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Current secondary structure prediction computations have a serious drawback. The calculated thermodynamically most stable structure often differs from that observed in solution or in crystal form. In this paper we suggest a way to partially over-come some of these limitations by simulating the RNA folding process and calculating the frequencies of occurrence of the various substructures obtained. The frequently recurring substructures are then selected to construct the secondary structure of the whole RNA. 142 tRNA molecules and an E. coli 16S rRNA molecule have been examined by this method. The percentage of successful prediction of the correct helices are significantly higher than those calculated previously. The secondary structures of intervening sequences (IVSs) excised from human alpha-like globin pre-mRNAs are also computed. Thus, in this method the secondary structures obtained are composed of the statistically more significant substructures. This has also been demonstrated by using randomly shuffled sequences. The secondary structures of each of the randomized sequences are computed and their mean and standard deviations are used in evaluating the significance of the substructures obtained in the folding of the biological sequence. Some potentially appealing structural features aligning adjacent exons for ligation have been found.

We describe a program for the analysis of RNA secondary structure. There are two new features in this program. (i) To get vector speeds on a vector pipeline machine (such as Cray X-MP/24) we have vectorized the secondary structure dynamic algorithm. (ii) The statistical significance of a locally 'optimal' secondary structure is assessed by a Monte Carlo method. The results can be depicted graphically including profiles of the stability of local secondary structures and the distribution of the potentially significant secondary structures in the RNA molecules. Interesting regions where both the potentially significant secondary structures and 'open' structures (single-stranded coils) occur can be identified by the plots mentioned above. Furthermore, the speed of the vectorized code allows repeated Monte Carlo simulations with different overlapping window sizes. Thus, the optimal size of the significant secondary structure occurring in the interesting region can be assessed by repeating the Monte Carlo simulation. The power of the program is demonstrated in the analysis of local secondary structures of human T-cell lymphotrophic virus type III (HIV).

A fixed-point alignment analysis technique is presented which is designed to locate common sequence motifs in collections of proteins or nucleic acids. Initially a program aligns a collection of sequences by a common sequence pattern or known biological feature. The common pattern or feature (fixed-point) may be a user-specified sequence string or a known sequence position like mRNA start site, which may be taken directly from the annotated feature table of GenBank. Once all alignment markers are located, the sequences are scanned for occurrences of given oligomers within a specified span both upstream and downstream of the fixed-point. The occurrences may then be plotted as a function of the position relative to the fixed-point, displayed as an actual sequence alignment or selectively summarized via various program options. Applications of the technique are discussed.

We have previously developed a general method based on the statistical technique of discriminant analysis to predict splice junctions in eukaryotic mRNA sequences [Nakata, K., Kanehisa, M. and DeLisi, C. (1985) Nucleic Acids Res., 13, 5327-5340]. In order to evaluate further applicability of this method, we now analyze the promoter region of Escherichia coli sequences. The attributes used for discrimination include the accuracy of consensus sequence patterns measured by the perceptron algorithm, the thermal stability map, the base composition and the Calladine-Dickerson rules for helical twist angle, roll angle, torsion angle and propeller twist angle. When applied to selected E. coli sequences in the GenBank database, the method correctly identifies 75% of the true promoter regions.