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At least 1,603 records · Page 89Linked to original sources
PubMed2005

Comparing protein-ligand docking programs is difficult.

There is currently great interest in comparing protein-ligand docking programs. A review of recent comparisons shows that it is difficult to draw conclusions of general applicability. Statistical hypothesis testing is required to ensure that differences in pose-prediction success rates and enrichment rates are significant. Numerical measures such as root-mean-square deviation need careful interpretation and may profitably be supplemented by interaction-based measures and visual inspection of dockings. Test sets must be of appropriate diversity and of good experimental reliability. The effects of crystal-packing interactions may be important. The method used for generating starting ligand geometries and positions may have an appreciable effect on docking results. For fair comparison, programs must be given search problems of equal complexity (e.g. binding-site regions of the same size) and approximately equal time in which to solve them. Comparisons based on rescoring require local optimization of the ligand in the space of the new objective function. Re-implementations of published scoring functions may give significantly different results from the originals. Ostensibly minor details in methodology may have a profound influence on headline success rates.

Algorithms↗
PubMed2026

Integrative pan-cancer analysis of transferrin reveals context-dependent prognostic associations and links to immune and metabolic disease-related programs.

BACKGROUND: Iron metabolism is closely linked to tumor biology, yet the pan-cancer significance of transferrin (TF), the major circulating iron-transport protein, remains insufficiently defined. Although TF has been implicated in cancer-related processes, its prognostic relevance, immune associations, and broader disease-related transcriptional context have not been systematically characterized across tumor types. OBJECTIVE: This study aimed to perform an integrative pan-cancer analysis of TF to characterize its expression patterns, clinical associations, immune context, pathway features, and pharmacogenomic correlations, and to explore whether TF-related signals extend to selected metabolic and chronic organ injury settings. METHODS: We used multiple public databases, including The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), Gene Expression Omnibus (GEO), and Cancer Cell Line Encyclopedia (CCLE), to integrate transcriptomic, proteomic, and clinical data across 33 tumor types and selected non-malignant conditions. TF expression was evaluated across normal tissues, tumors, and cell lines, followed by survival analysis, immune infiltration analysis, TMB/MSI and methylation assessment, pathway enrichment, and drug-response correlation. Independent GEO cohorts of non-alcoholic steatohepatitis (NASH), heart failure (HF), and liver cirrhosis (LC) were used for cross-disease extension. Selected findings were further explored in OA/PA-treated hepatocytes, 786-O renal carcinoma cells, and AC16 cardiomyocytes. RESULTS: TF showed pronounced tissue specificity and cancer-type-dependent dysregulation. Across pan-cancer cohorts, the most consistent adverse survival associations were observed in kidney renal clear cell carcinoma (KIRC) and stomach adenocarcinoma (STAD), where TF remained associated with overall survival (OS) in multivariable analyses. TF expression was also correlated with cancer-type-specific immune infiltration patterns and selected drug-response profiles. Across independent NASH, HF, and LC datasets, TF expression was elevated and TF-associated pathways partially overlapped with those observed in cancer. In vitro experiments provided preliminary support that TF modulation is associated with proliferative phenotypes in KIRC cells and stress- and metabolism-related phenotypes in hepatocyte and cardiomyocyte models. CONCLUSION: These findings support TF as a context-dependent biomarker candidate in cancer, with the most consistent prognostic relevance observed in KIRC and STAD. Rather than establishing a unified mechanism across diseases, this study provides an integrative framework suggesting that TF is associated with malignant behavior, immune context, and selected metabolic stress-related programs, and warrants further mechanistic investigation.

Iron metabolism↗
PubMed2005

Chemical biology on PINs and NeeDLes.

Systematic studies of the organization of biochemical networks that make up the living cell can be defined by studying the organization and dynamics of protein interaction networks (PINs). Here, we describe recent conceptual and experimental advances that can achieve this aim and how chemical perturbations of interactions can be used to define the organization of biochemical networks. Resulting perturbation profiles and subcellular locations of interactions allow us to 'place' each gene product at its relevant point in a network. We discuss how experimental strategies can be used in conjunction with other genome-wide analyses of physical and genetic protein interactions and gene transcription profiles to determine network dynamic linkage (NDL) in the living cell. It is through such dynamic studies that the intricate networks that make up the chemical machinery of the cell will be revealed.

Computational Biology↗
PubMed2003

Distinguishing enzyme structures from non-enzymes without alignments.

