●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

LOMETS (Local Meta-Threading Server, version 3) is a next-generation meta-server approach to template-based protein structure prediction and structure-based function annotation. The new program integrates multiple deep learning-based threading methods (CEthreader, DisCovER, EigenThreader, Hybrid-CEthreader, MapAlign) and state-of-the-art profile-based programs (FFAS3D, HHpred, HHsearch, MRFsearch, MUSTER, SparksX). For the first time, LOMETS3 is extended to handling multi-domain proteins by introducing the domain partition (FUpred and ThreaDom) and assembly (DEMO) modules. It also introduces a new module for fast full-length model construction using a gradient-based optimization program (DeepFold), which is guided by restraints from deep-learning (DeepPotential) and LOMETS top templates. Protein functions in LOMETS3 are predicted by the modified COFACTOR method, which adds the LOMETS3 threading templates associated with structure analogs as templates in COFACTOR structure-based pipelines. Large-scaled benchmark tests showed that the overall template-recognition and full-length model construction accuracy is significantly beyond its predecessors (LOMETS and LOMETS2), due to the integration of deep-learning and multi-domain threading techniques. LOMETS3 participated in CASP14 as 'Zhang-TBM' and was ranked as one of the top methods for automatic protein structure prediction. A detailed description of the LOMETS3 server can be seen on the About LOMETS page. Please post your questions and comments about LOMETS at the Service System Discussion Board.

The output of the LOMETS server includes (Example output):

• Secondary structure prediction.
• Solvent accessibility prediction.
• Contact-map and distance-map prediction by DeepPotential.
• Domain partition results shown in contact-map (optional).
• Individual domain threading/modeling results by LOMETS (optional).
• The best ten assembled templates from individual domain threading templates (optional).
• The best ten initial threading templates selected from 110 (=11x10) templates.
• Full-length models built by an L-BFGS system (DeepFold) using distance restraints from DeepPotential and LOMETS templates.
• The best ten similar structure identified by TM-align using the first LOMETS model, and the associated functional annotations.
• Functional annotations (Gene Ontology term, Enzyme Commission number, and Ligand Binding residues) derived from top-ranking threading templates.
• Function predictions by a modified COFACTOR method using LOMETS3 threading templates and structure analogs in COFACTOR structure-based pipelines.

LOMETS On-line (Example output)

Cut and paste your sequence here (<1500 residues in FASTA format): Example input


Or upload the sequence from your local computer:

Email: (not required but recommended, where results will be sent to)

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


Advanced options

• Exclude templates:
  keep all identified templates (recommended)
  remove templates sharing >30% sequence identity with target

• Automatic domain partition and assembly:
  run domain partition and assembly (recommended, see details)
  run threading without domain partition (if you believe your protein is of single-domain)

• Function prediction by COFACTOR:
  predict protein function (need additional 1-10 hours).



See server response time here

• Wei Zheng, Qiqige Wuyun, Xiaogen Zhou, Yang Li, Peter Freddolino, Yang Zhang. LOMETS3: Integrating deep-learning and profile-alignment for advanced protein template recognition and function annotation. Nucleic Acids Research, in press, 2022.
• Wei Zheng, Chengxin Zhang, Qiqige Wuyun, Robin Pearce, Yang Li, Yang Zhang. LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins. Nucleic Acids Research, 47: W429-W436 (2019) [PDF of manuscript] [PDF of Support Information].
• Sitao Wu, Yang Zhang. LOMETS: A local meta-threading-server for protein structure prediction. Nucleic Acids Research, 35: 3375-3382 (2007) [PDF of manuscript].