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Recent progress in biomolecular engineering.

During the next decade or so, there will be significant and impressive advances in biomolecular engineering, especially in our understanding of the biological roles of various biomolecules inside the cell. The advances in high throughput screening technology for discovery of target molecules and the accumulation of functional genomics and proteomics data at accelerating rates will enable us to design and discover novel biomolecules and proteins on a rational basis in diverse areas of pharmaceutical, agricultural, industrial, and environmental applications. As an applied molecular evolution technology, DNA shuffling will play a key role in biomolecular engineering. In contrast to the point mutation techniques, DNA shuffling exchanges large functional domains of sequences to search for the best candidate molecule, thus mimicking and accelerating the process of sexual recombination in the evolution of life. The phage-display system of combinatorial peptide libraries will be extensively exploited to design and create many novel proteins, as a result of the relative ease of screening and identifying desirable proteins. Even though this system has so far been employed mainly in screening the combinatorial antibody libraries, its application will be extended further into the science of protein-receptor or protein-ligand interactions. The bioinformatics for genome and proteome analyses will contribute substantially toward ever more accelerated advances in the pharmaceutical industry. Biomolecular engineering will no doubt become one of the most important scientific disciplines, because it will enable systematic and comprehensive analyses of gene expression patterns in both normal and diseased cells, as well as the discovery of many new high-value molecules. When the functional genomics database, EST and SAGE techniques, microarray technique, and proteome analysis by 2-dimensional gel electrophoresis or capillary electrophoresis in combination with mass spectrometer are all put to good use, biomolecular engineering research will yield new drug discoveries, improved therapies, and significantly improved or new bioprocess technology. With the advances in biomolecular engineering, the rate of finding new high-value peptides or proteins, including antibodies, vaccines, enzymes, and therapeutic peptides, will continue to accelerate. The targets for the rational design of biomolecules will be broad, diverse, and complex, but many application goals can be achieved through the expansion of knowledge based on biomolecules and their roles and functions in cells and tissues. Some engineered biomolecules, including humanized Mab's, have already entered the clinical trials for therapeutic uses. Early results of the trials and their efficacy are positive and encouraging. Among them, Herceptin, a humanized Mab for breast cancer treatment, became the first drug designed by a biomolecular engineering approach and was approved by the FDA. Soon, new therapeutic drugs and high-value biomolecules will be designed and produced by biomolecular engineering for the treatment or prevention of not-so-easily cured diseases such as cancers, genetic diseases, age-related diseases, and other metabolic diseases. Many more industrial enzymes, which will be engineered to confer desirable properties for the process improvement and manufacturing of high-value biomolecular products at a lower production cost, are also anticipated. New metabolites, including novel antibiotics that are active against resistant strains, will also be produced soon by recombinant organisms having de novo engineered biosynthetic pathway enzyme systems. The biomolecular engineering era is here, and many of benefits will be derived from this field of scientific research for years to come if we are willing to put it to good use.

Animals↗

In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists.

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.

Cluster Analysis↗

Identification of the complete coding sequence and genomic organization of the Treacher Collins syndrome gene.

Treacher Collins syndrome (TCS) is an autosomal dominant disorder of craniofacial development, the features of which include conductive hearing loss and cleft palate. Recently, the demonstration of a series of 10 mutations within a partial-length cDNA clone have indicated that the TCS gene (TCOF1) has been positionally cloned. Although it has been shown that the gene is expressed in a wide variety of fetal and adult tissues, database sequence comparisons have failed to provide significant information on the function of the gene. In the current investigation, a combination of cDNA library screening and rapid amplification of cDNA ends has permitted the isolation of the complete coding sequence of TCOF1, which is encoded by 26 exons and predicts a low complexity, serine/alanine-rich protein of approximately 144 kD. The use of a variety of bioinformatics tools has resulted in the identification of repeated units within the gene, each of which maps onto an individual exon. The predicted protein Treacle contains numerous potential phosphorylaiton sites, a number of which map to similar positions within the repeated units, and shows weak but significant homology to the nucleolar phosphoproteins. Although the precise function of Treacle remains unknown, these observations suggest that phosphorylation may be important for its role in early embryonic development and that it may play a role in nucleolar-cytoplasmic shuttling. The information presented in this study will allow continued mutation analysis in families with a history of TCS and should facilitate continued experimentation to shed further light on the function of the gene/protein during development of the craniofacial complex.

