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

I-TASSER I-TASSER-MTD C-I-TASSER CR-I-TASSER QUARK C-QUARK DMFold 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. The output model of LOMETS server is given by both PDB format and ModelCIF format now. Please post your questions and comments about LOMETS at the Service System Discussion Board.

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


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

LOMETS On-line (Example output)



LOMETS Resource:


References:

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