Harvest: an open-source tool for the validation and improvement of peptide identification metrics and fragmentation exploration

<p>Abstract</p> <p>Background</p> <p>Protein identification using mass spectrometry is an important tool in many areas of the life sciences, and in proteomics research in particular. Increasing the number of proteins correctly identified is dependent on the ability to include new knowledge about the mass spectrometry fragmentation process, into computational algorithms designed to separate true matches of peptides to unidentified mass spectra from spurious matches. This discrimination is achieved by computing a function of the various features of the potential match between the observed and theoretical spectra to give a numerical approximation of their similarity. It is these underlying "metrics" that determine the ability of a protein identification package to maximise correct identifications while limiting false discovery rates. There is currently no software available specifically for the simple implementation and analysis of arbitrary novel metrics for peptide matching and for the exploration of fragmentation patterns for a given dataset.</p> <p>Results</p> <p>We present Harvest: an open source software tool for analysing fragmentation patterns and assessing the power of a new piece of information about the MS/MS fragmentation process to more clearly differentiate between correct and random peptide assignments. We demonstrate this functionality using data metrics derived from the properties of individual datasets in a peptide identification context. Using Harvest, we demonstrate how the development of such metrics may improve correct peptide assignment confidence in the context of a high-throughput proteomics experiment and characterise properties of peptide fragmentation.</p> <p>Conclusions</p> <p>Harvest provides a simple framework in C++ for analysing and prototyping metrics for peptide matching, the core of the protein identification problem. It is not a protein identification package and answers a different research question to packages such as Sequest, Mascot, X!Tandem, and other protein identification packages. It does not aim to maximise the number of assigned peptides from a set of unknown spectra, but instead provides a method by which researchers can explore fragmentation properties and assess the power of novel metrics for peptide matching in the context of a given experiment. Metrics developed using Harvest may then become candidates for later integration into protein identification packages.</p

Regulation of the Mitogen Activated Protein Kinase Kinase (MEK)-1 by NAD-Dependent Deacetylases

Sirtuins are class III deacetylases that regulate many essential processes, including cellular stress, genome stability, and metabolism. Although these NAD+-dependent deacetylases control adaptive cellular responses, identification of sirtuin-regulated signaling targets remain under-studied. Here, we demonstrate that acetylation of the mitogen-activated protein kinase kinase-1 (MEK1) stimulates its kinase activity, and that acetylated MEK1 is under the regulatory control of the sirtuin family members SIRT1 and SIRT2. Treatment of cells with sirtuin inhibitors, or siRNA knockdown of SIRT1 or SIRT2 proteins, increases MEK1 acetylation and subsequent phosphorylation of the extracellular signal-regulated kinase (ERK). Generation of an acetyl-specific MEK1 antibody demonstrates that endogenous acetylated MEK1 is extensively enriched in the nucleus following epidermal growth factor (EGF) stimulation. An acetyl-mimic of MEK1 increases inappropriate growth properties, suggesting that acetylation of MEK1 has oncogenic potential

Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology

Biotic and abiotic retention, recycling and remineralization of metals in the ocean

Trace metals shape both the biogeochemical functioning and biological structure of oceanic provinces. Trace metal biogeochemistry has primarily focused on modes of external supply of metals from aeolian, hydrothermal, sedimentary and other sources. However, metals also undergo internal transformations such as abiotic and biotic retention, recycling and remineralization. The role of these internal transformations in metal biogeochemical cycling is now coming into focus. First, the retention of metals by biota in the surface ocean for days, weeks or months depends on taxon-specific metal requirements of phytoplankton, and on their ultimate fate: that is, viral lysis, senescence, grazing and/or export to depth. Rapid recycling of metals in the surface ocean can extend seasonal productivity by maintaining higher levels of metal bioavailability compared to the influence of external metal input alone. As metal-containing organic particles are exported from the surface ocean, different metals exhibit distinct patterns of remineralization with depth. These patterns are mediated by a wide range of physicochemical and microbial processes such as the ability of particles to sorb metals, and are influenced by the mineral and organic characteristics of sinking particles. We conclude that internal metal transformations play an essential role in controlling metal bioavailability, phytoplankton distributions and the subsurface resupply of metals

A proteomics approach to decipher the molecular nature of planarian stem cells

Background In recent years, planaria have emerged as an important model system for research into stem cells and regeneration. Attention is focused on their unique stem cells, the neoblasts, which can differentiate into any cell type present in the adult organism. Sequencing of the Schmidtea mediterranea genome and some expressed sequence tag projects have generated extensive data on the genetic profile of these cells. However, little information is available on their protein dynamics. Results We developed a proteomic strategy to identify neoblast-specific proteins. Here we describe the method and discuss the results in comparison to the genomic high-throughput analyses carried out in planaria and to proteomic studies using other stem cell systems. We also show functional data for some of the candidate genes selected in our proteomic approach. Conclusions We have developed an accurate and reliable mass-spectra-based proteomics approach to complement previous genomic studies and to further achieve a more accurate understanding and description of the molecular and cellular processes related to the neoblasts

Ultrastructural pattern of loricrin localization in the epidermis of the red kangaroo Macropus rufus

The ultrastructural immunolocalization of loricrin, a major protein of the cell corneous envelope is known for the epidermis of monotremes and placentalmammals, but not in the soft epidermis ofmarsupials. This information would allow a definitive generalization on the role of loricrin in the process of keratinization in mammalian epidermis. The present study on the hairy epidermis of the red kangaroo has shown that the immunolabelling for this protein is diffuse in the upper spinosus layer and is peripherally associated with keratohyalin granules in the granular layer. Immunolabelling, however, is poor or absent in the darker part of keratohyalin granules which likely contains pro-filaggrin and filaggrin. In transitional and corneous cells, loricrin concentrates along the forming cell corneous envelope as in the hairy epidermis of other mammals. However, in the softer part of the corneous layer, rich in lipids, the labelling tends to disappear. Loricrin therefore appears to be connected with the strengthening of the cell corneous envelope of mature corneocytes, but becomes poorly detected in soft corneocytes. Based on the present and previous information, the distribution of loricrin in the epidermis of different mammals and in the epidermis of birds and reptiles is discussed