●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

DRfold2 is a cutting-edge method for RNA tertiary structure prediction, built on deep learning and a novel composite language model. Given a query RNA sequence, DRfold2 leverages its pre-trained RNA Composite Language Model to capture co-evolutionary patterns and secondary structure information. Rotation matrices and translation vectors for each nucleotide are predicted through end-to-end deep learning frameworks, enabling precise global topology and base pairing modeling. The conformations are further refined using geometry-based optimization, significantly enhancing structure accuracy. Benchmark results show DRfold2 achieves up to 100% higher unsupervised contact precision compared to its predecessor. Moreover, DRfold2 complements AlphaFold3, providing statistically significant improvements when combined through our hybrid optimization framework. Check [Help] page for more details.

DRfold2 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)

• Yang Li, Chenjie Feng, Xi Zhang, Yang Zhang. "Ab initio RNA structure prediction with composite language model and denoised end-to-end learning", submitted.