Project description:Bottom-up proteomics relies on identification of peptides from tandem mass spectra, usually via matching against sequence databases. Confidence in a peptide-spectrum match can be characterized by a score value given by the database search engines, and it depends on the information content and the quality of the spectrum. The latter are influenced by experimental parameters, of which the collision energy is the most important one in the case of collision-induced dissociation. We examined how the identification score of the Byonic and Andromeda (MaxQuant) engines varies with collision energy for more than a thousand individual peptides from a HeLa tryptic digest on a QTof instrument. We thereby extended our earlier study on Mascot scores and corroborated its findings on the potential bimodal nature of this energy dependence. Optimal energies as a function of m/z show comparable linear trends for the three engines. On the basis of peptide-level results, we designed methods with one or two liquid chromatography-tandem mass spectrometry (LC-MS/MS) runs and various collision energy settings and assessed their practical performance in peptide and protein identification from the HeLa standard sample. A 10-40% gain in various measures, such as the number of identified proteins or sequence coverage, was obtained over the factory default settings. Best performing methods differ for the three engines, suggesting that the experimental parameters should be fine-tuned to the choice of the engine. We also recommend a simple approach and provide reference data to ease the transfer of the optimized methods to other mass spectrometers relevant for proteomics. We demonstrate the utility of this approach on an Orbitrap instrument. Data sets can be accessed via the MassIVE repository (MSV000086379).
Project description:Comparison among the following data independent acquisition modes provided on the Waters Synapt G2-S instrument: MSE, MSE with Ion Mobility (HDMSE), and MSE with Ion Mobility and drift time specific collision energies (UDMSE). The following on-column loads and LC gradient lengths have been used for the DDA data (1-20130710-63647): * 200 ng, 90 minutes gradient * 300 ng, 180 minutes gradient * 1000 ng, 180 minutes gradient. DDA data has been processed by using MaxQuant (v.1.3.0.5) software, searching against a organism specific (Homo Sapiens) Uniprot/Swissprot database. FDR has been controlled by using a pseudo-reverse (KR positions kept) database at both protein and peptide levels to 1%. Proteins with less than two peptides (minimum of 6 amino acids length, up to 2 missed cleavages and 3 variable modifications allowed. Variable identifications were : methionine oxidation, fixed modifications were : cysteine carbamidomethylation ) identified have been discarded. A match between runs of a 2 minutes windows among the three technical replicates has been performed.
Project description:RNA structural transitions are important in the function and regulation of RNAs. Here, we reveal a layer of transcriptome organization in the form of RNA folding energies. By probing yeast RNA structures at different temperatures, we obtained relative melting temperatures (Tm) for RNA structures in over 4000 transcripts. Specific signatures of RNA Tm demarcated the polarity of mRNA open reading frames, and highlighted numerous candidate regulatory RNA motifs in 3' untranslated regions. RNA Tm distinguished non-coding versus coding RNAs, identified mRNAs with distinct cellular functions. We identified thousands of putative RNA thermometers, and their presence is predictive of the pattern of RNA decay in vivo during heat shock. The exosome complex recognizes unpaired bases during heat shock to degrade these RNAs, coupling intrinsic structural stabilities to gene regulation. Thus, genome-wide structural dynamics of RNA can parse functional elements of the transcriptome and reveal diverse biological insights.
Project description:We describe a novel method utilizing ion mobility-based concentration of peptide fragment ions on a qTOF mass spectrometer improving the sensitivity of bottom-up proteomics by up to 10-fold. This enabled the identification of 7,500 human proteins within one day and 8,600 phosphorylation sites within 5h of LC-MS/MS time. The method also proved powerful for multiplexed quantification experiments exemplified by the chemoproteomic interaction analysis of HDACs with Trichostatin A.
Project description:RNA structural transitions are important in the function and regulation of RNAs. Here, we reveal a layer of transcriptome organization in the form of RNA folding energies. By probing yeast RNA structures at different temperatures, we obtained relative melting temperatures (Tm) for RNA structures in over 4000 transcripts. Specific signatures of RNA Tm demarcated the polarity of mRNA open reading frames, and highlighted numerous candidate regulatory RNA motifs in 3' untranslated regions. RNA Tm distinguished non-coding versus coding RNAs, identified mRNAs with distinct cellular functions. We identified thousands of putative RNA thermometers, and their presence is predictive of the pattern of RNA decay in vivo during heat shock. The exosome complex recognizes unpaired bases during heat shock to degrade these RNAs, coupling intrinsic structural stabilities to gene regulation. Thus, genome-wide structural dynamics of RNA can parse functional elements of the transcriptome and reveal diverse biological insights. RNA structure probing at 5 different temperatures (23M-BM-0C , 30M-BM-0C , 37M-BM-0C , 55M-BM-0C and 75M-BM-0C) using RNase V1 on polyA-selected log phase S288C yeast RNAs was followed by library construction using a modified SOLiD small RNA cloning kit and deep sequencing on the SOLiD platform. We performed 2 biological replicates for each temperature.
