Toshiyuki Tsuji

Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane–water interfaces

MOTIVATION An amphiphilicity index of amino acid residues was developed for improving the method of transmembrane helix prediction. RESULTS The transfer energy of a hydrocarbon stem group beyond the gamma-carbon was calculated from the accessible surface area, and used to index the amphiphilicity of the residue. Non-zero amphiphilicity index values were obtained for lysine, arginine, histidine, glutamic acid, glutamine, tyrosine and tryptophan. Those residues were found to be abundant in the end regions of transmembrane helices, indicating their preference for the membrane-water interface. The moving average of the amphiphilicity index actually showed significant peaks in the end regions of most transmembrane helices. A dispersion diagram of average amphiphilicity index versus average hydrophobicity index was devised to facilitate discrimination of transmembrane helices. AVAILABILITY The amphiphilicity index has been incorporated into a system, SOSUI, for the discrimination of membrane proteins and the prdiction of tranmembrane helical regions (http://sosui.proteome.bio.tuat.ac.jp/sosuiframe0.html).

SOSUI-GramN: high performance prediction for sub-cellular localization of proteins in gram-negative bacteria.

A predictive software system, SOSUI-GramN, was developed for assessing the subcellular localization of proteins in Gram-negative bacteria. The system does not require the sequence homology data of any known sequences; instead, it uses only physicochemical parameters of the N- and C-terminal signal sequences, and the total sequence. The precision of the prediction system for subcellular localization to extracellular, outer membrane, periplasm, inner membrane and cytoplasmic medium was 92.3%, 89.4%, 86.4%, 97.5% and 93.5%, respectively, with corresponding recall rates of 70.3%, 87.5%, 76.0%, 97.5% and 88.4%, respectively. The overall performance for precision and recall obtained using this method was 92.9% and 86.7%, respectively. The comparison of performance of SOSUI-GramN with that of other methods showed the performance of prediction for extracellular proteins, as well as inner and outer membrane proteins, was either superior or equivalent to that obtained with other systems. SOSUI-GramN particularly improved the accuracy for predictions of extracellular proteins which is an area of weakness common to the other methods.

Ratio of membrane proteins in total proteomes of prokaryota

The numbers of membrane proteins in the current genomes of various organisms provide an important clue about how the protein world has evolved from the aspect of membrane proteins. Numbers of membrane proteins were estimated by analyzing the total proteomes of 248 prokaryota, using the SOSUI system for membrane proteins (Hirokawa et al., Bioinformatics, 1998) and SOSUI-signal for signal peptides (Gomi et al., CBIJ, 2004). The results showed that the ratio of membrane proteins to total proteins in these proteomes was almost constant: 0.228. When amino acid sequences were randomized, setting the probability of occurrence of all amino acids to 5%, the membrane protein/total protein ratio decreased to about 0.085. However, when the same simulation was carried out, but using the amino acid composition of the above proteomes, this ratio was 0.218, which is nearly the same as that of the real proteomic systems. This fact is consistent with the birth, death and innovation (BDI) model for membrane proteins, in which transmembrane segments emerge and disappear in accordance with random mutation events.

Development of a High Performance Prediction Method for Single Spanning Membrane Proteins

Membrane proteins constitute 20-25% of open reading frames in a biological genome [1]. Previously we developed a membrane protein predictor SOSUI [2] and a signal peptide predictor SOSUIsignal [3] whose web site is visited by many researchers in the world. However, this system is not good at prediction of single spanning (TM1) membrane proteins. It is a common problem to all membrane protein prediction tools. TM1 membrane proteins occupy 30-35% of membrane proteins in a genome and have various important functions.