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PubMed2004

Computational physiology and the Physiome Project.

Bioengineering analyses of physiological systems use the computational solution of physical conservation laws on anatomically detailed geometric models to understand the physiological function of intact organs in terms of the properties and behaviour of the cells and tissues within the organ. By linking behaviour in a quantitative, mathematically defined sense across multiple scales of biological organization--from proteins to cells, tissues, organs and organ systems--these methods have the potential to link patient-specific knowledge at the two ends of these spatial scales. A genetic profile linked to cardiac ion channel mutations, for example, can be interpreted in relation to body surface ECG measurements via a mathematical model of the heart and torso, which includes the spatial distribution of cardiac ion channels throughout the myocardium and the individual kinetics for each of the approximately 50 types of ion channel, exchanger or pump known to be present in the heart. Similarly, linking molecular defects such as mutations of chloride ion channels in lung epithelial cells to the integrated function of the intact lung requires models that include the detailed anatomy of the lungs, the physics of air flow, blood flow and gas exchange, together with the large deformation mechanics of breathing. Organizing this large body of knowledge into a coherent framework for modelling requires the development of ontologies, markup languages for encoding models, and web-accessible distributed databases. In this article we review the state of the field at all the relevant levels, and the tools that are being developed to tackle such complexity. Integrative physiology is central to the interpretation of genomic and proteomic data, and is becoming a highly quantitative, computer-intensive discipline.

Biophysical Phenomena↗
PubMed2007

Mitoproteome: human heart mitochondrial protein sequence database.

The human mitochondrial proteome database has been developed by deriving data from a combination of public repositories and experimental and computational prediction methods. The experimental data is derived from highly purified mitochondria from human heart tissue, whereas predictions have been performed by MITOPRED, a genome-scale method for the prediction of nucleus-encoded mitochondrial proteins. Mitochondrial protein sequences from different sources have been clustered to generate a nonredundant dataset. Annotations related to the protein function, structure, disease association, pathways, and so on are collected from a number of public databases using commonly used UNIX and Perl scripts. This chapter provides a detailed description of various data sources and methods used to download, curate, parse, and generate meaningful annotations from primary as well as derived databases.

Computational Biology↗
PubMed2006

Optimizing search conditions for the mass fingerprint-based identification of proteins.

The two central problems in protein identification by searching a protein sequence collection with MS data are the optimal use of experimental information to allow for identification of low abundance proteins and the accurate assignment of the probability that a result is false. For comprehensive MS-based protein identification, it is necessary to choose an appropriate algorithm and optimal search conditions. We report a systematic study of the quality of PMF-based protein identifications under different sequence collection search conditions using the Probability algorithm, which assigns the statistical significance to each result. We employed 2244 PMFs from 2-DE-separated human blood plasma proteins, and performed identification under various search constraints: mass accuracy (0.01-0.3 Da), maximum number of missed cleavage sites (0-2), and size of the sequence collection searched (5.6 x 10(4)-1.8 x 10(5)). By counting the number of significant results (significance levels 0.05, 0.01, and 0.001) for each condition, we demonstrate the search condition impact on the successful outcome of proteome analysis experiments. A mass correction procedure utilizing mass deviations of albumin matching peptides was tested in an attempt to improve the statistical significance of identifications and iterative searching was employed for identification of multiple proteins from each PMF.

Blood Proteins↗
PubMed2005

Solution phase isoelectric fractionation in the multi-compartment electrolyser: a divide and conquer strategy for the analysis of complex proteomes.

Sample complexity frequently interferes with the analysis of low-abundance proteins by two-dimensional gel electrophoresis (2DGE). Ideally, high abundance proteins should be removed, allowing low-abundance proteins to be applied at much higher concentrations than is possible with the unfractionated sample. One approach is to partition the sample in a manner that segregates the bulk of extraneous proteins from the protein(s) of interest. Solution phase isoelectric focusing in the multi-compartment electrolyser generates fractions of discrete isoelectric point (pI) intervals allowing isolated narrow segments of a proteome to be analysed individually by 2DGE. It is particularly useful for the isolation of low-abundance proteins of extremely basic or acidic pI.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2002

Toxicoproteomics -- a new preclinical tool.

The publication of the human genome has presented the scientific community with an unprecedented amount of genetic information with the potential to revolutionize the drug discovery process. This information could be used to identify novel drug targets and disease markers or could aid in the development of personalized medicines. The realization that genetic changes must ultimately influence protein function has pushed the field of proteomics further into the limelight. In this review the applications of proteomics to the field of toxicology will be discussed. It is anticipated that, in the future, toxicologists will apply a range of genomic and proteomic techniques to address issues in toxicity.

