Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond

Single-stranded regions in RNA secondary structure are important for RNA–RNA and RNA–protein interactions. We present a probability profile approach for the prediction of these regions based on a statistical algorithm for sampling RNA secondary structures. For the prediction of phylogenetically-determined single-stranded regions in secondary structures of representative RNA sequences, the probability profile offers substantial improvement over the minimum free energy structure. In designing antisense oligonucleotides, a practical problem is how to select a secondary structure for the target mRNA from the optimal structure(s) and many suboptimal structures with similar free energies. By summarizing the information from a statistical sample of probable secondary structures in a single plot, the probability profile not only presents a solution to this dilemma, but also reveals ‘well-determined’ single-stranded regions through the assignment of probabilities as measures of confidence in predictions. In antisense application to the rabbit β-globin mRNA, a significant correlation between hybridization potential predicted by the probability profile and the degree of inhibition of in vitro translation suggests that the probability profile approach is valuable for the identification of effective antisense target sites. Coupling computational design with DNA–RNA array technique provides a rational, efficient framework for antisense oligonucleotide screening. This framework has the potential for high-throughput applications to functional genomics and drug target validation.

The neural development and organization of letter recognition: Evidence from functional neuroimaging, computational modeling, and behavioral studies

Although much of the brain’s functional organization is genetically predetermined, it appears that some noninnate functions can come to depend on dedicated and segregated neural tissue. In this paper, we describe a series of experiments that have investigated the neural development and organization of one such noninnate function: letter recognition. Functional neuroimaging demonstrates that letter and digit recognition depend on different neural substrates in some literate adults. How could the processing of two stimulus categories that are distinguished solely by cultural conventions become segregated in the brain? One possibility is that correlation-based learning in the brain leads to a spatial organization in cortex that reflects the temporal and spatial clustering of letters with letters in the environment. Simulations confirm that environmental co-occurrence does indeed lead to spatial localization in a neural network that uses correlation-based learning. Furthermore, behavioral studies confirm one critical prediction of this co-occurrence hypothesis, namely, that subjects exposed to a visual environment in which letters and digits occur together rather than separately (postal workers who process letters and digits together in Canadian postal codes) do indeed show less behavioral evidence for segregated letter and digit processing.

Sequence similarity analysis of Escherichia coli proteins: functional and evolutionary implications.

A computer analysis of 2328 protein sequences comprising about 60% of the Escherichia coli gene products was performed using methods for database screening with individual sequences and alignment blocks. A high fraction of E. coli proteins--86%--shows significant sequence similarity to other proteins in current databases; about 70% show conservation at least at the level of distantly related bacteria, and about 40% contain ancient conserved regions (ACRs) shared with eukaryotic or Archaeal proteins. For > 90% of the E. coli proteins, either functional information or sequence similarity, or both, are available. Forty-six percent of the E. coli proteins belong to 299 clusters of paralogs (intraspecies homologs) defined on the basis of pairwise similarity. Another 10% could be included in 70 superclusters using motif detection methods. The majority of the clusters contain only two to four members. In contrast, nearly 25% of all E. coli proteins belong to the four largest superclusters--namely, permeases, ATPases and GTPases with the conserved "Walker-type" motif, helix-turn-helix regulatory proteins, and NAD(FAD)-binding proteins. We conclude that bacterial protein sequences generally are highly conserved in evolution, with about 50% of all ACR-containing protein families represented among the E. coli gene products. With the current sequence databases and methods of their screening, computer analysis yields useful information on the functions and evolutionary relationships of the vast majority of genes in a bacterial genome. Sequence similarity with E. coli proteins allows the prediction of functions for a number of important eukaryotic genes, including several whose products are implicated in human diseases.