Open AccessBi-Factor Analysis Based on Noise-Reduction (BIFANR): A New Algorithm for Detecting Coevolving Amino Acid Sites in Proteins
Author(s) -
Juntao Liu,
Xiaoyun Duan,
Jianyang Sun,
Yanbin Yin,
Guojun Li,
Lushan Wang,
Bingqiang Liu
Publication year - 2013
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0079764
Subject(s) - independence (probability theory) , amino acid , robustness (evolution) , protein structure prediction , protein sequencing , protein structure , evolutionary algorithm , biology , peptide sequence , algorithm , bioinformatics , computer science , mathematics , statistics , genetics , machine learning , biochemistry , gene
Previous statistical analyses have shown that amino acid sites in a protein evolve in a correlated way instead of independently. Even though located distantly in the linear sequence, the coevolved amino acids could be spatially adjacent in the tertiary structure, and constitute specific protein sectors. Moreover, these protein sectors are independent of one another in structure, function, and even evolution. Thus, systematic studies on protein sectors inside a protein will contribute to the clarification of protein function. In this paper, we propose a new algorithm BIFANR (Bi-factor Analysis Based on Noise-reduction) for detecting protein sectors in amino acid sequences. After applying BIFANR on S1A family and PDZ family, we carried out internal correlation test, statistical independence test, evolutionary rate analysis, evolutionary independence analysis, and function analysis to assess the prediction. The results showed that the amino acids in certain predicted protein sector are closely correlated in structure, function, and evolution, while protein sectors are nearly statistically independent. The results also indicated that the protein sectors have distinct evolutionary directions. In addition, compared with other algorithms, BIFANR has higher accuracy and robustness under the influence of noise sites.