●I-TASSER ●D-I-TASSER ●I-TASSER-MTD ●C-I-TASSER ●CR-I-TASSER ●QUARK ●C-QUARK ●D-QUARK ●DRfold ●DRfold2 ●LOMETS ●MUSTER ●CEthreader ●SEGMER ●DeepFold ●DeepFoldRNA ●FoldDesign ●COFACTOR ●COACH ●MetaGO ●TripletGO ●IonCom ●FG-MD ●ModRefiner ●REMO ●DEMO ●DEMO-EM ●DMFold ●SPRING ●COTH ●Threpp ●PEPPI ●BSpred ●ANGLOR ●EDock ●BSP-SLIM ●SAXSTER ●FUpred ●ThreaDom ●ThreaDomEx ●EvoDesign ●BindProf ●BindProfX ●SSIPe ●GPCR-I-TASSER ●MAGELLAN ●ResQ ●STRUM ●DAMpred ●TCRfinder

●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 ●DeepMSA2 ●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 (Deep learning-based Iterative Threading ASSEmbly Refinement) is a new method extended from I-TASSER for deep learning-based, high-accuracy protein structure and function predictions. Starting from a query sequence, D-I-TASSER first creates multiple sequence alignments (MSAs) by DeepMSA2 via iteratively searching of genomics and metagenomics sequence databases, where inter-residue contact/distance maps and hydrogen-bond (HB) networks are generated by three complementary deep neural-network predictors from DeepPotential, AttentionPotential, and AlphaFold2 (optional in 'Advanced options'). Meanwhile, multiple template alignmens are identified from the PDB by the DeepMSA2-guided meta-threading program LOMETS3. The full-length structural models are finally constructed by iterative fragment assembly Monte Carlo simultions under the guidance of the I-TASSER force field and deep-learning contact/distance/HB restraints, where a new domain spliting and reassemly module is introduced for modelling large-size multi-domain proteins. Finally, the biological functions of the query protein are derived using the structure-based function annotation method COFACTOR.

The D-I-TASSER pipeline (as 'UM-TBM') ranked as the No. 1 server in both Single-domain and Multi-domain Sections in the most recent CASP15 experiment. Notably, D-I-TASSER achieves higher accuracy than both AlphaFold2 and AlphaFold3 in recent CASP experiments and large-scale benchmark evaluations. The server is freely accessible to all users, including commercial ones. Please report problems and questions at our Discussion Board, and our developers will study and answer the questions accordingly. ( >>More about the server ...)

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: (optional, where results will be sent to) ID: (optional, your given name of the protein) Advanced options [?] Predict protein function based on structure model (additional one or two days maybe applied). Using large IMG/JGI metagenomic database to build MSA (additional one or two days maybe applied). Using AlphaFold2 distances (additional one or two days maybe applied). Using domain partition and assembly module (additional one or two days maybe applied). (Please submit a new job only after your old job is completed)

• Wei Zheng, Qiqige Wuyun, Yang Li, Quancheng Liu, Xiaogen Zhou, Chunxiang Peng, Yiheng Zhu, Lydia Freddolino, Yang Zhang. Deep learning-based single- and multi-domain protein structure prediction with D-I-TASSER. Nature Biotechnology, in press (2025).