InterPreTS: protein Interaction Prediction through Tertiary Structure. Summary: InterPreTS (Interaction Prediction through Tertiary Structure) is a web-based version of our method for predicting protein–protein interactions (Aloy and Russell, 2002, Proc. Natl Acad. Sci. USA, 99, 5896–5901). Given a pair of query sequences, we first search for homologues in a database of interacting domains (DBID) of known three-dimensional complex structures. Pairs of sequences homologous to a known interacting pair are scored for how well they preserve the atomic contacts at the interaction interface. InterPreTS includes a useful interface for visualising molecular details of any predicted interaction. Availability:

References in zbMATH (referenced in 6 articles )

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  1. Gui, Yuanmiao; Wang, Rujing; Wei, Yuanyuan; Wang, Xue: DNN-PPI: a large-scale prediction of protein-protein interactions based on deep neural networks (2019)
  2. Rasti, Saeid; Vogiatzis, Chrysafis: A survey of computational methods in protein-protein interaction networks (2019)
  3. Mier, Pablo; Alanis-Lobato, Gregorio; Andrade-Navarro, Miguel A.: Protein-protein interactions can be predicted using coiled coil co-evolution patterns (2017)
  4. Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin: Adaptive compressive learning for prediction of protein-protein interactions from primary sequence (2011)
  5. Chang, Darby Tien-Hao; Syu, Yu-Tang; Lin, Po-Chang: Predicting the protein-protein interactions using primary structures with predicted protein surface (2010) ioport
  6. Andreopoulos, Bill; Winter, Christof; Labudde, Dirk; Schroeder, Michael: Triangle network motifs predict complexes by complementing high-error interactomes with structural information (2009) ioport