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M Kanehisa

Publications and source records attributed to M Kanehisa.

At least 55 records · Page 3Linked to original sources
PubMed1997

Organizing and computing metabolic pathway data in terms of binary relations.

A new database system named KEGG is being organised to computerize functional aspects of genes and genomes in terms of the binary relations of interacting molecules or genes. We are currently working on the metabolic pathway database that is composed of three interconnected sections: genes, molecules, and pathways, which are also linked to a number of existing databases through our DBGET retrieval system. Here we present the basic concept of binary relations and hierarchical classifications to represent the metabolic pathway data. The database operations are then defined as an extension of the relational operations, and the path computation problem is considered as a deduction from binary relations. An example of using KEGG for the functional prediction of genomic sequences is presented.

Animals↗
PubMed1996

The size differences among mammalian introns are due to the accumulation of small deletions.

In order to investigate the molecular mechanisms that alter intron size, we conducted an extensive interspecies comparison of homologous introns among three mammalian groups: human, artiodactyls, and rodents. The size differences of introns were statistically significant among all three groups (longest intron was for human and shortest for rodents), and appear to be due to the accumulation of small deletions, according to the separate count of insertion and deletion frequencies. The distribution of intron size differences also has a shape similar to that for the distribution of insertion/deletion sizes found in pseudogenes. It is suggested that introns are selectively neutral to small-scale changes of the genome size, which inherently contain the bias of favoring short deletions against short insertions.

Animals↗
PubMed1996

Construction and analysis of a profile library characterizing groups of structurally known proteins.

A new sequence motif library StrProf was constructed characterizing the groups of related proteins in the PDB three-dimensional structure database. For a representative member of each protein family, which was identified by cross-referencing the PDB with the PIR superfamily classification, a group of related sequences was collected by the BLAST search against the nonredundant protein sequence database. For every group, the motifs were identified automatically according to the criteria of conservation and uniqueness of pentapeptide patterns and with a dual dynamic programming algorithm. In the StrProf library, motifs are represented by profile matrices rather than consensus patterns to allow more flexible search capabilities. Another dynamic programming algorithm was then developed to search this motif library. When the computationally derived StrProf was compared with PROSITE, which is a manually derived motif library in the best consensus pattern representation, the numbers of identified patterns were comparable. StrProf missed about one third of the PROSITE motifs, but there were also new motifs lacking in PROSITE. The new library was incorporated in SMART (Sequence Motif Analysis and Retrieval Tool), a computer tool designed to help search and annotate biologically important sites in an unknown protein sequence. The client program is available free of charge through the Internet.

Algorithms↗
PubMed1996

Analysis of amino acid indices and mutation matrices for sequence comparison and structure prediction of proteins.

An amino acid index is a set of 20 numerical values representing any of the different physicochemical and biochemical properties of amino acids. As a follow-up to the previous study, we have increased the size of the database, which currently contains 402 published indices, and re-performed the single-linkage cluster analysis. The results basically confirmed the previous findings. Another important feature of amino acids that can be represented numerically is the similarity between them. Thus, a similarity matrix, also called a mutation matrix, is a set of 20 x 20 numerical values used for protein sequence alignments and similarity searches. We have collected 42 published matrices, performed hierarchical cluster analyses and identified several clusters corresponding to the nature of the data set and the method used for constructing the mutation matrix. Further, we have tried to reproduce each mutation matrix by the combination of amino acid indices in order to understand which properties of amino acids are reflected most. There was a relationship between the PAM units of Dayhoff's mutation matrix and the volume and hydrophobicity of amino acids. The database of 402 amino acid indices and 42 amino acid mutation matrices is made publicly available on the Internet.

Amino Acids↗
PubMed1995

A genetic algorithm based molecular modeling technique for RNA stem-loop structures.

