Structural Proteomics: High-Throughput Methods

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Preview — Structural Proteomics by Bostjan Kobe. The objective of this volume is to provide readers with a current view of all aspects of the 'pipeline' that takes protein targets to structures and how these have been optimised. This volume includes chapters describing, in-depth, the individual steps in the Structural Genomics pipeline, as well as less detailed overviews of individual Structural Genomics initiatives. It The objective of this volume is to provide readers with a current view of all aspects of the 'pipeline' that takes protein targets to structures and how these have been optimised.

It is the first book of protocols to cover techniques in a new and emerging field. Get A Copy.

Native mass spectrometry: towards high-throughput structural proteomics.

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High-Throughput Methods

Friend Reviews. To see what your friends thought of this book, please sign up. A tryptic digest of a whole-cell protein extract from human K cells was analyzed using microLC 0. Data extraction by using the rich library quantified K proteins, while proteins when using a library generated from HEK cells, and using a HeLa library, respectively. Signal variability expressed as fold change of proteins present in all datasets was compared throughout nine replicates.

Median coefficients of variation are between 7. Signal variability expressed as fold-change of proteins present in all datasets was compared throughout the six replicates.

Correlation between measured apex retention time and predicted retention time. Inset: Mean retention time standard deviation of 6 iRT peptides across injections is Red vertical lines indicate the interspersed quality control samples QC sample measurements needed for batch correction. The number of proteins in a SWATH dataset of 1 to yeast samples was determined with either sparse or complete Qvalue filtering, Qvalue percentile or Qvalue median filtering as implemented in Spectronaut software coloured lines. Coefficient of variation for fold changes of peptides was calculated in batch 1 green , batch 2 orange , batch 3 purple or across batches magenta prior to batch correction.

Q The quantification of yeast proteomes before batch correction. In a PCA, proteomes cluster according to the acquisition batch, with color-coded technical replicates clustering together. R The quantification of yeast proteomes after batch correction. After batch correction based on the combined quality control sample profiles, clustering according to batches is reduced, and proteomes cluster according to the color-coded yeast strain. Inset: Median coefficients of variation of peptide intensities between all 9 replicates of each strain are At this point it should be noted that, while more peptides are injected for microflow-chromatography, not more starting material is required for generating the samples.

Due to handling difficulties i. Thus, our microLC-based workflow does not require more input sample material. Instead a lower amount of injection replicates can be run per sample. For most applications of quantitative proteomics however, multiple injection replicates are not required, as statistical methods require biological or at least full technical replicates.

Injection replicates and are hence typically recorded only for method development purposes or for the generation of spectral libraries, but not on large sample series. Indeed, in practice, microLC-based proteomics may be able to handle more diluted samples as derived from highly limiting starting material, for the simple reason a higher spectrum of injection volumes can be handled chromatographically.

We continued with an optimisation of the microflow chromatography, focussing on finding a good compromise between number of identified analytes and gradient length for high throughput applications. Microflow-SWATH-MS is therefore competitive in regards to other proteomic technologies that use nanoflow chromatography on instruments of a comparable generation On a newer generation of ion trap mass spectrometers, yeast proteomic depth did, upon extensive fractionation, go beyond these detection limits These results imply that the obtained protein number values are not yet exhausted and will increase when the workflow is optimized for increasing proteomic depth instead of sample numbers and robustness, or when using a more sensitive mass spectrometer not the focus of this study.

While narrow peaks are generally desirable in chromatography, in proteomics they amplify the problem of undersampling, a situation that emerges when more peptides elute at any unit in time than the mass spectrometer can process Data-independent acquisition DIA strategies, like SWATH-MS where all precursors falling into an isolation window are fragmented simultaneously and chromatograms are reconstructed computationally post-acquisition, have been designed to overcome this problem Upon these modifications, our SWATH-MS method covered the typical microLC chromatographic peak with a critical number of 8—12 data points, yielding accurate peak representation for the application of fast chromatography Fig.

Protein identification numbers and the quality of quantification are both dependent on the recorded data but also, specifically in DIA acquisition, on the spectral libraries used to extract the data. Therefore, we processed the datasets using different library generation strategies. Tandem 29 and Comet 30 search engines. Peptide quantification numbers followed similar trends Suppl.

Figs 4 and 5. In parallel, we tested the performance of microLC-SWATH-MS on a standardized whole-proteome human cell line K tryptic digest, by extracting data using three publicly available spectral libraries generated by combining multiple tissues and fractionation 31 or by repetitive injection of tissue-specific cell digests of HEK or HeLa cells Spectronaut 26 repository. In small scale, the median coefficients of variation CVs for replicate injections of the yeast samples in all acquisition strategies and analysis libraries were 5.

L and Suppl. The precision was largely similar over the full dynamic range spanning five orders of magnitude Suppl. Figs 9 and Interestingly, proteins identified by DIA-Umpire, which in our samples were lower in number compared to other approaches, yielded a higher precision in the quantification experiments Fig.

This could be related to a better signal-to-noise ratio of high abundant analytes, or to the highly abundant part of the proteome being generally more stable. Indeed, we also detect the abundance bias in the peptides identified by DIA-Umpire, indicating its the quantification of more abundant peptides that results in more precise values Suppl.

In order to determine the performance characteristics of microLC-SWATH-MS for the intended application of acquiring large numbers of proteomes for data driven biology, we conducted two large studies to optimize strategies for retention time and batch correction, as well as peptide selection.

