peptide detection
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2022 ◽  
pp. 146906672110667
Author(s):  
Miroslav Hruska ◽  
Dusan Holub

Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact––increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.


Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7260
Author(s):  
Emmanouil Mavrogeorgis ◽  
Harald Mischak ◽  
Agnieszka Latosinska ◽  
Justyna Siwy ◽  
Vera Jankowski ◽  
...  

In recent years, capillary electrophoresis coupled to mass spectrometry (CE-MS) has been increasingly applied in clinical research especially in the context of chronic and age-associated diseases, such as chronic kidney disease, heart failure and cancer. Biomarkers identified using this technique are already used for diagnosis, prognosis and monitoring of these complex diseases, as well as patient stratification in clinical trials. CE-MS allows for a comprehensive assessment of small molecular weight proteins and peptides (<20 kDa) through the combination of the high resolution and reproducibility of CE and the distinct sensitivity of MS, in a high-throughput system. In this study we assessed CE-MS analytical performance with regards to its inter- and intra-day reproducibility, variability and efficiency in peptide detection, along with a characterization of the urinary peptidome content. To this end, CE-MS performance was evaluated based on 72 measurements of a standard urine sample (60 for inter- and 12 for intra-day assessment) analyzed during the second quarter of 2021. Analysis was performed per run, per peptide, as well as at the level of biomarker panels. The obtained datasets showed high correlation between the different runs, low variation of the ten highest average individual log2 signal intensities (coefficient of variation, CV < 10%) and very low variation of biomarker panels applied (CV close to 1%). The findings of the study support the analytical performance of CE-MS, underlining its value for clinical application.


2021 ◽  
Author(s):  
Lilian R. Heil ◽  
William E. Fondrie ◽  
Christopher D. McGann ◽  
Alexander J. Federation ◽  
William S. Noble ◽  
...  

Advances in library-based methods for peptide detection from data independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.


2021 ◽  
Vol MA2021-01 (63) ◽  
pp. 1677-1677
Author(s):  
Alexander George Zestos ◽  
Favian Alberto Liu ◽  
Thomas Asrat

2021 ◽  
Vol 20 (4) ◽  
pp. 1966-1971
Author(s):  
William E. Fondrie ◽  
William S. Noble

2021 ◽  
Author(s):  
MacKenzie D. Williams ◽  
Rhonda Bacher ◽  
Daniel J. Perry ◽  
C. Ramsey Grace ◽  
Kieran M. McGrail ◽  
...  

We and others previously demonstrated that a type 1 diabetes genetic risk score (GRS) improves the ability to predict disease progression and onset in at-risk subjects with islet autoantibodies. Here, we hypothesized that GRS and islet autoantibodies, combined with age at onset and disease duration, could serve as markers of residual β-cell function following type 1 diabetes diagnosis. Generalized estimating equations were used to investigate whether GRS along with insulinoma-associated protein-2 autoantibody (IA-2A), zinc transporter 8 autoantibody (ZnT8A) and GAD autoantibody (GADA) titers were predictive of C-peptide detection in a largely cross-sectional cohort of 401 subjects with type 1 diabetes (duration median = 4.5 years, range 0-60). Indeed, a combined model incorporating disease duration, age at onset, GRS, and titers of IA-2A, ZnT8A and GADA provided superior capacity to predict C-peptide detection (QIC=334.6) compared with disease duration, age at onset, and GRS as the sole parameters (QIC=359.2). These findings support the need for longitudinal validation of our combinatorial model. The ability to project the rate and extent of decline in residual C-peptide production for individuals with type 1 diabetes could critically inform enrollment and benchmarking for clinical trials seeking to preserve or restore endogenous β-cell function.


2021 ◽  
Author(s):  
MacKenzie D. Williams ◽  
Rhonda Bacher ◽  
Daniel J. Perry ◽  
C. Ramsey Grace ◽  
Kieran M. McGrail ◽  
...  

We and others previously demonstrated that a type 1 diabetes genetic risk score (GRS) improves the ability to predict disease progression and onset in at-risk subjects with islet autoantibodies. Here, we hypothesized that GRS and islet autoantibodies, combined with age at onset and disease duration, could serve as markers of residual β-cell function following type 1 diabetes diagnosis. Generalized estimating equations were used to investigate whether GRS along with insulinoma-associated protein-2 autoantibody (IA-2A), zinc transporter 8 autoantibody (ZnT8A) and GAD autoantibody (GADA) titers were predictive of C-peptide detection in a largely cross-sectional cohort of 401 subjects with type 1 diabetes (duration median = 4.5 years, range 0-60). Indeed, a combined model incorporating disease duration, age at onset, GRS, and titers of IA-2A, ZnT8A and GADA provided superior capacity to predict C-peptide detection (QIC=334.6) compared with disease duration, age at onset, and GRS as the sole parameters (QIC=359.2). These findings support the need for longitudinal validation of our combinatorial model. The ability to project the rate and extent of decline in residual C-peptide production for individuals with type 1 diabetes could critically inform enrollment and benchmarking for clinical trials seeking to preserve or restore endogenous β-cell function.


Soft Matter ◽  
2021 ◽  
Author(s):  
Samapan Sikdar ◽  
Manidipa Banerjee ◽  
Satyavani Vemparala

Understanding the viral peptide detection, partitioning and subsequent host membrane composition-based response is required for gaining insights into viral mechanism. Here, we probe the crucial role of presence of membrane...


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