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About LOMETS Sever
LOMETS (LOcal MEta-Threading-Server) is a meta-server method for protein
structure prediction . 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 .
Template ranking scheme and list of threading programs in LOMETS
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.
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 
2 HHpred 83.0 0.589 
3 SparksX 6.9 0.587 
4 FFAS3D 33.0 0.574 
5 wMUSTER 8.7 0.570
6 MUSTER 6.1 0.569 
7 HHsearch 10.0 0.567 
8 SP3 7.0 0.566 
9 PPAS 7.6 0.562 
10 PROSPECT2 3.2 0.558 
11 PRC 21.0 0.536 
Figure 1. Pipeline of LOMETS.
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.
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 derived from aligned templates.
Illustration of outputs
 Wu S, Zhang Y. LOMETS: A local meta-threading-server for protein structure prediction. Nucleic Acids Research. 35, 3375-3382 (2007).
Back to the LOMETS homepage
 Wu S, Zhang Y. MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information.
Proteins, 72, 547-556 (2008).
 Soding, J. (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics (Oxford, England), 21, 951-960.
 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.
 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.
 Xu, Y. and Xu, D. (2000) Protein threading using PROSPECT: design and evaluation. Proteins, 40, 343-354.
 Madera, M. (2008) Profile Comparer: a program for scoring and aligning profilehidden Markov models. Bioinformatics. 24(22):2630-2631.
 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.
 Zheng et al, in preparation.
 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)