PROMALS3D: a tool for multiple protein sequence and structure alignments

By Jimin Pei, Bong-Hyun Kim, Nick V. Grishin. 2008

1,296 citations recorded by the source corpus.

Although multiple sequence alignments (MSAs) are essential for a wide range of applications from structure modeling to prediction of functional sites, construction of accurate MSAs for distantly related proteins remains a largely unsolved problem. The rapidly increasing database of spatial structures is a valuable source to improve alignment quality. We explore the use of 3D structural information to guide sequence alignments constructed by our MSA program PROMALS. The resulting tool, PROMALS3D, automatically identifies homologs with known 3D structures for the input sequences, derives structural constraints through structure-based alignments and combines them with sequence constraints to construct consistency-based multiple sequence alignments. The output is a consensus alignment that brings together sequence and structural information about input proteins and their homologs. PROMALS3D can also align sequences of multiple input structures, with the output representing a multiple structure-based alignment refined in combination with sequence constraints. The advantage of PROMALS3D is that it gives researchers an easy way to produce high-quality alignments consistent with both sequences and structures of proteins. PROMALS3D outperforms a number of existing methods for constructing multiple sequence or structural alignments using both reference-dependent and reference-independent evaluation methods.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability (2013, methodology): “This was the basis of the 3D Coffee program (O’Sullivan et al. 2004), and later the PROMALS3D package (Pei et al. 2008).”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, background): “…1992; Panchenko et al., 2000; Peng and Xu, 2009, 2010; Sippl and Weitckus, 1992; Skolnick et al., 2000), protein alignment (Notredame et al., 2000; Pei et al., 2008; Wu and Zhang, 2008b; Xu, 2005; Zhang and Skolnick , 2005), model quality assessment (Jones and Thornton, 1996; Panchenko et al.,…”
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): “PROMALS3D and HHpred correctly assigned 282 and 291 positions (of a total of 360 residues visible in the Cgl1109 structure), respectively.”
MAFFT-DASH: integrated protein sequence and structural alignment (2019, methodology): “MSA software such as Expresso (8,9) in the TCoffee package and PROMALS3D (10) allow structural information to be incorporated in order to improve accuracy when aligning remote sequence homologs.”
Ligand Discovery from a Dopamine D3 Receptor Homology Model and Crystal Structure (2011, neutral): “The initial alignment was generated using PROMALS3”
DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment (2015, methodology): “More recent approaches that make use of solved protein structures are available, such as PROMALS3D [66].”
Evolution-Based Functional Decomposition of Proteins (2015, neutral): “Standard sequence database searching algorithms (BLAST, PSI-BLAST, etc.) (29) together with automated alignment tools (e.g. PRO-MALS (30)), or available databases of multiple sequence alignments such as PFAM (31) seem to provide suitable sequence alignments.”