Naoyuki Asakawa

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.

Characteristic amino acid distribution around segments unique to allergens.

Epitopes are located at the surface of allergens with which antibodies specifically bind. On the assumption that fragments unique to allergens have common, characteristic amino acid sequences, we compared the amino acid sequences of allergens with those of non-allergens. Segments around fragments unique to allergens showed wavelet-like distributions for several amino acids. Charged residues, alanine and glycine had positive peaks at the centre of the unique segments with small valleys on both sides, while aromatic residues, proline and cysteine showed the inverse distribution. Furthermore, the wavelet-like distribution of amino acids could be represented by a universal distribution function together with an index characterizing the intensity of the wavelet. Using the universal distribution function and the novel index of amino acids, we developed a simple method for extracting segments and fragments that are unique to allergens. The significance of the universal distribution function and the novel index is also discussed, by comparing the plot of the allergen-unique fragments index and dynamic fluctuation in the three dimensional structure of birch pollen allergen as both a single molecule and a complex with the corresponding antibody.