Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation.
BACKGROUND
With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched.


RESULTS
In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology.


CONCLUSIONS
By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.
State Key Laboratory for Molecular Virology and Genetic Engineering, Institute of Pathogen Biology, Chinese Academy Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Xianwen
X
Ren
Yong-Cui
YC
Wang
Yong
Y
Wang
Xiang-Sun
XS
Zhang
Nai-Yang
NY
Deng
D000595:Amino Acid Sequence; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
D011506:Proteins