Nucleotide preferences in sequence-specific recognition of DNA by c-myb protein.

Using a binding site selection procedure, we have found that sequence-specific DNA-binding by the mouse c-myb protein involves recognition of nucleotides outside of the previously identified hexanucleotide motif. Oligonucleotides containing a random nucleotide core were immunoprecipitated in association with c-Myb, amplified by the Polymerase Chain Reaction and cloned in plasmids prior to sequencing. By alignment of sequences it was apparent that additional preferences existed at each of three bases immediately 5' of the hexanucleotide consensus, allowing an extension of the preferred binding site to YGRCVGTTR. The contributions of these 5' nucleotides to binding affinity was established in bandshift analyses with oligonucleotides containing single base substitutions; in particular, it was found that replacement of the preferred guanine at position -2 with any other base greatly reduced c-Myb binding. We found that the protein encoded by the related B-myb gene bound the preferred c-Myb site with similar affinity; however, B-Myb and c-Myb showed distinct preferences for the identity of the nucleotide at position -1 relative to the hexanucleotide consensus. This study demonstrates that the c-Myb DNA-binding site is more extensive than recognised hitherto and points to similar but distinct nucleotide preferences in recognition of DNA by related Myb proteins.     

DNA motif elucidation using belief propagation

Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k = 8 ∼10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors’ websites: e.g. http://www.cs.toronto.edu/∼wkc/kmerHMM.     

Bayesian multiple-instance motif discovery with BAMBI: inference of recombinase and transcription factor binding sites

Finding conserved motifs in genomic sequences represents one of essential bioinformatic problems. However, achieving high discovery performance without imposing substantial auxiliary constraints on possible motif features remains a key algorithmic challenge. This work describes BAMBI-a sequential Monte Carlo motif-identification algorithm, which is based on a position weight matrix model that does not require additional constraints and is able to estimate such motif properties as length, logo, number of instances and their locations solely on the basis of primary nucleotide sequence data. Furthermore, should biologically meaningful information about motif attributes be available, BAMBI takes advantage of this knowledge to further refine the discovery results. In practical applications, we show that the proposed approach can be used to find sites of such diverse DNA-binding molecules as the cAMP receptor protein (CRP) and Din-family site-specific serine recombinases. Results obtained by BAMBI in these and other settings demonstrate better statistical performance than any of the four widely-used profile-based motif discovery methods: MEME, BioProspector with BioOptimizer, SeSiMCMC and Motif Sampler as measured by the nucleotide-level correlation coefficient. Additionally, in the case of Din-family recombinase target site discovery, the BAMBI-inferred motif is found to be the only one functionally accurate from the underlying biochemical mechanism standpoint. C++ and Matlab code is available at http://www.ee.columbia.edu/~guido/BAMBI or http://genomics.lbl.gov/BAMBI/.     

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.     

A consensus DNA-binding site for the androgen receptor.

We have used a DNA-binding site selection assay to determine a consensus binding sequence for the androgen receptor (AR). A purified fusion protein containing the AR DNA-binding domain was incubated with a pool of random sequence oligonucleotides, and complexes were isolated by gel mobility shift assays. Individually selected sites were characterised by nucleotide sequencing and compiled to give a consensus AR-binding element. This sequence is comprised of two 6-basepair (bp) asymmetrical elements separated by a 3-bp spacer, 5'-GGA/TACANNNTGTTCT-3', similar to that described for the glucocorticoid response element. Inspection of the consensus revealed a slight preference for G or A nucleotides at the +1 position in the spacer and for A and T nucleotides in the 3'-flanking region. Therefore, a series of oligonucleotides was designed in which the spacer and flanking nucleotides were changed to the least preferred sequence. Competition experiments with these oligonucleotides and the AR fusion protein indicated that an oligonucleotide with both the spacer and flanking sequences changed had greater than 3-fold less affinity than the consensus sequence. The functional activity of these oligonucleotides was also assessed by placing them up-stream of a reporter gene in a transient transfection assay and correlated with the affinity with which the AR fusion protein bound to DNA. Therefore, sequences surrounding the two 6-bp half-sites influence both the binding affinity for the receptor and the functional activity of the response element.