Automated Intensity Descent Algorithm for Interpretation of Complex High-Resolution Mass Spectra

This paper describes a new automated intensity descent algorithm for analysis of complex high-resolution mass spectra. The algorithm has been successfully applied to interpret Fourier transform mass spectra of proteins; however, it should be generally applicable to complex high-resolution mass spectra of large molecules recorded by other instruments. The algorithm locates all possible isotopic clusters by a novel peak selection method and a robust cluster subtraction technique according to the order of descending peak intensity after global noise level estimation and baseline correction. The peak selection method speeds up charge state determination and isotopic cluster identification. A Lorentzian-based peak subtraction technique resolves overlapping clusters in high peak density regions. A noise flag value is introduced to minimize false positive isotopic clusters. Moreover, correlation coefficients and matching errors between the identified isotopic multiplets and the averagine isotopic abundance distribution are the criteria for real isotopic clusters. The best fitted averagine isotopic abundance distribution of each isotopic cluster determines the charge state and the monoisotopic mass. Three high-resolution mass spectra were interpreted by the program. The results show that the algorithm is fast in computational speed, robust in identification of overlapping clusters, and efficient in minimization of false positives. In ∼2 min, the program identified 611 isotopic clusters for a plasma ECD spectrum of carbonic anhydrase. Among them, 50 new identified isotopic clusters, which were missed previously by other methods, have been discovered in the high peak density regions or as weak clusters by this algorithm. As a result, 18 additional new bond cleavages have been identified from the 50 new clusters of carbonic anhydrase

Automated Intensity Descent Algorithm for Interpretation of Complex High-Resolution Mass Spectra

This paper describes a new automated intensity descent algorithm for analysis of complex high-resolution mass spectra. The algorithm has been successfully applied to interpret Fourier transform mass spectra of proteins; however, it should be generally applicable to complex high-resolution mass spectra of large molecules recorded by other instruments. The algorithm locates all possible isotopic clusters by a novel peak selection method and a robust cluster subtraction technique according to the order of descending peak intensity after global noise level estimation and baseline correction. The peak selection method speeds up charge state determination and isotopic cluster identification. A Lorentzian-based peak subtraction technique resolves overlapping clusters in high peak density regions. A noise flag value is introduced to minimize false positive isotopic clusters. Moreover, correlation coefficients and matching errors between the identified isotopic multiplets and the averagine isotopic abundance distribution are the criteria for real isotopic clusters. The best fitted averagine isotopic abundance distribution of each isotopic cluster determines the charge state and the monoisotopic mass. Three high-resolution mass spectra were interpreted by the program. The results show that the algorithm is fast in computational speed, robust in identification of overlapping clusters, and efficient in minimization of false positives. In ∼2 min, the program identified 611 isotopic clusters for a plasma ECD spectrum of carbonic anhydrase. Among them, 50 new identified isotopic clusters, which were missed previously by other methods, have been discovered in the high peak density regions or as weak clusters by this algorithm. As a result, 18 additional new bond cleavages have been identified from the 50 new clusters of carbonic anhydrase

Automated Intensity Descent Algorithm for Interpretation of Complex High-Resolution Mass Spectra

This paper describes a new automated intensity descent algorithm for analysis of complex high-resolution mass spectra. The algorithm has been successfully applied to interpret Fourier transform mass spectra of proteins; however, it should be generally applicable to complex high-resolution mass spectra of large molecules recorded by other instruments. The algorithm locates all possible isotopic clusters by a novel peak selection method and a robust cluster subtraction technique according to the order of descending peak intensity after global noise level estimation and baseline correction. The peak selection method speeds up charge state determination and isotopic cluster identification. A Lorentzian-based peak subtraction technique resolves overlapping clusters in high peak density regions. A noise flag value is introduced to minimize false positive isotopic clusters. Moreover, correlation coefficients and matching errors between the identified isotopic multiplets and the averagine isotopic abundance distribution are the criteria for real isotopic clusters. The best fitted averagine isotopic abundance distribution of each isotopic cluster determines the charge state and the monoisotopic mass. Three high-resolution mass spectra were interpreted by the program. The results show that the algorithm is fast in computational speed, robust in identification of overlapping clusters, and efficient in minimization of false positives. In ∼2 min, the program identified 611 isotopic clusters for a plasma ECD spectrum of carbonic anhydrase. Among them, 50 new identified isotopic clusters, which were missed previously by other methods, have been discovered in the high peak density regions or as weak clusters by this algorithm. As a result, 18 additional new bond cleavages have been identified from the 50 new clusters of carbonic anhydrase

Demographic Characteristics of the Patient Population Stratified by the Outcome Measures.

