Coupling a Detergent Lysis/Cleanup Methodology with Intact Protein Fractionation for Enhanced Proteome Characterization

The expanding use of surfactants for proteome sample preparations has prompted the need to systematically optimize the application and removal of these MS-deleterious agents prior to proteome measurements. Here we compare four detergent cleanup methods (trichloroacetic acid (TCA) precipitation, chloroform/methanol/water (CMW) extraction, a commercial detergent removal spin column method (DRS) and filter-aided sample preparation (FASP)) to provide efficiency benchmarks with respect to protein, peptide, and spectral identifications in each case. Our results show that for protein-limited samples, FASP outperforms the other three cleanup methods, while at high protein amounts, all the methods are comparable. This information was used to investigate and contrast molecular weight-based fractionated with unfractionated lysates from three increasingly complex samples (Escherichia coli K-12, a five microbial isolate mixture, and a natural microbial community groundwater sample), all of which were prepared with an SDS-FASP approach. The additional fractionation step enhanced the number of protein identifications by 8% to 25% over the unfractionated approach across the three samples

Coupling a Detergent Lysis/Cleanup Methodology with Intact Protein Fractionation for Enhanced Proteome Characterization

The expanding use of surfactants for proteome sample preparations has prompted the need to systematically optimize the application and removal of these MS-deleterious agents prior to proteome measurements. Here we compare four detergent cleanup methods (trichloroacetic acid (TCA) precipitation, chloroform/methanol/water (CMW) extraction, a commercial detergent removal spin column method (DRS) and filter-aided sample preparation (FASP)) to provide efficiency benchmarks with respect to protein, peptide, and spectral identifications in each case. Our results show that for protein-limited samples, FASP outperforms the other three cleanup methods, while at high protein amounts, all the methods are comparable. This information was used to investigate and contrast molecular weight-based fractionated with unfractionated lysates from three increasingly complex samples (Escherichia coli K-12, a five microbial isolate mixture, and a natural microbial community groundwater sample), all of which were prepared with an SDS-FASP approach. The additional fractionation step enhanced the number of protein identifications by 8% to 25% over the unfractionated approach across the three samples

Coupling a Detergent Lysis/Cleanup Methodology with Intact Protein Fractionation for Enhanced Proteome Characterization

The expanding use of surfactants for proteome sample preparations has prompted the need to systematically optimize the application and removal of these MS-deleterious agents prior to proteome measurements. Here we compare four detergent cleanup methods (trichloroacetic acid (TCA) precipitation, chloroform/methanol/water (CMW) extraction, a commercial detergent removal spin column method (DRS) and filter-aided sample preparation (FASP)) to provide efficiency benchmarks with respect to protein, peptide, and spectral identifications in each case. Our results show that for protein-limited samples, FASP outperforms the other three cleanup methods, while at high protein amounts, all the methods are comparable. This information was used to investigate and contrast molecular weight-based fractionated with unfractionated lysates from three increasingly complex samples (Escherichia coli K-12, a five microbial isolate mixture, and a natural microbial community groundwater sample), all of which were prepared with an SDS-FASP approach. The additional fractionation step enhanced the number of protein identifications by 8% to 25% over the unfractionated approach across the three samples

Coupling a Detergent Lysis/Cleanup Methodology with Intact Protein Fractionation for Enhanced Proteome Characterization

The expanding use of surfactants for proteome sample preparations has prompted the need to systematically optimize the application and removal of these MS-deleterious agents prior to proteome measurements. Here we compare four detergent cleanup methods (trichloroacetic acid (TCA) precipitation, chloroform/methanol/water (CMW) extraction, a commercial detergent removal spin column method (DRS) and filter-aided sample preparation (FASP)) to provide efficiency benchmarks with respect to protein, peptide, and spectral identifications in each case. Our results show that for protein-limited samples, FASP outperforms the other three cleanup methods, while at high protein amounts, all the methods are comparable. This information was used to investigate and contrast molecular weight-based fractionated with unfractionated lysates from three increasingly complex samples (Escherichia coli K-12, a five microbial isolate mixture, and a natural microbial community groundwater sample), all of which were prepared with an SDS-FASP approach. The additional fractionation step enhanced the number of protein identifications by 8% to 25% over the unfractionated approach across the three samples

Coupling a Detergent Lysis/Cleanup Methodology with Intact Protein Fractionation for Enhanced Proteome Characterization

