title: Exploring Mouse Protein Function via Multiple Approaches.
abstract: Although the number of available protein sequences is growing exponentially, functional protein annotations lag far behind. Therefore, accurate identification of protein functions remains one of the major challenges in molecular biology. In this study, we presented a novel approach to predict mouse protein functions. The approach was a sequential combination of a similarity-based approach, an interaction-based approach and a pseudo amino acid composition-based approach. The method achieved an accuracy of about 0.8450 for the 1st-order predictions in the leave-one-out and ten-fold cross-validations. For the results yielded by the leave-one-out cross-validation, although the similarity-based approach alone achieved an accuracy of 0.8756, it was unable to predict the functions of proteins with no homologues. Comparatively, the pseudo amino acid composition-based approach alone reached an accuracy of 0.6786. Although the accuracy was lower than that of the previous approach, it could predict the functions of almost all proteins, even proteins with no homologues. Therefore, the combined method balanced the advantages and disadvantages of both approaches to achieve efficient performance. Furthermore, the results yielded by the ten-fold cross-validation indicate that the combined method is still effective and stable when there are no close homologs are available. However, the accuracy of the predicted functions can only be determined according to known protein functions based on current knowledge. Many protein functions remain unknown. By exploring the functions of proteins for which the 1st-order predicted functions are wrong but the 2nd-order predicted functions are correct, the 1st-order wrongly predicted functions were shown to be closely associated with the genes encoding the proteins. The so-called wrongly predicted functions could also potentially be correct upon future experimental verification. Therefore, the accuracy of the presented method may be much higher in reality.
authors.affiliation: Department of Mathematics, Shaoyang University, Shaoyang, Hunan, 422000, China.
authors.forename: Guohua
authors.initials: G
authors.lastname: Huang
authors.affiliation: Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
authors.forename: Chen
authors.initials: C
authors.lastname: Chu
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
authors.forename: Xiangyin
authors.initials: X
authors.lastname: Kong
authors.affiliation: College of Life Science, Anhui Agricultural University, Hefei, Anhui, 230036, China.
authors.forename: Yunhua
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Engineering, Tianjin Key Lab of Biomedical Engineering Measurement, Tianjin University, Tianjin, China.
authors.forename: Ning
authors.initials: N
authors.lastname: Zhang
authors.affiliation: School of Life Sciences, Shanghai University, 99 Shangda Road, Shanghai, 200444, China.
authors.forename: Yu-Dong
authors.identifier: http://orcid.org/0000-0001-5664-7979
authors.initials: YD
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence / Q000235:genetics; D000818:Animals; D019295:Computational Biology; D030562:Databases, Protein; D051379:Mice; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D020539:Sequence Analysis, Protein; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins