PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Bioinformatics”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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

At least 757 records · Page 42Linked to original sources

Bioinformatics of granzymes: sequence comparison and structural studies on granzyme family by homology modeling.

Cytotoxic lymphocytes (CTLs), the key players of cell mediated immunity, induce apoptosis by engaging death receptors or through exocytosis of cytolytic granules containing granzyme (proteases) and pore-forming protein (perforin). The crystal structure of granzyme B from human (B(h)) and rat (B(r)), as well as that of pro-granzyme K (K(h)) has been reported recently. In the present communication, we describe the homology modeling of granzyme family (in particular Gzm A(h), M(h), B(m), and C(m) from human and mouse) based on the crystal structural coordinates of trypsin, granzyme K (K(h)), and granzyme B (B(h)). These models have been used for establishing phylogenetic relationship as well as identifying characteristic features for designing specific inhibitors. The paper also highlights key residues at the S1, S2, and S2(') binding subsites in all granzyme, which may be involved in the structure-function relationship of this enzyme family. The predicted 3D homology models show a conserved two similar domain structure, i.e., an N-terminal domain and a C-terminal domain comprising predominantly of beta-sheet structure with a little alpha-helical content. Micro-heterogeneities have been observed in the vicinity of the active site in all granzymes as compared to granzyme B(h). For example, in granzyme M(h), valine is present at the S1 subsite instead of arginine. Similarly differences at S2 (Leu-->Phe), S3 (Ser-->Gly), and S4 (Arg-->Asn) subsites are quite apparent and appear to hold the potential for selective designing of inhibitors for possible therapeutic applications. Furthermore, analysis of the electrostatic surface potential on the shape of granzyme-inhibitor binding groove reveals clear differences at the reactive site. Additionally the different posttranslational modification sites such as phosphorylation (e.g., in granzyme M Thr101, Ser109), myristoylation (Gly22, 117, and 131), and glycosylation (Ser160) have been identified, as very little is known about the functional significance of these modifications in the granzyme family. Thus, glycosylation at Ser160 in granzyme M may influence the net charge of the enzyme, resulting in altered substrate binding as compared to granzyme B. Also this modification may influence the rate of complexation and binding affinity with proteoglycans. These studies are expected to contribute towards the basic understanding of functional associations of the granzymes with other molecules and their possible role in apoptosis.

Amino Acid Sequence↗

Proposed lipocalin fold for apolipoprotein M based on bioinformatics and site-directed mutagenesis.

Apolipoprotein M (apoM) is a novel apolipoprotein that is predominantly present in high-density lipoprotein. Sensitive sequence searches, threading and comparative model building experiments revealed apoM to be structurally related to the lipocalin protein family. In a 3D model, characterized by an eight-stranded anti-parallel beta-barrel, a segment including Asn135 could adopt a closed or open conformation. Using site-directed mutagenesis, we demonstrated Asn135 in wild-type apoM to be glycosylated, suggesting that the segment is solvent exposed. ApoM displays two strong acidic patches of potential functional importance, one around the N-terminus and the other next to the opening of the beta-barrel.

Amino Acid Sequence↗

Architecture of the herpes simplex virus major capsid protein derived from structural bioinformatics.

The dispositions of 39 alpha helices of greater than 2.5 turns and four beta sheets in the major capsid protein (VP5, 149 kDa) of herpes simplex virus type 1 were identified by computational and visualization analysis from the 8.5A electron cryomicroscopy structure of the whole capsid. The assignment of helices in the VP5 upper domain was validated by comparison with the recently determined crystal structure of this region. Analysis of the spatial arrangement of helices in the middle domain of VP5 revealed that the organization of a tightly associated bundle of ten helices closely resembled that of a domain fold found in the annexin family of proteins. Structure-based sequence searches suggested that sequences in both the N and C-terminal portions of the VP5 sequence contribute to this domain. The long helices seen in the floor domain of VP5 form an interconnected network within and across capsomeres. The combined structural and sequence-based informatics has led to an architectural model of VP5. This model placed in the context of the capsid provides insights into the strategies used to achieve viral capsid stability.

Amino Acid Motifs↗

Bioinformatic methods to exploit mass spectrometric data for proteomic applications.

The new technologies in mass spectrometric analysis of peptides and proteins necessary to accommodate proteomics-scale analyses require, in turn, concomitant development of informatics technologies suitable for very large-scale data handling and analysis. This chapter focuses on the data analysis tools available to the community for analysis of mass spectrometric proteomics data. Different database searching strategies are discussed for peptide and protein identification, and approaches and tools available for comparative quantitative analysis of samples are outlined.

Animals↗

Bioconductor: an open source framework for bioinformatics and computational biology.

This chapter describes the Bioconductor project and details of its open source facilities for analysis of microarray and other high-throughput biological experiments. Particular attention is paid to concepts of container and workflow design, connections of biological metadata to statistical analysis products, support for statistical quality assessment, and calibration of inference uncertainty measures when tens of thousands of simultaneous statistical tests are performed.

Animals↗

Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics.

The goal of this chapter is to introduce some of the available computational methods for expression analysis. Genomic and proteomic experimental techniques are briefly discussed to help the reader understand these methods and results better in context with the biological significance. Furthermore, a case study is presented that will illustrate the use of these analytical methods to extract significant biomarkers from high-throughput microarray data. Genomic and proteomic data analysis is essential for understanding the underlying factors that are involved in human disease. Currently, such experimental data are generally obtained by high-throughput microarray or mass spectrometry technologies among others. The sheer amount of raw data obtained using these methods warrants specialized computational methods for data analysis. Biomarker discovery for neurological diagnosis and prognosis is one such example. By extracting significant genomic and proteomic biomarkers in controlled experiments, we come closer to understanding how biological mechanisms contribute to neural degenerative diseases such as Alzheimers' and how drug treatments interact with the nervous system. In the biomarker discovery process, there are several computational methods that must be carefully considered to accurately analyze genomic or proteomic data. These methods include quality control, clustering, classification, feature ranking, and validation. Data quality control and normalization methods reduce technical variability and ensure that discovered biomarkers are statistically significant. Preprocessing steps must be carefully selected since they may adversely affect the results of the following expression analysis steps, which generally fall into two categories: unsupervised and supervised. Unsupervised or clustering methods can be used to group similar genomic or proteomic profiles and therefore can elucidate relationships within sample groups. These methods can also assign biomarkers to sub-groups based on their expression profiles across patient samples. Although clustering is useful for exploratory analysis, it is limited due to its inability to incorporate expert knowledge. On the other hand, classification and feature ranking are supervised, knowledge-based machine learning methods that estimate the distribution of biological expression data and, in doing so, can extract important information about these experiments. Classification is closely coupled with feature ranking, which is essentially a data reduction method that uses classification error estimation or other statistical tests to score features. Biomarkers can subsequently be extracted by eliminating insignificantly ranked features. These analytical methods may be equally applied to genetic and proteomic data. However, because of both biological differences between the data sources and technical differences between the experimental methods used to obtain these data, it is important to have a firm understanding of the data sources and experimental methods. At the same time, regardless of the data quality, it is inevitable that some discovered biomarkers are false positives. Thus, it is important to validate discovered biomarkers. The validation process may be slow; yet, the overall biomarker discovery process is significantly accelerated due to initial feature ranking and data reduction steps. Information obtained from the validation process may also be used to refine data analysis procedures for future iteration. Biomarker validation may be performed in a number of ways - bench-side in traditional labs, web-based electronic resources such as gene ontology and literature databases, and clinical trials.

Animals↗