Amino Acid Sequence↗

PROTEOME-3D: an interactive bioinformatics tool for large-scale data exploration and knowledge discovery.

Comprehensive understanding of biological systems requires efficient and systematic assimilation of high-throughput datasets in the context of the existing knowledge base. A major limitation in the field of proteomics is the lack of an appropriate software platform that can synthesize a large number of experimental datasets in the context of the existing knowledge base. Here, we describe a software platform, termed PROTEOME-3D, that utilizes three essential features for systematic analysis of proteomics data: creation of a scalable, queryable, customized database for identified proteins from published literature; graphical tools for displaying proteome landscapes and trends from multiple large-scale experiments; and interactive data analysis that facilitates identification of crucial networks and pathways. Thus, PROTEOME-3D offers a standardized platform to analyze high-throughput experimental datasets for the identification of crucial players in co-regulated pathways and cellular processes.

Computational Biology↗

A bioinformatics tool to select sequences for microarray studies of mouse models of oncogenesis.

UNLABELLED: One of the challenges to the effective utilization of cDNA microarray analysis in mouse models of oncogenesis is the choice of a critical set of probes that are informative for human disease. Given the thousands of genes with a potential role in human oncogenesis and the hundreds of thousands of mouse sequences available for use as probes, selection of an informative set of mouse probes can be an overwhelming task. We have developed a web based sequence mining tool using DataBase Independent (DBI) Perl to annotate publicly available sequences. The Mouse Oncochip Design Tool uses the Mouse Genome Database (MGD) developed and maintained by the Jackson Laboratories for mouse DNA sequences. There are over 380 000 sequences in their database. The output list has been ordered to present the genes more likely to be informative in a mouse model of human cancer using a candidate set of oncogenes to order the list. Mouse sequences that represent genes that are homologous with a member of a human oncogene set are listed first. In addition it provides a set of links for information on clone source gene function. CONTACT: http://nciarray.nci.nih.gov/cgi-bin/me/mouse_design.cgi

Animals↗

Identification of a novel class of annexin genes.

The annexins are a family of calcium- and phospholipid-binding proteins that have been widely studied in animals. Investigation of annexins in the fungus Aspergillus fumigatus identified a novel annexin-like gene (ANXC4) as well as two conventional annexins (ANXC3.1 and ANXC3.2). The genes were initially identified by bioinformatics, and sequences were then determined experimentally. Reverse transcription polymerase chain reaction indicated that all three genes were expressed. ANXC4 lacked calcium-binding consensus sequences and had a 553 residue N-terminal tail. However, bioinformatics indicated that ANXC4 is an annexin and homologues were identified in other filamentous fungi. ANXC4 therefore represents a new grouping within the annexin family.

Amino Acid Sequence↗

Prediction of missed cleavage sites in tryptic peptides aids protein identification in proteomics.

Protein identification via peptide mass fingerprinting (PMF) remains a key component of high-throughput proteomics experiments in post-genomic science. Candidate protein identifications are made using bioinformatic tools from peptide peak lists obtained via mass spectrometry (MS). These algorithms rely on several search parameters, including the number of potential uncut peptide bonds matching the primary specificity of the hydrolytic enzyme used in the experiment. Typically, up to one of these "missed cleavages" are considered by the bioinformatics search tools, usually after digestion of the in silico proteome by trypsin. Using two distinct, nonredundant datasets of peptides identified via PMF and tandem MS, a simple predictive method based on information theory is presented which is able to identify experimentally defined missed cleavages with up to 90% accuracy from amino acid sequence alone. Using this simple protocol, we are able to "mask" candidate protein databases so that confident missed cleavage sites need not be considered for in silico digestion. We show that that this leads to an improvement in database searching, with two different search engines, using the PMF dataset as a test set. In addition, the improved approach is also demonstrated on an independent PMF data set of known proteins that also has corresponding high-quality tandem MS data, validating the protein identifications. This approach has wider applicability for proteomics database searching, and the program for predicting missed cleavages and masking Fasta-formatted protein sequence databases has been made available via http:// ispider.smith.man.ac uk/MissedCleave.

