Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Joseph, Agnel Praveen; Srinivasan, Narayanaswamy; Brevern, Alexandre G. de

doi:10.1016/j.biochi.2012.05.028

Cited by 17 publications

(16 citation statements)

References 58 publications

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“…PBs are a structural alphabet composed of 16 local prototypes (Joseph et al, 2010) five residues in length. PBs give a reasonable approximation of all local protein 3D structures (de Brevern, Etchebest & Hazout, 2000) and are very efficient in protein superimpositions (Joseph, Srinivasan & de Brevern, 2012) and MD (Molecular Dynamics) analyses (de Brevern et al, 2005). They are labelled from a to p. PBs m and d can be roughly described as prototypes for α-helix and central β-strand, respectively.…”

Section: Local Conformational Analysismentioning

confidence: 99%

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction

Vattekatte

Shinada

Narwani

et al. 2020

PeerJ

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Antigen binding by antibodies requires precise orientation of the complementarity- determining region (CDR) loops in the variable domain to establish the correct contact surface. Members of the family Camelidae have a modified form of immunoglobulin gamma (IgG) with only heavy chains, called Heavy Chain only Antibodies (HCAb). Antigen binding in HCAbs is mediated by only three CDR loops from the single variable domain (VHH) at the N-terminus of each heavy chain. This feature of the VHH, along with their other important features, e.g., easy expression, small size, thermo-stability and hydrophilicity, made them promising candidates for therapeutics and diagnostics. Thus, to design better VHH domains, it is important to thoroughly understand their sequence and structure characteristics and relationship. In this study, sequence characteristics of VHH domains have been analysed in depth, along with their structural features using innovative approaches, namely a structural alphabet. An elaborate summary of various studies proposing structural models of VHH domains showed diversity in the algorithms used. Finally, a case study to elucidate the differences in structural models from single and multiple templates is presented. In this case study, along with the above-mentioned aspects of VHH, an exciting view of various factors in structure prediction of VHH, like template framework selection, is also discussed.

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Section: Local Conformational Analysismentioning

confidence: 99%

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction

Vattekatte

Shinada

Narwani

et al. 2020

PeerJ

Self Cite

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“…In particular we used a flexible structure alignment method [MATT; Menke et al, 2008]. This approach strips out rotational and translational information as well as the variability induced by flexible hinges, which has been shown to be more accurate than rigid body superimpositions in the assignment of homology [Menke et al, 2008, Konc and Janežič, 2010, Nguyen et al, 2011, Daniluk and Lesyng, 2011, Joseph et al, 2012, Shah and Sahinidis, 2012.…”

Section: Resultsmentioning

confidence: 99%

“…It does not allow any deformation of the structure (shape), like the flexibility that softwares like MATT have. Therefore, it is plausible to hypothesize that a flexible alignment will do better than GPS, as they do against non-flexible alignments [Menke et al, 2008, Konc and Janežič, 2010, Nguyen et al, 2011, Daniluk and Lesyng, 2011, Joseph et al, 2012, Shah and Sahinidis, 2012. Other flexible structural alignment softwares have shown a slightly better performance than MATT, however,the improvements are not significant [Joseph et al, 2012] and MATT returns more core residues than most of its competitors, as well as a statistical test of the "goodness" of the alignment [Menke et al, 2008].…”

Section: Comparing Matt Flexible Alignment and Gps: Results From The mentioning

confidence: 99%

Protein structures as shapes: Analysing protein structure variation using geometric morphometrics

Hleap

Blouin

2017

Preprint

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A phenotype is defined as an organism's physical traits. In the macroscopic world, an animal's shape is a phenotype. Geometric morphometrics (GM) can be used to analyze its shape. Let's pose protein structures as microscopic three dimensional shapes, and apply principles of GM to the analysis of macromolecules. In this paper we introduce a way to 1) abstract a structure as a shape; 2) align the shapes; and 3) perform statistical analysis to establish patterns of variation in the datasets. We show that general procrustes superimposition (GPS) can be replaced by multiple structure alignment without changing the outcome of the test. We also show that estimating the deformation of the shape (structure) can be informative to analyze relative residue variations. Finally, we show an application of GM for two protein structure datasets: 1) in the α-amylase dataset we demonstrate the relationship between structure, function, and how the 1 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/219030 doi: bioRxiv preprint first posted online Nov. 13, 2017; dependency of chloride has an important effect on the structure; and 2) in the Niemann-Pick disease, type C1 (NPC1) protein's molecular dynamic simulation dataset, we introduce a simple way to analyze the trajectory of the simulation by means of protein structure variation.

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“…PBs a through c primarily represent the N-cap of β-strand, whereas e and f correspond to C-caps; PBs g through j are specific to coils, PBs k and l correspond to N cap of α –helix and PBs n through p to C-caps. They have been used in various approaches, for example in protein superimposition (24,25), and for the analysis (26,27) or prediction (28,29) of protein binding sites. PB assignment was done with a slightly modified Python PBxplore tool (https://github.com/pierrepo/PBxplore).…”

Section: Methodsmentioning

confidence: 99%

PTM-SD: a database of structurally resolved and annotated posttranslational modifications in proteins

Craveur¹,

Rebehmed²,

Brevern³

2014

Database

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Posttranslational modifications (PTMs) define covalent and chemical modifications of protein residues. They play important roles in modulating various biological functions. Current PTM databases contain important sequence annotations but do not provide informative 3D structural resource about these modifications. Posttranslational modification structural database (PTM-SD) provides access to structurally solved modified residues, which are experimentally annotated as PTMs. It combines different PTM information and annotation gathered from other databases, e.g. Protein DataBank for the protein structures and dbPTM and PTMCuration for fine sequence annotation. PTM-SD gives an accurate detection of PTMs in structural data. PTM-SD can be browsed by PDB id, UniProt accession number, organism and classic PTM annotation. Advanced queries can also be performed, i.e. detailed PTM annotations, amino acid type, secondary structure, SCOP class classification, PDB chain length and number of PTMs by chain. Statistics and analyses can be computed on a selected dataset of PTMs. Each PTM entry is detailed in a dedicated page with information on the protein sequence, local conformation with secondary structure and Protein Blocks. PTM-SD gives valuable information on observed PTMs in protein 3D structure, which is of great interest for studying sequence–structure– function relationships at the light of PTMs, and could provide insights for comparative modeling and PTM predictions protocols.Database URL: PTM-SD can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/PTM-SD/.

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Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Cited by 17 publications

References 58 publications

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction

Protein structures as shapes: Analysing protein structure variation using geometric morphometrics

PTM-SD: a database of structurally resolved and annotated posttranslational modifications in proteins

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