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●I-TASSER ●QUARK ●LOMETS ●COACH ●COFACTOR ●MUSTER ●SEGMER ●FG-MD ●ModRefiner ●REMO ●SPRING ●COTH ●BSpred ●SVMSEQ ●ANGLOR ●BSP-SLIM ●SAXSTER ●ThreaDom ●EvoDesign ●GPCR-I-TASSER ●BindProf ●ResQ ●IonCom ●STRUM

●TM-score ●TM-align ●MMalign ●NWalign ●EDTSurf ●MVP ●MVP-Fit ●SPICKER ●HAAD ●PSSpred ●3DRobot ●I-TASSER-MR

●BioLiP ●E. coli ●GLASS ●GPCR-HGmod ●GPCR-RD ●GPCR-EXP ●TM-fold ●DECOYS ●POTENTIAL ●RW ●HPSF ●CASP7 ●CASP8 ●CASP9 ●CASP10 ●CASP11

LOMETS combines the result of the following state-of-the-art threading programs:

• MUSTER (MUS) ^[2]
  
• HHSEARCH (IIIe) ^[3]
  
• HHSEARCH-I (IIIj) ^[3]
  
• HHSEARCH-2 (HHP) ^[3]
  
• SPARKS-X (SPX) ^[4]
  
• SP3 (VVV) ^[5]
  
• PROSPECT2 (BBB) ^[6]
  
• cdPPAS (WWW) ^[7]
  
• Neff-PPAS (JJJb) ^[7]
  
• wdPPAS (RRR6) ^[7]
  
• FFAS03 (RRR3) ^[8]
  
• SAM (PRC) ^[9]
  
• pGenTHREADER (pgen) ^[10]
  
• FFAS-3D (FF3) ^[11]
  
• RAPTOR-X (RAP2) ^[12]
  There are three settings of HHsearch implemented in LOMETS: HHP, IIIj, IIIe. LOMETS input is sequence. But HHsearch input is Hidden Markov Model (HMM). Therefore, a multiple sequence alignment (MSA) has to be built for query sequence to generate the HMM. HHP, IIIj, IIIe differ in the way they generates the MSA. HHP uses hhblits to generate MSA. IIIj uses legacy PSI-BLAST (blastpgp) to generate MSA. IIIe uses an in-house program provided by Dr J Soeding which also uses blastpgp to generate MSA. In the HHsearch step, IIIe emphasizes local alignment quality, while IIIj emphasizes global alignment quailty.

Advanced Options.
Exclude templates: LOMETS derive models from known PDB structures (templates). If "remove templates sharing >30% sequence identity with target" was choosen, templates will not be generated from template structures that are highly homologous to target sequence. In general, excluding homologous templates will make structure prediction harder. So this option is only for benchmarking purposes.

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References:
[1] Zhang, Yang. "Interplay of I‐TASSER and QUARK for template‐based and ab initio protein structure prediction in CASP10." Proteins: Structure, Function, and Bioinformatics 82.S2 (2014): 175-187.
[2] Wu, Sitao, and Yang Zhang. "MUSTER: improving protein sequence profile–profile alignments by using multiple sources of structure information." Proteins: Structure, Function, and Bioinformatics 72.2 (2008): 547-556.
[3] Soding, Johannes. "Protein homology detection by HMM–HMM comparison." Bioinformatics 21.7 (2005): 951-960.
[4] Zhou, Hongyi, and Yaoqi Zhou. "Single‐body residue‐level knowledge‐based energy score combined with sequence‐profile and secondary structure information for fold recognition." Proteins: Structure, Function, and Bioinformatics 55.4 (2004): 1005-1013.
[5] Zhou, Hongyi, and Yaoqi Zhou. "Fold recognition by combining sequence profiles derived from evolution and from depth‐dependent structural alignment of fragments." Proteins: Structure, Function, and Bioinformatics 58.2 (2005): 321-328.
[6] Xu, Ying, and Dong Xu. "Protein threading using PROSPECT: design and evaluation." Proteins: Structure, Function, and Bioinformatics 40.3 (2000): 343-354.
[7] Yan, Renxiang, et al. "A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction." Scientific reports 3 (2013).
[8] Jaroszewski, Lukasz, et al. "FFAS03: a server for profile–profile sequence alignments." Nucleic acids research 33.suppl 2 (2005): W284-W288.
[9] Madera, Martin. "Profile Comparer: a program for scoring and aligning profile hidden Markov models." Bioinformatics 24.22 (2008): 2630-2631.
[10] Lobley, Anna, Michael I. Sadowski, and David T. Jones. "pGenTHREADER and pDomTHREADER: new methods for improved protein fold recognition and superfamily discrimination." Bioinformatics 25.14 (2009): 1761-1767.
[11] Xu, Dong, et al. "FFAS-3D: improving fold recognition by including optimized structural features and template re-ranking." Bioinformatics (2013): btt578.
[12] Ma, Jianzhu, et al. "Protein threading using context-specific alignment potential." Bioinformatics 29.13 (2013): i257-i265.