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Amino acid sequence of the penicillin-binding protein/DD-peptidase of Streptomyces K15. Predicted secondary structures of the low Mr penicillin-binding proteins of class A.

ABSTRACT: The low-Mr penicillin-binding protein (PBP)/DD-transpeptidase of Streptomyces K15 is synthesized in the form of a 291-amino acid-residue precursor possessing a cleavable 29-amino acid-residue signal peptide. Sequence-similarity searches and hydrophobic-cluster analysis show that the Streptomyces K15 enzyme, the Escherichia coli PBPs/DD-carboxy-peptidases 5 and 6, the Bacillus subtilis PBP/DD-carboxypeptidase 5 and the spoIIA product (a putative PBP involved in the sporulation of B. subtilis) are structurally related and form a distinct class A of low-Mr PBPs/DD-peptidases. The distribution of the hydrophobic clusters along the amino acid sequences also shows that the Streptomyces K15 PBP, and by extension the other PBPs of class A, have similarity in the polypeptide folding, with the beta-lactamases of class A, with as reference the Streptomyces albus G and Staphylococcus aureus beta-lactamases of known three-dimensional structure. This comparison allows one to predict most of the secondary structures in the PBPs and the amino acid motifs that define the enzyme active sites.

SUBMITTER: Palomeque-Messia P

PROVIDER: S-EPMC1151569 | biostudies-other | 1991 Oct

REPOSITORIES: biostudies-other

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Project description:BackgroundRNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins. Protein-RNA interactions are typically characterized using in vivo and in vitro experiments but these may not detect all binding partners. Therefore, computational methods that capture the protein-dependent nature of such binding interactions could help to predict potential binding partners in silico.ResultsWe have developed three methods to predict whether an RNA can interact with a particular RNA-binding protein using support vector machines and different features based on the sequence (the Oli method), the motif score (the OliMo method) and the secondary structure (the OliMoSS method). We applied these approaches to different experimentally-derived datasets and compared the predictions with RNAcontext and RPISeq. Oli outperformed OliMoSS and RPISeq, confirming our protein-specific predictions and suggesting that tetranucleotide frequencies are appropriate discriminative features. Oli and RNAcontext were the most competitive methods in terms of the area under curve. A precision-recall curve analysis achieved higher precision values for Oli. On a second experimental dataset including real negative binding information, Oli outperformed RNAcontext with a precision of 0.73 vs. 0.59.ConclusionsOur experiments showed that features based on primary sequence information are sufficiently discriminating to predict specific RNA-protein interactions. Sequence motifs and secondary structure information were not necessary to improve these predictions. Finally we confirmed that protein-specific experimental data concerning RNA-protein interactions are valuable sources of information that can be used for the efficient training of models for in silico predictions. The scripts are available upon request to the corresponding author.

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Amino acid sequence of the penicillin-binding protein/DD-peptidase of Streptomyces K15. Predicted secondary structures of the low Mr penicillin-binding proteins of class A.

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