[6] Blocks database and its applications

doi:10.1016/S0076-6879(96)66008-X

Methods in Enzymology

Volume 266, 1996, Pages 88-105

https://doi.org/10.1016/S0076-6879(96)66008-X Get rights and content

Abstract

Protein blocks consist of multiply aligned sequence segments without gaps that represent the most highly conserved regions of protein families. A database of blocks has been constructed by successive application of the fully automated PROTOMAT system to lists of protein family members obtained from Prosite documentation. Currently, Blocks 8.0 based on protein families documented in Prosite 12 consists of 2884 blocks representing 770 families. Searches of the Blocks Database are carried out using protein or DNA sequence queries, and results are returned with measures of significance for both single and multiple block hits. The database has also proved useful for derivation of amino acid substitution matrices (the Blosum series) and other sets of parameters. WWW and E-mail servers provide access to the database and associated functions, including a block maker for sequences provided by the user.

References (45)

P. Green
Curr. Opin. Struct. Biol.
(1994)
S. Henikoff et al.
Genomics
(1994)
S. Henikoff et al.
J. Mol. Biol.
(1994)
S. Henikoff et al.
Gene
(1995)
D.M. Nikoloff et al.
J. Biol. Chem.
(1994)
S.F. Altschul et al.
J. Mol. Biol.
(1990)
T.F. Smith et al.
J. Mol. Biol.
(1981)
S.F. Altschul
J. Mol. Biol.
(1991)
M.P. Brown et al.
P.R. Sibbald et al.
J. Mol. Biol.
(1990)

M. Gerstein et al.

J. Mol. Biol.

(1994)

S.F. Altschul et al.

J. Mol. Biol.

(1989)

E.V. Koonin et al.

EMBO J.

(1994)

M.D. Adams et al.

Science

(1991)

S.G. Oliver et al.

Nature (London)

(1992)

S. Henikoff et al.

Nucleic Acids Res.

(1991)

S. Henikoff et al.

T.D. Schneider et al.

Nucleic Acids Res.

(1990)

S. Henikoff et al.

I.B. Dodd et al.

Nucleic Acids Res.

(1990)

A. Bairoch

Nucleic Acids Res.

(1992)

Cited by (112)

