PROMALS: towards accurate multiple sequence alignments of distantly related proteins

By Jimin Pei, Nick V. Grishin. 2007

356 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment (2015, unknown): “PROMALS [27] was substantially slower than the other programs that do not rely on a large external database of sequences.”
Improved accuracy of multiple ncRNA alignment by incorporating structural information into a MAFFT-based framework (2008, background): “Moreover, the accuracy of multiple alignment is improved by using information of homologs, probably because homologs make family-specific information available and enrich the profiles used in the multiple alignment processes [20-26].”
Recent developments in the MAFFT multiple sequence alignment program (2008, methodology): “SPEM [17], MUMMALS [70], PROMALS [18] and other methods incorporate predicted structural information into an alignment, like the RNA aligners noted above.”
T-Coffee: a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension (2011, result): “These include ProbCons (9), PCMA (10), MAFFT (the slow accurate mode) (11), PROMALS (12) and Pecan (13).”
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, methodology): “profile of each position of an alignment, represented as the estimated amino acid frequencies, was calculated as described before [97].”
Accurate multiple sequence alignment of transmembrane proteins with PSI-Coffee (2012, background): “Pei J, Grishin NV: PROMALS: towards accurate multiple sequence alignments of distantly related proteins.”
GOMoDo: A GPCRs Online Modeling and Docking Webserver (2013, methodology): “We have used the program PROMALS [22], following the same methodology as in refs.”
Fast Identification of Possible Drug Treatment of Coronavirus Disease −19 (COVID-19) Through Computational Drug Repurposing Study (2020, methodology): “The MSA was generated by using the Promals3D web server.(41) The structure of COVID-19 protease is found to be most similar to those of SARS protease (PDB Code 3TNT(42)) and less similar to MERS protease (PDB Code 5WKK(43)) (Figure 9A).”
The Structure, Stability and Pheromone Binding of the Male Mouse Protein Sex Pheromone Darcin (2014, methodology): “In addition comparative analysis of the MUP family employed PROMALS3D [42,43] for secondary structure driven sequence alignment and Multiprot [44] for homologous structure alignment.”
PROMALS3D: a tool for multiple protein sequence and structure alignments (2008, methodology): “790 on the PREFAB set of alignments [best among programs not using 3D structural information (10)], as compared to 0.”
A global averaging method for dynamic time warping, with applications to clustering (2011, unknown): “…for return m[S,T] Algorithm 2. first Require: p ¼ (a, b): couple return a Algorithm 3. second Require: p ¼ (a, b): couple return b Algorithm 4. minVal Require: v1, v2, v3: couple if firstðv1Þrminðfirstðv2Þ,firstðv3ÞÞ then return v1 else if firstðv2Þr firstðv3Þ then return v2 else return v3 end if”
Computational prediction of secondary and supersecondary structures. (2013, background): “These characteristics include multiple alignment (17), prediction of protein-ligand interactions (18-20), prediction of residue depth (21, 22), structural classes and folds (23-25), residue contacts (26, 27), disorder (28-30), folding rates and types (31-33), and target selection for structural geno”