In Silico Biology

The basic linear treatment of sequence comparisons limits the ability of contemporary sequence alignment algorithms to detect non-order-conserving recombinations. Here, we introduce the algorithm combAlign which addresses the assessment of pairwise sequence similarity on non-order-conserving recombinations on a large scale. Emphasizing a two-level approach, combAlign first detects locally well conserved subsequences in a target and a source sequence. Subsequently, the relative placement of alignments is mapped to a graph. Concatenating local alignments to reassemble the target sequence to the fullest extent, the maximum scoring path through the graph denotes the best attainable combAlignment. Parameters influencing this process can be set to meet the user's specific demands. combAlign is applied to examples demonstrating the possibility to reflect evolutionary kinship of proteins even if their domains and motifs are strongly rearranged.

The translation initiation site (TIS) prediction problem is about how to correctly identify TIS in mRNA, cDNA, or other types of genomic sequences. High prediction accuracy can be helpful in a better understanding of protein coding from nucleotide sequences. This is an important step in genomic analysis to determine protein coding from nucleotide sequences. In this paper, we present an in silico method to predict translation initiation sites in vertebrate cDNA or mRNA sequences. This method consists of three sequential steps as follows. In the first step, candidate features are generated using k-gram amino acid patterns. In the second step, a small number of top-ranked features are selected by an entropy-based algorithm. In the third step, a classification model is built to recognize true TISs by applying support vector machines or ensembles of decision trees to the selected features. We have tested our method on several independent data sets, including two public ones and our own extracted sequences. The experimental results achieved are better than those reported previously using the same data sets. Our high accuracy not only demonstrates the feasibility of our method, but also indicates that there might be "amino acid" patterns around TIS in cDNA and mRNA sequences.

In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofestädt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism.

Mathematical modeling is a powerful approach for understanding the complexity of biological systems. Recently, several successful attempts have been made for simulating complex biological processes like metabolic pathways, gene regulatory networks and cell signaling pathways. The pathway models have not only generated experimentally verifiable hypothesis but have also provided valuable insights into the behavior of complex biological systems. Many recent studies have confirmed the phenotypic variability of organisms to an inherent stochasticity that operates at a basal level of gene expression. Due to this reason, development of novel mathematical representations and simulations algorithms are critical for successful modeling efforts in biological systems. The key is to find a biologically relevant representation for each representation. Although mathematically rigorous and physically consistent, stochastic algorithms are computationally expensive, they have been successfully used to model probabilistic events in the cell. This paper offers an overview of various mathematical and computational approaches for modeling stochastic phenomena in cellular systems.

Molecular chaperones are a wide group of unrelated protein families whose role is to assist others proteins. Comparably, under environmental stress, stress proteins behave as biocatalysts of protein stabilization. Stress proteins include a large class of proteins that were originally termed heat shock proteins (HSPs) due to their initial discovery in tissues exposed to elevated temperatures. Many, but not all, stress proteins and HSPs are molecular chaperones. Moreover, not all HSPs are derivable from stress. HSPs are structurally diversified by the contribution of various domains having specific roles. HSPs have been grouped, mainly on the basis of their molecular masses, into specific families that include small HSPs (sHSPs)/α-crystallins, HSP10s, HSP40s, HSP60s, HSP70s, HSP90s, HSP100s and HSP110s. The names of these major families are historical artefacts with limited information content. Using the current databases, names and proteic domains of many molecular chaperones in different species were analyzed. Although traditional names of HSPs are trivial, it is unrealistic to suggest replacing them, because they are preferred and widely used. Here we suggest that these traditional names be chaperoned, in silico, by a systematic nomenclature. Thus, for example, with the same intent of use of [trioxygen: O $_3$ ] for ozone, we propose here C7HSP70[Ehsa]ER-P11021 for GRP78 (78 kDa endoplasmic Human molecular chaperone in HSP70 superfamily with P11021 as its accession number in the database of the National Center for Biotechnology Information (NCBI)). The proposed systematic computer-oriented naming and classification method is designed for HSPs and also their partners based on the number of amino acids, domain structure, phylogenetic domain, localization in the cell and accession number as stated in the NCBI. Arabidopsis thaliana was analyzed as a model, because it contains a large number of various HSPs localized in several organelles. Overall, this naming system helps in building, optimizing and managing a novel online database entirely devoted to HSPs. The purported taxonomy, coupled with the newly constructed database, can contribute to studies involving large amounts of stored data on HSPs.

