High resolution mass spectrometry in molecular structure and stereochemical studies - Effect of stereochemistry on the fragmentation of epimeric derivatives of azabicycloalkanes

High resolution mass spectrometry in studies of stereochemistry effect on fragmentation of epimeric derivatives of azabicycloalkane

Experiment requirements: Vitamin D metabolites and bone demineralization, Spacelab 2, experiment no. 1

As a contribution toward an understanding of the molecular basis of bone loss, mineral imbalance, and increasing fecal calcium under conditions of prolonged space flight, the blood levels of biologically active vitamin D metabolites of flight crew members will be quantitatively measured. Prior to the mission, the refinement of existing and the development of new techniques for the assay of all vitamin D metabolites will provide an arsenal of methods suitable for a wide range of metabolite levels. In terms of practical application, the analysis of human and animal plasma samples, Spacelab crew plasma samples, and flight hardware are envisioned

The isolation and structural elucidation of voacristine hydroxyindolenine

Isolation and characterization of voacristine hydroxyindolenin

The organic geochemistry of ancient sediments, part II

Chemical analysis of sediment and oil hydrocarbon content by gas chromatography and mass spectrometry to establish inception period of bio-organic evolutio

AntiFam: a tool to help identify spurious ORFs in protein annotation

As the deluge of genomic DNA sequence grows the fraction of protein sequences that have been manually curated falls. In turn, as the number of laboratories with the ability to sequence genomes in a high-throughput manner grows, the informatics capability of those labs to accurately identify and annotate all genes within a genome may often be lacking. These issues have led to fears about transitive annotation errors making sequence databases less reliable. During the lifetime of the Pfam protein families database a number of protein families have been built, which were later identified as composed solely of spurious open reading frames (ORFs) either on the opposite strand or in a different, overlapping reading frame with respect to the true protein-coding or non-coding RNA gene. These families were deleted and are no longer available in Pfam. However, we realized that these may perform a useful function to identify new spurious ORFs. We have collected these families together in AntiFam along with additional custom-made families of spurious ORFs. This resource currently contains 23 families that identified 1310 spurious proteins in UniProtKB and a further 4119 spurious proteins in a collection of metagenomic sequences. UniProt has adopted AntiFam as a part of the UniProtKB quality control process and will investigate these spurious proteins for exclusion

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs which represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.Comment: 34 pages, 7 figure

Re-Annotation Is an Essential Step in Systems Biology Modeling of Functional Genomics Data

One motivation of systems biology research is to understand gene functions and interactions from functional genomics data such as that derived from microarrays. Up-to-date structural and functional annotations of genes are an essential foundation of systems biology modeling. We propose that the first essential step in any systems biology modeling of functional genomics data, especially for species with recently sequenced genomes, is gene structural and functional re-annotation. To demonstrate the impact of such re-annotation, we structurally and functionally re-annotated a microarray developed, and previously used, as a tool for disease research. We quantified the impact of this re-annotation on the array based on the total numbers of structural- and functional-annotations, the Gene Annotation Quality (GAQ) score, and canonical pathway coverage. We next quantified the impact of re-annotation on systems biology modeling using a previously published experiment that used this microarray. We show that re-annotation improves the quantity and quality of structural- and functional-annotations, allows a more comprehensive Gene Ontology based modeling, and improves pathway coverage for both the whole array and a differentially expressed mRNA subset. Our results also demonstrate that re-annotation can result in a different knowledge outcome derived from previous published research findings. We propose that, because of this, re-annotation should be considered to be an essential first step for deriving value from functional genomics data

Accurate Protein Structure Annotation through Competitive Diffusion of Enzymatic Functions over a Network of Local Evolutionary Similarities

High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor and 5-fold lower than sequence-based annotations. In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. A web server is at http://mammoth.bcm.tmc.edu/networks

Testing the Ortholog Conjecture with Comparative Functional Genomic Data from Mammals

A common assumption in comparative genomics is that orthologous genes share greater functional similarity than do paralogous genes (the “ortholog conjecture”). Many methods used to computationally predict protein function are based on this assumption, even though it is largely untested. Here we present the first large-scale test of the ortholog conjecture using comparative functional genomic data from human and mouse. We use the experimentally derived functions of more than 8,900 genes, as well as an independent microarray dataset, to directly assess our ability to predict function using both orthologs and paralogs. Both datasets show that paralogs are often a much better predictor of function than are orthologs, even at lower sequence identities. Among paralogs, those found within the same species are consistently more functionally similar than those found in a different species. We also find that paralogous pairs residing on the same chromosome are more functionally similar than those on different chromosomes, perhaps due to higher levels of interlocus gene conversion between these pairs. In addition to offering implications for the computational prediction of protein function, our results shed light on the relationship between sequence divergence and functional divergence. We conclude that the most important factor in the evolution of function is not amino acid sequence, but rather the cellular context in which proteins act