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PubMed2005

Application of phylogenetic algorithms to assess Rab functional relationships.

Researchers looking to solve biological problems have access to enormous amounts of sequence information and the desktop computational infrastructure to personally interrogate and analyze large datasets. Many powerful bioinformatics tools are available online; however, this discourages the customized analysis of data that is necessary for the experimental scientist to make maximally effective use of the information. In addition, a customized environment facilitates the critical evaluation of bioinformatic methods. This chapter presents a protocol developed to aid in classification of subfamilies and subclasses of a superfamily using the personal desktop computer. The visual representation of the qualitative and quantitative results of data analyses is also considered. The examples are focused on Rab GTPases but are more widely applicable to the classification of any given protein family.

Algorithms↗

PubMed2005

Identification of ubiquitin ligase substrates by in vitro expression cloning.

The number of identified E3 ubiquitin ligases has dramatically increased in recent years. However, the substrates targeted for degradation by these particular ligases have not been easily identified. One reason for the inability of matching substrates and ligases is the finding that E3 recognition elements in substrates are often poorly defined. This minimizes the likelihood that bioinformatic approaches will lead to the identification of E3 substrates. For example, the multi-subunit complex the anaphase promoting complex (APC) is an E3 that recognizes destruction boxes (RXXLXXXXD/N/E) or KEN motifs within substrates (Glotzer et al., 1991; Pfleger and Kirschner, 2000). However, many proteins that contain either a potential destruction or a KEN motif are not recognized by the APC in vitro or in vivo, suggesting that there are other, less well-defined characteristics of substrates that contribute to their ability to serve as APC substrates (Ayad, Rankin, and Kirschner, unpublished observations). Aside from bioinformatic approaches of identifying APC substrates, several groups have also attempted to use affinity techniques to discover novel APC substrates. This has not been widely successful, because many APC substrates are not abundant. Also, as is the case with many ligase-substrate interactions, the affinity of substrates for the APC is likely to be very low. All these considerations have motivated a search for other techniques to assist in identifying substrates of this particular E3 ligase. Here, we describe the use of in vitro expression cloning to identify novel APC substrates.

Amino Acid Sequence↗

PubMed2006

Comprehensive enzymatic characterization of glycosyltransferases with a beta3GT or beta4GT motif.

Bioinformatics is a very powerful tool in the field of glycoproteomics, as well as genomics and proteomics. The bioinformatics technique accelerates the comprehensive identification and in silico cloning of human glycogenes containing glycosyltransferases, glycolytic enzymes, sugar-nucleotide synthetases, sugar-nucleotide transporters, and so forth. Glycosyltransferase genes play central roles in carbohydrate chain biosynthesis and have been analyzed for their biological functions. At present, over 180 human glycosyltransferases were identified, cloned, and expressed in various expression systems to detect the activity for carbohydrate synthesis. The recombinant proteins for glycosyltransferase were successfully identified for their enzyme activities and substrate specificities. Their substrate specificities were determined using various donor substrates and acceptors. This section reviews the functions, substrate specificities, and enzymatic reactions of glycosyltransferases such as beta1,3-glycosyltransferase family and beta1,4-glycosyltransferase family.

Amino Acid Motifs↗

PubMed2001

A computer system to perform structure comparison using TOPS representations of protein structure.

We describe the design and implementation of a fast topology-based method for protein structure comparison. The approach uses the TOPS topological representation of protein structure, aligning two structures using a common discovered pattern and generating measure of distance derived from an insert score. Heavy use is made of a constraint-based pattern-matching algorithm for TOPS diagrams that we have designed and described elsewhere (Bioinformatics 15(4) (1999) 317). The comparison system is maintained at the European Bioinformatics Institute and is available over the Web at tops.ebi.ac.uk/tops. Users submit a structure description in Protein Data Bank (PDB) format and can compare it with structures in the entire PDB or a representative subset of protein domains, receiving the results by email.

Algorithms↗

PubMed2003

In silico identification of components of the Toll-like receptor (TLR) signaling pathway in clustered chicken expressed sequence tags (ESTs).

We have described a bioinformatic approach that involves the clustering of expressed sequence tags (ESTs) to reveal homologs of the Toll-like receptor (TLR) pathway in the chicken. Homology searching of proteins, predicted to be encoded by these EST clusters, resulted in the in silico identification of full-length sequences for Toll-interacting protein (Tollip), IL-1 receptor-associated kinase 4 (IRAK-4), myeloid differentiation factor 88 adapter-like (Mal), TGF beta-activated kinase 1 binding protein 1 (TAB1). We also determined partial sequence information for myeloid differentiation factor 88 (MyD88), two novel TLRs, TNF receptor-associated factor 6 (TRAF6), TGF beta-activated kinase 1 (TAK1), TAB2, inhibitor of nuclear factor kappa B kinase alpha (IKK alpha) and IKK beta. This bioinformatics study has confirmed the evolutionary conservation of the TLR pathway in chicken and demonstrated its essential homology to the TLR pathway in mammals. We have identified in silico the full-length sequence for liver-expressed antimicrobial peptide 2 (LEAP-2). This is the first time a non-mammalian LEAP-2 has been described.

