Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Computational proteomics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11Linked to original sources

An alternative approach to deal with geometric uncertainties in computer analysis of two-dimensional electrophoresis gels.

With the growing importance of proteomics in biomedical and pharmaceutical sciences a need has emerged for computing tools that are capable of digitally visualizing and analyzing protein spot patterns within two-dimensional electrophoresis (2-DE) gel. Matching programs need to meet requirements such as interlaboratory comparison and the comparison of samples from different origins. For such research purposes, we have developed the CAROL system that implements new algorithms for spot detection and matching, which enable researchers to take a different approach to protein spot identification and comparison. The present short communication discusses how the system deals with uncertain geometric spot information that arises from streaks and complex spot regions and how this can be amplified for the matching procedure.

Algorithms↗

Improved peptide charge state assignment.

We present a new approach capable of assigning charge states to peptides based on both their intact mass spectrum and their fragmentation mass spectrum. More specifically, our approach aims at fully exploiting available information to improve correct charge assignment rate. This is achieved by using information provided by the fragmentation spectrum extensively. For low-resolution spectra, charge assignment based on fragmentation mass spectrum is better than charge assignment based on intact peptide signal only. We introduce two methods that allow to integrate information contributing to successful peptide charge state assignment. We demonstrate the performance of our algorithms on large ion trap data sets. The application of these algorithms to large-scale proteomics projects can save significant computation time and have a positive impact on identification false positive rates.

Algorithms↗

Vaccinology: past achievements, present roadblocks and future promises.

Of all the branches of modern medicine, vaccinology can claim to be the one that has contributed most to the relief of human misery and the spectacular increase in life expectancy in the last two centuries. It is the only science that has eradicated an infectious disease-smallpox-responsible for 8-20% of all deaths in several European countries in the 18th century. Other disabling and lethal diseases, like poliomyelitis and measles, are targeted for eradication. Currently, it is estimated that immunization saves the lives of 3 million children a year but 2 million more lives could be saved by existing vaccines. The success of vaccines in controlling and eliminating diseases has, paradoxically, been the cause of a revival of the anti-vaccination movement which in the absence, in developed countries, of many erstwhile common infectious diseases such as diphtheria, tetanus, polio, pertussis, measles, rubella and mumps has come to believe that vaccination is not only no longer necessary but is even dangerous. This is because it accepts, as "reactions", any untoward health event that occurs after administration of a vaccine. Most vaccine "reactions", therefore, appear to be more frequent than vaccine-preventable diseases. Public Health Authorities, aware of the great value of vaccines to society, are facing an uphill battle to get them accepted by a growing proportion of so-called educated minorities, thus endangering disease elimination. Other developments, in the last two decades, that have hampered vaccine usage have been the exploding costs of research, development and manufacture of new vaccines and the emphasis still placed on therapy in preference to prevention in medicine. This has led to the erroneous perception that vaccines are expensive although they are, in most cases, more cost-effective than the popular wait-see-treat approach. A favorable trend for vaccinology has been fueled by recent major breakthroughs in the sciences of immunology, molecular biology, genomics, proteomics, physico-chemistry and computers that promise a bright future for prevention, not only of acute infectious diseases, but also treatment of conditions like chronic infections, allergy, auto-immune diseases and cancer where some malfunctioning of the immune system is thought to play a part. Vaccines are being made more user-friendly by the development of combined vaccines and less painful and invasive inoculation techniques than the traditional syringe and needle. Recent new initiatives, like the Global Alliance on Vaccines and Immunization (GAVI),which are gathering new sources of funding for vaccination, should be beneficial for vaccinology.

Bacterial Infections↗

Moss systems biology en route: phytohormones in Physcomitrella development.