Algorithms↗

SSEP-2.0: Secondary Structural Elements of Proteins.

The Secondary Structural Elements of Proteins (SSEP) database is an integrated and comprehensive knowledge base for accessing information related to all the secondary-structural elements present in non-redundant (25 and 90%) protein chains. The new version 2.0 of the database contains 2485 and 8595 protein chains from the 25 and 90% non-redundant data sets, respectively. The necessary web interfaces have been developed that enable users to visualize the three-dimensional structure of the secondary-structural element in the client machine using the free molecular-visualization program RASMOL. This source is updated at regular intervals and can be accessed through the bioinformatics web server at the URL http://cluster.physics.iisc.ernet.in/ssep or http://144.16.71.148/ssep/.

Artificial Intelligence↗

Unlocking the mysteries of virus-host interactions: does functional genomics hold the key?

The interactions between viruses and the cells they infect are complex and multifaceted. While viruses strive to usurp cellular functions to their advantage, the cell strives to thwart these efforts by mounting a variety of defensive responses. These responses may include the induction of interferon, stress response, or apoptotic pathways, all of which are accompanied by changes in gene expression. Some viruses consistently win this tug of war, whereas others succumb to cellular defense mechanisms. The viral and cellular factors that determine the outcome are for the most part still unknown. With the advent of functional genomics, potent new technologies are now available to probe the complexities of virus-host interactions in ever increasing depth and detail. We describe here our efforts to use microarrays, proteomics, and bioinformatics to focus in on the changes in gene expression and protein production that occur in a virus-infected cell and to use these technologies to unlock the mysteries of virus-host interactions.

Animals↗

BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data.

The microarray technique requires the organization and analysis of vast amounts of data. These data include information about the samples hybridized, the hybridization images and their extracted data matrices, and information about the physical array, the features and reporter molecules. We present a web-based customizable bioinformatics solution called BioArray Software Environment (BASE) for the management and analysis of all areas of microarray experimentation. All software necessary to run a local server is freely available.

Database Management Systems↗

Thrombin-like effect of an important green pit viper toxin, albolabrin: a bioinformatic study.

The green pit viper venom has a major effect on the hematological system. Clinical features of venomous snakebites vary from asymptomatic to fatal bleeding. The venom is found to have a thrombin-like effect in vitro. Here, the author performs a bioinformatic analysis on the green pit viper venom focusing on its thrombin-like effect. Sequence comparison between green pit viper venom, albolabrin and thrombin was performed. In addition, the author performed a search for other human proteins closely relating to the thrombin and created a multiple sequence alignment phylogenetic tree to present the family tree of the thrombin, albolabrin and those proteins recorded in the genomic database. In conclusion, the comparative sequence analysis between green pit viper venom and thrombin gives several identities. The reported relationship on the phylogenetic tree can match with the reported in vivo function of green pit viper. Explanations on the effect of green pit viper toxin on the hemostasis can be derived from this study. Furthermore, future researches based on the reported identities can be expected.

Amino Acid Sequence↗

Reconsidering the human immunoglobulin heavy-chain locus: 1. An evaluation of the expressed human IGHD gene repertoire.

We have used a bioinformatics approach to evaluate the completeness and functionality of the reported human immunoglobulin heavy-chain IGHD gene repertoire. Using the hidden Markov-model-based iHMMune-align program, 1,080 relatively unmutated heavy-chain sequences were aligned against the reported repertoire. These alignments were compared with alignments to 1,639 more highly mutated sequences. Comparisons of the frequencies of gene utilization in the two databases, and analysis of features of aligned IGHD gene segments, including their length, the frequency with which they appear to mutate, and the frequency with which specific mutations were seen, were used to determine the reliability of alignments to the less commonly seen IGHD genes. Analysis demonstrates that IGHD4-23 and IGHD5-24, which have been reported to be open reading frames of uncertain functionality, are represented in the expressed gene repertoire; however, the functionality of IGHD6-25 must be questioned. Sequence similarities make the unequivocal identification of members of the IGHD1 gene family problematic, although all genes except IGHD1-14*01 appear to be functional. On the other hand, reported allelic variants of IGHD2-2 and of the IGHD3 gene family appear to be nonfunctional, very rare, or nonexistent. Analysis also suggests that the reported repertoire is relatively complete, although one new putative polymorphism (IGHD3-10*p03) was identified. This study therefore confirms a surprising lack of diversity in the available IGHD gene repertoire, and restriction of the germline sequence databases to the functional set described here will substantially improve the accuracy of IGHD gene alignments and therefore the accuracy of analysis of the V-D-J junction.

