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PubMed2002

Proteomics and its trends facing nature's complexity.

The complexity of nature is tremendous, particularly at the epigenetic level. Proteomic studies must therefore complement genomic discoveries to better understand biological processes. Because of the very large number of modified proteins and their great variability in physico-chemical properties, no single method can be used to analyze all of them. Mass spectrometry has demonstrated its superior ability to rapidly identify and partially characterize numerous proteins in low abundance and has become a central element in most proteomic projects. Studies of protein function are necessary to understand biological pathways and this is being tackled using several approaches such as two hybrid systems, phage technology or affinity methods. Finally, mathematical and bioinformatic developments will be essential to study nature's complex systems.

Computational Biology↗
PubMed2006

Arabidopsis cell wall proteome defined using multidimensional protein identification technology.

With the completion of the sequencing of the Arabidopsis genome and the recent advances in proteomic technology, the identification of proteins from highly complex mixtures is now possible. Rather than using gel electrophoresis and peptide mass fingerprinting, we have used multidimensional protein identification technology (MudPIT) to analyse the "tightly-bound" proteome for purified cell walls from Arabidopsis cell suspension cultures. Using bioinformatics for the prediction of signal peptides for targeting to the secretory pathway and for the absence of ER retention signal, 89 proteins were selected as potential extracellular proteins. Only 33% of these were identified in previous proteomic analyses of Arabidopsis cell walls. A functional classification revealed that a large proportion of the proteins were enzymes, notably carbohydrate active enzymes, peroxidases and proteases. Comparison of all the published proteomic analyses for the Arabidopsis cell wall identified 268 non-redundant genes encoding wall proteins. Sixty of these (22%) were derived from our analysis of tightly-bound wall proteins.

Arabidopsis↗
PubMed2005

Pharmacogenomics and clinical biomarkers in drug discovery and development.

The fields of pathology and clinical pharmacology are in an era of rapid evolution, reflecting continuous technological advancements in molecular diagnostics focused on improving drug efficacy and reducing toxicity. The discovery and refinement of the human genome sequence, further understanding of epigenetic events, and the expansion of proteomics research combined with emerging technologies such as functional imaging, biosensors, and sophisticated computational biology are having an unprecedented impact on the pharmaceutical industry. This review focuses on the discovery and development of DNA-based gene sequence tests and RNA-based gene expression profiles as applied to the prediction of response, resistance, and toxicity of both new and existing anticancer, cardiovascular, and anti-inflammatory drugs. This review will also consider the potential of emerging biomarkers designed to assist in the clinical development of these agents.

Biomarkers↗
PubMed2007

Cooperativity and saturation in biochemical networks: a saturable formalism using Taylor series approximations.

Cooperative and saturable systems are common in molecular biology. Nevertheless, common canonical formalisms for kinetic modeling that are theoretically well justified do not have a saturable form. Modeling and fitting data from saturable systems are widely done using Hill-like equations. In practice, there is no theoretical justification for the generalized use of these equations, other than their ability to fit experimental data. Thus it is important to find a canonical formalism that is (a) theoretically well supported, (b) has a saturable functional form, and (c) can be justifiably applicable to any biochemical network. Here we derive such a formalism using Taylor approximations in a special transformation space defined by power-inverses and logarithms of power-inverses. This formalism is generalized for processes with n-variables, leading to a useful mathematical representation for molecular biology: the Saturable and Cooperative Formalism (SC formalism). This formalism provides an appropriate representation that can be used for modeling processes with cooperativity and saturation. We also show that the Hill equation can be seen as a special case within this formalism. Parameter estimation for the SC formalism requires information that is also necessary to build Power-Law models, Metabolic Control Analysis descriptions or (log)linear and Lin-log models. In addition, the saturation fraction of the relevant processes at the operating point needs to be considered. The practical use of the SC formalism for modeling is illustrated with a few examples. Similar models are built using different formalisms and compared to emphasize advantages and limitations of the different approaches.

Algorithms↗
PubMedDate not supplied

A computational analysis of substrate binding strength by phosphorylase kinase and protein kinase A.