The moss Physcomitrella patens has become a powerful model system in modern plant biology. Highly standardized cell culture techniques, as well as the necessary tools for computational biology, functional genomics and proteomics have been established. Large EST collections are available and the complete moss genome will be released soon. A simple body plan and the small number of different cell types in Physcomitrella facilitate the study of developmental processes. In the filamentous juvenile moss tissue, developmental decisions rely on the differentiation of single cells. Developmental steps are controlled by distinct phytohormones and integration of environmental signals. Especially the phytohormones auxin, cytokinin, and abscisic acid have distinct effects on early moss development. In this article, we review current knowledge about phytohormone influences on early moss development in an attempt to fully unravel the complex regulatory signal transduction networks underlying the developmental decisions of single plant cells in a holistic systems biology approach.

Abscisic Acid↗

Early gene m18, a novel player in the immune response to murine cytomegalovirus.

The identification of all antigenic peptides encoded by a pathogen, its T cell 'immunome', is a research aim for rational vaccine design. Screening of proteome-spanning peptide libraries or computational prediction is used to identify antigenic peptides recognized by CD8 T cells. Based on their high coding capacity, cytomegaloviruses (CMVs) could specify numerous antigenic peptides. Yet, current evidence indicates that the memory CD8 T cell response in a given haplotype is actually focused on a few viral proteins. CMVs actively interfere with antigen processing and presentation by the expression of immune evasion proteins. In the case of murine CMV (mCMV), these proteins are effectual in the early (E) phase of the virus replication cycle and should thus preclude the presentation of peptides derived from E proteins. Notably, the m18 gene is here added to a growing list of mCMV E genes that encode antigenic peptides in spite of the E phase immune evasion strategies of the virus.

Animals↗

A combined approach to drug metabolism and toxicity assessment.

The challenge of predicting the metabolism or toxicity of a drug in humans has been approached using in vivo animal models, in vitro systems, high throughput genomics and proteomics methods, and, more recently, computational approaches. Understanding the complexity of biological systems requires a broader perspective rather than focusing on just one method in isolation for prediction. Multiple methods may therefore be necessary and combined for a more accurate prediction. In the field of drug metabolism and toxicology, we have seen the growth, in recent years, of computational quantitative structure-activity relationships (QSARs), as well as empirical data from microarrays. In the current study we have further developed a novel computational approach, MetaDrug, that 1) predicts metabolites for molecules based on their chemical structure, 2) predicts the activity of the original compound and its metabolites with various absorption, distribution, metabolism, excretion, and toxicity models, 3) incorporates the predictions with human cell signaling and metabolic pathways and networks, and 4) integrates networks and metabolites, with relevant toxicogenomic or other high throughput data. We have demonstrated the utility of such an approach using recently published data from in vitro metabolism and microarray studies for aprepitant, 2(S)-((3,5-bis(trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-triazol-5-yl)methyl)morpholine (L-742694), trovofloxacin, 4-hydroxytamoxifen, and artemisinin and other artemisinin analogs to show the predicted interactions with cytochromes P450, pregnane X receptor, and P-glycoprotein, and the metabolites and the networks of genes that are affected. As a comparison, we used a second computational approach, MetaCore, to generate statistically significant gene networks with the available expression data. These case studies demonstrate the combination of QSARs and systems biology methods.

Drug-Related Side Effects and Adverse Reactions↗

[Importance of development of ecotoxicogenomics in understanding molecular mechanisms of chemicals in developing animals].

