A Genetic Algorithm for Scale-Based Translocon Simulation

Discriminating between secreted and membrane proteins is a challenging task. A recent and important discovery to understand the machinery responsible of the insertion of membrane proteins was the results of Hessa experiments [9]. The authors developed a model system for measuring the ability of insertion of engineered hydrophobic amino acid segments in the membrane. The main results of these experiments are summarized in a new ”biological hydrophobicity scale”. In this scale, each amino acid is represented by a curve that indicates its contribution to the process of protein insertion according to its position inside the membrane. We follow the same hypothesis as Hessa but we propose to determine “in silico” the hydrophobicity scale. This goal is formalized as an optimization problem, where we try to define a set of curves that gives the best discrimination between signal peptide and protein segments which cross the membrane. This paper describes the genetic algorithm that we developed to solve this problem and the experiments that we conducted to assess its performance

In silico evaluation of the influence of the translocon on partitioning of membrane segments

Background The locations of the TM segments inside the membrane proteins are the consequence of a cascade of several events: the localizing of the nascent chain to the membrane, its insertion through the translocon, and the conformation adopted to reach its stable state inside the lipid bilayer. Even though the hydrophobic h-region of signal peptides and a typical TM segment are both composed of mostly hydrophobic side chains, the translocon has the ability to determine whether a given segment is to be inserted into the membrane. Our goal is to acquire robust biological insights into the influence of the translocon on membrane insertion of helices, obtained from the in silico discrimination between signal peptides and transmembrane segments of bitopic proteins. Therefore, by exploiting this subtle difference, we produce an optimized scale that evaluates the tendency of each amino acid to form sequences destined for membrane insertion by the translocon. Results The learning phase of our approach is conducted on carefully chosen data and easily converges on an optimal solution called the PMIscale (Potential Membrane Insertion scale). Our study leads to two striking results. Firstly, with a very simple sliding-window prediction method, PMIscale enables an efficient discrimination between signal peptides and signal anchors. Secondly, PMIscale is also able to identify TM segments and to localize them within protein sequences. Conclusions Despite its simplicity, the localization method based on PMIscale nearly attains the highest level of TM topography prediction accuracy as the current state-of-the-art prediction methods. These observations confirm the prominent role of the translocon in the localization of TM segments and suggest several biological hypotheses about the physical properties of the translocon

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