D-I-TASSER (Deep learning-based 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 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, Please report problems and questions at our Discussion Board.
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