The Struct2Net program predicts protein-protein interactions (PPI) by integrating structure-based information with other functional annotations, e.g. GO, co-expression and co-localization etc. The structure-based protein interaction prediction is conducted using a protein threading server RAPTOR plus logistic regression.
Coev2Net is a general framework to predict, assess and boost confidence in individual interactions inferred from a HTP experiment. For every pair of interaction in the HTP screen, Coev2Net provides a score to assess their likelihood of being co-evolved from interacting homologous sequences.
LocFuse is a novel ensemble learning method of human protein-protein interaction prediction via classifier fusion using protein localization information.
The iLoops Server uses the loop classification as defined in ArchDB and/or the SCOP classification of domains to predict whether or not a pair of proteins interact.
HIPPIE (Human Integrated Protein-Protein Interaction rEference) is a human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets.
ProDGe (Protein Domain Gene) visualizes existing and suggests novel domaindomain interactions and protein-protein interactions at the domain level. The comprehensive dataset behind ProDGe consists of protein, domain and interaction information for both layers, collected and combined appropriately from UniProt, Pfam, DOMINE and IntAct. Based on known domain interactions, ProDGe suggests novel protein interactions and assigns them to four confidence classes, depending on the reliability of the underlying domain interaction. Furthermore, ProDGe is able to identify potential homologous interaction partners in other species, which is particularly helpful when investigating poorly annotated species.
ModLink+ is a software to predict the fold of a target protein sequence. ModLink+includes an improved procedure for extrapolating links that iteratively varies the number of interactions required to consider a protein as a hub. This new algorithm, that comprises a ‘self-adaptive’ definition of hub proteins, has increased applicability without affecting its accuracy.
NOXclass is a classifier identifying protein-protein interaction types (biological obligate, biological non-obligate and crystal packing) implemented using a support vector machine (SVM) algorithm. NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available.