Developing organisms are sensitive to estrogenic chemicals. Exposure to estrogens or estrogenic chemicals during critical periods of development induces persistent changes in reproductive as well as nonreproductive organs, including persistent molecular alterations. Chemicals released into the environment potentially disrupt the endocrine system in wild animals and humans, some of which exhibit estrogenic or anti-androgenic activity by binding to estrogen or androgen receptors. Estrogen-responsive genes and critical developmental windows of various animal species, therefore, should be identified to understand the molecular basis of estrogenic activity during embryonic development. To understand molecular mechanisms of estrogenic chemicals in developing organisms, we identified estrogen-responsive genes using cDNA microarrays and a quantitative reverse transcriptase-polymerase chain reaction, and genes related to estrogen-independent vaginal changes in mice induced by estrogens during a critical window. We also analyzed genes in Xenopus laevis embryos related to abnormalities induced by estrogenic chemicals using cDNA microarrays. Estrogen receptors and other steroid hormone receptors were cloned in various species, including the American alligator, crocodile, Florida red-bellied turtle, Silurana tropicalis, roach, Fundulus and mosquito fish. An environmental androgen, trenbolone, was found to induce persistent changes in the gonopodium and ovotestis in mosquito fish. In Daphnia magna, juvenile hormone agonists induce the production of male offspring. Tributyltin (TBT) induces imposex in rockshell through retinoid X receptor (RXR) activation. Microarray technology is a powerful tool for understanding the molecular mechanism of receptor-mediated toxicology in various animal species. Ecotoxicogenomics is a multidisciplinary field concerned with elucidating how the entire genome is involved in the biological response of wild animals exposed to environmental toxicants and stressors. Ecotoxicogenomics combines information from studies of genomic-scale mRNA profiling by microarray analysis (transcriptomics), protein profiling (proteomics), metabolite profiling (metabolonomics) and computational models to understand the roles of gene-environment interaction in the chemical toxicity. To understand the molecular mechanism of chemicals in developing wildlife, the establishment of ecotoxicogenomics is essential in the near future.

Amphibians↗

Systems biology: new approaches to old environmental health problems.

The environment plays a pivotal role as a human health determinant and presence of hazardous pollutants in the environment is often implicated in human disease. That pollutants cause human diseases however is often controversial because data connecting exposure to environmental hazards and human diseases are not well defined, except for some cancers and syndromes such as asthma. Understanding the complex nature of human-environment interactions and the role they play in determining the state of human health is one of the more compelling problems in public health. We are becoming more aware that the reductionist approach promulgated by current methods has not, and will not yield answers to the broad questions of population health risk analysis. If substantive applications of environment-gene interactions are to be made, it is important to move to a systems level approach, to take advantage of epidemiology and molecular genomic advances. Systems biology is the integration of genomics, transcriptomics, proteomics, and metabolomics together with computer technology approaches to elucidate environmentally caused disease in humans. We discuss the applications of environmental systems biology as a route to solution of environmental health problems.

Environmental Exposure↗

High performance GRID based implementation for genomics and protein analysis.

Starting from the genomic and proteomic sequence data, a complex computational infrastructure as been established with the objective to develop a GRID based system to to automate the analysis, prediction and annotation processes of genomic DNA. To support of this type of analysis, several algorithms as been used to recognize biological signals involved in the identification of genes and proteins. The system implemented can be use to analyse the content of the large number of genomic sequences. For this reason, the system realized is capable of using a computational architecture specifically designed for intensive computing based on GRID technologies developed throughout the BIOINFOGRID European project. We developed a GRID based workflow to correlate different kind of Bioinformatics data, going from the Genomics Nucleotide to the Protein Sequence. The first step in the workflow consists of submitting a nucleotide sequence that is elaborated by a specific software for gene prediction. In particular this tool performs a search in the nucleotide sequence to find out the key components of gene. The predicted gene is then translated in the corresponding protein sequence. Based on protein sequence is then possible to identify the domains that characterize the protein functionality using specific tools of domain prediction. Protein domains classification are very important in the analysis of the macromolecular functionality. To analyze a whole protein family from large genome of various organism means to elaborate a large amount of data that requires huge computational resources. To analyze all this data we suggest the use of a high performance platform based on grid technology. We have implemented our applications on a wide area grid platform for scientific applications [http://www.grid.it and http://grid-it.cnaf.infn.it] composed of about 1000 CPU's. The grid infrastructure consists in a collection of computing elements and storage elements that jointly concur to define a platform for high performance elaboration. In this study a grid based application is presented to compute the protein domain analysis in a distributed way. This approach has high performance because the protein domains are checked with different software in parallel in different grid sites.