Large amounts of knowledge about genes have been stored in public databases. One of the most challenging problems in Bioinformatics is, given all the information about the genes in the databases, determining the relationships between the genes. For example, how can we determine if genes are related and how closely they are related based on existing knowledge about their biological roles. We developed GeneInfoViz, a web tool for batch retrieval of gene information and construction and visualization of gene relation networks. We created a database containing compiled Gene Ontology information for the genes of several model organisms. Users can batch search for a group of genes and get the Gene Ontology terms that are associated with the genes. Directed acyclic graphs are generated to show the hierarchical structure of the Gene Ontology tree. GeneInfoViz calculates an adjacency matrix to determine whether the genes are related and, if so, how closely they are related based on biological processes, molecular functions, or cellular components they are associated with and then displays a dynamic graph layout of the network among the selected genes.

The use of Bayesian Network methods to recover transcriptional regulatory networks from static microarray data is an active area of bioinformatics research. However, early work in this area lacked realistic analysis of the effects of data set size on learning performance and ignored the potentially immense benefits of using prior biological knowledge. More recent work which has utilized such information has tended to focus on qualitative descriptions of the results.

In this paper, we construct a detailed, realistic model for glucose homeostasis and use this model to generate static, synthetic gene expression data. We then use a Bayesian Network method to reconstruct this genetic network from the synthetic microarray data utilizing various amounts and types of prior knowledge.

By quantitatively analyzing the effects of data set size and the incorporation of different types of prior biological knowledge on our ability to reconstruct the original network, we show that characteristic portions of genetic networks can be reconstructed from microarray data. Incorporating prior knowledge into the learning scheme greatly reduces the data required, allowing these reverse engineering techniques to be used to learn regulatory interactions from microarray data sets of realistic size.

Complete genome sequences of several pathogenic bacteria have been determined, and many more such projects are currently under way. While these data potentially contain all the determinants of host-pathogen interactions and possible drug targets, computational tools for selecting suitable candidates for further experimental analyses are currently limited. Detection of bacterial genes that are non-homologous to human genes, and are essential for the survival of the pathogen represents a promising means of identifying novel drug targets. We have used three-way genome comparisons to identify essential genes from Pseudomonas aeruginosa. Our approach identified 306 essential genes that may be considered as potential drug targets. The resultant analyses are in good agreement with the results of systematic gene deletion experiments. This approach enables rapid potential drug target identification, thereby greatly facilitating the search for new antibiotics. These results underscore the utility of large genomic databases for in silico systematic drug target identification in the post-genomic era.

DNA curvature is known to play a biological role in gene regulation, in particular, initiation of transcription. We applied the software CURVATURE based on the wedge model to predict whether promoter regions of certain prokaryotes may be characterized by higher intrinsic DNA curvature located within or upstream to these regions. The main purpose was to verify our earlier hypothesis that the DNA curvature plays a biological role in gene regulation in mesophilic as compared to hyperthermophilic prokaryotes, i.e., DNA curvature presumably has a functional adaptive significance determined by temperature selection. Therefore, we analyzed all available complete prokaryotic genomes. The analysis showed that there is a group of genomes with a relatively high average DNA curvature upstream of start of genes. Remarkably, all organisms of this group appeared to be mesophilic, which is a full confirmation of the former hypothesis. The conservative patterns of genomic curvature distribution across different mesophilic bacterial and archaeal genomes presented in this study provide a new, convincing indication that curved DNA is evolutionarily preserved and determined by temperature selection. Moreover, we found a rather peculiar property of hyperthermophilic prokaryotes: the coding regions are predicted to be significantly more curved than it would be expected from their dinucleotide composition.

Thermo-search is an online web tool for the analysis of proteomes and individual proteins according to the ratio of two couplets of preferred and avoided amino acids in hyperthermophiles, thermophiles and mesophiles. It displays the ratio between glutamic acid plus lysine (E+K) and glutamine plus histidine (Q+H), which is higher in thermophilic proteomes and thermostable proteins than in mesophilic proteomes and thermo labile proteins. Thermo-search allows a rapid screen of the CRM database for thermostable proteins in their functional categories and a visualization of the (E+K)/(Q+H) average ratio between organisms, allowing a comparison of their lifestyles.

A statistical analysis of the PDB structures has led us to define a new set of small 3D structural prototypes called Protein Blocks (PBs). This structural alphabet includes 16 PBs, each one is defined by the (Φ, Ψ) dihedral angles of 5 consecutive residues. The amino acid distributions observed in sequence windows encompassing these PBs are used to predict by a Bayesian approach the local 3D structure of proteins from the sole knowledge of their sequences. LocPred is a software which allows the users to submit a protein sequence and performs a prediction in terms of PBs. The prediction results are given both textually and graphically.