●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

D-I-TASSER (Distance-guided Iterative Threading ASSEmbly Refinement) is a new method extended from I-TASSER for high-accuracy protein structure and function predictions. Starting from a query sequence, D-I-TASSER first generates inter-residue contact maps, distance maps and hydrogen-bond (HB) networks using multiple deep neural-network predictors, including AttentionPotential (self-attention network built on MSA transformer) and DeepPotential. Meanwhile, it identifies structural templates from the PDB by the meta-threading LOMETS3 approach. The full-length atomic models are finally assembled by iterative fragment assembly Monte Carlo simultions under the guidance of I-TASSER force field and deep-learning contact/distance/HB restraints, where biological functions of the query protein are derived from the structure models by COFACTOR. The large-scale benchmark tests showed that D-I-TASSER generates significantly more accurate models than I-TASSER, especially for the sequences that do not have homologous templates in the PDB. D-I-TASSER server provides an optional D-I-TASSER-AF2 pipeline, which incorporates AlphaFold2 restraints with D-I-TASSER and generates models with average accuracy higher than both D-I-TASSER and AlphaFold2. Please report problems and questions at our Discussion Board.

Copy and paste your sequence below ([10, 750] residues in FASTA format). Click here for a sample input: Or upload the sequence from your local computer: Email: (mandatory, where results will be sent to) ID: (optional, your given name of the protein) Advanced options [?] Predict protein function based on structure model (running time may be doubled). Using large IMG/JGI metagenomic database to build MSA (additional one or two days maybe applied). Using D-I-TASSER-AF2 pipeline (additional one or two days maybe applied). (Please submit a new job only after your old job is completed)

• Wei Zheng, Yang Li, Qiqige Wuyun, Xiaogen Zhou, Chengxin Zhang, Robin Pearce, Eric W. Bell, Yiheng Zhu, Yang Zhang. Integrating deep neural network models with I-TASSER for accurate protein structure prediction. Submitted, 2022.