Computer applications in the biosciences : CABIOS最新文献

Motivation: Substitution rates estimated from aligned DNA data can be used as genetic distances to investigate the phylogenetic relationship of those sequences. For this purpose, a Markov model of nucleotide substitution has to be assumed that describes this process most adequately.

Results: A program is presented that estimates substitution rates and their standard errors for a variety of Markov models. The model introduced by Hasegawa et al. (J. Mol. Evol., 22, 160-174, 1985) is the only one for which distances and standard deviations need to be calculated numerically, since analytical formulae cannot be derived. Each model is implemented in two different variants: (i) assuming rate homogeneity or (ii) starting from Gamma-distributed substitution rates across sequence sites. The estimation of heterogeneous substitution rates is based on a method suggested by Tamura and Nei (Mol. Biol. Evol., 10, 512-526, 1993). All required parameters are estimated from sequence data, hence the user is not asked to supply any additional input. One goal of the program is to support the user when choosing a particular model that describes most adequately the evolution of the given data set. For this purpose, a more detailed analysis of this model fit is provided. Phylogenetic trees reconstructed from the inferred distances using the neighbor-joining algorithm are also available.

Motivation: Using the genetic algorithm (GA) for RNA folding on a massively parallel supercomputer, MasPar MP-2 with 16,384 processors, we successfully predicted the existence of H-type pseudoknots in several sequences.

Results: The GA is applied to folding the tRNA-like 3' end of turnip yellow mosaic virus (TYMV) RNA sequence with 82 nucleotides, the 3' UTRs of satellite tobacco necrosis virus (STNV)-2 RNA sequence with 619 nucleotides and STNV-I RNA sequence with 622 nucleotides, and the bacteriophage T2, T4 and T6 gene 32 mRNA sequences with 946, 1340 and 946 nucleotides, respectively. The GA's results match the phylogenetically supported tertiary structures of these sequences.

Biologists have long made use of linguistic metaphors in describing and naming cellular processes involving nucleic acid and protein sequences. Indeed, it is very natural to view the genetic 'text' and its sequential transliterations in these terms. However, a metaphor is not a tool, and it is necessary to ask whether the techniques used in analyzing other kinds of languages, such as human and computer languages, can in fact be of any use in tackling problems in molecular biology. This paper reviews the work of the author and others in applying the methods of computational linguistics to biological sequences.

Two approximations were introduced into the double dynamic programming algorithm, in order to reduce the computational time for structural alignment. One of them was the so-called distance cut-off, which approximately describes the structural environment of each residue by its local environment. In the approximation, a sphere with a given radius is placed at the center of the side chain of each residue. The local environment of a residue is constituted only by the residues with side chain centers that are present within the sphere, which is expressed by a set of center-to-center distances from the side chain of the residue to those of all the other constituent residues. The residues outside the sphere are neglected from the local environment. Another approximation is associated with the distance cut-off, which is referred to here as the delta N cut-off. If two local environments are similar to each other, the numbers of residues constituting the environments are expected to be similar. The delta N cut-off was introduced based on the idea. If the difference between the numbers of the constituent residues of two local environments is greater than a given threshold value, delta N, the evaluation of the similarity between the local environments is skipped. The introduction of the two approximations dramatically reduced the computational time for structural alignment by the double dynamic programming algorithm. However, the approximations also decreased the accuracy of the alignment. To improve the accuracy with the approximations, a program with a two-step alignment algorithm was constructed. At first, an alignment was roughly constructed with the approximations. Then, the epsilon-suboptimal region for the alignment was determined. Finally, the double dynamic programming algorithm with full structural environments was applied to the residue pairs within the epsilon-suboptimal region to produce an improved alignment.

Motivation: We needed an efficient way to explore the binding reactions leading to protein complexes of known composition and structure.

