scholarly journals Dysgalacticin: a novel, plasmid-encoded antimicrobial protein (bacteriocin) produced by Streptococcus dysgalactiae subsp. equisimilis

Microbiology ◽  
2006 ◽  
Vol 152 (7) ◽  
pp. 1991-2001 ◽  
Author(s):  
Nicholas C. K. Heng ◽  
Nancy L. Ragland ◽  
Pearl M. Swe ◽  
Hayley J. Baird ◽  
Megan A. Inglis ◽  
...  
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Amino Acid Sequence ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Antimicrobial Protein ◽  
Streptococcus Dysgalactiae ◽  
Indigenous Plasmid ◽  
Inhibitory Spectrum ◽  
Dependent Transport

Dysgalacticin is a novel bacteriocin produced by Streptococcus dysgalactiae subsp. equisimilis strain W2580 that has a narrow spectrum of antimicrobial activity directed primarily against the principal human streptococcal pathogen Streptococcus pyogenes. Unlike many previously described bacteriocins of Gram-positive bacteria, dysgalacticin is a heat-labile 21.5 kDa anionic protein that kills its target without inducing lysis. The N-terminal amino acid sequence of dysgalacticin [Asn-Glu-Thr-Asn-Asn-Phe-Ala-Glu-Thr-Gln-Lys-Glu-Ile-Thr-Thr-Asn-(Asn)-Glu-Ala] has no known homologue in publicly available sequence databases. The dysgalacticin structural gene, dysA, is located on the indigenous plasmid pW2580 of strain W2580 and encodes a 220 aa preprotein which is probably exported via a Sec-dependent transport system. Natural dysA variants containing conservative amino acid substitutions were also detected by sequence analyses of dysA elements from S. dysgalactiae strains displaying W2580-like inhibitory profiles. Production of recombinant dysgalacticin by Escherichia coli confirmed that this protein is solely responsible for the inhibitory activity exhibited by strain W2580. A combination of in silico secondary structure prediction and reductive alkylation was employed to demonstrate that dysgalacticin has a novel structure containing a disulphide bond essential for its biological activity. Moreover, dysgalacticin displays similarity in predicted secondary structure (but not primary amino acid sequence or inhibitory spectrum) with another plasmid-encoded streptococcal bacteriocin, streptococcin A-M57 from S. pyogenes, indicating that dysgalacticin represents a prototype of a new class of antimicrobial proteins.

scholarly journals Protein Secondary Structure Prediction using Recurrent Neural Networks

2020 ◽  
Vol 8 (5) ◽  
pp. 660-663
Keyword(s):  
Neural Networks ◽  
Amino Acid ◽  
Secondary Structure ◽  
Amino Acid Sequence ◽  
Recurrent Neural Networks ◽  
Protein Sequence ◽  
Structure Prediction ◽  
Short Term Memory ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure

In bioinformatics the prediction of the secondary structure of the protein from its primary amino acid sequence is very difficult, which has a huge impact on the field of science and medicine. The hardest part is how to learn the most effective and correct protein features to improve prediction. Here, we carry out a deep learning model to enhance structure prediction. The core achievement of this paper is a group of recurrent neural networks (RNNs) that can manage high-level relational features from a pair of input protein sequence and target protein sequences. This paper contrasts the different type of recurrent network in recurrent neural networks (RNNs). In addition, the emphasis is on more advanced systems which incorporate a gating utility is called long short term memory (LSTM) unit and the newly added gated recurrent unit (GRU). This recurrent units has been calculated on the basis of predicting protein secondary structure using an amino acid sequence. The dataset has been taken from a publicly available database server (RCSB), and this study shows that advanced recurrent units LSTM is better than GRU for a long protein sequence.

scholarly journals The complete amino acid sequence of three alcohol dehydrogenase alleloenzymes (AdhN-11, AdhS and AdhUF) from the fruitfly Drosophila melanogaster

1980 ◽  
Vol 187 (3) ◽  
pp. 875-883 ◽  
Author(s):  
D R Thatcher
Keyword(s):  
Drosophila Melanogaster ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Alcohol Dehydrogenase ◽  
Aspartic Acid ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
British Library ◽  
Horse Liver ◽  
And Staphylococcus Aureus

The sequence of three alcohol dehydrogenase alleloenzymes from the fruitfly Drosophila melanogaster has been determined by the sequencing of peptides produced by trypsin, chymotrypsin, thermolysin, pepsin and Staphylococcus aureus-V8-proteinase digestion. The amino acid sequence shows no obvious homology with the published sequences of the horse liver and yeast enzymes, and secondary structure prediction suggests that the nucleotide-binding domain is located in the N-terminal half of the molecule. The amino acid substitutions between AdhN-11 (a point mutation of AdhF), AdhS and AdhUF alleloenzymes were identified. AdhN-11 alcohol dehydrogenase differed from the other two by a glycine-14-(AdhS and AdhUF)-to-aspartic acid substitution, the AdhS enzyme from AdhN-11 and AdhUF enzymes by a threonine-192-(AdhN-11 and AdhUF)-to-lysine (AdhS) substitution and the AdhUF enzyme was found to differ by an alanine-45-(AdhS and AdhN-11)-to-aspartic acid (AdhUF) charge substitution and a ‘silent’ asparagine-8-(AdhS and AdhN-11)-to-alanine (AdhUF) substitution. Detailed sequence evidence has been deposited as Supplementary Publication SUP 50107 (36 pages) at the British Library Lending Division, Boston Spa, Wetherby, West Yorkshire LS23 7BQ, U.K., from whom copies can be obtained on the terms indicated in Biochem. J. (1978) 169, 5.

CLUSTERING OF AMINO ACIDS FOR PROTEIN SECONDARY STRUCTURE PREDICTION

2004 ◽  
Vol 02 (02) ◽  
pp. 333-342 ◽  
Author(s):  
WEI-MOU ZHENG
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Markov Models ◽  
Secondary Structure Prediction ◽  
Length Distribution ◽  
Protein Secondary Structure ◽  
Coarse Graining ◽  
High Prediction ◽  
Duration Effect

Simple hidden Markov models are proposed for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of length distribution, and focus on inclusion of short range correlations of residues and of conformation states in the models. Conformation-independent and -dependent amino acid coarse-graining schemes are designed for the models by means of proper mutual information. We compare models of different level of complexity, and establish a practical model with a high prediction accuracy.

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