A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces
AbstractUnderstanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set. View Full-Text
- Supplementary File 1:
Supplementary (ZIP, 665 KB)
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Melo, R.; Fieldhouse, R.; Melo, A.; Correia, J.D.G.; Cordeiro, M.N.D.S.; Gümüş, Z.H.; Costa, J.; Bonvin, A.M.J.J.; Moreira, I.S. A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces. Int. J. Mol. Sci. 2016, 17, 1215.
Melo R, Fieldhouse R, Melo A, Correia JDG, Cordeiro MNDS, Gümüş ZH, Costa J, Bonvin AMJJ, Moreira IS. A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces. International Journal of Molecular Sciences. 2016; 17(8):1215.Chicago/Turabian Style
Melo, Rita; Fieldhouse, Robert; Melo, André; Correia, João D.G.; Cordeiro, Maria N.D.S.; Gümüş, Zeynep H.; Costa, Joaquim; Bonvin, Alexandre M.J.J.; Moreira, Irina S. 2016. "A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces." Int. J. Mol. Sci. 17, no. 8: 1215.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.