Joint modeling of DNA sequence and physical properties to improve eukaryotic promoter recognition

We present an approach to integrate physical properties of DNA, such as DNA bendability or GC content, into our probabilistic promoter recognition system McPROMOTER. In the new model, a promoter is represented as a sequence of consecutive segments represented by joint likelihoods for DNA sequence and profiles of physical properties. Sequence likelihoods are modeled with interpolated Markov chains, physical properties with Gaussian distributions. The background uses two joint sequence/profile models for coding and non-coding sequences, each consisting of a mixture of a sense and an anti-sense submodel. On a large Drosophila test set, we achieved a reduction of about 30% of false positives when compared with a model solely based on sequence likelihoods.     

On the role of structural information in remote homology detection and sequence alignment: new methods using hybrid sequence profiles

Structural alignments often reveal relationships between proteins that cannot be detected using sequence alignment alone. However, profile search methods based entirely on structural alignments alone have not been found to be effective in finding remote homologs. Here, we explore the role of structural information in remote homolog detection and sequence alignment. To this end, we develop a series of hybrid multidimensional alignment profiles that combine sequence, secondary and tertiary structure information into hybrid profiles. Sequence-based profiles are profiles whose position-specific scoring matrix is derived from sequence alignment alone; structure-based profiles are those derived from multiple structure alignments. We compare pure sequence-based profiles to pure structure-based profiles, as well as to hybrid profiles that use combined sequence-and-structure-based profiles, where sequence-based profiles are used in loop/motif regions and structural information is used in core structural regions. All of the hybrid methods offer significant improvement over simple profile-to-profile alignment. We demonstrate that both sequence-based and structure-based profiles contribute to remote homology detection and alignment accuracy, and that each contains some unique information. We discuss the implications of these results for further improvements in amino acid sequence and structural analysis.     

A structural similarity analysis of double-helical DNA

A database of the structural properties of all 32,896 unique DNA octamer sequences has been calculated, including information on stability, the minimum energy conformation and flexibility. The contents of the database have been analysed using a variety of Euclidean distance similarity measures. A global comparison of sequence similarity with structural similarity shows that the structural properties of DNA are much less diverse than the sequences, and that DNA sequence space is larger and more diverse than DNA structure space. Thus, there are many very different sequences that have very similar structural properties, and this may be useful for identifying DNA motifs that have similar functional properties that are not apparent from the sequences. On the other hand, there are also small numbers of almost identical sequences that have very different structural properties, and these could give rise to false-positives in methods used to identify function based on sequence alignment. A simple validation test demonstrates that structural similarity can differentiate between promoter and non-promoter DNA. Combining structural and sequence similarity improves promoter recall beyond that possible using either similarity measure alone, demonstrating that there is indeed information available in the structure of double-helical DNA that is not readily apparent from the sequence.