Characterization of the calf thymus hairpin-binding factor involved in histone pre-mRNA 3' end processing

Rigorous assessment and integration of the sequence and structure based features to predict hot spots

Background

Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need.

Results

In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes.

Conclusion

Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.

A gene recommender algorithm to identify coexpressed genes in C. elegans

One of the most important uses of whole-genome expression data is for the discovery of new genes with similar function to a given list of genes (the query) already known to have closely related function. We have developed an algorithm, called the gene recommender, that ranks genes according to how strongly they correlate with a set of query genes in those experiments for which the query genes are most strongly coregulated. We used the gene recommender to find other genes coexpressed with several sets of query genes, including genes known to function in the retinoblastoma complex. Genetic experiments confirmed that one gene (JC8.6) identified by the gene recommender acts with lin-35 Rb to regulate vulval cell fates, and that another gene (wrm-1) acts antagonistically. We find that the gene recommender returns lists of genes with better precision, for fixed levels of recall, than lists generated using the C. elegans expression topomap.

Assembly of U7 small nuclear ribonucleoprotein particle and histone RNA 3' processing in Xenopus egg extracts

In animals, replication-dependent histone genes are expressed in dividing somatic cells during S phase to maintain chromatin condensation. Histone mRNA 3'-end formation is an essential regulatory step producing an mRNA with a hairpin structure at the 3'-end. This requires the interaction of the U7 small nuclear ribonucleoprotein particle (snRNP) with a purine-rich spacer element and of the hairpin-binding protein with the hairpin element, respectively, in the 3'-untranslated region of histone RNA. Here, we demonstrate that bona fide histone RNA 3' processing takes place in Xenopus egg extracts in a reaction dependent on the addition of synthetic U7 RNA that is assembled into a ribonucleoprotein particle by protein components available in the extract. In addition to reconstituted U7 snRNP, Xenopus hairpin-binding protein SLBP1 is necessary for efficient processing. Histone RNA 3' processing is not affected by addition of non-destructible cyclin B, which drives the egg extract into M phase, but SLBP1 is phosphorylated in this extract. SPH-1, the Xenopus homologue of human p80-coilin found in coiled bodies, is associated with U7 snRNPs. However, this does not depend on the U7 RNA being able to process histone RNA and also occurs with U1 snRNPs; therefore, association of SPH1 cannot be considered as a hallmark of a functional U7 snRNP.

Positive and negative mutant selection in the human histone hairpin-binding protein using the yeast three-hybrid system

We have used the yeast three-hybrid system in a positive selection for mutants of the human histone hairpin-binding protein (HBP) capable of interacting with non-canonical hairpins and in a negative selection for loss-of-binding mutants. Interestingly, all mutations from the positive selection are located in the N- and C-terminal regions flanking a minimal RNA-binding domain (RBD) previously defined between amino acids 126 and 198. Further, in vitro binding studies demonstrate that the RBD, which shows no obvious similarity to other RNA-binding motifs, has a relaxed sequence specificity compared to full-length HBP, allowing it to bind to mutant hairpin RNAs not normally found in histone genes. These findings indicate that the sequences flanking the RBD are important for restricting binding to the highly conserved histone hairpin structure. Among the loss-of-binding mutations, about half are nonsense mutations distributed throughout the N-terminal part and the RBD whereas the other half are missense mutations restricted to the RBD. Whereas the nonsense mutations permit a more precise definition of the C-terminal border of the RBD, the missense mutations identify critical residues for RNA binding within the RBD.