Home Research COVID-19 Services Publications People Teaching Job Opening News Forum Lab Only
Online Services

I-TASSER QUARK LOMETS COACH COFACTOR MetaGO MUSTER CEthreader SEGMER FG-MD ModRefiner REMO DEMO SPRING COTH BSpred ANGLOR EDock BSP-SLIM SAXSTER FUpred ThreaDom ThreaDomEx EvoDesign GPCR-I-TASSER MAGELLAN BindProf BindProfX SSIPe ResQ IonCom STRUM DAMpred

TM-score TM-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 WDL-RF ATPbind DockRMSD DeepMSA FASPR EM-Refiner

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 new generation of meta-server approach to template-based protein structure prediction and structure-based function annotation, which 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). To model sequences without homologous templates, an L-BFGS folding system is introduced to construct full-length models from deep-learning contact/distance restraints by DeepPotential and LOMETS top templates. Large-scaled benchmark tests showed that the overall template-recognition accuracy is significantly beyond its predecessors (LOMETS and LOMETS2), due to the integration of deep-learning 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.

Update Notes: LOMETS has been updated to LOMETS3 with major updates, including:

  1. Template library: While template libraries in former LOMETS are generated separately for different threading programs, which can result in inconsistent update and completeness of template structures, a unified and comprehensive template library is now created and weekly updated for all threading programs.
  2. MSA profile: A deep multiple sequence alignment (DeepMSA) approach is developed to create deep sequence profiles from metagenome sequence databases for all template proteins, which significantly improves the accuracy of almost all the profile- and deep learning-based threading alignments.
  3. Threading programs: More than half of the old threading programs were renewed and/or replaced by the state-of-the-art methods, including those combining the cutting-edge deep-learning techniques.
  4. Re-ranking method: Residue-Residue distances, contacts, and hydrogen bond geometries are predicted from DeepPotential. A new scoring function, which combines residue distances, contacts, hydrogen bonds, and a profile score, is used to re-rank the templates for profile-based threadings.
  5. Ab initio structure modeling: An L-BFGS system is introduced to construct full-length structure models for non-homologous target sequences based on spatial restraints predicted by DeepPotential and those deduced from top threading templates.
  6. Atomic model refinement: New refinement pipeline based on FG-MD and FASPR is used to refine and re-pack the side-chain conformation of the final models.
  7. Structural analogs: TM-align is used to search the first LOMETS3 model through all structures in the PDB library, where the top 10 protein structures with the closest structural similarity, i.e., the highest TM-score, to the target are reported.
  8. Functional annotations: Completely redesigned output page, which now contains structure-based function annotations (including Gene Ontology term, Enzyme Commission number, and Ligand Binding residues) derived from threading templates.

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

Tips for modeling multi-domain proteins which are usually have >500 AA:

  1. Use FUpred, ThreaDom or ThreaDomEx servers to predict the domain boundary for your sequence, and then split the full-length sequence into domain-level sequences by FUpred, ThreaDom or ThreaDomEx domain partition information.
  2. Submit the domain-level sequences to LOMETS server to get the domain-level models, respectively.
  3. Submit the full-length sequence and domain-level models to DEMO server, which will automatically assemble the full-length model.
(You can also directly submit the full-length sequence to LOMETS server to get the model. Next, manually determine domain partitions based on the structure model of LOMETS as structural models generally show clear domain boundaries. Finally, repeat Step 2 and 3.)


[Example output]   [About LOMETS]   [Forum]   [Check Previous Jobs]

LOMETS On-line (Example output)



LOMETS Resource: LOMETS Download:


References:

yangzhanglabumich.edu | (734) 647-1549 | 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218