The expanding use of surfactants for proteome sample preparations has prompted the need to systematically optimize the application and removal of these MS-deleterious agents prior to proteome measurements. Here we compare four detergent cleanup methods (trichloroacetic acid (TCA) precipitation, chloroform/methanol/water (CMW) extraction, a commercial detergent removal spin column method (DRS) and filter-aided sample preparation (FASP)) to provide efficiency benchmarks with respect to protein, peptide, and spectral identifications in each case. Our results show that for protein-limited samples, FASP outperforms the other three cleanup methods, while at high protein amounts, all the methods are comparable. This information was used to investigate and contrast molecular weight-based fractionated with unfractionated lysates from three increasingly complex samples (Escherichia coli K-12, a five microbial isolate mixture, and a natural microbial community groundwater sample), all of which were prepared with an SDS-FASP approach. The additional fractionation step enhanced the number of protein identifications by 8% to 25% over the unfractionated approach across the three samples

Coupling a Detergent Lysis/Cleanup Methodology with Intact Protein Fractionation for Enhanced Proteome Characterization

The expanding use of surfactants for proteome sample preparations has prompted the need to systematically optimize the application and removal of these MS-deleterious agents prior to proteome measurements. Here we compare four detergent cleanup methods (trichloroacetic acid (TCA) precipitation, chloroform/methanol/water (CMW) extraction, a commercial detergent removal spin column method (DRS) and filter-aided sample preparation (FASP)) to provide efficiency benchmarks with respect to protein, peptide, and spectral identifications in each case. Our results show that for protein-limited samples, FASP outperforms the other three cleanup methods, while at high protein amounts, all the methods are comparable. This information was used to investigate and contrast molecular weight-based fractionated with unfractionated lysates from three increasingly complex samples (Escherichia coli K-12, a five microbial isolate mixture, and a natural microbial community groundwater sample), all of which were prepared with an SDS-FASP approach. The additional fractionation step enhanced the number of protein identifications by 8% to 25% over the unfractionated approach across the three samples

Dynamic control of NFV forwarding graphs with end-to-end deadline constraints

There is a strong industrial drive to use cloud computing technologies and concepts for providing timing sensitive services in the networking domain since it would provide the means to share the physical resources among multiple users and thus increase the elasticity and reduce the costs. In this work, we develop a mathematical model for user-stateless virtual network functions forming a forwarding graph. The model captures uncertainties of the performance of these virtual resources as well as the time-overhead needed to instantiate them. The model is used to derive a service controller for horizontal scaling of the virtual resources as well as an admission controller that guarantees that packets exiting the forwarding graph meet their end-to-end deadline. The Automatic Service and Admission Controller (AutoSAC) developed in this work uses feedback and feedforward making it robust against uncertainties of the underlying infrastructure. Also, it has a fast reaction time to changes in the input

Defining the Boundaries and Characterizing the Landscape of Functional Genome Expression in Vascular Tissues of <i>Populus</i> using Shotgun Proteomics

Current state-of-the-art experimental and computational proteomic approaches were integrated to obtain a comprehensive protein profile of <i>Populus</i> vascular tissue. This featured: (1) a large sample set consisting of two genotypes grown under normal and tension stress conditions, (2) bioinformatics clustering to effectively handle gene duplication, and (3) an informatics approach to track and identify single amino acid polymorphisms (SAAPs). By applying a clustering algorithm to the <i>Populus</i> database, the number of protein entries decreased from 64689 <i>proteins</i> to a total of 43069 <i>protein groups</i>, thereby reducing 7505 identified proteins to a total of 4226 protein groups, in which 2016 were singletons. This reduction implies that ∼50% of the measured proteins shared extensive sequence homology. Using conservative search criteria, we were able to identify 1354 peptides containing a SAAP and 201 peptides that become tryptic due to a K or R substitution. These newly identified peptides correspond to 502 proteins, including 97 previously unidentified proteins. In total, the integration of deep proteome measurements on an extensive sample set with protein clustering and peptide sequence variants provided an exceptional level of proteome characterization for <i>Populus</i>, allowing us to spatially resolve the vascular tissue proteome

COG classification of <i>S. thermophilus</i> proteomes across infection time points.

<p>Proteins were grouped into functional categories by COG assignments. Percentages were calculated using normalized spectral counts averaged between two technical replicates.</p

Cas proteins changing in response to phage 2972 infection.

<p>Values are normalized spectral counts averaged between two technical replicates. Untreated cells MOI = 0, green bars, infected cells MOI = 0, maroon bars, and infected cells at MOI = 1, blue bars. Lines of the same color represent optical density measurements for each group. From top left to bottom right: Cas9 (ST89_070900) from CRISPR1 locus, Cas9 (ST89_097000) from CRISPR3 locus, Cas6e (ST89_103830) and Cas7 (ST89_103850) from CRISPR4 locus.</p