Alleles↗

PlyC: a multimeric bacteriophage lysin.

Lysins are murein hydrolases produced by bacteriophage that act on the bacterial host cell wall to release progeny phage. When added extrinsically in their purified form, these enzymes produce total lysis of susceptible Gram-positive bacteria within seconds, suggesting a unique antimicrobial strategy. All known Gram-positive lysins are produced as a single polypeptide containing a catalytic activity domain, which cleaves one of the four major peptidoglycan bonds, and a cell-wall-binding domain, which may bind a species-specific carbohydrate epitope in the cell wall. Here, we have cloned and expressed a unique lysin from the streptococcal bacteriophage C(1), termed PlyC. Molecular characterization of the plyC operon reveals that PlyC is, surprisingly, composed of two separate gene products, PlyCA and PlyCB. Based on biochemical and biophysical studies, the catalytically active PlyC holoenzyme is composed of eight PlyCB subunits for each PlyCA. Inhibitor studies predicted the presence of an active-site cysteine, and bioinformatic analysis revealed a cysteine, histidine-dependent amidohydrolase/peptidase domain within PlyCA. Point mutagenesis confirmed that PlyCA is responsible for the observed catalytic activity, and Cys-333 and His-420 are the active-site residues. PlyCB was found to self-assemble into an octamer, and this complex alone was able to direct streptococcal cell-wall-specific binding. Similar to no other proteins in sequence databases, PlyC defines a previously uncharacterized structural family of cell-wall hydrolases.

Cell Wall↗

Models@Home: distributed computing in bioinformatics using a screensaver based approach.

MOTIVATION: Due to the steadily growing computational demands in bioinformatics and related scientific disciplines, one is forced to make optimal use of the available resources. A straightforward solution is to build a network of idle computers and let each of them work on a small piece of a scientific challenge, as done by Seti@Home (http://setiathome.berkeley.edu), the world's largest distributed computing project. RESULTS: We developed a generally applicable distributed computing solution that uses a screensaver system similar to Seti@Home. The software exploits the coarse-grained nature of typical bioinformatics projects. Three major considerations for the design were: (1) often, many different programs are needed, while the time is lacking to parallelize them. Models@Home can run any program in parallel without modifications to the source code; (2) in contrast to the Seti project, bioinformatics applications are normally more sensitive to lost jobs. Models@Home therefore includes stringent control over job scheduling; (3) to allow use in heterogeneous environments, Linux and Windows based workstations can be combined with dedicated PCs to build a homogeneous cluster. We present three practical applications of Models@Home, running the modeling programs WHAT IF and YASARA on 30 PCs: force field parameterization, molecular dynamics docking, and database maintenance.

Computational Biology↗

Novel phylogenetic studies of genomic sequence fragments derived from uncultured microbe mixtures in environmental and clinical samples.

A self-organizing map (SOM) was developed as a novel bioinformatics strategy for phylogenetic classification of sequence fragments obtained from pooled genome samples of uncultured microbes in environmental and clinical samples. This phylogenetic classification was possible without either orthologous sequence sets or sequence alignments. We first constructed SOMs for tetranucleotide frequencies in 210,000 5 kb sequence fragments obtained from 1502 prokaryotes for which at least 10 kb of genomic sequence has been deposited in public DNA databases. The sequences could be classified primarily according to phylogenetic groups without information regarding the species. We used the SOM method to classify sequence fragments derived from environmental samples of the Sargasso Sea and of an acidophilic biofilm growing in acid mine drainage. Phylogenetic diversity of the environmental sequences was effectively visualized on a single map. Sequences that were derived from a single genome but cloned independently could be reassociated in silico. G + C% has been used for a long period as a fundamental parameter for phylogenetic classification of microbes, but the G + C% is apparently too simple a parameter to differentiate a wide variety of known species. Oligonucleotide frequency can be used to distinguish the species because oligonucleotide frequencies vary significantly among their genomes.