<p>All values are reported as: N(%), where N indicates the number of observations.</p>†<p>Values are expressed as: Mean (±standard deviation).</p

Proteomic Analysis of Temperature Dependent Extracellular Proteins from <i>Aspergillus fumigatus</i> Grown under Solid-State Culture Condition

Fungal species of the genus <i>Aspergillus</i> are filamentous ubiquitous saprophytes that play a major role in lignocellulosic biomass recycling and also are considered as cell factories for the production of organic acids, pharmaceuticals, and industrially important enzymes. Analysis of extracellular secreted biomass degrading enzymes using complex lignocellulosic biomass as a substrate by solid-state fermentation could be a more practical approach to evaluate application of the enzymes for lignocellulosic biorefinery. This study isolated a fungal strain from compost, identified as <i>Aspergillus fumigatus</i>, and further analyzed it for lignocellulolytic enzymes at different temperatures using label free quantitative proteomics. The profile of secretome composition discovered cellulases, hemicellulases, lignin degrading proteins, peptidases and proteases, and transport and hypothetical proteins; while protein abundances and further their hierarchical clustering analysis revealed temperature dependent expression of these enzymes during solid-state fermentation of sawdust. The enzyme activities and protein abundances as determined by exponentially modified protein abundance index (emPAI) indicated the maximum activities at the range of 40–50 °C, demonstrating the thermophilic nature of the isolate <i>A. fumigatus</i> LF9. Characterization of the thermostability of secretome suggested the potential of the isolated fungal strain in the production of thermophilic biomass degrading enzymes for industrial application

Schematic representation of the experimental design. ERLIC, electrostatic repulsion hydrophilic interaction chromatography.

<p>Schematic representation of the experimental design. ERLIC, electrostatic repulsion hydrophilic interaction chromatography.</p

Proteomic Analysis of Temperature Dependent Extracellular Proteins from <i>Aspergillus fumigatus</i> Grown under Solid-State Culture Condition

Fungal species of the genus <i>Aspergillus</i> are filamentous ubiquitous saprophytes that play a major role in lignocellulosic biomass recycling and also are considered as cell factories for the production of organic acids, pharmaceuticals, and industrially important enzymes. Analysis of extracellular secreted biomass degrading enzymes using complex lignocellulosic biomass as a substrate by solid-state fermentation could be a more practical approach to evaluate application of the enzymes for lignocellulosic biorefinery. This study isolated a fungal strain from compost, identified as <i>Aspergillus fumigatus</i>, and further analyzed it for lignocellulolytic enzymes at different temperatures using label free quantitative proteomics. The profile of secretome composition discovered cellulases, hemicellulases, lignin degrading proteins, peptidases and proteases, and transport and hypothetical proteins; while protein abundances and further their hierarchical clustering analysis revealed temperature dependent expression of these enzymes during solid-state fermentation of sawdust. The enzyme activities and protein abundances as determined by exponentially modified protein abundance index (emPAI) indicated the maximum activities at the range of 40–50 °C, demonstrating the thermophilic nature of the isolate <i>A. fumigatus</i> LF9. Characterization of the thermostability of secretome suggested the potential of the isolated fungal strain in the production of thermophilic biomass degrading enzymes for industrial application

Discovery of Prognostic Biomarker Candidates of Lacunar Infarction by Quantitative Proteomics of Microvesicles Enriched Plasma

