●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

Methods

Starting from a query amino acid sequence, DeepMSA2 is used to search the query against multiple whole-genome and metagenome sequence databases to create a multiple sequence alignment (MSA). The MSA is then used by DeepPotential to derive input features based on co-evolutionary analyses for the deep ResNet training. DeepPotential outputs the probability distribution of Cβ-Cβ/Cα-Cα contact and distance maps, as well as the inter-residue orientations. These restraint potentials along with the inherent statistical energy function are used to guide the L-BFGS folding simulations to final full-length structure model construction (see Fig. 1).

Figure 1. Pipeline of DeepFold for deep-learning based ab initio protein structure prediction.

DeepFold was tested on a set of 221 Hard threading targets collected from SCOPe and CASP9-12 as well as the CASP13 FM targets (Figure 2). The results show that the modeling performance of DeepFold was significantly greater than other deep learning approaches such as DMPfold and trRosetta, as well as the leading template-based modeling methods such as I-TASSER. It also outperformed AlphaFold v1.0 on the CASP13 FM targets (see Fig. 2).

Figure 2. Head-to-head TM-score comparisons between DeepFold and other protein structure prediction methods: A) I-TASSER; B) C-I-TASSER; C) DMPfold; D) trRosetta; E) AlphaFold. (A-D) are based on the 221 Hard benchmark proteins, while (E) is on 31 FM targets from CASP13.

The user needs to paste the fasta-formatted amino acid sequence into the input box, or upload the amino acid sequence of the query protein using the "Choose file" button (see Fig. 3).


Figure 3. Illustration of input for the DeepFold server.

The output of the DeepFold server include:
• The full-length atomic model
• Predicted secondary structures
• Predicted contact, distance, and orientation maps
• Top 10 proteins in the PDB that are structurally closest to the predicted models
• Predicted Enzyme Classification and the confidence score (if you check the "Predict protein function based on structure model (running time may be doubled)." option of the input)
• Predicted GO terms and the confidence score (if you check the "Predict protein function based on structure model (running time may be doubled)." option of the input)
• Predicted ligand-binding sites and the confidence score (if you check the "Predict protein function based on structure model (running time may be doubled)." option of the input)
An illustrative example of the DeepFold output can be seen from below (Figs. 4-7):
• Secondary structure and contact/distance/orientation map information:
  
  

  Figure 4. Secondary structure, contact map, distance Map, and orientation information in the DeepFold output.

  
  

• Templates, final models, and analog structures:
  
  
  

  Figure 5. Final model and analog information in the DeepFold output.
  

  
  

• Gene Ontology (GO) Term prediction information:
  
  

  Figure 6. Gene Ontology (GO) Term prediction information in the DeepFold output. This information is output only after you check the "Predict protein function based on structure model (running time may be doubled)" option of the input.
  
  

• Enzyme Commission (EC) and ligand binding site prediction information:
  
  Figure 7. Enzyme Commission (EC) and ligand binding site prediction information in the DeepFold output. This information is output only after you check the "Predict protein function based on structure model (running time may be doubled)." option of the input.
  

• Robin Pearce, Yang Li, Gilbert S. Omenn, Yang Zhang. Fast and Accurate Ab Initio Protein Structure Prediction Using Deep Learning Potentials (submitted).