●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

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.

DeepPotential server (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)

Predict protein structure based on DeepPotential (running time may be increased).

DeepPotential Download:

• Download the DeepPotential standalone package
• Download the PotentialFold standalone package
• Download the training set of DeepPotential
• Download the CASP13 FM test set
• Download the CAMEO hard test set

DeepPotential News:

• 2021/11/23: DeepPotential server and standalone package were released online.
• 2020/11/30: DeepPotential server (Group ID: 010 ) was ranked at the top-1 position in 14th community-wide CASP experiment for automated protein contact/distance-map prediction.

• Yang Li, Chengxin Zhang, Dong-Jun Yu, Yang Zhang. Deep learning geometrical potential for high-accuracy ab initio protein structure prediction. iScience, in press (2022)