Animals↗
PubMed2006

NovelFam3000--uncharacterized human protein domains conserved across model organisms.

BACKGROUND: Despite significant efforts from the research community, an extensive portion of the proteins encoded by human genes lack an assigned cellular function. Most metazoan proteins are composed of structural and/or functional domains, of which many appear in multiple proteins. Once a domain is characterized in one protein, the presence of a similar sequence in an uncharacterized protein serves as a basis for inference of function. Thus knowledge of a domain's function, or the protein within which it arises, can facilitate the analysis of an entire set of proteins. DESCRIPTION: From the Pfam domain database, we extracted uncharacterized protein domains represented in proteins from humans, worms, and flies. A data centre was created to facilitate the analysis of the uncharacterized domain-containing proteins. The centre both provides researchers with links to dispersed internet resources containing gene-specific experimental data and enables them to post relevant experimental results or comments. For each human gene in the system, a characterization score is posted, allowing users to track the progress of characterization over time or to identify for study uncharacterized domains in well-characterized genes. As a test of the system, a subset of 39 domains was selected for analysis and the experimental results posted to the NovelFam3000 system. For 25 human protein members of these 39 domain families, detailed sub-cellular localizations were determined. Specific observations are presented based on the analysis of the integrated information provided through the online NovelFam3000 system. CONCLUSION: Consistent experimental results between multiple members of a domain family allow for inferences of the domain's functional role. We unite bioinformatics resources and experimental data in order to accelerate the functional characterization of scarcely annotated domain families.

Animals↗
PubMed2006

Tracing the origin of functional and conserved domains in the human proteome: implications for protein evolution at the modular level.

BACKGROUND: The functional repertoire of the human proteome is an incremental collection of functions accomplished by protein domains evolved along the Homo sapiens lineage. Therefore, knowledge on the origin of these functionalities provides a better understanding of the domain and protein evolution in human. The lack of proper comprehension about such origin has impelled us to study the evolutionary origin of human proteome in a unique way as detailed in this study. RESULTS: This study reports a unique approach for understanding the evolution of human proteome by tracing the origin of its constituting domains hierarchically, along the Homo sapiens lineage. The uniqueness of this method lies in subtractive searching of functional and conserved domains in the human proteome resulting in higher efficiency of detecting their origins. From these analyses the nature of protein evolution and trends in domain evolution can be observed in the context of the entire human proteome data. The method adopted here also helps delineate the degree of divergence of functional families occurred during the course of evolution. CONCLUSION: This approach to trace the evolutionary origin of functional domains in the human proteome facilitates better understanding of their functional versatility as well as provides insights into the functionality of hypothetical proteins present in the human proteome. This work elucidates the origin of functional and conserved domains in human proteins, their distribution along the Homo sapiens lineage, occurrence frequency of different domain combinations and proteome-wide patterns of their distribution, providing insights into the evolutionary solution to the increased complexity of the human proteome.

Animals↗
PubMed2026

Unveiling the Molecular Secrets of Seaweeds: A Comprehensive Review of Bioinformatics Applications in Algal Research.

Recent advances in high-throughput sequencing, bioinformatics, and multi-omics technologies have transformed seaweed research by overcoming long-standing challenges associated with complex genomes, diverse life cycles, and limited genomic resources. This review provides a comprehensive overview of bioinformatics approaches used to investigate seaweed genomics, transcriptomics, proteomics, metabolomics, microbiomes, and functional genomics, with emphasis on the computational tools and databases that support these analyses. Applications of bioinformatics in phylogenetics, drug discovery, microbiome characterization, and the development of biofuels, nutraceuticals, pharmaceuticals, and sustainable agriculture are also discussed. Particular attention is given to emerging strategies involving multi-omics integration, genome editing, artificial intelligence, machine learning, and synthetic biology that are reshaping seaweed research. The review further examines current challenges, including incomplete genomic resources, data standardization, and the need for experimental validation of computational predictions. Collectively, these advances highlight the growing role of bioinformatics in enabling systems-level understanding of seaweed biology and accelerating their translation into sustainable biotechnological and marine bioeconomy applications.

macroalgal genomics↗
PubMed2001

Computational cell biology in the post-genomic era.

Rapid accumulation of biological data from novel high throughput technologies characteristic of genomic and proteomic research as well as advances in more traditional biological disciplines are leading to wider use of detailed and complex computational models of cell behavior. These models address a variety of dynamic intracellular processes ranging from interactions within a gene regulation network to intracellular and intercellular signal transduction. This review focuses on the current trends in computation cell biology, particularly emphasizing the role of experimental validation. The recent successes and future challenges facing computational cell biology are also discussed.