Evaluating potential risks of food allergy of novel food sources based on comparison of proteins predicted from genomes and compared to www.AllergenOnline.org
2021, Food and Chemical Toxicology
Potential proteins from three novel food sources (Chlorella variabilis, Galdieria sulphuraria, and Fusarium strain flavolapis) were predicted from genomic sequences and were evaluated for potential risks of allergic cross-reactivity by comparing the predicted amino acid sequences against the allergens in the www.AllergenOnline.org (AOL) database. The preliminary analysis used CODEX Alimentarius limits of >35% identity over 80 amino acids to evaluate the predicted proteins which include many evolutionarily conserved proteins. Regulators might expect clinical serum IgE tests based on identity matches above the criteria if the proteins were introduced in genetically engineered crops. Some regulators have the same expectations for proteins in novel foods. To address the inequality of extensively conserved sequences, we compared the predicted proteins from curated genomes of 23 highly diverse allergenic species from animals, plants and arthropods as well as humans to AOL sequences and compiled identities. Identity matches greater than CODEX limits (>35% ID over 80 AA) are common for many proteins that are conserved through extensive evolution but are not predictive of published allergy risks based on observed taxonomic cross-reactivity. Therefore, we recommend changes in the allergen databases or methods of identifying matches for risk evaluation of new food sources. Our results provide critical data for redefining allergens in AOL or for providing guidance on more predictive sequence identity matches for risk assessment of possible risks of food allergy.
Ranking and clustering of Drosophila olfactory receptors using mathematical morphology
2019, Genomics
Citation Excerpt :
Different methods of similarity among the DORs allow inferring functional and evolutionary relationships among the sequences [5-9]. There exist some methodologies for the analysis of protein sequences in classifying them [10-17]. The next-generation sequencing technologies produce huge datasets that have to be systematically analyzed with novel computational algorithms.
This article introduces an alignment-free clustering method in order to cluster all the 66 DORs sequentially diverse protein sequences. Two different methods are discussed: one is utilizing twenty standard amino acids (without grouping) and another one is using chemical grouping of amino acids (with grouping). Two grayscale images (representing two protein sequences by order pair frequency matrices) are compared to find the similarity index using morphology technique. We could achieve the correlation coefficients of 0.9734 and 0.9403 for without and with grouping methods respectively with the ClustalW result in the ND5 dataset, which are much better than some of the existing alignment-free methods. Based on the similarity index, the 66 DORs are clustered into three classes - Highest, Moderate and Lowest - which are seen to be best fitted for 66 DORs protein sequences. OR83b is the distinguished olfactory receptor expressed in divergent insect population which is substantiated through our investigation.
Assessing the best edit in perturbation-based iterative refinement algorithms to compute the median string
2019, Pattern Recognition Letters
Different pattern recognition techniques such as clustering, k-nearest neighbors classification or instance reduction algorithms require prototypes to represent pattern classes. In many applications, strings are used to encode instances, for example, in contour representations or in biological data such as DNA, RNA and protein sequences. Median strings have been used as representatives of a set of strings in different domains. Finding the median string is an NP-Complete problem for several formulations. Alternatively, heuristic approaches that iteratively refine an initial coarse solution by applying edit operations have been proposed. We propose here a novel algorithm that outperforms state of the art heuristic approximations to the median string in terms of convergence speed by estimating the effect of a perturbation in the minimization of the expressions that define the median strings. We present comparative experiments to validate these results.
Bioinformatics analysis to assess potential risks of allergenicity and toxicity of HRAP and PFLP proteins in genetically modified bananas resistant to Xanthomonas wilt disease
2017, Food and Chemical Toxicology
Citation Excerpt :
The full-length FASTA3 was the primary search which gives optimum alignments using the default criteria defined by Pearson (1999). The default scoring matrix is BLOSUM 50 (Henikoff and Henikoff, 1992, 1996). The penalty for each gap inserted into query or searched sequences to obtain optimal alignments is calculated as (q + r*k), where q (10) is an initial penalty for each independent gap, r (2) is a penalty for each aa position within the gap and k is the number of aa positions within the gap (Reese and Pearson, 2002).
Banana Xanthomonas wilt (BXW) disease threatens banana production and food security throughout East Africa. Natural resistance is lacking among common cultivars. Genetically modified (GM) bananas resistant to BXW disease were developed by inserting the hypersensitive response-assisting protein (Hrap) or/and the plant ferredoxin-like protein (Pflp) gene(s) from sweet pepper (Capsicum annuum). Several of these GM banana events showed 100% resistance to BXW disease under field conditions in Uganda. The current study evaluated the potential allergenicity and toxicity of the expressed proteins HRAP and PFLP based on evaluation of published information on the history of safe use of the natural source of the proteins as well as established bioinformatics sequence comparison methods to known allergens (www.AllergenOnline.org and NCBI Protein) and toxins (NCBI Protein). The results did not identify potential risks of allergy and toxicity to either HRAP or PFLP proteins expressed in the GM bananas that might suggest potential health risks to humans. We recognize that additional tests including stability of these proteins in pepsin assay, nutrient analysis and possibly an acute rodent toxicity assay may be required by national regulatory authorities.
Evaluation of Bar, Barnase, and Barstar recombinant proteins expressed in genetically engineered Brassica juncea (Indian mustard) for potential risks of food allergy using bioinformatics and literature searches
2015, Food and Chemical Toxicology
Citation Excerpt :
The default search and scoring described in the Allergenonline.org database website were used. The default scoring matrix is BLOSUM 50 (Henikoff and Henikoff, 1992, 1996). The gap penalty for each inserted gap in the query or aligned protein sequences is calculated as (−q + −r*k), where q (10) is an initial penalty for each independent gap, r (2) is a penalty for each amino acid position within the gap and k is the number of amino acid positions within the gap (Reese and Pearson, 2002), default word size (ktup) two (Pearson, 2000), and an expectation value score (E-value) of 1 (highest number, representing similarity potential significant alignment in the small AOL database).
The potential allergenicity of Bar, Barnase, and Barstar recombinant proteins expressed in genetically engineered mustard for pollination control in plant breeding was evaluated for regulatory review. To evaluate the potential allergenicity of the Bar, Barnase and Barstar proteins amino acid sequence comparisons were made to those of known and putative allergens, and search for published evidence to the sources of the genes using the AllergenOnline.org database. Initial comparisons in 2012 were performed with version 12 by methods recommended by the Codex Alimentarius Commission and the Indian Council of Medical Research, Government of India. Searches were repeated with version 15 in 2015. A literature search was performed using PubMed to identify reports of allergy associated with the sources of the three transgenes. Potential open reading frames at the DNA insertion site were evaluated for matches to allergens. No significant sequence identity matches were identified with Bar, Barnase or Barstar proteins or potential fusion peptides at the genomic-insert junctions compared to known allergens. No references were identified that associated the sources of the genes with allergy. Based on these results we conclude that the Bar, Barnase and Barstar proteins are unlikely to present any significant risk of food allergy to consumers.
Combinations of long peptide sequence blocks can be used to describe toxin diversification in venomous animals
2015, Toxicon
An important mechanism for the evolution of toxins in venomous animals is believed to be the acquisition of genes encoding proteins that switch from physiological to toxic roles following gene duplication. The ‘reverse recruitment’ hypothesis pertains that these genes can also revert back to physiological functions, although such events are thought to be rare. A non-supervised homology searching method was developed which allowed the peptide diversity of animal toxins to be described as combinations between limited numbers of amino-acid sequence blocks we called ‘tox-bits’. Taking the phospholipase A2 (PLA2) protein family as an example, a Bernoulli Trial was used to test if ‘tox-bits’ were robust enough to distinguish between peptides with physiological or toxin functions. The analysis revealed that discrimination was indeed possible, and supports the very recent ‘restriction’ hypothesis whereby genes with the potential to encode toxic functions have likely been independently recruited into venom systems and therefore require few, if any, reverse recruitment events. The development of ‘tox-bits’ provides a novel bioinformatics tool to allow recognition of toxins from other proteins in genome sequences, facilitating the study of gene recruitment and duplication strategies in venom diversification. The ‘tox-bits’ library is freely available at http://bioserv.pbf.hr/blocks.zip.

View all citing articles on Scopus

View full text

[6] Blocks database and its applications

Abstract

Curr. Opin. Struct. Biol.

Genomics

J. Mol. Biol.

Gene

J. Biol. Chem.

J. Mol. Biol.

J. Mol. Biol.

J. Mol. Biol.

J. Mol. Biol.

J. Mol. Biol.

J. Mol. Biol.

EMBO J.

Science

Nature (London)

Nucleic Acids Res.

Nucleic Acids Res.

Nucleic Acids Res.

Nucleic Acids Res.