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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

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DeepPotential is a method for protein inter-residue geometry and full-length 3D structure prediction. For a query sequence, DeepPotential starts with the collection of deep multiple sequence alignments (MSAs) through whole-genome and metagenome sequence databases. Next, a complimentary set of coevolutionay feature matrices extracted from the selected MSAs and are used to predict geometry maps with deep multi-tasking ResNet. The full-length structure is quickly constructed by PotentialFold through the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm.


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