Computational Biology↗
PubMed2026

A Bayesian framework for multivariate differential analysis.

Differential analysis is a routine procedure in the statistical analysis toolbox across many applied fields, including quantitative proteomics, the main illustration of the present paper. The state-of-the-art limma approach uses a hierarchical formulation with moderated-variance estimators for each analyte directly injected into the t-statistic. While standard hypothesis testing strategies are recognised for their low computational cost, allowing for quick extraction of the most differential among thousands of elements, they generally overlook key aspects such as handling missing values, inter-element correlations, and uncertainty quantification. The present paper proposes a fully Bayesian framework for differential analysis, leveraging a conjugate hierarchical formulation for both the mean and the variance. Inference is performed by computing the posterior distribution of compared experimental conditions and sampling from the distribution of differences. This approach provides well-calibrated uncertainty quantification at a similar computational cost as hypothesis testing by leveraging closed-form equations. Furthermore, a natural extension enables multivariate differential analysis that accounts for possible inter-element correlations. We also demonstrate that, in this Bayesian treatment, missing at random data should generally be ignored in univariate settings, and further derive a tailored approximation that handles multiple imputation for the multivariate setting. We argue that probabilistic statements in terms of effect size and associated uncertainty are better suited to practical decision-making. Therefore, we finally propose simple and intuitive inference criteria, such as the overlap coefficient, which express group similarity as a probability rather than traditional, and often misleading, p-values. The performance of this approach is evaluated through an extensive empirical study using both synthetic and controlled real-world proteomics datasets. Overall, we believe that this Bayesian framework for (multivariate) differential analysis provides a valuable and intuitive counterpart to standard methods at a comparable computational cost.

Bayes Theorem↗
PubMed2004

Tree analysis of mass spectral urine profiles discriminates transitional cell carcinoma of the bladder from noncancer patient.

BACKGROUND: Recent advances in proteomic profiling technologies, such as surface-enhanced laser desorption/ionization mass spectrometry (SELDI), have allowed preliminary profiling and identification of tumor markers in biological fluids in several cancer types and establishment of clinically useful diagnostic computational models. We developed a bioinformatics tool and used it to identify proteomic patterns in urine that distinguish transitional cell carcinoma (TCC) from noncancer. METHODS: Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionization). A preliminary "training" set of spectra derived from analysis of urine from 46 TCC patients, 32 patients with benign urogenital diseases (BUD), and 40 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that identified a fine-protein mass pattern that discriminated cancer from noncancer effectively. A blinded test set, including 38 new cases, was used to determine the sensitivity and specificity of the classification system. RESULTS: The algorithm identified a cluster pattern that, in the training set, segregated cancer from noncancer with sensitivity of 84.8% and specificity of 91.7%. The discriminatory pattern correctly identified. A sensitivity of 93.3% and a specificity of 87.0% for the blinded test were obtained when comparing the TCC vs. noncancer. CONCLUSIONS: These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for bladder cancer in high-risk and general populations.

Adult↗
PubMed2005

Understanding platelets. Lessons from proteomics, genomics and promises from network analysis.

New large-scale analysis techniques such as bioinformatics, mass spectrometry and SAGE data analysis will allow a new framework for understanding platelets. This review analyses some important options and tasks for these tools and examines an outline of the new, refined picture of the platelet outlined by these new techniques. Looking at the platelet-specific building blocks of genome, (active) transcriptome and proteome (notably secretome and phospho-proteome), we summarize current bioinformatical and biochemical approaches, tasks as well as their limitations. Understanding the surprisingly complex platelet regarding compartmentalization, key cascades, and pathways including clinical implications will remain an exciting and hopefully fruitful challenge for the future.

Animals↗
PubMed2004

Decoupling dynamical systems for pathway identification from metabolic profiles.

RATIONALE: Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, time-dense profiles of metabolites or proteins that are measurable, for instance, with mass spectrometry, nuclear magnetic resonance or protein kinase phosphorylation. These profiles contain a wealth of information about the structure and dynamics of the pathway or network from which the data were obtained. The retrieval of this information requires a combination of computational methods and mathematical models, which are typically represented as systems of ordinary differential equations. RESULTS: We show that, for the purpose of structure identification, the substitution of differentials with estimated slopes in non-linear network models reduces the coupled system of differential equations to several sets of decoupled algebraic equations, which can be processed efficiently in parallel or sequentially. The estimation of slopes for each time series of the metabolic or proteomic profile is accomplished with a 'universal function' that is computed directly from the data by cross-validated training of an artificial neural network (ANN). CONCLUSIONS: Without preprocessing, the inverse problem of determining structure from metabolic or proteomic profile data is challenging and computationally expensive. The combination of system decoupling and data fitting with universal functions simplifies this inverse problem very significantly. Examples show successful estimations and current limitations of the method. AVAILABILITY: A preliminary Web-based application for ANN smoothing is accessible at http://bioinformatics.musc.edu/webmetabol/. S-systems can be interactively analyzed with the user-friendly freeware PLAS (http://correio.cc.fc.ul.pt/~aenf/plas.html) or with the MATLAB module BSTLab (http://bioinformatics.musc.edu/bstlab/), which is currently being beta-tested.

