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The SBP2 and 15.5 kD/Snu13p proteins share the same RNA binding domain: identification of SBP2 amino acids important to SECIS RNA binding.

ABSTRACT: Selenoprotein synthesis in eukaryotes requires the selenocysteine insertion sequence (SECIS) RNA, a hairpin in the 3' untranslated region of selenoprotein mRNAs. The SECIS RNA is recognized by the SECIS-binding protein 2 (SBP2), which is a key player in this specialized translation machinery. The objective of this work was to obtain structural insight into the SBP2-SECIS RNA complex. Multiple sequence alignment revealed that SBP2 and the U4 snRNA-binding protein 15.5 kD/Snu13p share the same RNA binding domain of the L7A/L30 family, also found in the box H/ACA snoRNP protein Nhp2p and several ribosomal proteins. In corollary, we have detected a similar secondary structure motif in the SECIS and U4 RNAs. Combining the data of the crystal structure of the 15.5 kD-U4 snRNA complex, and the SBP2/15.5 kD sequence similarities, we designed a structure-guided strategy predicting 12 SBP2 amino acids that should be critical for SECIS RNA binding. Alanine substitution of these amino acids followed by gel shift assays of the SBP2 mutant proteins identified four residues whose mutation severely diminished or abolished SECIS RNA binding, the other eight provoking intermediate down effects. In addition to identifying key amino acids for SECIS recognition by SBP2, our findings led to the proposal that some of the recognition principles governing the 15.5 kD-U4 snRNA interaction must be similar in the SBP2-SECIS RNA complex.

SUBMITTER: Allmang C

PROVIDER: S-EPMC1370339 | biostudies-other | 2002 Oct

REPOSITORIES: biostudies-other

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The SBP2 and 15.5 kD/Snu13p proteins share the same RNA binding domain: identification of SBP2 amino acids important to SECIS RNA binding.

Allmang Christine C Carbon Philippe P Krol Alain A

RNA (New York, N.Y.) 20021001 10

Selenoprotein synthesis in eukaryotes requires the selenocysteine insertion sequence (SECIS) RNA, a hairpin in the 3' untranslated region of selenoprotein mRNAs. The SECIS RNA is recognized by the SECIS-binding protein 2 (SBP2), which is a key player in this specialized translation machinery. The objective of this work was to obtain structural insight into the SBP2-SECIS RNA complex. Multiple sequence alignment revealed that SBP2 and the U4 snRNA-binding protein 15.5 kD/Snu13p share the same RNA ...[more]

PMID: 12403468

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Project description:BACKGROUND: Many learning approaches to predicting RNA-binding residues in a protein sequence construct a non-redundant training dataset based on the sequence similarity. The sequence similarity-based method either takes a whole sequence or discards it for a training dataset. However, similar sequences or even identical sequences can have different interaction sites depending on their interaction partners, and this information is lost when the sequences are removed. Furthermore, a training dataset constructed by the sequence similarity-based method may contain redundant data when the remaining sequence contains similar subsequences within the sequence. In addition to the problem with the training dataset, most approaches do not consider the interacting partner (i.e., RNA) of a protein when they predict RNA-binding amino acids. Thus, they always predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNA molecules. RESULTS: We developed a feature vector-based method that removes data redundancy for a non-redundant training dataset. The feature vector-based method constructed a larger training dataset than the standard sequence similarity-based method, yet the dataset contained no redundant data. We identified effective features of protein and RNA (the interaction propensity of amino acid triplets, global features of the protein sequence, and RNA feature) for predicting RNA-binding residues. Using the method and features, we built a support vector machine (SVM) model that predicted RNA-binding residues in a protein sequence. Our SVM model showed an accuracy of 84.2%, an F-measure of 76.1%, and a correlation coefficient of 0.41 with 5-fold cross validation on a non-redundant dataset from 3,149 protein-RNA interacting pairs. In an independent test dataset that does not include the 3,149 pairs and were not used in training the SVM model, it achieved an accuracy of 90.3%, an F-measure of 72.8%, and a correlation coefficient of 0.24. Comparison with other methods on the same datasets demonstrated that our model was better than the others. CONCLUSIONS: The feature vector-based redundancy reduction method is powerful for constructing a non-redundant training dataset for a learning model since it generates a larger dataset with non-redundant data than the standard sequence similarity-based method. Including the features of both RNA and protein sequences in a feature vector results in better performance than using the protein features only when predicting the RNA-binding residues in a protein sequence.

Project description:BackgroundWell-performing automated protein function recognition approaches usually comprise several complementary techniques. Beside constructing better consensus, their predictive power can be improved by either adding or refining independent modules that explore orthogonal features of proteins. In this work, we demonstrated how the exploration of global atomic distributions can be used to indicate functionally important residues.ResultsUsing a set of carefully selected globular proteins, we parametrized continuous probability density functions describing preferred central distances of individual protein atoms. Relative preferred burials were estimated using mixture models of radial density functions dependent on the amino acid composition of a protein under consideration. The unexpectedness of extraordinary locations of atoms was evaluated in the information-theoretic manner and used directly for the identification of key amino acids. In the validation study, we tested capabilities of a tool built upon our approach, called SurpResi, by searching for binding sites interacting with ligands. The tool indicated multiple candidate sites achieving success rates comparable to several geometric methods. We also showed that the unexpectedness is a property of regions involved in protein-protein interactions, and thus can be used for the ranking of protein docking predictions. The computational approach implemented in this work is freely available via a Web interface at http://www.bioinformatics.org/surpresi.ConclusionsProbabilistic analysis of atomic central distances in globular proteins is capable of capturing distinct orientational preferences of amino acids as resulting from different sizes, charges and hydrophobic characters of their side chains. When idealized spatial preferences can be inferred from the sole amino acid composition of a protein, residues located in hydrophobically unfavorable environments can be easily detected. Such residues turn out to be often directly involved in binding ligands or interfacing with other proteins.

Dataset Information

The SBP2 and 15.5 kD/Snu13p proteins share the same RNA binding domain: identification of SBP2 amino acids important to SECIS RNA binding.

Publications

The SBP2 and 15.5 kD/Snu13p proteins share the same RNA binding domain: identification of SBP2 amino acids important to SECIS RNA binding.

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