parviz Abdolmaleki

Affiliation:

Keywords: Bioinformatics, example prediction, membrane proteins, machine learning, performance comparison, protein interaction sites.

Abstract: Protein-protein interactions at membranes play an inevitable role in the function of proteins there. However, the task of studying the interactions is of many difficulties in case of the membrane proteins, due to their hydrophobic environment. This is why the use of bioinformatics methods, especially machine learning approaches is essential to understand the membrane proteins' function. However, the machine learning methods for prediction of the interaction sites of membrane proteins are faced with numerous challenges. This paper aims to describe the current state of machine learning applications in inferring the membrane protein interaction sites, and to assess the methods up to now proposed. We have introduced the membrane protein interaction site prediction methods, presented the contribution degrees of parameters at membrane protein interaction interfaces, and compared the performance of methods through several case predictions.

Cite this article as:

Asadabadi Barzegari Ebrahim and Abdolmaleki Parviz, A Review and Comparative Assessment of Machine Learning Approaches for Interaction Site Prediction in Membrane Proteins, Current Bioinformatics 2015; 10 (3) . https://dx.doi.org/10.2174/157489361003150723132234