Prediction of lipid-binding regions in cytoplasmic and extracellular loops of membrane proteins as exemplified by protein translocation membrane proteins.

The presence of possible lipid-binding regions in the cytoplasmic or extracellular loops of membrane proteins with an emphasis on protein translocation membrane proteins was investigated in this study using bioinformatics. Recent developments in approaches recognizing lipid-binding regions in proteins were found to be promising. In this study a total bioinformatics approach specialized in identifying lipid-binding helical regions in proteins was explored. Two features of the protein translocation membrane proteins, the position of the transmembrane regions and the identification of additional lipid-binding regions, were analyzed. A number of well-studied protein translocation membrane protein structures were checked in order to demonstrate the predictive value of the bioinformatics approach. Furthermore, the results demonstrated that lipid-binding regions in the cytoplasmic and extracellular loops in protein translocation membrane proteins can be predicted, and it is proposed that the interaction of these regions with phospholipids is important for proper functioning during protein translocation.

The prediction of novel multiple lipid-binding regions in protein translocation motor proteins: a possible general feature.

Protein translocation is an important cellular process. SecA is an essential protein component in the Sec system, as it contains the molecular motor that facilitates protein translocation. In this study, a bioinformatics approach was applied in the search for possible lipid-binding helix regions in protein translocation motor proteins. Novel lipid-binding regions in Escherichia coli SecA were identified. Remarkably, multiple lipid-binding sites were also identified in other motor proteins such as BiP, which is involved in ER protein translocation. The prokaryotic signal recognition particle receptor FtsY, though not a motor protein, is in many ways related to SecA, and was therefore included in this study. The results demonstrate a possible general feature for motor proteins involved in protein translocation.

New user-friendly approach to obtain an Eisenberg plot and its use as a practical tool in protein sequence analysis.

The Eisenberg plot or hydrophobic moment plot methodology is one of the most frequently used methods of bioinformatics. Bioinformatics is more and more recognized as a helpful tool in Life Sciences in general, and recent developments in approaches recognizing lipid binding regions in proteins are promising in this respect. In this study a bioinformatics approach specialized in identifying lipid binding helical regions in proteins was used to obtain an Eisenberg plot. The validity of the Heliquest generated hydrophobic moment plot was checked and exemplified. This study indicates that the Eisenberg plot methodology can be transferred to another hydrophobicity scale and renders a user-friendly approach which can be utilized in routine checks in protein-lipid interaction and in protein and peptide lipid binding characterization studies. A combined approach seems to be advantageous and results in a powerful tool in the search of helical lipid-binding regions in proteins and peptides. The strength and limitations of the Eisenberg plot approach itself are discussed as well. The presented approach not only leads to a better understanding of the nature of the protein-lipid interactions but also provides a user-friendly tool for the search of lipid-binding regions in proteins and peptides.

The role and significance of potential lipid-binding regions in the mitochondrial protein import motor: an in-depth in silico study.

Over the last two decades, an impressive progress has been made in the identification of novel factors in the translocation machineries of the mitochondrial protein import and their possible roles. The role of lipids and possible protein-lipids interactions remains a relatively unexplored territory. Investigating the role of potential lipid-binding regions in the sub-units of the mitochondrial motor might help to shed some more light in our understanding of protein-lipid interactions mechanistically. Bioinformatics results seem to indicate multiple potential lipid-binding regions in each of the sub-units. The subsequent characterization of some of those regions in silico provides insight into the mechanistic functioning of this intriguing and essential part of the protein translocation machinery. Details about the way the regions interact with phospholipids were found by the use of Monte Carlo simulations. For example, Pam18 contains one possible transmembrane region and two tilted surface bound conformations upon interaction with phospholipids. The results demonstrate that the presented bioinformatics approach might be useful in an attempt to expand the knowledge of the possible role of protein-lipid interactions in the mitochondrial protein translocation process.

Identification and in silico analysis of helical lipid binding regions in proteins belonging to the amphitropic protein family.

The role of protein-lipid interactions is increasingly recognized to be of importance in numerous biological processes. Bioinformatics is being increasingly used as a helpful tool in studying protein-lipid interactions. Especially recently developed approaches recognizing lipid binding regions in proteins can be implemented. In this study one of those bioinformatics approaches specialized in identifying lipid binding helical regions in proteins is expanded. The approach is explored further by features which can be easily obtained manually. Some interesting examples of members of the amphitropic protein family have been investigated in order to demonstrate the additional features of this bioinformatics approach. The results in this study seem to indicate interesting characteristics of amphitropic proteins and provide insight into the mechanistic functioning and overall understanding of this intriguing class of proteins. Additionally, the results demonstrate that the presented bioinformatics approach might be either an interesting starting point in protein-lipid interactions studies or a good tool for selecting new focus points for more detailed experimental research of proteins with known overall protein-lipid binding abilities.