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Including quality control QC samples, this benchmark span over whole-proteome samples, recorded in three batches upon coupling the QTOF mass spectrometer to a commercial nanoLC system nanoAcquity, Waters that had been converted to a microLC by exchanging capillaries of larger diameter. Even before applying retention time normalization, the converted nanoLC yielded highly stable retention times in microflow standard deviation of apex retention times This value was further improved using linear retention time normalisation using standard peptides 20 , upon which an excellent agreement with theoretically expected retention times was achieved Fig.

Furthermore, to address the batch effects that unavoidably confound quantitative proteomics when conducted in large scale 34 , 35 , we established a batch correction strategy that repetitively includes a mixture of a combined sample QC control every 10—12 injections. Then, we applied an empirical Bayes framework 36 , 37 to correct data for observed batch effects associated with acquisition dates by specifying covariates.

Structural Proteomics. High-Throughput Methods - PDF Free Download

Batches spanning to whole-proteome acquisitions were each acquired over a period of around 9 days net acquisition time 6 days. A typical problem of large-scale data analysis is that, ideally, it should yield the consistent quantification of a given analyte across all samples. However, this procedure artificially reduces proteome depth, as it over-rates the FDR in one sample over the recurrence of a signal in hundreds of samples. On the other hand, sparse filtering, i.

In our hands, the best compromise was found when filtering by the median Qvalue, which gives both consistent results and retains a high number of quantified proteins Fig. Indeed, when also evaluating proteome depth by controlling protein FDR, we found that median Qvalue filtering is more conservative than controlling protein FDR 38 , 39 , Finally, we evaluated the quantitative precision of our workflow, which is arguably an equal or even more important benchmark compared to peptide identification numbers for judging the quality of large scale proteomics experiments produced for data-driven applications.

For this, we determined coefficient of variation CV values for the obtained peptide quantities. This characteristic equals, or even exceeds, precision values typically obtained in label and label-free proteomic experiments, even when conducted in small scale 25 , 41 , 42 , In order to also demonstrate the retention of the biological variability upon batch correction, we analysed the differences between the technical CVs and total CVs, which include both technical and biological variability.

Figs 12 and In parallel we tested the platform to see whether we can distinguish differences in proteomes of so called redundant biological signalling molecules by exploiting semi-targeted feature extractions from data-independent mass spectrometry data. For this benchmark, we selected all yeast strains deleted for a protein kinase that are viable in minimal medium We measured their proteome by microLC-SWATH-MS in exponential growth phase, and extracted the quantities of metabolic enzymes, as these constitute one of the largest functional class of proteins in the high-abundant fraction of the proteome.

In order to yield precision on the enzymome dataset, we selected the peptides for quantification not by abundance, but by highest pairwise correlation. This strategy assumes that all peptides that are derived from the same protein are suitable for quantification; this strategy excludes peptides that behave inconsistently across the measured samples, i. We used a correlation-dependent peptide selection strategy, as developed by us earlier in combination with an MS E workflow An analogous strategy is used in the mapDIA workflow, for example Then we optimized the batch correction strategy for the semi-targeted approach.

We used parametric adjustment of the raw signals as in supervised surrogate variable SVA analysis Estimated surrogate effects were regressed out from the peptide signal. Finally, for each protein, the signals of all peptide groups were geometrically averaged. Following this semi-targeted strategy, metabolic enzyme levels were quantified across measured proteomes over a period of four months.

A manuscript describing the biological meaning of enzyme abundance changes as measured in all viable yeast kinase knockout strains is submitted for publication elsewhere Zelezniak et al.

2018 Day 5 am 09 00 Aebersold High Throughput Proteomics

In conclusion, we show that microLC-based proteomics is able to achieve broad protein identification characteristics. In combination with a microflow-optimized data independent acquisition workflow, a platform is obtained that can capture a quantitative snapshot of a yeast or human proteome in an hour or less, and at the same time maintain high quantitative precision that remains consistent over months and over hundreds of proteomes.

It is important to mention that due to a more exhaustive usage of the created tryptic digests, the workflow does not need more starting material compared to conventional nanoLC based proteomic workflows, and is economic by not being dependent on the latest instrument hardware and the use of microflow-columns that have longer run times compared to their nanoLC-counterpart, reducing both material costs as well as hands-on time, while increasing throughput.

It further empowers the analysis of large time-series and cohorts of comparative studies in basic and clinical research, e. Indeed, the high flow rate renders microLC-SWATH-MS a highly robust proteomic technology with low instrument downtime and maintenance cycles, increasing data quality simply through minimizing instrumental bias and leading to higher productivity and cost effectiveness. Cells were harvested by centrifugation and snap-frozen in aliquots equaling 10 OD units. Sample preparation was performed as described 33 with the following modifications.

MS compatible human protein digest from K cells was obtained from Promega. Before analysis, samples were spiked with 0. After injecting samples onto the analytical column, peptides were separated on linear gradients detailed in Suppl. Collision-induced dissociation was triggered by rolling collision energy. Briefly, spectral data acquired in IDA mode was centroided using qtofpeakpicker Centroided files were searched with X! Search results were scored using PeptideProphet and combined with iProphet. The final spectral library was assembled using SpectraST 49 by retaining spectra above iProphet FDR controlled cutoff and normalizing chromatography to iRT peptide retention time reference.

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