A new modeling technique for arriving at the three dimensional (3-D) structure of an RNA stem-loop has been developed based on a conformational search by a genetic algorithm and the following refinement by energy minimization. The genetic algorithm simultaneously optimizes a population of conformations in the predefined conformational space and generates 3-D models of RNA. The fitness function to be optimized by the algorithm has been defined to reflect the satisfaction of known conformational constraints. In addition to a term for distance constraints, the fitness function contains a term to constrain each local conformation near to a prepared template conformation. The technique has been applied to the two loops of tRNA, the anticodon loop and the T-loop, and has found good models with small root mean square deviations from the crystal structure. Slightly different models have also been found for the anticodon loop. The analysis of a collection of alternative models obtained has revealed statistical features of local variations at each base position.

Algorithms↗
PubMed1994

Distribution profiles of GC content around the translation initiation site in different species.

We have analyzed the distribution of guanine-cytosine (GC) content around the translation initiation site in genomic DNA sequences of different species. A set of sequences belonging to one species is aligned at the translation initiation site, and the average GC content is calculated for 100 base windows over a range of 500 bases each for upstream and downstream region. Consistent with previous observations that coding regions are more GC-rich than non-coding regions, we observe a jump in the GC content at the translation initiation site, except for vertebrate sequences. It was also found that the overall shape of the GC content profile is similar within each organism group even though the average GC contents can be very different. Furthermore, by examining different profiles for different species, we have found a negative correlation between the average GC content and the jump size at the translation initiation site. Apparently, more AT-rich genomes, which tend to lack macroscopic mosaic structures, exhibit more marked differences in the GC content at the microscopic level.

Animals↗
PubMed1993

An approach to systematic detection of protein structural motifs.

A procedure to detect similar local structures of proteins from C alpha coordinates is presented. First, the conformations of seven-residue peptide segments are approximated by a limited number of representatives, each of which is assigned a symbol. Thus, the overall conformation of a protein is represented by a symbol string. The comparison of these symbol strings using a sequence alignment technique then gives pairs of similar local structures. These pairs are considered candidates of structural motifs. The application of the procedure to the analysis of 93 proteins gave 858 pairs of similar local structures, which included several well-known structural motifs such as the nucleotide-binding beta alpha beta-unit and the calcium-binding EF hand. The characterization of amino acid patterns of similar local structures given by the procedure should be useful for the development of protein structure prediction based on the acquisition of empirical rules from a large-scale database.

Algorithms↗
PubMed1993

Multiple sequence alignment by parallel simulated annealing.

We have developed simulated annealing algorithms to solve the problem of multiple sequence alignment. The algorithm was shown to give the optimal solution as confirmed by the rigorous dynamic programming algorithm for three-sequence alignment. To overcome long execution times for simulated annealing, we utilized a parallel computer. A sequential algorithm, a simple parallel algorithm and the temperature parallel algorithm were tested on a problem. The results were compared with the result obtained by a conventional tree-based algorithm where alignments were merged by two-way dynamic programming. Every annealing algorithm produced a better energy value than the conventional algorithm. The best energy value, which probably represents the optimal solution, was reached within a reasonable time by both of the parallel annealing algorithms. We consider the temperature parallel algorithm of simulated annealing to be the most suitable for finding the optimal multiple sequence alignment because the algorithm does not require any scheduling for optimization. The algorithm is also useful for refining multiple alignments obtained by other heuristic methods.

Algorithms↗
PubMed1992

An assessment of neural network and statistical approaches for prediction of E. coli promoter sites.

We have constructed a perceptron type neural network for E. coli promoter prediction and improved its ability to generalize with a new technique for selecting the sequence features shown during training. We have also reconstructed five previous prediction methods and compared the effectiveness of those methods and our neural network. Surprisingly, the simple statistical method of Mulligan et al. performed the best amongst the previous methods. Our neural network was comparable to Mulligan's method when false positives were kept low and better than Mulligan's method when false negatives were kept low. We also showed the correlation between the prediction rates of neural networks achieved by previous researchers and the information content of their data sets.

Base Sequence↗
PubMed1992

A knowledge base for predicting protein localization sites in eukaryotic cells.

