期刊
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
卷 17, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/ijms17081215
关键词
protein-protein interfaces; hot-spots; machine learning; Solvent Accessible Surface Area (SASA); evolutionary sequence conservation
资金
- Fundacao para a Ciencia e a Tecnologia [FCT-SFRH/BPD/97650/2013]
- FCT [UID/Multi/04349/2013, IF/00578/2014, UID/NEU/04539/2013]
- European Social Fund
- Programa Operacional Potencial Humano
- Marie Sklodowska-Curie Individual Fellowship [MEMBRANEPROT 659826]
- FEDER (Programa Operacional Factores de Competitividade-COMPETE)
- Center for Basic and Translational Research on Disorders of the Digestive System, Rockefeller University
- Icahn School of Medicine at Mount Sinai
Understanding 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.
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