Literature DB >> 15905278

Computational approaches for identification of conserved/unique binding pockets in the A chain of ricin.

Carol L Ecale Zhou1, Adam T Zemla, Diana Roe, Malin Young, Marisa Lam, Joseph S Schoeniger, Rod Balhorn.   

Abstract

MOTIVATION: Specific and sensitive ligand-based protein detection assays that employ antibodies or small molecules such as peptides, aptamers or other small molecules require that the corresponding surface region of the protein be accessible and that there be minimal cross-reactivity with non-target proteins. To reduce the time and cost of laboratory screening efforts for diagnostic reagents, we developed new methods for evaluating and selecting protein surface regions for ligand targeting.
RESULTS: We devised combined structure- and sequence-based methods for identifying 3D epitopes and binding pockets on the surface of the A chain of ricin that are conserved with respect to a set of ricin A chains and unique with respect to other proteins. We (1) used structure alignment software to detect structural deviations and extracted from this analysis the residue-residue correspondence, (2) devised a method to compare corresponding residues across sets of ricin structures and structures of closely related proteins, (3) devised a sequence-based approach to determine residue infrequency in local sequence context and (4) modified a pocket-finding algorithm to identify surface crevices in close proximity to residues determined to be conserved/unique based on our structure- and sequence-based methods. In applying this combined informatics approach to ricin A, we identified a conserved/unique pocket in close proximity (but not overlapping) the active site that is suitable for bi-dentate ligand development. These methods are generally applicable to identification of surface epitopes and binding pockets for development of diagnostic reagents, therapeutics and vaccines.

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Substances:

Year:  2005        PMID: 15905278     DOI: 10.1093/bioinformatics/bti498

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  6 in total

1.  A reinforced merging methodology for mapping unique peptide motifs in members of protein families.

Authors:  Hao-Teng Chang; Tun-Wen Pai; Tan-chi Fan; Bo-Han Su; Pei-Chih Wu; Chuan-Yi Tang; Chun-Tien Chang; Shi-Hwei Liu; Margaret Dah-Tsyr Chang
Journal:  BMC Bioinformatics       Date:  2006-01-25       Impact factor: 3.169

2.  MvirDB--a microbial database of protein toxins, virulence factors and antibiotic resistance genes for bio-defence applications.

Authors:  C E Zhou; J Smith; M Lam; A Zemla; M D Dyer; T Slezak
Journal:  Nucleic Acids Res       Date:  2006-11-07       Impact factor: 16.971

3.  MannDB - a microbial database of automated protein sequence analyses and evidence integration for protein characterization.

Authors:  Carol L Ecale Zhou; Marisa W Lam; Jason R Smith; Adam T Zemla; Matthew D Dyer; Thomas A Kuczmarski; Elizabeth A Vitalis; Thomas R Slezak
Journal:  BMC Bioinformatics       Date:  2006-10-17       Impact factor: 3.169

Review 4.  Artificial intelligence and big data facilitated targeted drug discovery.

Authors:  Benquan Liu; Huiqin He; Hongyi Luo; Tingting Zhang; Jingwei Jiang
Journal:  Stroke Vasc Neurol       Date:  2019-11-07

5.  Structural re-alignment in an immunogenic surface region of ricin A chain.

Authors:  Adam T Zemla; Carol L Ecale Zhou
Journal:  Bioinform Biol Insights       Date:  2008-02-01

6.  CombAlign: a code for generating a one-to-many sequence alignment from a set of pairwise structure-based sequence alignments.

Authors:  Carol L Ecale Zhou
Journal:  Source Code Biol Med       Date:  2015-08-05
  6 in total

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