Using RNA secondary structures to guide sequence motif finding towards single-stranded regions

RNA binding proteins recognize RNA targets in a sequence specific manner. Apart from the sequence, the secondary structure context of the binding site also affects the binding affinity. Binding sites are often located in single-stranded RNA regions and it was shown that the sequestration of a binding motif in a double-strand abolishes protein binding. Thus, it is desirable to include knowledge about RNA secondary structures when searching for the binding motif of a protein. We present the approach MEMERIS for searching sequence motifs in a set of RNA sequences and simultaneously integrating information about secondary structures. To abstract from specific structural elements, we precompute position-specific values measuring the single-strandedness of all substrings of an RNA sequence. These values are used as prior knowledge about the motif starts to guide the motif search. Extensive tests with artificial and biological data demonstrate that MEMERIS is able to identify motifs in single-stranded regions even if a stronger motif located in double-strand parts exists. The discovered motif occurrences in biological datasets mostly coincide with known protein-binding sites. This algorithm can be used for finding the binding motif of single-stranded RNA-binding proteins in SELEX or other biological sequence data.     

Global signatures of protein binding on structured RNAs in Saccharomyces cerevisiae

Protein binding is essential to the transport,decay and regulation of almost all RNA molecules.However,the structural preference of protein binding on RNAs and their cellular functions and dynamics upon changing environmental conditions are poorly understood.Here,we integrated various high-throughput data and introduced a computational framework to describe the global interactions between RNA binding proteins(RBPs)and structured RNAs in yeast at single-nucleotide resolution.We found that on average,in terms of percent total lengths,~15%of mRNA untranslated regions(UTRs),~37%of canonical non-coding RNAs(ncRNAs)and~11%of long ncRNAs(lncRNAs)are bound by proteins.The RBP binding sites,in general,tend to occur at single-stranded loops,with evolutionarily conserved signatures,and often facilitate a specific RNA structure conformation in vivo.We found that four nucleotide modifications of tRNA are significantly associated with RBP binding.We also identified various structural motifs bound by RBPs in the UTRs of mRNAs,associated with localization,degradation and stress responses.Moreover,we identified>200 novel lncRNAs bound by RBPs,and about half of them contain conserved secondary structures.We present the first ensemble pattern of RBP binding sites in the structured non-coding regions of a eukaryotic genome,emphasizing their structural context and cellular functions.     

Predicting protein post-translational modifications using meta-analysis of proteome scale data sets

Protein post-translational modifications are an important biological regulatory mechanism, and the rate of their discovery using high throughput techniques is rapidly increasingly. To make use of this wealth of sequence data, we introduce a new general strategy designed to predict a variety of post-translational modifications in several organisms. We used the motif-x program to determine phosphorylation motifs in yeast, fly, mouse, and man and lysine acetylation motifs in man. These motifs were then scanned against proteomic sequence data using a newly developed tool called scan-x to globally predict other potential modification sites within these organisms. 10-fold cross-validation was used to determine the sensitivity and minimum specificity for each set of predictions, all of which showed improvement over other available tools for phosphoprediction. New motif discovery is a byproduct of this approach, and the phosphorylation motif analyses provide strong evidence of evolutionary conservation of both known and novel kinase motifs.     

The fragment transformation method to detect the protein structural motifs

To identify functional structural motifs from protein structures of unknown function becomes increasingly important in recent years due to the progress of the structural genomics initiatives. Although certain structural patterns such as the Asp-His-Ser catalytic triad are easy to detect because of their conserved residues and stringently constrained geometry, it is usually more challenging to detect a general structural motifs like, for example, the betabetaalpha-metal binding motif, which has a much more variable conformation and sequence. At present, the identification of these motifs usually relies on manual procedures based on different structure and sequence analysis tools. In this study, we develop a structural alignment algorithm combining both structural and sequence information to identify the local structure motifs. We applied our method to the following examples: the betabetaalpha-metal binding motif and the treble clef motif. The betabetaalpha-metal binding motif plays an important role in nonspecific DNA interactions and cleavage in host defense and apoptosis. The treble clef motif is a zinc-binding motif adaptable to diverse functions such as the binding of nucleic acid and hydrolysis of phosphodiester bonds. Our results are encouraging, indicating that we can effectively identify these structural motifs in an automatic fashion. Our method may provide a useful means for automatic functional annotation through detecting structural motifs associated with particular functions.