DMFold: deep multiple sequence alignment based protein folding

DMFold (also known as DMFold-Multimer) is a deep learning-based approach to protein complex structure and function prediction built on deep multiple sequence alignments (MSAs). The core of the pipeline is the integration of DeepMSA2 with a modified structure module of AlphaFold2. Starting from a set of query sequences, DMFold first creates deep monomeric MSAs using an iterative search procedure through multiple whole-genome (Uniclust30 and UniRef90) and metagenome (Metaclust, BFD, Mgnify, TaraDB, MetaSourceDB and JGIclust) databases, where multimeric MSAs are then constructed by pairing the monomeric MSAs based on species annotations. Next, complex structure models are predicted by integrating the multimetic MSAs with structural modules of AlphaFold2-Multimer, where funtional annotations, including Gene Ontology, Enzyme Commission and Ligand Binding Sites, are generated by COFACTOR2 and US-align ased on the top DMFold structure models. DMFold participated (as "Zheng") in CASP15 and ranked as the No. 1 method for PPI complex structure prediction, with accuracy significantly higher than the state-of-the-art AlphaFold2 program (i.e., "NBIS-AF2-multimer" in CASP15). Although DMFold focuses on multi-chain protein complexes, it also accepts single-chain monomer sequences ('DMFold-Monomer' pipeline). The server is freely accessible to all users, including commercial ones.

[Example of complex] [Example of monomer] [Check Jobs]

1. Copy and paste your sequence (in FASTA format) below (currently accepting both monomer and complex sequences with length in [30, 1400 AA] for monomers, and in [30, 2000 AA] for complexes with < 10 chains): [Example inputs of monomer] [Example inputs of complex]

Or you can upload your fasta file. [Click for an example monomer fasta file] [Click for an example complex fasta file]

Email: (mandatory, where results will be sent to)

ID: (optional, your given name of the protein)

Advanced options

(Please submit a new job only after your old job is completed)

References:

Wei Zheng, Qiqige Wuyun, Yang Li, Chengxin Zhang, P Lydia Freddolino, Yang Zhang. Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data. Nature Methods, 21: 279-289 (2024). (and Sipporting Information)
Wei Zheng, Quancheng Liu, Qiqige Wuyun, P. Lydia Freddolino, Yang Zhang. DMFold: A deep learning platform for protein complex structure and function predictions based on DeepMSA2. In preparation.

1. About DMFold

2. Submission form

3. Reference