Prediction of non-classical secreted proteins using informative physicochemical properties.
The prediction of non-classical secreted proteins is a significant problem for drug discovery and development of disease diagnosis. The characteristic of non-classical secreted proteins is they are leaderless proteins without signal peptides in N-terminal. This characteristic makes the prediction of non-classical proteins more difficult and complicated than the classical secreted proteins. We identify a set of informative physicochemical properties of amino acid indices cooperated with support vector machine (SVM) to find discrimination between secreted and non-secreted proteins and to predict non-classical secreted proteins. When the sequence identity of dataset was reduced to 25%, the prediction accuracy on training dataset is 85% which is much better than the traditional sequence similarity-based BLAST or PSI-BLAST tool. The accuracy of independent test is 82%. The most effective features of prediction revealed the fundamental differences of physicochemical properties between secreted and non-secreted proteins. The interpretable and valuable information could be beneficial for drug discovery or the development of new blood biochemical examinations.
Hung
Chiung-Hui
CH
Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.
Huang
Hui-Ling
HL
Hsu
Kai-Ti
KT
Ho
Shinn-Jang
SJ
Ho
Shinn-Ying
SY
D000595:Amino Acid Sequence; D000596:Amino Acids / Q000032:analysis*; D055808:Drug Discovery; D006403:Hematologic Tests / Q000379:methods; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods*; D060388:Support Vector Machine
D000596:Amino Acids; D011506:Proteins