Biology subjects

Payne, S. H.

Publications and source records attributed to Payne, S. H..

4 recordsLinked to original sources

bioRxiv2018

Rapidly Assessing the Quality of Targeted Proteomics Experiments Through Monitoring Stable-isotope Labeled Standards

Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in clinical biomarker, cellular modeling and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards which are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved MS instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy labeled peptides and flags those that fail quality control metrics. Using four metrics, the tool detects problems both with individual SRM transitions and the collective group of transitions that monitor a single peptide. The programs speed enables its use at the point of data acquisition and can be ideally run immediately upon the completion of an LC-SRM-MS analysis.

bioinformatics↗

bioRxiv2018

Identification of novel protein lysine acetyltransferasesin Escherichia coli

Post-translational modifications, such as N{varepsilon}-lysine acetylation, regulate protein function. N{varepsilon}-lysine acetylation can occur either non-enzymatically or enzymatically. The non-enzymatic mechanism uses acetyl phosphate (AcP) or acetyl coenzyme A (AcCoA) as acetyl donors to modify an N{varepsilon}-lysine residue of a protein. The enzymatic mechanism uses N{varepsilon}-lysine acetyltransferases (KATs) to specifically transfer an acetyl group from AcCoA to N{varepsilon}-lysine residues on proteins. To date, only one KAT (YfiQ, also known as Pka and PatZ) has been identified in E. coli. Here, we demonstrate the existence of 4 additional E. coli KATs: RimI, YiaC, YjaB, and PhnO. In a genetic background devoid of all known acetylation mechanisms (most notably AcP and YfiQ) and one deacetylase (CobB), overexpression of these putative KATs elicited unique patterns of protein acetylation. We mutated key active site residues and found that most of them eliminated enzymatic acetylation activity. We used mass spectrometry to identify and quantify the specificity of YfiQ and the four novel KATs. Surprisingly, our analysis revealed a high degree of substrate specificity. The overlap between KAT-dependent and AcP-dependent acetylation was extremely limited, supporting the hypothesis that these two acetylation mechanisms play distinct roles in the post-translational modification of bacterial proteins. We further showed that these novel KATs are conserved across broad swaths of bacterial phylogeny. Finally, we determined that one of the novel KATs (YiaC) and the known KAT (YfiQ) can negatively regulate bacterial migration. Together, these results emphasize distinct and specific non-enzymatic and enzymatic protein acetylation mechanisms present in bacteria.\n\nImportanceN{varepsilon}-lysine acetylation is one of the most abundant and important post-translational modifications across all domains of life. One of the best-studied effects of acetylation occurs in eukaryotes, where acetylation of histone tails activates gene transcription. Although bacteria do not have true histones, N{varepsilon}-lysine acetylation is prevalent; however, the role of these modifications is mostly unknown. We constructed an E. coli strain that lacked both known acetylation mechanisms to identify four new N{varepsilon}-lysine acetyltransferases (RimI, YiaC, YjaB, and PhnO). We used mass spectrometry to determine the substrate specificity of these acetyltransferases. Structural analysis of selected substrate proteins revealed site-specific preferences for enzymatic acetylation that had little overlap with the preferences of the previously reported acetyl-phosphate non-enzymatic acetylation mechanism. Finally, YiaC and YfiQ appear to regulate flagellar-based motility, a phenotype critical for pathogenesis of many organisms. These acetyltransferases are highly conserved and reveal deeper and more complex roles for bacterial post-translational modification.

microbiology↗

bioRxiv2017

A protein standard that emulates homology for the characterization of protein inference algorithms

A natural way to benchmark the performance of an analytical experimental setup is to use samples of known content, and see to what degree one can correctly infer the content of such a sample from the data. For shotgun proteomics, one of the inherent problems of interpreting data is that the measured analytes are peptides and not the actual proteins themselves. As some proteins share proteolytic peptides, there might be more than one possible causative set of proteins resulting in a given set of peptides and there is a need for mechanisms that infer proteins from lists of detected peptides. A weakness of commercially available samples of known content is that they consist of proteins that are deliberately selected for producing tryptic peptides that are unique to a single protein. Unfortunately, such samples do not expose any complications in protein inference. For a realistic benchmark of protein inference procedures, there is, therefore, a need for samples of known content where the present proteins share peptides with known absent proteins. Here, we present such a standard, that is based on E. coli expressed human protein fragments. To illustrate the usage of this standard, we benchmark a set of different protein inference procedures on the data. We observe that inference procedures excluding shared peptides provide more accurate estimates of errors compared to methods that include information from shared peptides, while still giving a reasonable performance in terms of the number of identified proteins. We also demonstrate that using a sample of known protein content without proteins with shared tryptic peptides can give a false sense of accuracy for many protein inference methods.

bioinformatics↗

bioRxiv2017

Blazing Signature Filter: a library for fast pairwise similarity comparisons

Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. Whether the task is to identify co-expressed genes in large-scale expression surveys or to predict combinations of gene knockouts which would elicit a similar phenotype, the underlying computational task is often a multi-dimensional similarity test. As datasets continue to grow, improvements to the efficiency, sensitivity or specificity of such computation will have broad impacts as it allows scientists to more completely explore the wealth of scientific data. A significant practical drawback of large-scale data mining is that the vast majority of pairwise comparisons are unlikely to be relevant, meaning that they do not share a signature of interest. It is therefore essential to efficiently identify these unproductive comparisons as rapidly as possible and exclude them from more time-intensive similarity calculations. The Blazing Signature Filter (BSF) is a highly efficient pairwise similarity algorithm which enables extensive data mining within a reasonable amount of time. The algorithm transforms datasets into binary metrics, allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity. As a result, the BSF can scale to high dimensionality and rapidly filter unproductive pairwise comparison. Two bioinformatics applications of the tool are presented to demonstrate the ability to scale to billions of pairwise comparisons and the usefulness of this approach.

bioinformatics↗