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

●TM-score ●TM-align ●US-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 ●DeepPotential ●WDL-RF ●ATPbind ●DockRMSD ●DeepMSA ●FASPR ●EM-Refiner ●GPU-I-TASSER

●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

ResPRE is an algorithm for protein residue-residue contact-map prediction. Starting from a query sequence, multiple sequence alignments (MSAs) are collected from sequence databases. The inverse covariance matrix, or precision matrix, of the MSAs are then used to train the contact models through deep residual convolutional neural networks. The major advantage of ResPRE is in the use of precision matrix which helps to rule out translational noises of covariance correlations, while the the residual network with parallel shortcut layer connections increases the learning efficiency of the deep convolutional neural network training.

ResPRE On-line ( View example output)

Cut and paste your sequence, in plain text format. Example input


Or upload sequence from your computer:

Email: (mandatory, where results will be sent to.)

ID: (optional, name of the protein)

ResPRE Download:

• Standalone package of ResPRE: Download
• Training set of ResPRE: Download
• Test set of ResPRE: Download

• Yang Li, Jun Hu, Chengxin Zhang, Dong-Jun Yu, and Yang Zhang. ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks. Bioinformatics, 35: 4647-4655 (2019). (Download PDF and Support Information).