MUSTER: Improving protein sequence profile–profile alignments by using multiple sources of structure information

By Sitao Wu, Yang Zhang. 2008

391 citations recorded by the source corpus.

We develop a new threading algorithm MUSTER by extending the previous sequence profile–profile alignment method, PPA. It combines various sequence and structure information into single‐body terms which can be conveniently used in dynamic programming search: (1) sequence profiles; (2) secondary structures; (3) structure fragment profiles; (4) solvent accessibility; (5) dihedral torsion angles; (6) hydrophobic scoring matrix. The balance of the weighting parameters is optimized by a grading search based on the average TM‐score of 111 training proteins which shows a better performance than using the conventional optimization methods based on the PROSUP database. The algorithm is tested on 500 nonhomologous proteins independent of the training sets. After removing the homologous templates with a sequence identity to the target >30%, in 224 cases, the first template alignment has the correct topology with a TM‐score >0.5. Even with a more stringent cutoff by removing the templates with a sequence identity >20% or detectable by PSI‐BLAST with an E‐value <0.05, MUSTER is able to identify correct folds in 137 cases with the first model of TM‐score >0.5. Dependent on the homology cutoffs, the average TM‐score of the first threading alignments by MUSTER is 5.1–6.3% higher than that by PPA. This improvement is statistically significant by the Wilcoxon signed rank test with a P‐value < 1.0 × 10−13, which demonstrates the effect of additional structural information on the protein fold recognition. The MUSTER server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/MUSTER. Proteins 2008. © 2008 Wiley‐Liss, Inc.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

A position-specific distance-dependent statistical potential for protein structure and functional study (2012, methodology): “The window size that we used is consistent with what was used by the Zhang group (Wu and Zhang, 2008a), and the numbers of hidden neurons are not very different from what was used by the Zhou group (Xue et al., 2009).”
Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia (2011, methodology): “These results were refined with a model performed at the Muster serve”
Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021, methodology): “include torsion angles, secondary structure, and solvent accessibility [2].”
Generating Triangulated Macromolecular Surfaces by Euclidean Distance Transform (2009, methodology): “This provides an essential frame for matching the predicted solvent accessibility with that of template structures in our fold-recognition algorithm [ 34 ].”
Comparative Protein Structure Modeling Using MODELLER (2016, methodology): “Commonly used threading programs are GenTHREADER (Jones, 1999; McGufﬁn and Jones, 2003), 3D-PSSM (Kelley et al., 2000), FUGUE (Shi et al., 2001), SP3 (Zhou and Zhou, 2005), SAM-T08 multi-track HMM (Karchin et al., 2003; Karplus et al., 2003), and MUSTER (Wu and Zhang, 2008).”
ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction (2008, methodology): “We have recently combined the phi and psi angle predictions into a profile-profile alignment algorithm [30], where the input features for the angle prediction are similar as ANGLOR but both phi and psi predictions were trained by SVM [8].”
Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, positive): “It integrates 11 state-of-the-art threading programs: one contact-based threading program CEthreader (Zheng et al., 2019b), three proﬁle HMM-based threading programs HHpred (Meier and So¨ding, 2015), HHsearch (So¨ding, 2005), and PRC (Madera, 2008), and seven proﬁle-based threading programs FFAS3D (”
iFC2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content (2010, background): “2005), discrimination of outer membrane proteins (Gromiha 2005a, b; Gromiha and Suwa 2005), target selection for structural genomics (Mizianty and Kurgan 2009b; Kurgan and Mizianty 2009), and in prediction of tertiary structure (Chou 2004; Wu and Zhang 2008), residue–residue contacts (Björkholm et a”
CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction (2016, methodology): “These methods can either rely on sequences only or employ structure features as well, where the latter is also called threading (Cheng and Baldi, 2006; Jo et al., 2015; Karplus et al., 1998; Wang et al., 2012a,b; Wu and Zhang, 2008).”
Toward the solution of the protein structure prediction problem (2021, methodology): “…both the approach of Bowie et al. and PSI-BLAST by using local structural features, either predicted for the protein of interest or derived from templates, and sequence pro ﬁ les, represented by PSSMs or HMMs, to identify distantly homologous templates for a given protein sequence (91, 93, 94).”