The ability to predict protein function from structure is becoming increasingly important as the number of structures resolved is growing more rapidly than our capacity to study function. Current methods for predicting protein function are mostly reliant on identifying a similar protein of known function. For proteins that are highly dissimilar or are only similar to proteins also lacking functional annotations, these methods fail. Here, we show that protein function can be predicted as enzymatic or not without resorting to alignments. We describe 1178 high-resolution proteins in a structurally non-redundant subset of the Protein Data Bank using simple features such as secondary-structure content, amino acid propensities, surface properties and ligands. The subset is split into two functional groupings, enzymes and non-enzymes. We use the support vector machine-learning algorithm to develop models that are capable of assigning the protein class. Validation of the method shows that the function can be predicted to an accuracy of 77% using 52 features to describe each protein. An adaptive search of possible subsets of features produces a simplified model based on 36 features that predicts at an accuracy of 80%. We compare the method to sequence-based methods that also avoid calculating alignments and predict a recently released set of unrelated proteins. The most useful features for distinguishing enzymes from non-enzymes are secondary-structure content, amino acid frequencies, number of disulphide bonds and size of the largest cleft. This method is applicable to any structure as it does not require the identification of sequence or structural similarity to a protein of known function.

Algorithms↗
PubMed2003

Assessment of the role of computations in structural biology.

Computations are now an integrated part of structural biology and are used in data gathering, data processing, and data storage as well as in a full spectrum of theoretical pursuits. In this review, we focus on areas of great promise and call attention to important issues of internal consistency and error analysis.

Computational Biology↗
PubMed2003

The complete Corynebacterium glutamicum ATCC 13032 genome sequence and its impact on the production of L-aspartate-derived amino acids and vitamins.

The complete genomic sequence of Corynebacterium glutamicum ATCC 13032, well-known in industry for the production of amino acids, e.g. of L-glutamate and L-lysine was determined. The C. glutamicum genome was found to consist of a single circular chromosome comprising 3282708 base pairs. Several DNA regions of unusual composition were identified that were potentially acquired by horizontal gene transfer, e.g. a segment of DNA from C. diphtheriae and a prophage-containing region. After automated and manual annotation, 3002 protein-coding genes have been identified, and to 2489 of these, functions were assigned by homologies to known proteins. These analyses confirm the taxonomic position of C. glutamicum as related to Mycobacteria and show a broad metabolic diversity as expected for a bacterium living in the soil. As an example for biotechnological application the complete genome sequence was used to reconstruct the metabolic flow of carbon into a number of industrially important products derived from the amino acid L-aspartate.

Amino Acid Sequence↗
PubMed2002

Protein sequence analysis in silico: application of structure-based bioinformatics to genomic initiatives.

The current pace of high-throughput genome sequencing programs coupled with high-throughput functional genomic screens has provided researchers with a bewildering array of sequence and biological data to contend with. Identification of proteins of interest from a particular biological study requires the application of bioinformatic tools to process and prioritise the data. From a protein function standpoint, transfer of annotation from known proteins to a novel target is currently the only practical way to convert vast quantities of raw sequence data into meaningful information. New bioinformatics tools now provide more sophisticated methods to transfer functional annotation, integrating sequence, family profile and structural search methodology. The importance of these approaches to medical research is increasing as we move to annotate the proteome through functional and structural genomic efforts.

Animals↗
PubMed2004

Identification of protein coding regions of rice genes using alternative spectral rotation measure and linear discriminant analysis.

An improved method, called Alternative Spectral Rotation (ASR) measure, for predicting protein coding regions in rice DNA has been developed. The method is based on the Spectral Rotation (SR) measure proposed by Kotlar and Lavner, and its accuracy is higher than that of the SR measure and the Spectral Content (SC) measure proposed by Tiwari et al. In order to increase the identifying accuracy, we chose three different coding characters, namely the asymmetric, purine, and stop-codon variables as parameters, and an approving result was presented by the method of Linear Discriminant Analysis (LDA).

Codon↗
PubMedDate not supplied

Protein variability in Meloidogyne spp. (Nematoda:Meloidogynidae) revealed by two-dimensional gel electrophoresis and mass spectrometry.