Results: A new program is described that allows the user to define a set of protein elements and to link these elements into an oligomeric 'ball-and-stick' assembly in a graphical interface. Once the structure of the oligomer has been defined, the program then employs a novel algorithm to deduce the binding reactions and intermediate complexes needed to make the oligomer from its starting protein components. The program also finds the equilibrium state of the system, using either default starting concentrations and Kd values or data supplied by the user.

Motivation: Realistic simulation of the kinetic properties of metabolic pathways requires rate equations to be expressed in reversible form, because substrate and product elasticities are drastically different in reversible and irreversible reactions. This presents no special problem for reactions that follow reversible Michaelis-Menten kinetics, but for enzymes showing cooperative kinetics the full reversible rate equations are extremely complicated, and anyway in virtually all cases the full equations are unknown because sufficiently complete kinetic studies have not been carried out. There is a need, therefore, for approximate reversible equations that allow convenient simulation without violating thermodynamic constraints.

Results: We show how the irreversible Hill equation can be generalized to a reversible form, including effects of modifiers. The proposed equation leads to behaviour virtually indistinguishable from that predicted by a kinetic form of the Adair equation, despite the fact that the latter is a far more complicated equation. By contrast, a reversible form of the Monod-Wyman-Changeux equation that has sometimes been used leads to predictions for the effects of modifiers at high substrate concentration that differ qualitatively from those given by the Adair equation.

Motivation: Modeling families of related biological sequences using Hidden Markov models (HMMs), although increasingly widespread, faces at least one major problem: because of the complexity of these mathematical models, they require a relatively large training set in order to accurately recognize a given family. For families in which there are few known sequences, a standard linear HMM contains too many parameters to be trained adequately.

Results: This work attempts to solve that problem by generating smaller HMMs which precisely model only the conserved regions of the family. These HMMs are constructed from motif models generated by the EM algorithm using the MEME software. Because motif-based HMMs have relatively few parameters, they can be trained using smaller data sets. Studies of short chain alcohol dehydrogenases and 4Fe-4S ferredoxins support the claim that motif-based HMMs exhibit increased sensitivity and selectivity in database searches, especially when training sets contain few sequences.

A new method for the prediction of protein secondary structure is proposed, which relies totally on the global aspect of a protein. The prediction scheme is as follows. A structural library is first scanned with a query sequence by the 3D-1D compatibility method developed before. All the structures examined are sorted with the compatibility score and the top 50 in the list are picked out. Then, all the known secondary structures of the 50 proteins are globally aligned against the query sequence, according to the 3D-1D alignments. Prediction of either alpha helix, beta strand or coil is made by taking the majority among the observations at each residue site. Besides 325 proteins in the structural library, 77 proteins were selected from the latest release of the Brookhaven Protein Data Bank, and they were divided into three data sets. Data set 1 was used as a training set for which several adjustable parameters in the method were optimized. Then, the final form of the method was applied to a testing set (data set 2) which contained proteins of chain length < or = 400 residues. The average prediction accuracy was as high as 69% in the three-state assessment of alpha, beta and coil. On the other hand, data set 3 contains only those proteins of length > 400 residues, for which the present method would not work properly because of the size effect inherent in the 3D-1D compatibility method. The proteins in data set 3 were, therefore, subdivided into constituent domains (data set 4) before being fed into the prediction program. The prediction accuracy for data set 4 was 66% on average, a few percent lower than that for data set 2. Possible causes for this discrepancy are discussed.

This paper describes a new method for determining the consensus sequences that signal the start of translation and the boundaries between exons and introns (donor and acceptor sites) in eukaryotic mRNA. The method takes into account the dependencies between adjacent bases, in contrast to the usual technique of considering each position independently. When coupled with a dynamic program to compute the most likely sequence, new consensus sequences emerge. The consensus sequence information is summarized in conditional probability matrices which, when used to locate signals in uncharacterized genomic DNA, have greater sensitivity and specificity than conventional matrices. Species-specific versions of these matrices are especially effective at distinguishing true and false sites.