To automate examination of massive amounts of sequence data for biological function, it is important to computerize interpretation based on empirical knowledge of sequence-function relationships. For this purpose, we have been constructing a knowledge base by organizing various experimental and computational observations as a collection of if-then rules. Here we report an expert system, which utilizes this knowledge base, for predicting localization sites of proteins only from the information on the amino acid sequence and the source origin. We collected data for 401 eukaryotic proteins with known localization sites (subcellular and extracellular) and divided them into training data and testing data. Fourteen localization sites were distinguished for animal cells and 17 for plant cells. When sorting signals were not well characterized experimentally, various sequence features were computationally derived from the training data. It was found that 66% of the training data and 59% of the testing data were correctly predicted by our expert system. This artificial intelligence approach is powerful and flexible enough to be used in genome analyses.

Algorithms↗
PubMed1992

Construction of a dictionary of sequence motifs that characterize groups of related proteins.

An automatic procedure is proposed to identify, from the protein sequence database, conserved amino acid patterns (or sequence motifs) that are exclusive to a group of functionally related proteins. This procedure is applied to the PIR database and a dictionary of sequence motifs that relate to specific superfamilies constructed. The motifs have a practical relevance in identifying the membership of specific superfamilies without the need to perform sequence database searches in 20% of newly determined sequences. The sequence motifs identified represent functionally important sites on protein molecules. When multiple blocks exist in a single motif they are often close together in the 3-D structure. Furthermore, occasionally these motif blocks were found to be split by introns when the correlation with exon structures was examined.

Amino Acid Sequence↗
PubMed1991

Expert system for predicting protein localization sites in gram-negative bacteria.

We have developed an expert system that makes use of various kinds of knowledge organized as "if-then" rules for predicting protein localization sites in Gram-negative bacteria, given the amino acid sequence information alone. We considered four localization sites: the cytoplasm, the inner (cytoplasmic) membrane, the periplasm, and the outer membrane. Most rules were derived from experimental observations. For example, the rule to recognize an inner membrane protein is the presence of either a hydrophobic stretch in the predicted mature protein or an uncleavable N-terminal signal sequence. Lipoproteins are first recognized by a consensus pattern and then assumed present at either the inner or outer membrane. These two possibilities are further discriminated by examining an acidic residue in the mature N-terminal portion. Furthermore, we found an empirical rule that periplasmic and outer membrane proteins were successfully discriminated by their different amino acid composition. Overall, our system could predict 83% of the localization sites of proteins in our database.

Bacterial Outer Membrane Proteins↗
PubMed1990

Fragment peptide library for classification and functional prediction of proteins.

From protein sequence comparison data found in the literature, a library was organized using peptide fragment sequences which are common to related proteins. Each of the fragments was then examined for its occurrence in all the protein superfamilies defined by the NBRF-PIR data base. We have selected those fragment peptides that appear exclusively in one or a few superfamilies, and thus made a library of fragment peptides that characterize specific superfamilies. Such characteristic peptides are, in general, five to seven residues long and contain unusually high proportions of glycine and cysteine. This collection is a useful resource for the classification and functional prediction of protein molecules.

Amino Acid Sequence↗
PubMed1988

Discriminant analysis of promoter regions in Escherichia coli sequences.

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.

Algorithms↗
PubMed1988

Prediction of in-vivo modification sites of proteins from their primary structures.

In order to make better use of the information contained in rapidly expanding amino acid sequence data, a new method to predict various modification sites of proteins from their primary structures is presented. It is also applicable to the prediction of other functional sites in proteins. Here we show the examples of N-glycosylation and serine/threonine phosphorylation sites. The method is essentially an elaboration of consensus sequence pattern matching based on stepwise discriminant analysis. The occurring amino acids near a potential modification site are represented by six numerical values which reflect various properties of amino acids. Longer-range effects around these sites are also considered. The stepwise procedure enabled us to automatically select effective features for discrimination. A computer program with our method first identifies potential modification sites by a sequence pattern, NX(S/T) for N-glycosylation or (S/T) for phosphorylation, and then decides by discriminant analysis whether a potential site is likely to be a true modification site. The prediction accuracy in the second step of discrimination was about 60% for glycosylation sites and about 80% for phosphorylation sites.

Amino Acid Sequence↗