Biomedical subjects

V R Akmaev

Publications and source records attributed to V R Akmaev.

3 recordsLinked to original sources

PubMed2000

Improved statistical methods reveal direct interactions between 16S and 23S rRNA.

Recent biochemical studies have indicated a number of regions in both the 16S and 23S rRNA that are exposed on the ribosomal subunit surface. In order to predict potential interactions between these regions we applied novel phylogenetically-based statistical methods to detect correlated nucleotide changes occurring between the rRNA molecules. With these methods we discovered a number of highly significant correlated changes between different sets of nucleotides in the two ribosomal subunits. The predictions with the highest correlation values belong to regions of the rRNA subunits that are in close proximity according to recent crystal structures of the entire ribosome. We also applied a new statistical method of detecting base triple interactions within these same rRNA subunit regions. This base triple statistic predicted a number of new base triples not detected by pair-wise interaction statistics within the rRNA molecules. Our results suggest that these statistical methods may enhance the ability to detect novel structural elements both within and between RNA molecules.

Animals↗

PubMed2000

Phylogenetically enhanced statistical tools for RNA structure prediction.

MOTIVATION: Methods that predict the structure of molecules by looking for statistical correlation have been quite effective. Unfortunately, these methods often disregard phylogenetic information in the sequences they analyze. Here, we present a number of statistics for RNA molecular-structure prediction. Besides common pair-wise comparisons, we consider a few reasonable statistics for base-triple predictions, and present an elaborate analysis of these methods. All these statistics incorporate phylogenetic relationships of the sequences in the analysis to varying degrees, and the different nature of these tests gives a wide choice of statistical tools for RNA structure prediction. RESULTS: Starting from statistics that incorporate phylogenetic information only as independent sequence evolution models for each position of a multiple alignment, and extending this idea to a joint evolution model of two positions, we enhance the usual purely statistical methods (e.g. methods based on the Mutual Information statistic) with the use of phylogenetic information available in the sequences. In particular, we present a joint model based on the HKY evolution model, and consequently a X(2) test of independence for two positions. A significant part of this work is devoted to some mathematical analysis of these methods. We tested these statistics on regions of 16S and 23S rRNA, and tRNA.

Base Sequence↗

PubMed1999

A phylogenetic approach to RNA structure prediction.

Methods based on the Mutual Information statistic (MI methods) predict structure by looking for statistical correlations between sequence positions in a set of aligned sequences. Although MI methods are often quite effective, these methods ignore the underlying phylogenetic relationships of the sequences they analyze. Thus, they cannot distinguish between correlations due to structural interactions, and spurious correlations resulting from phylogenetic history. In this paper, we introduce a method analogous to MI that incorporates phylogenetic information. We show that this method accurately recovers the structures of well-known RNA molecules. We also demonstrate, with both real and simulated data, that this phylogenetically-based method outperforms standard MI methods, and improves the ability to distinguish interacting from non-interacting positions in RNA. This method is flexible, and may be applied to the prediction of protein structure given the appropriate evolutionary model. Because this method incorporates phylogenetic data, it also has the potential to be improved with the addition of more accurate phylogenetic information, although we show that even approximate phylogenies are helpful.

Computer Simulation↗