MUSTANG: A multiple structural alignment algorithm

By Arun Siddharth Konagurthu, James C. Whisstock, Peter James Stuckey, Arthur M. Lesk. 2006

718 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs (2020, methodology): “As seen, despite using sequence-only (ClustalOmega [27] and Muscle [28]), structure-only (Mustang [30]), or automated blended alignments (T-Coffee, TM-PSI, and Espresso (PDB Mode) [29]), the knowledge-based alignment performs the best in all regions tested.”
Advances and pitfalls of protein structural alignment. (2009, methodology): “Consistency scoring [36] has been used in the generation of multiple alignments from libraries of pairwise alignments [37,38].”
Fast Multiple Alignment of Protein Structures Using Conformational Letter Blocks (2009, methodology): “Most existing methods of multiple structural alignment combine a pairwise alignment and some heuristic with a progressive-type layout to merge pairwise alignments into a multiple alignment [6, 7, 13-16].”
MUSTANG-MR Structural Sieving Server: Applications in Protein Structural Analysis and Crystallography (2010, methodology): “MUSTANG-MR web server benefits from an intuitive interface to MUSTANG”
Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors (2013, methodology): “A structure-based sequence alignment was generated of all 14 GPCR templates using MUSTANG [28] as seen in Figure S2 (Figure 1, Step 1).”
Application of DEN refinement and automated model building to a difficult case of molecular-replacement phasing: the structure of a putative succinyl-diaminopimelate desuccinylase from Corynebacterium glutamicum (2012, methodology): “We generated a structure-based alignment between the template 1vgy-A and Cgl1109 using MUSTANG (Konagurthu et al., 2006) and compared it with predicted alignments.”
Exploiting sequence and stability information for directing nanobody stability engineering (2017, methodology): “To identify corresponding residue positions in different nanobody structures and sequences, we created a manually curated profile-to-profile alignment of a structural alignment of all nanobody chains (determined using Mustang [57]) with the nanobody MSA used for GSS analysis.”
DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment (2015, methodology): “This motivated me to look at the difference between the original benchmarks and the same sequences realigned with the sequence-independent structural alignment program MUSTANG [40].”
VIPERdb2: an enhanced and web API enabled relational database for structural virology (2008, methodology): “All the IFMSAs were computed with the package T-Coffee (9), using a consensus of several pairwise structure alignment libraries built with SAP (14), MUSTANG ( 15 ) and TM-align (16).”