iPBA: a tool for protein structure comparison using sequence alignment strategies

Gelly, Jean‐Christophe; Joseph, Agnel Praveen; Srinivasan, Narayanaswamy; Brevern, Alexandre G. de

doi:10.1093/nar/gkr333

Cited by 92 publications

(83 citation statements)

References 42 publications

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“…The SRCN_3247 model contained 9 beta-sheets, 11 alpha and 5 3,10 helices while SRCN_5165 contains 9 beta-sheets, 11 alpha and 6 3,10 helices. Using the improved Protein Block Alignment (iPBA) webserver [58], structural superposition of the models to the top-ranking templates was performed for prediction of pair-wise structural alignment. The structural similarity score was measured using root mean square deviation (RMSD) between both Cα atom of the modeled protein and the corresponding template.…”

Section: Structural Modelingmentioning

confidence: 99%

<em>Sarcocystis neurona</em> Protein Kinases: Computational Identification, Evolutionary Analysis and Putative Inhibitor Docking

Murungi

Kariithi

2017

Preprint

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Abstract:The apicomplexan parasite, Sarcocystis neurona causes the degenerative neurological equine protozoal myeloencephalitis (EPM) disease of horses. Due to its host range expansion, S. neurona is an emerging threat that requires close monitoring. In apicomplexans, protein kinases (PKs) have been implicated in a myriad of critical functions such as host cell invasion, cell cycle progression and host immune responses evasion. Here, we used various bioinformatics methods to define the kinome of S. neurona and phylogenetic relatedness of its PKs to other apicomplexans. Further, three-dimensional (3D) homology models for selected S. neurona putative PKs were constructed and evaluated for inhibitor docking. We identified 92 putative PKs clustering within the AGC, CAMK, CK1, CMGC, STE, TKL, aPK and OPK groups. Although containing the universally conserved PKA (AGC group), S. neurona kinome was devoid of PKB and PKC, but contained the six apicomplexan conserved CDPKs (CAMK group). The OPK group was represented by ROPKs 19A, 27, 30, 33, 35 and 37, but was devoid of the virulence-associated ROPKs 5, 6, 18 and 38. Two out of the three S. neurona CK1 enzymes had high sequence similarities to T. gondii TgCK1-α and TgCK1-β and the Plasmodium PfCK1. Docking of four inhibitors onto homology models of putative ROP27 and PKA indicated that inhibition of S. neurona PKs is feasible, but needs to be experimentally tested. The essentiality of apicomplexan PKs makes the elucidation of S. neurona kinome a key milestone for development of novel therapeutics for EPM.

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Section: Structural Modelingmentioning

confidence: 99%

<em>Sarcocystis neurona</em> Protein Kinases: Computational Identification, Evolutionary Analysis and Putative Inhibitor Docking

Murungi

Kariithi

2017

Preprint

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“…This structural alphabet allows a reasonable approximation of local protein 3D structures [34] with a root mean square deviation (rmsd) recently evaluated to be 0.42 Å [46]. PBs [46] have been assigned using in-house Python software as in the previous studies [41,42]. Figure 1 gives an outline of the steps involved in mulPBA alignment approach.…”

Section: Protein Blocksmentioning

confidence: 99%

“…The pairwise alignments are obtained using iPBA which performs an anchor based alignment by finding structurally conserved regions, identified as local alignments [41,42]. A combination of local [47] and global [39] dynamic programming algorithms is used for the alignment.…”

Section: Pairwise Alignmentsmentioning

confidence: 99%

“…The remaining segments between anchors (linkers) are then aligned with relaxed gap penalties, using Needleman-Wunsch algorithm. The PB substitution matrix was generated using substitution frequencies obtained from alignments of domain pairs in PALI [48] with no more than 40% sequence identity [41,42].…”

Section: Pairwise Alignmentsmentioning

confidence: 99%

“…It first identifies all high scoring and structurally favorable local alignments (anchors) and then aligns the segments between them to obtain a global alignment. This improved PB based structure alignment approach (iPBA) outperformed other established methods when tested on benchmark datasets [41,42].…”

Section: Introductionmentioning

confidence: 99%

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

2012

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Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string.A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition.Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.3

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β‐Bulges: Extensive structural analyses of β‐sheets irregularities

et al. 2013

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b-Sheets are quite frequent in protein structures and are stabilized by regular main-chain hydrogen bond patterns. Irregularities in b-sheets, named b-bulges, are distorted regions between two consecutive hydrogen bonds. They disrupt the classical alternation of side chain direction and can alter the directionality of b-strands. They are implicated in protein-protein interactions and are introduced to avoid b-strand aggregation. Five different types of b-bulges are defined. Previous studies on b-bulges were performed on a limited number of protein structures or one specific family. These studies evoked a potential conservation during evolution. In this work, we analyze the bbulge distribution and conservation in terms of local backbone conformations and amino acid composition. Our dataset consists of 66 times more b-bulges than the last systematic study (Chan et al. Protein Science 1993, 2:1574-1590. Novel amino acid preferences are underlined and local structure conformations are highlighted by the use of a structural alphabet. We observed that bbulges are preferably localized at the N-and C-termini of b-strands, but contrary to the earlier studies, no significant conservation of b-bulges was observed among structural homologues. Displacement of b-bulges along the sequence was also investigated by Molecular Dynamics simulations.

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iPBA: a tool for protein structure comparison using sequence alignment strategies

Cited by 92 publications

References 42 publications

<em>Sarcocystis neurona</em> Protein Kinases: Computational Identification, Evolutionary Analysis and Putative Inhibitor Docking

<em>Sarcocystis neurona</em> Protein Kinases: Computational Identification, Evolutionary Analysis and Putative Inhibitor Docking

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

β‐Bulges: Extensive structural analyses of β‐sheets irregularities

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