Amino Acid Sequence↗

PubMed2000

Application of genomics and proteomics for identification of bacterial gene products as potential vaccine candidates.

The ability of bioinformatics to characterize genomic sequences from pathogenic bacteria for prediction of genes that may encode vaccine candidates, e.g. surface localized proteins, has been evaluated. By applying appropriate tools for genomic mining to the published sequence of Haemophilus influenzae Rd genome, it was possible to identify a putative vaccine candidate, the outer membrane lipoprotein, P6. Proteomics complements genomics by offering abilities to rapidly identify the products of predicted genes, e.g. proteins in outer membrane preparations. The ability to identify the P6 protein uniquely from entries in a sequence database from the expected peptide-mass fingerprint of P6 demonstrates the power of proteomics. The application of proteomics for identification of vaccine candidates for another pathogenic bacterium, Helicobacter pylori using two different approaches is described. The first involves rapid identification of a series of monoclonal antibody reactive proteins from N-terminal sequence tags. The other approach involves identification of proteins in outer membrane preparations by 2-D electrophoresis followed by trypsin digestion and peptide mass map analysis. Our combined studies demonstrate that utilization of genome sequences by application of bioinformatics through genomics and proteomics can expedite the vaccine discovery process by rapidly providing a set of potential candidates for further testing.

Antibodies, Bacterial↗

PubMed2003

Structural genomics of Mycobacterium tuberculosis: a preliminary report of progress at UCLA.

The growing list of fully sequenced genomes, combined with innovations in the fields of structural biology and bioinformatics, provides a synergy for the discovery of new drug targets. With this background, the TB Structural Genomics Consortium has been formed. This international consortium is comprised of laboratories from 31 universities and institutes in 13 countries. The goal of the consortium is to determine the structures of over 400 potential drug targets from the genome of Mycobacterium tuberculosis and analyze their structures in the context of functional information. We summarize the efforts of the UCLA consortium members. Potential drug targets were selected using a variety of bioinformatics methods and screened for certain physical and species-specific properties to yield a starting group of protein targets for structure determination. Target determination methods include protein phylogenetic profiles and Rosetta Stone methods, and the use of related biochemical pathways to select genes linked to essential prokaryotic genes. Criteria imposed on target selection included potential protein solubility, protein or domain size, and targets that lack homologs in eukaryotic organisms. In addition, some protein targets were chosen that are specific to M. tuberculosis, such as PE and PPE domains. Thus far, the UCLA group has cloned 263 targets, expressed 171 proteins and purified 40 proteins, which are currently in crystallization trials. Our efforts have yielded 13 crystals and eight structures. Seven structures are summarized here. Four of the structures are secreted proteins: antigen 85B; MPT 63, which is one of the three major secreted proteins of M. tuberculosis; a thioredoxin derivative Rv2878c; and potentially secreted glutamate synthetase. We also report the structures of three proteins that are potentially essential to the survival of M. tuberculosis: a protein involved in the folate biosynthetic pathway (Rv3607c); a protein involved in the biosynthesis of vitamin B5 (Rv3602c); and a pyrophosphatase, Rv2697c. Our approach to the M. tuberculosis structural genomics project will yield information for drug design and vaccine production against tuberculosis. In addition, this study will provide further insights into the mechanisms of mycobacterial pathogenesis.

Bacterial Proteins↗

PubMed2003

Model selection methodology in supervised learning with evolutionary computation.

The expressive power, powerful search capability, and the explicit nature of the resulting models make evolutionary methods very attractive for supervised learning applications in bioinformatics. However, their characteristics also make them highly susceptible to overtraining or to discovering chance relationships in the data. Identification of appropriate criteria for terminating evolution and for selecting an appropriately validated model is vital. Some approaches that are commonly applied to other modelling methods are not necessarily applicable in a straightforward manner to evolutionary methods. An approach to model selection is presented that is not unduly computationally intensive. To illustrate the issues and the technique two bioinformatic datasets are used, one relating to metabolite determination and the other to disease prediction from gene expression data.

Algorithms↗

PubMed1998

CINEMA--a novel colour INteractive editor for multiple alignments.