<div><p>Background</p><p>Lacunar infarction (LACI) is a subtype of acute ischemic stroke affecting around 25% of all ischemic stroke cases. Despite having an excellent recovery during acute phase, certain LACI patients have poor mid- to long-term prognosis due to the recurrence of vascular events or a decline in cognitive functions. Hence, blood-based biomarkers could be complementary prognostic and research tools.</p><p>Methods and Finding</p><p>Plasma was collected from forty five patients following a non-disabling LACI along with seventeen matched control subjects. The LACI patients were monitored prospectively for up to five years for the occurrence of adverse outcomes and grouped accordingly (i.e., LACI-no adverse outcome, LACI-recurrent vascular event, and LACI-cognitive decline without any recurrence of vascular events). Microvesicles-enriched fractions isolated from the pooled plasma of four groups were profiled by an iTRAQ-guided discovery approach to quantify the differential proteome. The data have been deposited to the ProteomeXchange with identifier PXD000748. Bioinformatics analysis and data mining revealed up-regulation of brain-specific proteins including myelin basic protein, proteins of coagulation cascade (e.g., fibrinogen alpha chain, fibrinogen beta chain) and focal adhesion (e.g., integrin alpha-IIb, talin-1, and filamin-A) while albumin was down-regulated in both groups of patients with adverse outcome.</p><p>Conclusion</p><p>This data set may offer important insight into the mechanisms of poor prognosis and provide candidate prognostic biomarkers for validation on larger cohort of individual LACI patients.</p></div

Proteomic Analysis of Temperature Dependent Extracellular Proteins from <i>Aspergillus fumigatus</i> Grown under Solid-State Culture Condition

Fungal species of the genus Aspergillus are filamentous ubiquitous saprophytes that play a major role in lignocellulosic biomass recycling and also are considered as cell factories for the production of organic acids, pharmaceuticals, and industrially important enzymes. Analysis of extracellular secreted biomass degrading enzymes using complex lignocellulosic biomass as a substrate by solid-state fermentation could be a more practical approach to evaluate application of the enzymes for lignocellulosic biorefinery. This study isolated a fungal strain from compost, identified as Aspergillus fumigatus, and further analyzed it for lignocellulolytic enzymes at different temperatures using label free quantitative proteomics. The profile of secretome composition discovered cellulases, hemicellulases, lignin degrading proteins, peptidases and proteases, and transport and hypothetical proteins; while protein abundances and further their hierarchical clustering analysis revealed temperature dependent expression of these enzymes during solid-state fermentation of sawdust. The enzyme activities and protein abundances as determined by exponentially modified protein abundance index (emPAI) indicated the maximum activities at the range of 40–50 °C, demonstrating the thermophilic nature of the isolate A. fumigatus LF9. Characterization of the thermostability of secretome suggested the potential of the isolated fungal strain in the production of thermophilic biomass degrading enzymes for industrial application

Proteomic Analysis of Temperature Dependent Extracellular Proteins from <i>Aspergillus fumigatus</i> Grown under Solid-State Culture Condition

Fungal species of the genus <i>Aspergillus</i> are filamentous ubiquitous saprophytes that play a major role in lignocellulosic biomass recycling and also are considered as cell factories for the production of organic acids, pharmaceuticals, and industrially important enzymes. Analysis of extracellular secreted biomass degrading enzymes using complex lignocellulosic biomass as a substrate by solid-state fermentation could be a more practical approach to evaluate application of the enzymes for lignocellulosic biorefinery. This study isolated a fungal strain from compost, identified as <i>Aspergillus fumigatus</i>, and further analyzed it for lignocellulolytic enzymes at different temperatures using label free quantitative proteomics. The profile of secretome composition discovered cellulases, hemicellulases, lignin degrading proteins, peptidases and proteases, and transport and hypothetical proteins; while protein abundances and further their hierarchical clustering analysis revealed temperature dependent expression of these enzymes during solid-state fermentation of sawdust. The enzyme activities and protein abundances as determined by exponentially modified protein abundance index (emPAI) indicated the maximum activities at the range of 40–50 °C, demonstrating the thermophilic nature of the isolate <i>A. fumigatus</i> LF9. Characterization of the thermostability of secretome suggested the potential of the isolated fungal strain in the production of thermophilic biomass degrading enzymes for industrial application