Dataset Information

Quantifying Homologous Proteins and Proteoforms.

ABSTRACT: Many proteoforms-arising from alternative splicing, post-translational modifications (PTM), or paralogous genes-have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid these biases, we developed and implemented a first-principles model (HIquant) for quantifying proteoform stoichiometries. We characterized when MS data allow inferring proteoform stoichiometries by HIquant and derived an algorithm for optimal inference. We applied this algorithm to infer proteoform stoichiometries in two experimental systems that supported rigorous bench-marking: alkylated proteoforms spiked-in at known ratios and endogenous histone 3 PTM proteoforms quantified relative to internal heavy standards. When compared with the benchmarks, the proteoform stoichiometries interfered by HIquant without using external standards had relative error of 5-15% for simple proteoforms and 20-30% for complex proteoforms. A HIquant server is implemented at: https://web.northeastern.edu/slavov/2014HIquant/.

SUBMITTER: Malioutov D

PROVIDER: S-EPMC6317479 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Quantifying Homologous Proteins and Proteoforms.

Malioutov Dmitry D Chen Tianchi T Airoldi Edoardo E Jaffe Jacob J Budnik Bogdan B Slavov Nikolai N

Molecular & cellular proteomics : MCP 20181003 1

Many proteoforms-arising from alternative splicing, post-translational modifications (PTM), or paralogous genes-have distinct biological functions, such as histone PTM proteoforms. However, their quantification by existing bottom-up mass-spectrometry (MS) methods is undermined by peptide-specific biases. To avoid these biases, we developed and implemented a first-principles model (HI<i>quant</i>) for quantifying proteoform stoichiometries. We characterized when MS data allow inferring proteoform ...[more]

PMID: 30282776

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Project description:BackgroundClustering sequences into groups of putative homologs (families) is a critical first step in many areas of comparative biology and bioinformatics. The performance of clustering approaches in delineating biologically meaningful families depends strongly on characteristics of the data, including content bias and degree of divergence. New, highly scalable methods have recently been introduced to cluster the very large datasets being generated by next-generation sequencing technologies. However, there has been little systematic investigation of how characteristics of the data impact the performance of these approaches.ResultsUsing clusters from a manually curated dataset as reference, we examined the performance of a widely used graph-based Markov clustering algorithm (MCL) and a greedy heuristic approach (UCLUST) in delineating protein families coded by three sets of bacterial genomes of different G+C content. Both MCL and UCLUST generated clusters that are comparable to the reference sets at specific parameter settings, although UCLUST tends to under-cluster compositionally biased sequences (G+C content 33% and 66%). Using simulated data, we sought to assess the individual effects of sequence divergence, rate heterogeneity, and underlying G+C content. Performance decreased with increasing sequence divergence, decreasing among-site rate variation, and increasing G+C bias. Two MCL-based methods recovered the simulated families more accurately than did UCLUST. MCL using local alignment distances is more robust across the investigated range of sequence features than are greedy heuristics using distances based on global alignment.ConclusionsOur results demonstrate that sequence divergence, rate heterogeneity and content bias can individually and in combination affect the accuracy with which MCL and UCLUST can recover homologous protein families. For application to data that are more divergent, and exhibit higher among-site rate variation and/or content bias, MCL may often be the better choice, especially if computational resources are not limiting.

Dataset Information

Quantifying Homologous Proteins and Proteoforms.

Publications

Quantifying Homologous Proteins and Proteoforms.

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