CINEMA is a new editor for manipulating and generating multiple sequence alignments. The program provides both an interface to existing databases of alignments on the Internet and a tool for constructing and modifying alignments locally. It is written in Java, so executable code will run on most major desktop platforms without modification. The implementation is highly flexible, so the applet can be easily customised with additional functions; and the object classes are reusable, promoting rapid development of program extensions. Formerly, such extended functionality might have been provided via browser plug-ins, which have to be downloaded and installed on every client before loading data. Now, for the first time, an applet is available that allows interactive client-side processing of an alignment, which can then be stored or processed automatically on the server. The program is embedded in a comprehensive help file and is accessible both as a stand-alone tool on UCL's Bioinformatics Server; http:/(/)www.biochem.ucl.ac.uk/bsm/dbbrowser+ ++/CINEMA2.02/, and as an integral part of the PRINTS protein fingerprint database. Exploitation of such novel technologies revolutionises the way users may interact with public databases in the future: bioinformatics centres need not simply provide data, but are now able to offer the means by which information is visualised and manipulated, without the requirement for users to install software.

Color Perception↗

PubMed1997

Computational gene discovery and human disease.

Bioinformatics is now an essential tool in many aspects of human molecular genetics research. Methods for the prediction of gene structure are essential components in genomic sequencing projects and provide the key to deriving protein sequence and locating intron/exon junctions. Sequence comparison and database searching are the pre-eminent approaches for predicting the likely biochemical function of new genes, although sequence profiles derived from families of aligned sequences have advantages in the detection of remote sequence relationships. The use of sequence database analysis for large-scale comparative analysis of genome sequence data from model organisms is emerging as the most important recent development in the application of bioinformatics methods for characterizing candidate disease genes.

Animals↗

PubMed2003

Ethical issues in modern biological technologies.

Today's biology-based technologies have emerged from a historical imperative and as an inevitable consequence of developments in modern biology beginning in the last half-century. They can be classified into almost 30 different areas, ranging from the use of gene therapy for human beings, enzyme engineering, stem cells and cloning, to marine biotechnology, bioinformatics, nanotechnology and biological warfare among many others. Many of them have major sociopolitico-economic, moral, ethical and legal implications. They include genetic engineering, gene therapy, tissue culture, stem cell work, the new DNA technologies, commercialization of traditional plant-based drug formulations, assisted reproduction techniques, cloning technologies, organ transplantation, bioinformatics, and biological weapons. Examples of the ethical implications of several of these items will be considered. They will be assessed with special reference to ethical implications in respect of assisted reproduction techniques, of worldwide importance today, particularly for a country such as India.

Biological Warfare↗

PubMedDate not supplied

The construction and assessment of a statistical model for the prediction of protein assay data.

The focus of this work is the development of a statistical model for a bioinformatics database whose distinctive structure makes model assessment an interesting and challenging problem. The key components of the statistical methodology, including a fast approximation to the singular value decomposition and the use of adaptive spline modeling and tree-based methods, are described, and preliminary results are presented. These results are shown to compare favorably to selected results achieved using comparitive methods. An attempt to determine the predictive ability of the model through the use of cross-validation experiments is discussed. In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented.

Computational Biology↗

PubMedDate not supplied

Active learning with support vector machine applied to gene expression data for cancer classification.

There is growing interest in the application of machine learning techniques in bioinformatics. The supervised machine learning approach has been widely applied to bioinformatics and gained a lot of success in this research area. With this learning approach researchers first develop a large training set, which is a time-consuming and costly process. Moreover, the proportion of the positive examples and negative examples in the training set may not represent the real-world data distribution, which causes concept drift. Active learning avoids these problems. Unlike most conventional learning methods where the training set used to derive the model remains static, the classifier can actively choose the training data and the size of training set increases. We introduced an algorithm for performing active learning with support vector machine and applied the algorithm to gene expression profiles of colon cancer, lung cancer, and prostate cancer samples. We compared the classification performance of active learning with that of passive learning. The results showed that employing the active learning method can achieve high accuracy and significantly reduce the need for labeled training instances. For lung cancer classification, to achieve 96% of the total positives, only 31 labeled examples were needed in active learning whereas in passive learning 174 labeled examples were required. That meant over 82% reduction was realized by active learning. In active learning the areas under the receiver operating characteristic (ROC) curves were over 0.81, while in passive learning the areas under the ROC curves were below 0.50.

Artificial Intelligence↗

PubMedDate not supplied

Genomic messaging system language including command extensions for clinical data categories.

This paper, in the area of clinical bioinformatics, highlights relatively efficient means of storing, exchanging, protecting, and searching human and other genomic data, so as to make the data securely accessible to researchers while respecting patient privacy. One important idea is that the GMSL language can be considered as an extension of the way DNA and protein sequences are written so as to carry with them the wishes of the patient in regard to fine-grained consent (as well as retaining the medical experts' cautions, instructions for use, and annotation), and this is carried, whatever environment (e.g., XML) that the data is from or whatever it is going to. At the deepest level, a stream of data expressed in GMSL resembles highly compressed stream of self-checking machine code. For the reader less familiar with the computational aspects, some simple examples illustrate how the raw language looks and works as a raw stream of (interpreted) bytes. The bioinformatics applications are not confined to the clinical domain. This paper completes the initial specification of the language as previously presented and reports on some important extensions including clinical data categories.