Protein kinases exhibit various degrees of substrate specificity. The large number of different protein kinases in the eukaryotic proteomes makes it impractical to determine the specificity of each enzyme experimentally. To test if it were possible to discriminate potential substrates from non-substrates by simple computational techniques, we analysed the binding enthalpies of modelled enzyme-substrate complexes and attempted to correlate it with experimental enzyme kinetics measurements. The crystal structures of phosphorylase kinase and cAMP-dependent protein kinase were used to generate models of the enzyme with a series of known peptide substrates and non-substrates, and the approximate enthalpy of binding assessed following energy minimization. We show that the computed enthalpies do not correlate closely with kinetic measurements, but the method can distinguish good substrates from weak substrates and non-substrates.

Binding Sites↗
PubMed2004

Functional and topological characterization of protein interaction networks.

The elucidation of the cell's large-scale organization is a primary challenge for post-genomic biology, and understanding the structure of protein interaction networks offers an important starting point for such studies. We compare four available databases that approximate the protein interaction network of the yeast, Saccharomyces cerevisiae, aiming to uncover the network's generic large-scale properties and the impact of the proteins' function and cellular localization on the network topology. We show how each database supports a scale-free, topology with hierarchical modularity, indicating that these features represent a robust and generic property of the protein interactions network. We also find strong correlations between the network's structure and the functional role and subcellular localization of its protein constituents, concluding that most functional and/or localization classes appear as relatively segregated subnetworks of the full protein interaction network. The uncovered systematic differences between the four protein interaction databases reflect their relative coverage for different functional and localization classes and provide a guide for their utility in various bioinformatics studies.

Algorithms↗
PubMed2026

High-Plex Tissue Imaging with Conventional Immunofluorescence Platforms and Open-Source Software via Iterative Bleaching Extends Multiplexity (IBEX).

Iterative bleaching extends multiplexity (IBEX) is an easy-to-use, highly multiplex immunofluorescent tissue imaging method that employs widely available microscopy platforms, commercial reagents, and open-source software. In this article, we describe how to implement this method in a laboratory that has minimal experience with immunohistochemistry.

Software↗
PubMed2006

Phosphoproteomic profiling of human SH-SY5Y neuroblastoma cells during response to 6-hydroxydopamine-induced oxidative stress.

Dopaminergic neurons are known to be vulnerable to age-related neuronal disorders due to reactive oxygen species (ROS) generated during dopamine metabolism. However, it remains unclear what kinds of proteins are involved in the response to oxidative stress. We examined changes in whole proteins and phosphoproteins in the human dopaminergic neuroblastoma cell line SH-SY5Y under oxidative stress induced by the dopaminergic neurotoxin 6-hydroxydopamine (6-OHDA). Proteins of SH-SY5Y cells at various stages of oxidative stress were separated by two-dimensional gel electrophoresis for comparative analysis. Increase in glutathione-S-transferase pi was detected on SYPRO Ruby-stained gels by computer-aided image analysis. Stress-induced alterations in phosphoproteins were detected by Pro-Q Diamond staining. Elongation factor 2, lamin A/C, T-complex protein 1, and heterogeneous nuclear ribonucleoprotein H3 were identified by MALDI-TOF mass spectrometry as stress-responsive elements.

Blotting, Western↗
PubMed2006

Integrated analysis of multiple data sources reveals modular structure of biological networks.

It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks.

Algorithms↗
PubMed2005

Learning Petri net models of non-linear gene interactions.

Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and disease, the problem of automatically building the best biochemical model or "explanation" of the relationship has received less attention. In this paper, I describe a method based on random hill climbing that automatically builds Petri net models of non-linear (or multi-factorial) disease-causing gene-gene interactions. Petri nets are a suitable formalism for this problem, because they are used to model concurrent, dynamic processes analogous to biochemical reaction networks. I show that this method is routinely able to identify perfect Petri net models for three disease-causing gene-gene interactions recently reported in the literature.

Algorithms↗
PubMed2006

New metrics for comparative genomics.

The availability of genome sequences from a variety of organisms presents an opportunity to apply this sequence information to solving the key problems of molecular biology. One of the principal roadblocks on this path is the lack of appropriate descriptors and metrics that could succinctly represent the new knowledge stemming from the genomic data. Several new metrics have recently been used in comparative genome analysis, yet challenges remain in finding an appropriate language for the emerging discipline of systems biology.

Animals↗
PubMed2005

A medical bioinformatics approach for metabolic disorders: biomedical data prediction, modeling, and systematic analysis.