Total protein variation as revealed by two-dimensional electrophoresis (2D-E) was studied in 18 isolates from populations of Meloidogyne arenaria (six isolates), Meloidogyne incognita (10 isolates), and Meloidogyne javanica (one isolate) plus an unclassified isolate. Gels (80 x 60 x 0.75 mm) were silverstained and digitized in order to compare their protein patterns. Optical density and position of protein patterns were measured using statistical cluster analysis and computer-assisted image analysis software. Only those protein stains or positions that were clearly defined (i.e., without background) were considered. The number of positions in gels ranged from 86 to 203. Each of these positions had 95 clearly expressed proteins that were present in at least two replicates for each isolate. Spot position was considered a taxonomical character with two different states: presence (1) and absence (0). Accordingly, genetic distance was estimated among isolates and species, and a phylogenetic tree was constructed following the cladistic approach based on maximum parsimony analysis. Isolates of M. arenaria--M. javanica--Meloidogyne sp. and of M. incognita formed two separate monophyletic groups. Both groups were clearly defined on the basis of two sets of protein positions that can be considered as diagnostic characters. An attempt to identify these proteins by mass spectrometry was made. Group diagnostic proteins for M. incognita and M. arenaria (and for other proteins common to all isolates) were distinguished by protonated mass signals in the MALDI fingerprinting spectrum.

Animals↗
PubMedDate not supplied

Biophysical constraints for protein structure prediction.

Though highly desirable, neither a single experimental technique nor a computational approach can be sufficient enough to rationalize a protein structure. The incorporation of biophysical constraints, which can be rationalized based on conventional biophysical measurements, might lead to considerable improvement of the simulation procedures. In this regard, our analysis of 180 proteins in different conformational states allows prediction of the overall protein dimension based on the chain length, i.e., the protein molecular weight, with an accuracy of 10%.

Algorithms↗
PubMed2004

Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles.

Metabonomics can be viewed as the process of defining multivariate metabolic trajectories that describe the systemic response of organisms to physiological perturbations through time. We have explored the hypothesis that the homothetic geometry of a metabolic trajectory, i.e., the metabolic response irrespective of baseline values and overall magnitude, defines the mode of response of the organism to treatment and is hence the key property when considering the similarity between two sets of measurements. A modeling strategy to test for homothetic geometry, called scaled-to-maximum, aligned, and reduced trajectories (SMART) analysis, is presented that together with principal components analysis (PCA) facilitates the visualization of multivariate response similarity and hence the interpretation of metabonomic data. Several examples of the utility of this approach from toxicological studies are presented as follows: interlaboratory variation in hydrazine response, CCl(4) dose-response relationships, and interspecies comparison of bromobenzene toxicity. In each case, the homothetic trajectories hypothesis is shown to be an important concept for the successful multivariate modeling and interpretation of systemic metabolic change. Overall, geometric trajectory analysis based on a homothetic modeling strategy like SMART facilitates the amalgamation and comparison of metabonomic data sets and can improve the accuracy and precision of classification models based on metabolic profile data. Because interlaboratory variation, normal physiological variation, dose-response relationships, and interspecies differences are also key areas of concern in genomic and proteomic as well as metabonomic studies, the methods presented here may also have an impact on many other multilaboratory efforts to produce screenable "-omics" databases useful for gauging toxicity in safety assessment and drug discovery.

Animals↗
PubMed2000

Breadth plus depth.

Explore the source record for details and available documents.

Databases as Topic↗
PubMed2006

Modeling cellular machinery through biological network comparison.

Molecular networks represent the backbone of molecular activity within the cell. Recent studies have taken a comparative approach toward interpreting these networks, contrasting networks of different species and molecular types, and under varying conditions. In this review, we survey the field of comparative biological network analysis and describe its applications to elucidate cellular machinery and to predict protein function and interaction. We highlight the open problems in the field as well as propose some initial mathematical formulations for addressing them. Many of the methodological and conceptual advances that were important for sequence comparison will likely also be important at the network level, including improved search algorithms, techniques for multiple alignment, evolutionary models for similarity scoring and better integration with public databases.

Algorithms↗
PubMed2004

Toxicogenomics and systems toxicology: aims and prospects.

Toxicogenomics combines transcript, protein and metabolite profiling with conventional toxicology to investigate the interaction between genes and environmental stress in disease causation. The patterns of altered molecular expression that are caused by specific exposures or disease outcomes have revealed how several toxicants act and cause disease. Despite these success stories, the field faces noteworthy challenges in discriminating the molecular basis of toxicity. We argue that toxicology is gradually evolving into a systems toxicology that will eventually allow us to describe all the toxicological interactions that occur within a living system under stress and use our knowledge of toxicogenomic responses in one species to predict the modes-of-action of similar agents in other species.

Animals↗