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I-TASSER I-TASSER-MTD C-I-TASSER CR-I-TASSER QUARK C-QUARK LOMETS MUSTER CEthreader SEGMER 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


DeepFold is a deep-learning based method for ab initio protein structure prediction. Starting from a query sequence, it first collects multiple sequence alignments (MSAs) from whole- and meta-genome sequence libraries. Spatial restraints (contact/distance maps and inter-residue orientations) are then predicted by DeepPotential, a convolutional residual neural network model. Finally, full-length structural models are constructed using an L-BFGS folding algorithm.

[Download Standalone DeepFold Package] [Download Benchmark Dataset] [Help] [Forum]

DeepFold Online Server (View example output):
References:

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