Algorithms↗
PubMed2005

Shear stress biology of the endothelium.

The relationships between blood flow, mechanotransduction, and the localization of arterial lesions can now be advanced by the incorporation of new technologies and the refinement of existing methods in imaging modalities, computational modeling, fluid dynamics, and high throughput genomics and proteomics. When combined with traditional cell and molecular technologies, a powerful palette of investigative approaches is available to address shear stress biology of the endothelium at levels extending from nanoscale subcellular detailed mechanistic responses through to higher organizational levels of regional endothelial phenotypes and heterogeneous vascular beds.

Animals↗
PubMed2006

Application of FTTP to alpha-helix or beta-strand motifs.

Information concerning protein structure is widely dispersed and cannot easily and rapidly be processed by the biological community. We present a database of tendentious factors of three states of tripeptide units from PDB database, called a bank of tendentious factors of three states of three-peptide units (FTTP). The FTTP database was constructed based on conformational dihedral angle (varphi,psi) library of 20(3) peptide triplets by exhaustively searching through PDB databases. We introduce the FTTP database for the analysis of characteristics common to relative conformational biases of all peptide triplets, especially finding some motifs apt to alpha-helix and beta-strand. Our results show that this will provide a platform for studies of short peptide motifs, folding codons, secondary structure and three-dimensional (3D) structure of proteins. Moreover, FTTP is a unique resource that will allow a comprehensive characterization of peptide triplets and thus improve our understanding of sequence-structure relationship, refined domains, 3D structures, and their associated function. We believe the FTTP database will help biologists in increasing the efficiency of finding useful and relevant information regarding structure-function relationship of proteins. Therefore, this approach will play an important role in protein folding, protein engineering, molecular design, and proteomics.

Amino Acid Motifs↗
PubMed2005

TB subunit vaccines--putting the pieces together.

The search for a new and improved vaccine against tuberculosis (TB) is currently a very active field of research, which in the last 10 years has benefited tremendously from the completed Mycobacterium tuberculosis genome and the progress in molecular biology and computer science. In this review, we discuss how Genomics, Proteomics and Transcriptomics have accelerated the pace of antigen discovery and vaccine development and have changed this field completely, resulting in the identification of a large number of antigens with potential in TB vaccines. The next phase of this work has now started--putting the most relevant molecules back together as fusion molecules and cocktails. This requires carefully monitoring aspects as immunodominance, recognition in different populations as well as vaccine manufacturing.

Antigens, Bacterial↗
PubMed2025

Using Large Genomic Biobanks to Generate Insights into Genetic Kidney Disease.

Chronic kidney disease (CKD) affects approximately 9% of the global population, leading to increased risks of end-stage kidney disease (ESKD), cardiovascular disease (CVD), and mortality. Patients with CKD are a huge burden on health care resources globally. CKD is a complex condition influenced by a combination of genetic, environmental, and traditional risk factors. Family studies have suggested heritability rates for CKD ranging from 30% to 75%, and large genomic biobank studies have proven essential in identifying genes with substantial effects on CKD risk and in capturing cumulative genetic risk through polygenic risk scores. These biobanks are crucial for discovering new genes associated with kidney health and disease, and their growing size enhances the power to detect novel genetic associations. Integrating multi-omics technologies such as transcriptomics, metabolomics, and proteomics further enriches our understanding of CKD, while advanced computational tools continue to expand our insights into genetic data. Polygenic risk scores, derived from hundreds of genetic variants with small effect sizes, can help identify individuals at high risk of CKD. Genomic biobanks offer valuable opportunities for early identification and personalized treatment of monogenic kidney disorders, such as autosomal dominant polycystic kidney disease and Alport syndrome. These biobanks help fill knowledge gaps, particularly in individuals with milder or asymptomatic presentations who are often underrepresented in traditional studies. Expanding genomic biobank efforts globally, especially in diverse populations, is vital to enhancing our understanding of the genetic underpinnings of kidney disease. This review highlights the significant contributions of genomic biobanks to advancing our comprehension of the genetics of CKD.

Humans↗