Project description:Molecular dynamics (MD) simulations based on coarse-grained (CG) particle models of molecular liquids generally predict accelerated dynamics and misrepresent the time scales for molecular vibrations and diffusive motions. The parametrization of Generalized Langevin Equation (GLE) thermostats based on the microscopic dynamics of the fine-grained model provides a promising route to address this issue, in conjunction with the conservative interactions of the CG model obtained with standard coarse graining methods, such as iterative Boltzmann inversion, force matching, or relative entropy minimization. We report the application of a recently introduced bottom-up dynamic coarse graining method, based on the Mori-Zwanzig formalism, which provides accurate estimates of isotropic GLE memory kernels for several CG models of liquid water. We demonstrate that, with an additional iterative optimization of the memory kernels (IOMK) for the CG water models based on a practical iterative optimization technique, the velocity autocorrelation function of liquid water can be represented very accurately within a few iterations. By considering the distinct Van Hove function, we demonstrate that, with the presented methods, an accurate representation of structural relaxation can be achieved. We consider several distinct CG potentials to study how the choice of the CG potential affects the performance of bottom-up informed and iteratively optimized models.
Project description:Isobaric chemical tag labeling (e.g., iTRAQ and TMT) is a commonly used approach in quantitative proteomics research. Typically, peptides are covalently labeled with isobaric chemical tags, and quantification is enabled through detection of low-mass reporter ions generated after MS2 fragmentation. Recently, we have introduced and optimized a platform for intact protein-level TMT labeling that demonstrated >90% labeling efficiency in complex sample with top-down proteomics. Higher-energy collisional dissociation (HCD) is a commonly utilized fragmentation method for peptide-level isobaric chemical tag labeling because it produces accurate reporter ion intensities and avoids the loss of low mass ions. HCD energies have been optimized for peptide-level isobaric chemical tag labeling; however, fragmentation energies have not been systematically evaluated for TMT-labeled intact proteins for both protein identification and quantitation. In this study, we report a systematic evaluation of normalized HCD fragmentation energies on TMT-labeled HeLa lysate with top-down proteomics. Our results suggested that reporter ions often require higher collisional energy for higher ion intensities while most of intact proteins fragment when normalized HCD energies are between 30% and 50%. We further demonstrated that a stepped HCD fragmentation scheme with energies between 30 and 50% resulted in the optimized quantitation and identification for TMT-labeled intact HeLa protein lysate by providing average reporter ion intensity as > 3.60 E4 and average PrSM as > 1000 PrSM counts with high confidence.
Project description:Comparison of LeuEnk, enolase, and HeLa tryptic peptides MS/MS spectra obtained on a Bruker QTof CID and a Thermo Q-Exactive Focus Orbitrap HCD instrument as a function of collision energy using the similarity index.
Project description:We performed LC-MS/MS measurements on a Waters Select Series Cyclic IMS QTof mass spectrometer with varied CE settings both with and without IMS. We investigated the CE dependence of identification score, using Byonic search engine, for more than 1000 tryptic peptides from HeLa digest standard. We determined the optimal CE values, giving the highest identification score, for both setups (i.e., with and without IMS). Further, the two CID fragmentation cell of the instrument, located before and after the IMS cell, were also compared.
Project description:Extending chip performance beyond current limits of miniaturisation requires new materials and functionalities that integrate well with the silicon platform. Germanium fits these requirements and has been proposed as a high-mobility channel material, a light emitting medium in silicon-integrated lasers, and a plasmonic conductor for bio-sensing. Common to these diverse applications is the need for homogeneous, high electron densities in three-dimensions (3D). Here we use a bottom-up approach to demonstrate the 3D assembly of atomically sharp doping profiles in germanium by a repeated stacking of two-dimensional (2D) high-density phosphorus layers. This produces high-density (10(19) to 10(20) cm(-3)) low-resistivity (10(-4)? · cm) metallic germanium of precisely defined thickness, beyond the capabilities of diffusion-based doping technologies. We demonstrate that free electrons from distinct 2D dopant layers coalesce into a homogeneous 3D conductor using anisotropic quantum interference measurements, atom probe tomography, and density functional theory.