Proceedings. International Conference on Intelligent Systems for Molecular Biology最新文献

Traditionally, biochemical systems are modelled using kinetics and differential equations in a quantitative simulator. However, for many biological processes detailed quantitative information is not available, only qualitative or fuzzy statements about the nature of interactions. In a previous paper we have shown the applicability of qualitative reasoning methods for molecular biological regulatory processes. Now, we present a newly developed simulation environment, BioSim, that is written in Prolog using constraint logic programming techniques. The simulator combines the basic ideas of two main approaches to qualitative reasoning and integrates the contents of a molecular biology knowledge base, EcoCyc. We show that qualitative reasoning can be combined with automatic transformation of contents of genomic databases into simulation models to give an interactive modelling system that reasons about the relations and interactions of biological entities. This is demonstrated on the glycolytic pathway.

We describe a prototype system (Poly-X) for assisting an expert user in modeling protein repeats. Poly-X reduces the large number of degrees of freedom required to specify a protein motif in complete atomic detail. The result is a small number of parameters that are easily understood by, and under the direct control of, a domain expert. The system was applied to the polyglutamine (poly-Q) repeat in the first exon of huntingtin, the gene implicated in Huntington's disease. We present four poly-Q structural motifs: two poly-Q beta-sheet motifs (parallel and antiparallel) that constitute plausible alternatives to a similar previously published poly-Q beta-sheet motif, and two novel poly-Q helix motifs (alpha-helix and pi-helix). To our knowledge, helical forms of polyglutamine have not been proposed before. The motifs suggest that there may be several plausible aggregation structures for the intranuclear inclusion bodies which have been found in diseased neurons, and may help in the effort to understand the structural basis for Huntington's disease.

Existing query interfaces for biological databases are either based on fixed forms or textual query languages. Users of a fixed form-based query interface are limited to performing some pre-defined queries providing a fixed view of the underlying database, while users of a free text query language-based interface have to understand the underlying data models, specific query languages and application schemas in order to formulate queries. Further, operations on application-specific complex data (e.g., DNA sequences, proteins), which are usually provided by a variety of software packages with their own format requirements and peculiarities, are not available as part of, nor integrated with biological query interfaces. In this paper, we describe generic tools that provide powerful and flexible support for interactively exploring biological databases in a uniform and consistent way, that is via common data models, formats, and notations, in the framework of the Object-Protocol Model (OPM). These tools include (i) a Java graphical query construction tool with support for automatic generation of Web query forms that can be either used for further specifying conditions, or can be saved and customized; (ii) query processors for interpreting and executing queries that may involve complex application-specific objects, and that could span multiple heterogeneous databases and file systems; and (iii) utilities for automatic generation of HTML pages containing query results, that can be browsed using a Web browser. These tools avoid the restrictions imposed by traditional fixed-form query interfaces, while providing users with simple and intuitive facilities for formulating ad-hoc queries across heterogeneous databases, without the need to understand the underlying data models and query languages.

DNA sequence analysis depends on the accurate assembly of fragment reads for the determination of a consensus sequence. This report examines the possibility of analyzing multiple, independent restriction digests as a method for testing the fidelity of sequence assembly. A dynamic programming algorithm to determine the maximum likelihood alignment of error prone electrophoretic mobility data to the expected fragment mobilities given the consensus sequence and restriction enzymes is derived and used to assess the likelihood of detecting rearrangements in genomic sequencing projects. The method is shown to reliably detect errors in sequence fragment assembly without the necessity of making reference to an overlying physical map. An html form-based interface is available at http:/(/)www.ibc.wustl.edu/services/validate. html.

IMGT, the international ImMunoGeneTics database (http:(/)/imgt.cnusc.fr:8104), created by Marie-Paule Lefranc, Montpellier, France, is an integrated database specializing in antigen receptors and MHC of all vertebrate species. IMGT includes LIGM-DB, developed for Immunoglobulins and T-cell-receptors. LIGM-DB distributes high quality data with an important increment value added by the LIGM expert annotations. LIGM-DB accurate immunogenetics data is based on the standardization of biological knowledge related to keywords, annotation labels and gene identification. The management of such data resulting from biological research requires an high flexible implementation to quickly reflect up-to-date results, and to integrate new knowledge. We developed a systematized approach and defined LIGM-DB systems which manage and realize the major tasks for the database survey. In this paper, we will focus on the coherence system, which became absolutely crucial to maintain data quality as the database is growing up and as the biological knowledge continues to improve, and on the distribution system which makes LIGM-DB data easy to access, download and reuse. Efforts have been done to improve the data distribution procedures and adapt them to the current bioinformatics needs. Recently, we have developed an API which allows Java programmers to remotely access and integrate LIGM-DB data in other computer environments.