Computational Biology↗

Complex graph matrix representations and characterizations of proteomic maps and chemically induced changes to proteomes.

We have presented a complex graph matrix representation to characterize proteomics maps obtained from 2D-gel electrophoresis. In this method, each bubble in a 2D-gel proteomics map is represented by a complex number with components which are charge and mass. Then, a graph with complex weights is constructed by connecting the vertices in the relative order of abundance. This yields adjacency matrices and distance matrices of the proteomics graph with complex weights. We have computed the spectra, eigenvectors, and other properties of complex graphs and the Euclidian/graph distance obtained from the complex graphs. The leading eigenvalues and eigenvectors and, likewise, the smallest eigenvalues and eigenvectors, and the entire graph spectral patterns of the complex matrices derived from them yield novel weighted biodescriptors that characterize proteomics maps with information of charge and masses of proteins. We have also applied these eigenvector and eigenvalue maps to contrast the normal cells and cells exposed to four peroxisome proliferators, namely, clofibrate, diethylhexyl phthalate (DEHP), perfluorodecanoic acid (PFDA), and perfluoroctanoic acid (PFOA). Our complex eigenspectra show that the proteomic response induced by DEHP differs from the corresponding responses of other three chemicals consistent with their chemical structures and properties.

Algorithms↗

Integrative proteomics: structure, function, and interaction report on the 3rd joint meeting of the British Society for Proteome Research and the European Bioinformatics Institute, July 2006.

This report summarizes the highlights of the recent British Society for Proteome Research (BSPR) meeting jointly organized with the European Bioinformatics Institute (EBI) which was held at the Wellcome Trust Genome Campus, Hinxton, Cambridge, UK in July 2006. This was the third annual scientific meeting organized by the BSPR and EBI and the theme of this years meeting was Integrative Proteomics: Structure, function and interaction. A wealth of local and overseas speakers were invited to discuss both their own work and specific challenges present in modern day proteomic based experiments.

Computational Biology↗

DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.

Paired spatial multi-omics provides a supervised basis for learning RNA-protein correspondence in situ, but predicting protein abundance from spatial transcriptomic data alone remains challenging across tissue contexts and protein panels. Here, we present DPAS-Graph, an adaptive relation-learning framework for spatial RNA-to-protein prediction. Rather than directly merging spatial proximity and transcriptomic similarity as fixed graph priors, DPAS-Graph represents them as two relation channels on a shared edge support and updates their contributions during representation learning for protein prediction. Its Niche-Coupled Field Encoder combines layer-wise edge-relation modeling, intra-branch relation refinement, and cross-branch residual correction to learn spot representations for protein abundance prediction. In a leave-one-dataset-out benchmark across seven paired spatial multi-omics datasets, DPAS-Graph achieved lower aggregate prediction errors and improved spot-level agreement of protein expression profiles, with gains mainly reflected in error-based metrics and PCC-Spot. Spatial autocorrelation and protein-derived domain agreement analyses were further used to characterize the spatial behavior of the predicted protein maps. When applied to external RNA-only spatial sections, DPAS-Graph generated qualitatively interpretable marker-level virtual protein maps, illustrating its use as a complementary tool for protein-level interpretation of transcriptomics-only spatial data.

RNA↗

APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19.

MOTIVATION: Computational analyses of bulk and single-cell omics provide translational insights into complex diseases, such as COVID-19, by revealing molecules, cellular phenotypes, and signalling patterns that contribute to unfavourable clinical outcomes. Current in silico approaches dovetail differential abundance, biostatistics, and machine learning, but often overlook nonlinear proteomic dynamics, like post-translational modifications, and provide limited biological interpretability beyond feature ranking. RESULTS: We introduce APNet, a novel computational pipeline that combines differential activity analysis based on SJARACNe co-expression networks with PASNet, a biologically informed sparse deep learning model, to perform explainable predictions for COVID-19 severity. The APNet driver-pathway network ingests SJARACNe co-regulation and classification weights to aid result interpretation and hypothesis generation. APNet outperforms alternative models in patient classification across three COVID-19 proteomic datasets, identifying predictive drivers and pathways, including some confirmed in single-cell omics and highlighting under-explored biomarker circuitries in COVID-19. AVAILABILITY AND IMPLEMENTATION: APNet's R, Python scripts, and Cytoscape methodologies are available at https://github.com/BiodataAnalysisGroup/APNet.

