LOMETS: A local meta-threading-server for protein structure prediction

By Sitao Wu, Yang Zhang. 2007

797 citations recorded by the source corpus.

We developed LOMETS, a local threading meta-server, for quick and automated predictions of protein tertiary structures and spatial constraints. Nine state-of-the-art threading programs are installed and run in a local computer cluster, which ensure the quick generation of initial threading alignments compared with traditional remote-server-based meta-servers. Consensus models are generated from the top predictions of the component-threading servers, which are at least 7% more accurate than the best individual servers based on TM-score at a t-test significance level of 0.1%. Moreover, side-chain and C-alpha (Cα) contacts of 42 and 61% accuracy respectively, as well as long- and short-range distant maps, are automatically constructed from the threading alignments. These data can be easily used as constraints to guide the ab initio procedures such as TASSER for further protein tertiary structure modeling. The LOMETS server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/LOMETS.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, methodology): “The IntFOLD5-TS method was newly developed for CASP13 and worked via iterative multi-template based modelling (19) using the target-template alignments from 14 alternative methods (SP3 (31), SPARKS2 (31), HHsearch (32), COMA (33), SPARKSX (34), CNFsearch (35) and the eight alternative threading meth”
I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation (2017, methodology): “There were four targets (PDB ID: 1NNX, 2O1Q, 3B79, 3DOJ) in the test cases for which the LOMETS-based MR succeeded but I-TASSER-MR failed.”
I-TASSER server: new development for protein structure and function predictions (2015, methodology): “For a given query sequence, I-TASSER first identifies structural templates or super secondary structure motifs from the PDB library (19) using LOMETS (20), a meta-threading program that consists of multiple threading algorithms.”
Ab Initio Protein Structure Assembly Using Continuous Structure Fragments and Optimized Knowledge-based Force Field (2012, neutral): “22 We manually check the native structures of the 665 protein chains and exclude the targets which have obvious broken chains or incompact shapes.”
Template-based protein structure prediction in CASP11 and retrospect of I-TASSER in the last decade (2015, methodology): “The query sequence of the target protein is first threaded through the PDB library by LOMETS,7 a meta-threading approach containing multiple individual The flowchart of the template based modeling (TBM) by “Zhang-Server” in CASP11.”
Deep-learning contact-map guided protein structure prediction in CASP13 (2019, methodology): “Meanwhile, templates were identified for each domain by threading them through LOMETS, 22 where the domains were classified as “ Trivial, ” “ Easy, ” “ Hard, ” or “ Very Hard ” 23 based on the quality and number of threading alignments.”
Toward the solution of the protein structure prediction problem (2021, methodology): “For example, I-TASSER, which is an extension of TASSER, uses multiple templates identi ﬁ ed by LOMETS; compared with TASSER, the main difference of I-TASSER is that, following clustering of the low-energy decoys and selection of the cluster centroid, the centroid is searched through the PDB library ”
Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13 (2019, methodology): “The core methodology of ThreaDom is threading the query sequence through the PDB library using LOMETS 40 (22).”
Protein model discrimination using mutational sensitivity derived from deep sequencing. (2012, methodology): “Homology-based modeling and threading are two widely used approaches to derive structural models for a given protein sequence (Eswar et al., 2006; Kiefer et al., 2009; Lobley et al., 2009; Meller and Elber, 2001; Sali and Blundell, 1993; Wu and Zhang, 2007).”
Strategies and molecular tools to fight antimicrobial resistance: resistome, transcriptome, and antimicrobial peptides (2013, background): “…servers threading (PPA-I, SP3, PPA-II, sparks2, PROSPECT2, FUGUE, HHSEARCH, PAINT, SAM-T02), allowing the selection of models through research for 30 models for each of the individual servers, excluding short alignments, and defining models of greater structural similarity (Wu and Zhang, 2007).”
ResQ: An approach to unified estimation of B-factor and residue-specific error in protein structure prediction (2015, methodology): “Starting from the query sequence, I-TASSER first identifies multiple template structures from the PDB by LOMETS, which consists of multiple threading programs [3].”
Recognizing protein substructure similarity using segmental threading. (2010, background): “The long-range distance restraints are taken from the first four templates with an average error as reported earlier (Wu and Zhang, 2007).”
Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12 (2017, methodology): “15 If the target is classified as “ hard ” or “ very-hard ” based on the significance and consensus of the templates identified by LOMETS, the templates are re-ordered by their structural similarity to the QUARK models of the target.”
Atomic-level protein structure refinement using fragment guided molecular dynamics conformation sampling (2011, methodology): “In the contemporary TBM, multiple templates are often identified through metathreading techniques (Fischer, 2003; Ginalski et al., 2003; Wu and Zhang, 2007), and full-length models are built by combining the structural fragments/restraints from multiple templates (Cheng, 2008; Raman et al.”
MUSTER: Improving protein sequence profile–profile alignments by using multiple sources of structure information (2008, methodology): “…meta-server development, we show that the performance of PPA is comparable to the best single threading algorithms used in LOMETS30 which includes FUGUE,15 HHSEARCH,18 PROSPECT2,23 SAM-T02,65SPARKS2,13 SP3,14 PAINT,30 although the template libraries used in these method are not the same as PPA.”
Using iterative fragment assembly and progressive sequence truncation to facilitate phasing and crystal structure determination of distantly related proteins. (2016, methodology): “…structure library derived from the Protein Data Bank (PDB) to search for structural templates and supersecondary-structure motifs using LOMETS (Wu & Zhang, 2007), a meta-threading algorithm consisting of eight distinct programs: FFAS-3D (Xu et al., 2014), HHsearch (Söding, 2005), MUSTER (Wu…”
Detecting distant-homology protein structures by aligning deep neural-network based contact maps (2019, methodology): “Based on LOMETS [38], which categorizes proteins as Easy or Hard threading targets depending on the significance scores of multiple threading programs, this dataset contained 403 Easy targets (for which at least one program in LOMETS had a significant template hit) and 211 Hard targets (for which no”