Base Sequence↗

PubMedDate not supplied

"Plasmo2D": an ancillary proteomic tool to aid identification of proteins from Plasmodium falciparum.

Bioinformatics tools to aid gene and protein sequence analysis have become an integral part of biology in the post-genomic era. Release of the Plasmodium falciparum genome sequence has allowed biologists to define the gene and the predicted protein content as well as their sequences in the parasite. Using pI and molecular weight as characteristics unique to each protein, we have developed a bioinformatics tool to aid identification of proteins from Plasmodium falciparum. The tool makes use of a Virtual 2-DE generated by plotting all of the proteins from the Plasmodium database on a pI versus molecular weight scale. Proteins are identified by comparing the position of migration of desired protein spots from an experimental 2-DE and that on a virtual 2-DE. The procedure has been automated in the form of user-friendly software called "Plasmo2D". The tool can be downloaded from http://144.16.89.25/Plasmo2D.zip.

Animals↗

PubMed2006

Counting the zinc-proteins encoded in the human genome.

Metalloproteins are proteins capable of binding one or more metal ions, which may be required for their biological function, or for regulation of their activities or for structural purposes. Genome sequencing projects have provided a huge number of protein primary sequences, but, even though several different elaborate analyses and annotations have been enabled by a rich and ever-increasing portfolio of bioinformatic tools, metal-binding properties remain difficult to predict as well as to investigate experimentally. Consequently, the present knowledge about metalloproteins is only partial. The present bioinformatic research proposes a strategy to answer the question of how many and which proteins encoded in the human genome may require zinc for their physiological function. This is achieved by a combination of approaches, which include: (i) searching in the proteome for the zinc-binding patterns that, on their turn, are obtained from all available X-ray data; (ii) using libraries of metal-binding protein domains based on multiple sequence alignments of known metalloproteins obtained from the Pfam database; and (iii) mining the annotations of human gene sequences, which are based on any type of information available. It is found that 1684 proteins in the human proteome are independently identified by all three approaches as zinc-proteins, 746 are identified by two, and 777 are identified by only one method. By assuming that all proteins identified by at least two approaches are truly zinc-binding and inspecting the proteins identified by a single method, it can be proposed that ca. 2800 human proteins are potentially zinc-binding in vivo, corresponding to 10% of the human proteome, with an uncertainty of 400 sequences. Available functional information suggests that the large majority of human zinc-binding proteins are involved in the regulation of gene expression. The most abundant class of zinc-binding proteins in humans is that of zinc-fingers, with Cys4 and Cys2His2 being the most common types of coordination environment.

Computational Biology↗

PubMed2006

Protein intrinsic disorder and human papillomaviruses: increased amount of disorder in E6 and E7 oncoproteins from high risk HPVs.

It is recognized now that many functional proteins or their long segments are devoid of stable secondary and/or tertiary structure and exist instead as very dynamic ensembles of conformations. They are known by different names including natively unfolded, intrinsically disordered, intrinsically unstructured, rheomorphic, pliable, and different combinations thereof. Many important functions and activities have been associated with these intrinsically disordered proteins (IDPs), including molecular recognition, signaling, and regulation. It is also believed that disorder of these proteins allows function to be readily modified through phosphorylation, acetylation, ubiquitination, hydroxylation, and proteolysis. Bioinformatics analysis revealed that IDPs comprise a large fraction of different proteomes. Furthermore, it is established that the intrinsic disorder is relatively abundant among cancer-related and other disease-related proteins and IDPs play a number of key roles in oncogenesis. There are more than 100 different types of human papillomaviruses (HPVs), which are the causative agents of benign papillomas/warts, and cofactors in the development of carcinomas of the genital tract, head and neck, and epidermis. With respect to their association with cancer, HPVs are grouped into two classes, known as low (e.g., HPV-6 and HPV-11) and high-risk (e.g., HPV-16 and HPV-18) types. The entire proteome of HPV includes six nonstructural proteins [E1, E2, E4, E5, E6, and E7 (the latter two are known to function as oncoproteins in the high-risk HPVs)] and two structural proteins (L1 and L2). To understand whether intrinsic disorder plays a role in the oncogenic potential of different HPV types, we have performed a detailed bioinformatics analysis of proteomes of high-risk and low-risk HPVs with the major focus on E6 and E7 oncoproteins. The results of this analysis are consistent with the conclusion that high-risk HPVs are characterized by the increased amount of intrinsic disorder in transforming proteins E6 and E7.

Algorithms↗

PubMed2007

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↗