COVID-19↗

pTARGET: a web server for predicting protein subcellular localization.

The pTARGET web server enables prediction of nine distinct protein subcellular localizations in eukaryotic non-plant species. Predictions are made using a new algorithm [C. Guda and S. Subramaniam (2005) pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes. Bioinformatics, 21, 3963-3969], which is primarily based on the occurrence patterns of location-specific protein functional domains in different subcellular locations. We have implemented a relational database, PreCalcDB, to store pre-computed prediction results for all eukaryotic non-plant protein sequences in the public domain that includes about 770,000 entries. Queries can be made by entering protein sequences or by uploading a file containing up to 5000 protein sequences in FASTA format. Prediction results for queries with matching entries in the PreCalcDB will be retrieved instantly; while for the missing ones new predictions will be computed and sent by email. Pre-computed predictions can also be downloaded for complete proteomes of Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, Mus musculus and Homo sapiens. The server, its documentation and the data are accessible from http://bioinformatics.albany.edu/~ptarget.

Algorithms↗

Multivariate information bottleneck.

The information bottleneck (IB) method is an unsupervised model independent data organization technique. Given a joint distribution, p(X, Y), this method constructs a new variable, T, that extracts partitions, or clusters, over the values of X that are informative about Y. Algorithms that are motivated by the IB method have already been applied to text classification, gene expression, neural code, and spectral analysis. Here, we introduce a general principled framework for multivariate extensions of the IB method. This allows us to consider multiple systems of data partitions that are interrelated. Our approach utilizes Bayesian networks for specifying the systems of clusters and which information terms should be maintained. We show that this construction provides insights about bottleneck variations and enables us to characterize the solutions of these variations. We also present four different algorithmic approaches that allow us to construct solutions in practice and apply them to several real-world problems.

Algorithms↗

Computational identification of beta-barrel outer-membrane proteins in Mycobacterium tuberculosis predicted proteomes as putative vaccine candidates.

Mycobacterial porins and other beta-barrel outer-membrane proteins are represented by the structure of Mycobacterium smegmatis porin MspA. On the basis of existing knowledge of beta-barrel outer-membrane proteins, several state of the art prediction methods, as well as a new in-house program (PROB) were employed for the systematic exploration of Mycobacterium tuberculosis predicted proteomes for potential beta-barrel structures. PROB allowed parameter optimization while functioning with an adaptive algorithm for the detection of outer-membrane beta-barrel proteins in highly divergent proteomes. As a result of the predictions, 114 proteins in total were predicted to be beta-barrel structures; of these, 40 were PE-PPE proteins, 8 Mce proteins, 24 hypothetical, 11 probable membrane proteins, 10 transporters, 4 lipoproteins, and 14 classified as other. The congruence among three of the predictors, PROB, TMB-Hunt, and BOMP, was low with only three proteins (MT0318, MT0356, and MT2423) predicted by the three. Overall, 79 new proteins for which no previous experimental work has been performed are reported. At least 10 of these have high potential of being not only surface-exposed but also served as putative vaccine candidates as determined by in silico predictions of CD4T cell MHC-II restricted epitopes.

Algorithms↗

Network genomics--a novel approach for the analysis of biological systems in the post-genomic era.

Network Genomics studies genomics and proteomics foundations of cellular networks in biological systems. It complements systems biology in providing information on elements, their interaction and their functional interplay in cellular networks. The relationship between genomic and proteomic high-throughput technologies and computational methods are described, as well as several examples of specific network genomic application are presented.

Animals↗