Abstract:
:Identification and analysis of types of biological protein-protein interactions and their interfaces to predict obligate and non-obligate complexes is a problem that has drawn the attention of the research community in the past few years. In this paper, we propose a prediction approach to predict these two types of complexes. We use desolvation energies - amino acid and atom type - of the residues present in the interface. The prediction is performed via two state-of-the-art classification techniques, namely linear dimensionality reduction (LDR) and support vector machines (SVM). The results on a newly compiled data set, namely BPPI, which is a joint and modified version of two well-known data sets consisting of 213 obligate and 303 non-obligate complexes, show that the best prediction is achieved with SVM (76.94% accuracy) when using desolvation energies of atom-type features. Also, the proposed approach outperforms the previous solvent accessible area-based approaches using SVM (75% accuracy) and LDR (73.06% accuracy). Moreover, a visual analysis of desolvation energies in obligate and non-obligate complexes shows that a few atom-type pairs are good descriptors for these types of complexes.
journal_name
Proteomicsjournal_title
Proteomicsauthors
Aziz MM,Maleki M,Rueda L,Raza M,Banerjee Sdoi
10.1002/pmic.201100186subject
Has Abstractpub_date
2011-10-01 00:00:00pages
3802-10issue
19eissn
1615-9853issn
1615-9861journal_volume
11pub_type
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