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

Imai K, Asakawa N, Tsuji T, Akazawa F, Ino A, Sonoyama M, Mitaku S - Bioinformation (2008)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Physics, Graduate School of Engineering, Nagoya University, Furocho, Chikusa-ku, Nagoya 464-8606, Japan. imai@bp.nuap.nagoya-u.ac.jp

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

A simplified SOSUI-GramN flowchart.
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Related In: Results  -  Collection


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Figure 1: A simplified SOSUI-GramN flowchart.

Mentions: The prediction procedure of the SOSUI-GramN system consists of three layers of filters may reflects several pathways for the same subcellular localization site as shown in Figure 1. The first layer is the filter, consisting of SOSUI [1,2], required for distinguishing inner membrane proteins from the other classes of proteins. Some physiochemical parameter such as the distribution of hydrophobicty and amphiphilicity index [11,12] of amino acids around transmembrane regions and the size of protein, were combined in SOSUI to discriminate an inner membrane protein from other types of proteins. SOSUI achieved 98.0% accuracy of prediction for inner (or cytoplasmic) membrane protein of Prokayote. Because of its high accuracy, this system can distinctly differentiate inner membrane proteins from other proteins.


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

Imai K, Asakawa N, Tsuji T, Akazawa F, Ino A, Sonoyama M, Mitaku S - Bioinformation (2008)

A simplified SOSUI-GramN flowchart.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2533062&req=5

Figure 1: A simplified SOSUI-GramN flowchart.
Mentions: The prediction procedure of the SOSUI-GramN system consists of three layers of filters may reflects several pathways for the same subcellular localization site as shown in Figure 1. The first layer is the filter, consisting of SOSUI [1,2], required for distinguishing inner membrane proteins from the other classes of proteins. Some physiochemical parameter such as the distribution of hydrophobicty and amphiphilicity index [11,12] of amino acids around transmembrane regions and the size of protein, were combined in SOSUI to discriminate an inner membrane protein from other types of proteins. SOSUI achieved 98.0% accuracy of prediction for inner (or cytoplasmic) membrane protein of Prokayote. Because of its high accuracy, this system can distinctly differentiate inner membrane proteins from other proteins.

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Physics, Graduate School of Engineering, Nagoya University, Furocho, Chikusa-ku, Nagoya 464-8606, Japan. imai@bp.nuap.nagoya-u.ac.jp

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus