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I-TASSER C-I-TASSER QUARK C-QUARK LOMETS COACH COFACTOR MetaGO MUSTER CEthreader SEGMER FG-MD ModRefiner REMO DEMO SPRING COTH Threpp BSpred ANGLOR EDock BSP-SLIM SAXSTER FUpred ThreaDom ThreaDomEx EvoDesign GPCR-I-TASSER MAGELLAN BindProf BindProfX SSIPe ResQ IonCom STRUM DAMpred

TM-score TM-align MM-align RNA-align NW-align LS-align EDTSurf MVP MVP-Fit SPICKER HAAD PSSpred 3DRobot MR-REX I-TASSER-MR SVMSEQ NeBcon ResPRE TripletRes WDL-RF ATPbind DockRMSD DeepMSA FASPR EM-Refiner

BioLiP E. coli GLASS GPCR-HGmod GPCR-RD GPCR-EXP Tara-3D TM-fold DECOYS POTENTIAL RW/RWplus EvoEF HPSF THE-DB ADDRESS Alpaca-Antibody CASP7 CASP8 CASP9 CASP10 CASP11 CASP12 CASP13 CASP14




Threpp (Multimeric Threading based Protein-protein Interaction Predictor) is a computational algorithm for protein-protein interaction (PPI) prediction. Starting from a pair of protein sequences, it does two things: (1), it will judge whether the two proteins interact with each other by calculating the likelihood through a naive Bayes classifier model which combines the Threpp threading score and available high-throughput experimental (HTE) data. (2), it creates the quaternary stuctural models of the PPIs by reassembling the monomeric threading templates with the identified PPI frameworks. Large-scale benchmark tests showed that Threpp can significantly improve the precision and recall of both HTE and multimeric threading, and therefore reduce the false positive rate for the current PPI modeling approaches. The performance of the current Threpp server is optimal for predicting PPIs in E. coli, for which the integrated HTE datasets are constructed. In case that HTE data is not available, Threpp will only use the dimeric threading score to assess the PPI likelihood.

Threpp On-Line Server (An example of the Threpp output):



Threpp Download



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