| Autor: |
Ward KM; Department of Biology, Brigham Young University, Provo, UT 84602, USA., Pickett BD; Department of Biology, Brigham Young University, Provo, UT 84602, USA., Ebbert MTW; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA.; Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY 40506, USA.; Department of Neuroscience, University of Kentucky, Lexington, KY 40506, USA., Kauwe JSK; Department of Biology, Brigham Young University, Provo, UT 84602, USA., Miller JB; Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536, USA.; Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY 40506, USA.; Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY 40506, USA. |
| Abstrakt: |
Protein-protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins ( p = 1.03 × 10 -108 ) and 1.94 times higher in bacteria ( p = 1.22 × 10 -35 ). We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer's disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups. |
