ELM: the status of the 2010 eukaryotic linear motif resource

Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a ‘Bar Code’ format, which also displays known instances from homologous proteins through a novel ‘Instance Mapper’ protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation

PM source apportionment by Positive Matrix Factorization (PMF) using an extended aerosol chemical characterization including specific molecular markers

Airborne PM pollution has emerged out as a critical issue all across the world. Quantitative and qualitative source analysis is becoming imperative to imply effective emission control strategies to reduce ambient air pollutants. Receptor oriented models, based on the statistical approach, have been developed to analyze various characteristics of the pollutants measured at the receptor site and to estimate their contributions to the source. Among the multivariate statistical receptor models used for PM source apportionment, Positive Matrix Factorization (PMF) has been adopted world wide as one of the most convenient technique. PMF has a non negative constraint and is able to quantify the factor contribution directly without a subsequent use of multiple regression analysis. More than 40% of European source apportionment studies have applied PMF (Belis et al. 2013). Recent advancements have proposed the use of new organic molecular markers in PMF to better investigate the contribution of biogenic and/or secondary organic aerosols. It has been observed that the use of these compounds improves the efficacy of PM source apportionment (Waked et al. 2014). The main objective of this study was to apportion specific PM10 sources, by using a wide variety of such organic molecular markers as PMF input data, for samples collected at an urban station “Les Frenes” of a local air quality network (Air Rhône-Alpes), considered as representative of a densely populated urban area Grenoble (France). PM10 samples were collected every third day (24 h-basis sampling) on quartz filters over a one year period (2013) and extended chemical characterization was performed including the quantification of species such as OC/EC, ions/cations (Na+, Mg2+, NH4+, Cl-, SO42-, NO3-), Polycyclic Aromatic Hydrocarbon (PAH), oxy-PAH, nitro-PAH, polyols (arabitol, mannitol), Methane Sulfonic Acid (MSA), levoglucosan, sulfur-containing PAH (Benzo[b]naphtha[2,1-d]thiophene, BNT), oxalate, higher odd number alkanes (C27, C29, C31), metals (Ba, Cu, Cr, Zn, Sb, Ni, V, Al, Ti, Fe, Mn, Rb, Ca, K). Results showed that the 10-factor profiles have given the best fit in the PMF analysis including biogenic emissions (marine, soil, plant debris), secondary inorganic (nitrate and sulfate factors) and organic aerosols, dust and aged sea salt particles and anthropogenic sources (oil combustion, traffic exhaust, biomass burning, industry…) (Figure 1). The highest percentage contribution to PM is made by secondary inorganic aerosol (~20%). It is interesting to note that Secondary PAH-aerosol factor accounts for ~ 4%. Discussion will further underline the factor contribution on seasonal basis and the stability of the chosen solution

PM source apportionment by Positive Matrix Factorization (PMF) using an extended aerosol chemical characterization including specific molecular markers

Airborne PM pollution has emerged out as a critical issue all across the world. Quantitative and qualitative source analysis is becoming imperative to imply effective emission control strategies to reduce ambient air pollutants. Receptor oriented models, based on the statistical approach, have been developed to analyze various characteristics of the pollutants measured at the receptor site and to estimate their contributions to the source. Among the multivariate statistical receptor models used for PM source apportionment, Positive Matrix Factorization (PMF) has been adopted world wide as one of the most convenient technique. PMF has a non negative constraint and is able to quantify the factor contribution directly without a subsequent use of multiple regression analysis. More than 40% of European source apportionment studies have applied PMF (Belis et al. 2013). Recent advancements have proposed the use of new organic molecular markers in PMF to better investigate the contribution of biogenic and/or secondary organic aerosols. It has been observed that the use of these compounds improves the efficacy of PM source apportionment (Waked et al. 2014). The main objective of this study was to apportion specific PM10 sources, by using a wide variety of such organic molecular markers as PMF input data, for samples collected at an urban station “Les Frenes” of a local air quality network (Air Rhône-Alpes), considered as representative of a densely populated urban area Grenoble (France). PM10 samples were collected every third day (24 h-basis sampling) on quartz filters over a one year period (2013) and extended chemical characterization was performed including the quantification of species such as OC/EC, ions/cations (Na+, Mg2+, NH4+, Cl-, SO42-, NO3-), Polycyclic Aromatic Hydrocarbon (PAH), oxy-PAH, nitro-PAH, polyols (arabitol, mannitol), Methane Sulfonic Acid (MSA), levoglucosan, sulfur-containing PAH (Benzo[b]naphtha[2,1-d]thiophene, BNT), oxalate, higher odd number alkanes (C27, C29, C31), metals (Ba, Cu, Cr, Zn, Sb, Ni, V, Al, Ti, Fe, Mn, Rb, Ca, K). Results showed that the 10-factor profiles have given the best fit in the PMF analysis including biogenic emissions (marine, soil, plant debris), secondary inorganic (nitrate and sulfate factors) and organic aerosols, dust and aged sea salt particles and anthropogenic sources (oil combustion, traffic exhaust, biomass burning, industry…) (Figure 1). The highest percentage contribution to PM is made by secondary inorganic aerosol (~20%). It is interesting to note that Secondary PAH-aerosol factor accounts for ~ 4%. Discussion will further underline the factor contribution on seasonal basis and the stability of the chosen solution

ELM server: a new resource for investigating short functional sites in modular eukaryotic proteins

Multidomain proteins predominate in eukaryotic proteomes. Individual functions assigned to different sequence segments combine to create a complex function for the whole protein. While on-line resources are available for revealing globular domains in sequences, there has hitherto been no comprehensive collection of small functional sites/motifs comparable to the globular domain resources, yet these are as important for the function of multidomain proteins. Short linear peptide motifs are used for cell compartment targeting, protein–protein interaction, regulation by phosphorylation, acetylation, glycosylation and a host of other post-translational modifications. ELM, the Eukaryotic Linear Motif server at http://elm.eu.org/, is a new bioinformatics resource for investigating candidate short non-globular functional motifs in eukaryotic proteins, aiming to fill the void in bioinformatics tools. Sequence comparisons with short motifs are difficult to evaluate because the usual significance assessments are inappropriate. Therefore the server is implemented with several logical filters to eliminate false positives. Current filters are for cell compartment, globular domain clash and taxonomic range. In favourable cases, the filters can reduce the number of retained matches by an order of magnitude or more