Online Services

●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

Back to the LOMETS homepage

About LOMETS Sever

Overview

LOMETS (LOcal MEta-Threading-Server) is a meta-server method for protein structure prediction [1]. It generates protein structure predictions by ranking and selecting models from multiple state-of-the-art threading programs. Starting from a query sequence, deep multiple sequence alignments (MSAs) are generated by iterative sequence homolog searches through multiple sequence databases. 11 programs, which are all locally installed in our cluster, are implemented to identify structure templates from the PDB library, where top templates are ranked and selected by a score combining Z-score of alignment, confidence of programs and the sequence identity to the query. The functional annotations (including gene ontology and enzyme commission) are generated by matching the template structures with the function library [10].

A flowchart of the LOMETS pipeline is depicted in Figure 1, where user can refer individual methods to the references given at the bottom of the page.

Template ranking scheme and list of threading programs in LOMETS

For a given target, 220 templates are generated by 11 component servers, where each server generates 20 templates as sorted by their Z-scores in each algorithm. The best 10 templates are finally selected from the 220 templates based on the following scoring function:

     score(i,j)=Z(i,j)/Z0(i) * conf(i)+seq_id(i,j)

where Z(i,j) is the Z-score of j-th tmplate of i-th server, Z0(i) is the Z-score cutoff for defining good/bad templates for the i-th server, conf(i) is the confidence of i-th server which is defined the average TM-score to native of all predictions calculated from a large-scale benchmark test. seq_id(i,j) is the sequence identity to query of j-th template of i-th server. The parameters are listed in the following table:

         i  Server(i)    Z0(i)  conf(i)  Reference
         -  ---------   ------  -------  ---------
         1  CEthreader    5.6    0.617   [9]
         2  HHpred       83.0    0.589   [3]
         3  SparksX       6.9    0.587   [5]
         4  FFAS3D       33.0    0.574   [8]
         5  wMUSTER       8.7    0.570   
         6  MUSTER        6.1    0.569   [2]
         7  HHsearch     10.0    0.567   [3]
         8  SP3           7.0    0.566   [4]
         9  PPAS          7.6    0.562   [1]
        10  PROSPECT2     3.2    0.558   [6]
        11  PRC          21.0    0.536   [7]

LOMETS flowchart
Figure 1. Pipeline of LOMETS.

User Inputs

The user need to paste the fasta format amino acid sequence to input box, or upload the amino acid sequence of the query protein through browse button.

Figure 2. User inputs.

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.

Content in output page

The outputs of LOMETS contain:

(i) top 10 consensus templates and associated alignments;
(ii) top 5 full-length models;
(iii) top-rank templates from each threading program;
(iv) functional annotations^[10] derived from aligned templates.

Illustration of outputs

• LOMETS Top Templates:

  
  Figure 3. LOMETS summary output.
  

  
  
• Full-length Models:

  
  Figure 4. Models.
  

  
  
• Individual Threadings And Fuctional Annotations:

  
  Figure 5. Individual threadings and fuctional annotations.
  

---

References:

[1] Wu S, Zhang Y. LOMETS: A local meta-threading-server for protein structure prediction. Nucleic Acids Research. 35, 3375-3382 (2007).
[2] Wu S, Zhang Y. MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins, 72, 547-556 (2008).
[3] Soding, J. (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics (Oxford, England), 21, 951-960.
[4] Zhou, H. and Zhou, Y. (2004) Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition. Proteins, 55, 1005-1013.
[5] Zhou, H. and Zhou, Y. (2005) Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins, 58, 321-328.
[6] Xu, Y. and Xu, D. (2000) Protein threading using PROSPECT: design and evaluation. Proteins, 40, 343-354.
[7] Madera, M. (2008) Profile Comparer: a program for scoring and aligning profilehidden Markov models. Bioinformatics. 24(22):2630-2631.
[8] Xu D, Jaroszewski L, Li Z, Godzik A. (2014) FFAS-3D: improving fold recognition by including optimized structural features and template re-ranking. Bioinformatics. 30(5): 660-7.
[9] Zheng et al, in preparation.
[10] Yang J, Roy A, Zhang Y. BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions, Nucleic Acids Research, 41: D1096-D1103 (2013)

Back to the LOMETS homepage