Algorithms↗

Genotyping of Campylobacter jejuni using seven single-nucleotide polymorphisms in combination with flaA short variable region sequencing.

This investigation describes the development of a generally applicable, bioinformatics-driven, single-nucleotide polymorphism (SNP) genotyping assay for the common bacterial gastrointestinal pathogen Campylobacter jejuni. SNPs were identified in silico using the program 'Minimum SNPs', which selects for polymorphisms providing the greatest resolution of bacterial populations based on Simpson's index of diversity (D). The high-D SNPs identified in this study were derived from the combined C. jejuni/Campylobacter coli multilocus sequence typing (MLST) database. Seven SNPs were found that provided a D of 0.98 compared with full MLST characterization, based on 959 sequence types (STs). The seven high-D SNPs were interrogated using allele-specific real-time PCR (AS kinetic PCR), which negates the need for expensive labelled primers or probes and requires minimal assay optimization. The total turnaround time of the SNP typing assay was approximately 2 h. Concurrently, 69 C. jejuni isolates were subjected to MLST and flagellin A short variable region (flaA SVR) sequencing and combined with a population of 84 C. jejuni and C. coli isolates previously characterized by these methods. Within this collection of 153 isolates, 19 flaA SVR types (D=0.857) were identified, compared with 40 different STs (D=0.939). When MLST and flaA SVR sequencing were used in combination, the discriminatory power was increased to 0.959. In comparison, SNP typing of the 153 isolates alone provided a D of 0.920 and was unable to resolve a small number of unrelated isolates. However, addition of the flaA SVR locus to the SNP typing procedure increased the resolving power to 0.952 and clustered isolates similarly to MLST/flaA SVR. This investigation has shown that a seven-member C. jejuni SNP typing assay, used in combination with sequencing of the flaA SVR, efficiently discriminates C. jejuni isolates.

Alleles↗

Probable epitopes: Relationships between myelin basic protein antigenic determinants and viral and bacterial proteins.

Nature holds numerous viral and bacterial proteins with regions of similarity to myelin basic protein antigenic determinants. Bioinformatic technology, including sequence similarity searches, may allow for the detection of biochemical and biophysical relationships between these peptides. Understanding these relationships is essential to understanding immune-mediated disease and, consequently, may be used to elucidate the etiology of pathological demyelinating diseases such as multiple sclerosis. Studies of experimental autoimmune encephalomyelitis have been used to identify antigenic determinants. We have used these determinants to search available databases of viral and bacterial proteins. Our results indicate numerous viral and bacterial protein segments with probabilistic sequence similarity to myelin basic protein antigenic determinants.

Amino Acid Sequence↗

Progress in bioinformatics and the importance of being earnest.

In silico biology has gathered momentum as, worldwide, scientists have united in a common quest to sequence, store and analyse complete genomes. This year, a pivotal achievement of this cooperative endeavour was realised in the release of a public draft of the human genome, and with it the promises to improve our understanding of diverse aspects of biology and to yield a healthier future with safe personalized medicines. Key to these goals will be the need to elucidate and characterise the genes and gene products encoded not just in the human genome, but in many genomes. These tasks are underpinned by the concepts and processes of genome and gene/protein evolution, regulation of gene expression, mechanisms of protein folding, the manifestation of protein function, and so on, all of which must be understood in the context of complex, dynamic biological systems. Our use of computers to model such concepts and systems must be placed in the context of the current limits of our understanding of them:- it is important to recognise, for example, that we don't have a common understanding either of what constitutes a gene or a protein function; we can't invariably say that a particular sequence or fold has arisen via divergent or convergent evolution; and we don't fully understand the rules of protein folding. Accepting what we can't do in silico is essential in appreciating what we can do. Without this understanding, it is easy to be misled, as notions of what particular computational approaches can achieve are sometimes rather optimistic. There are valuable lessons to be learned here from the field of Artificial Intelligence, principal among which is the realisation that capturing and representing complex knowledge is time consuming, expensive and hard. Thus, we argue here that if bioinformatics is to tackle biological complexity in earnest, it would be wise to absorb the experience distilled from decades of artificial intelligence research, and to approach the road ahead with caution, rigour and pragmatism.

Artificial Intelligence↗