Multi-neighborhood search for discrimination of signal peptides and transmembrane segments

Abstract. A key step in study of biosynthesis of membrane proteins is to look for the code that could be used to explain and predict which proteins would eventually be inserted in the membrane and which proteins would be secreted into the ER lumen when they cross the translocon channel. The aim of this work is to present an improvement of a previous method based on a local search approach. The proposed method relies on new in-depth biological observations to design a new search space for the local search algorithm. Experiments conducted on a dedicated dataset show that our new approach leads to improved outcomes in terms of prediction rates

A Local Search Appproach for Transmembrane Segment and Signal Peptide Discrimination

Abstract. Discriminating between secreted and membrane proteins is a challenging task. This is particularly true for discriminating between transmembrane segments and signal peptides because they have common biochemical properties. In this paper, we introduce a new predictive method called LSTranslocon (Local Search Translocon) based on a Local Search methodology. The method takes advantage of the latest knowledge in the field to model the biological behaviors of proteins with the aim of ensuring good prediction. The LS Prediction approach is assessed on a constructed data set from Swiss-Prot database and compared with one of the best methods from the literature