UNLABELLED: During the past century, studies of metabolic disorders have focused research efforts to improve clinical diagnosis and management, to illuminate metabolic mechanisms, and to find effective treatments. The availability of human genome sequences and transcriptomic, proteomic, and metabolomic data provides us with a challenging opportunity to develop computational approaches for systematic analysis of metabolic disorders. In this paper, we present a strategy of bioinformatics analysis to exploit the current data available both on genomic and metabolic levels and integrate these at novel levels of understanding of metabolic disorders. PathAligner is applied to predict biomedical data based on a given disorder. A case study on urea cycle disorders is demonstrated. A Petri net model is constructed to estimate the regulation both on genomic and metabolic levels. We also analyze the transcription factors, signaling pathways and associated disorders to interpret the occurrence and regulation of the urea cycle. AVAILABILITY: PathAligner's metabolic disorder analyzer is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner/pathaligner_MDA.html. Supplementary materials are available at http://www.techfak.uni-bielefeld.de/~mchen/metabolic_disorders.

Animals↗
PubMed2004

A description scheme of biological processes based on elementary bricks of action.

With the fast growth of high-throughput strategies in Biology, there is a strong need to accelerate knowledge acquisition and organization of molecular functions. Unfortunately, although we know that there is a correlation between protein molecules and their functions, we are unable to clearly identify this link. Here, we revisit the current views of protein functions as well as their annotation, and we show that they are incompatible with unambiguous interpretations and the use of this knowledge. We describe herein a description scheme for biological processes based on elementary bricks of action that may be associated with biological molecules. To retrieve the descriptive quality found in annotations of other kinds of biological data, it was decided to develop a scheme involving four levels of abstraction: Basic Elements of Action, Biological Activities, Biological Functionalities and Biological Roles. This multi-level organization is a generic method; it allows for a description of biological processes by using a limited number of elementary bricks of action. Moreover, by using this description of biological processes, it should now be possible to clearly identify unambiguous relationships between the organization of biological processes and the structural or functional organizations of biological molecules.

Algorithms↗
PubMed2005

Mimicking cellular sorting improves prediction of subcellular localization.

Predicting the native subcellular compartment of a protein is an important step toward elucidating its function. Here we introduce LOCtree, a hierarchical system combining support vector machines (SVMs) and other prediction methods. LOCtree predicts the subcellular compartment of a protein by mimicking the mechanism of cellular sorting and exploiting a variety of sequence and predicted structural features in its input. Currently LOCtree does not predict localization for membrane proteins, since the compositional properties of membrane proteins significantly differ from those of non-membrane proteins. While any information about function can be used by the system, we present estimates of performance that are valid when only the amino acid sequence of a protein is known. When evaluated on a non-redundant test set, LOCtree achieved sustained levels of 74% accuracy for non-plant eukaryotes, 70% for plants, and 84% for prokaryotes. We rigorously benchmarked LOCtree in comparison to the best alternative methods for localization prediction. LOCtree outperformed all other methods in nearly all benchmarks. Localization assignments using LOCtree agreed quite well with data from recent large-scale experiments. Our preliminary analysis of a few entirely sequenced organisms, namely human (Homo sapiens), yeast (Saccharomyces cerevisiae), and weed (Arabidopsis thaliana) suggested that over 35% of all non-membrane proteins are nuclear, about 20% are retained in the cytosol, and that every fifth protein in the weed resides in the chloroplast.

Animals↗
PubMed2004

Biomarker discovery and validation: technologies and integrative approaches.

The emerging field of biomarkers has applications in the diagnosis, staging, prognosis and monitoring of disease progression, as well as in the monitoring of clinical responses to a therapeutic intervention and the development and delivery of personalized treatments to reduce attrition in clinical trials. Moreover, biomarkers have a positive impact on health economics. The word "biomarker" has been used extensively across therapeutic areas and many disciplines, and its nature takes into consideration clinical, physiological, biochemical, developmental, morphological and molecular measures. In drug trials, biomarkers have been proposed for use in efficacy determination and patient population stratification, in deducing pharmacokinetic-pharmacodynamic relationships and in safety monitoring. The interfacing and integration of different technologies for data collection and analysis are pivotal to biomarker identification, characterization, validation and application. "Integrative functional informatics" represents a novel direction in such technology integration.

Biomarkers↗