A hidden Markov model of signal peptides has been developed. It contains submodels for the N-terminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our data, the length distributions for the three regions are significantly different from expectations. For instance, the assigned hydrophobic region is between 8 and 12 residues long in almost all eukaryotic signal peptides. This analysis also makes obvious the difference between eukaryotes, Gram-positive bacteria, and Gram-negative bacteria. The model can be used to predict the location of the cleavage site, which it finds correctly in nearly 70% of signal peptides in a cross-validated test--almost the same accuracy as the best previous method. One of the problems for existing prediction methods is the poor discrimination between signal peptides and uncleaved signal anchors, but this is substantially improved by the hidden Markov model when expanding it with a very simple signal anchor model.

A computational system is described that predicts the structure of protein/protein and protein/DNA complexes starting from unbound coordinate sets. The approach is (i) a global search with rigid-body docking for complexes with shape complementarity and favourable electrostatics; (ii) use of distance constraints from experimental (or predicted) knowledge of critical residues; (iii) use of pair potential to screen docked complexes and (iv) refinement and further screening by protein-side chain optimisation and interfacial energy minimisation. The system has been applied to model ten protein/protein and eight protein-repressor/DNA (steps i to iii only) complexes. In general a few complexes, one of which is close to the true structure, can be generated.

The availability of large EST (Expressed Sequence Tag) databases has led to a revolution in the way new genes are cloned. Difficulties arise, however, due to high error rates and redundancy of raw EST data. For these reasons, one of the first tasks performed by a scientist investigating any EST of interest is to gather contiguous ESTs and assemble them into a larger virtual cDNA. The REX (Recursive EST eXtender) algorithm described in this paper completely automates this process by finding ESTs that can be clustered on the basis of overlapping bases, and then assembling the contigs into a consensus sequence. By combining the clustering and assembly steps, REX can quickly generate assemblies from EST databases that are frequently updated without having to preprocess the data. A consensus assembly method is used to correct miscalled bases and remove indel errors. A unique feature of this method is that it addresses the issues of splice variants and unspliced cDNA data. Since REX is a fast greedy algorithm, it can address the problem of generating a database of assembled sequences from very large collections of EST data. A procedure is described for creating and maintaining an Assembled Consensus EST database (ACE) that is useful for characterizing the large body of data that exists in EST databases.

We investigate the space of all protein sequences. We combine the standard measures of similarity (SW, FASTA, BLAST), to associate with each sequence an exhaustive list of neighboring sequences. These lists induce a (weighted directed) graph whose vertices are the sequences. The weight of an edge connecting two sequences represents their degree of similarity. This graph encodes much of the fundamental properties of the sequence space. We look for clusters of related proteins in this graph. These clusters correspond to strongly connected sets of vertices. Two main ideas underlie our work: i) Interesting homologies among proteins can be deduced by transitivity. ii) Transitivity should be applied restrictively in order to prevent unrelated proteins from clustering together. Our analysis starts from a very conservative classification, based on very significant similarities, that has many classes. Subsequently, classes are merged to include less significant similarities. Merging is performed via a novel two phase algorithm. First, the algorithm identifies groups of possibly related clusters (based on transitivity and strong connectivity) using local considerations, and merges them. Then, a global test is applied to identify nuclei of strong relationships within these groups of clusters, and the classification is refined accordingly. This process takes place at varying thresholds of statistical significance, where at each step the algorithm is applied on the classes of the previous classification, to obtain the next one, at the more permissive threshold. Consequently, a hierarchical organization of all proteins is obtained. The resulting classification splits the space of all protein sequences into well defined groups of proteins. The results show that the automatically induced sets of proteins are closely correlated with natural biological families and super families. The hierarchical organization reveals finer sub-families that make up known families of proteins as well as many interesting relations between protein families. The hierarchical organization proposed may be considered as the first map of the space of all protein sequences. An interactive web site including the results of our analysis has been constructed, and is now accessible through http:/(/)www.protomap.cs.huji.ac.il

The incorporation of experimentally-determined constraints into structure-prediction methods based on energy minimization leads to both improved selectivity with empirical potential functions and structure determination with far fewer constraints than are required for distance-geometry calculations. Some methods will be described for using both distance and angle constraints with the hierarchical minimization algorithm. The simulation is based on a combination of Monte Carlo Simulated Annealing and Genetic Algorithm techniques which are integrated into a single framework. The selection cycle of the genetic algorithm is carried out at the same temperature as the mutations, or alternatively the crossover cycle can be considered as a type of Monte Carlo trial move, such that each temperature annealing step corresponds to a new generation. The sequence is divided up into segments, and the mutation step consists of replacing an entire segment with a choice from a pre-selected list. This list is in turn constructed from a list of smaller segments, and the number of overall conformations can thus be pruned at each level of selection. Results will be shown for test cases using a small number of flexible distance constraints used as an additional term in the potential, and for restrictions placed on backbone dihedral angles used as an additional screening criterion for constructing trial moves.