Dataset Information

Bait correlation improves interactor identification by TMT-AP-MS

ABSTRACT: The quantitative multiplexing capacity of isobaric Tandem Mass Tags (TMT) has increased the throughput of affinity purification mass spectrometry (AP-MS) to characterize protein interaction networks of immunoprecipitated baits. However, variable bait levels between replicates can convolute interactor identification. We compared the Student's t difference test and Pearson's R correlation as methods to generate t-statistics and assessed the significance of interactors following TMT-AP-MS. Using a simple linear model of protein recovery in immunoprecipitates to simulate reporter ion ratio distributions, we found that correlation-derived t-statistics protect against bait variance while robustly controlling Type I errors (false positives). We experimentally determined the performance of these two approaches for determining t-statistics under two experimental conditions: irreversible prey association to the Hsp40 mutant DNAJB8H31Q followed by stringent washing, and reversible association to 14 3 3 with gentle washing. Correlation-derived t-statistics performed at least as well as difference test t-statistics for each sample, with substantial improvement in performance for experiments with high bait level variance. Deliberately varying bait levels over a large range fails to improve selectivity but does increase robustness between runs. The use of correlation-derived t-statistics should improve identification of interactors using TMT-AP-MS.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Joseph Genereux

LAB HEAD: Joseph Genereux

PROVIDER: PXD016613 | Pride | 2020-06-22

REPOSITORIES: Pride

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Publications

Bait Correlation Improves Interactor Identification by Tandem Mass Tag-Affinity Purification-Mass Spectrometry.

Mei Liangyong L Montoya Maureen R MR Quanrud Guy M GM Tran Minh M Villa-Sharma Athena A Huang Ming M Genereux Joseph C JC

Journal of proteome research 20200325 4

The quantitative multiplexing capacity of isobaric tandem mass tags (TMT) has increased the throughput of affinity purification mass spectrometry (AP-MS) to characterize protein interaction networks of immunoprecipitated bait proteins. However, variable bait levels between replicates can convolute interactor identification. We compared the Student's t-test and Pearson's R correlation as methods to generate t-statistics and assessed the significance of interactors following T ...[more]

PMID: 32138514

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Project description:Microarray data analysis Intensity of 21,939 gene features per array was extracted from scanned microarray images using Feature Extraction 5.1.1 software (Agilent Technologies), which performs background subtractions and dye normalization. This normalization method is targeted at detecting changes in relative expression of individual genes rather than global expression. Global expression change would require external normalization controls (van de Peppel et al., 2003). Text output was processed using an application developed in-house to perform ANOVA analysis (http://lgsun.grc.nia.nih.gov/ANOVA/). Intensity of features measured with a >50% error were replaced with missing values except features with very low intensity. Surrogate values equal to mean error were inserted for values that were negative or less than the probe error. Data were analyzed using ANOVA with embryonic stage as a factor. The small number of biological replications typical in expression profiling experiments results in a highly variable error variance, and this problem is usually addressed by log-ratio thresholds (Schena et al., 1995) that require subjective decisions about biological significance, or by Bayesian adjustment of error variance (Baldi and Long, 2001), which may still underestimate error variance and result in false positive results. To reduce false-positives, we opted for a very conservative error model in which error variance that is used for estimating F-statistics is the maximum of the actual error variance for this gene and the average error variance in 500 genes with similar average intensity. Statistical significance was determined using the False Discovery Rate (FDR = 10%) method (Benjamini and Hochberg, 1995). Pair-wise mean comparison was done with t-statistics and FDR=10%. Further data processing including scatter plots, hierarchical clustering, and principal component analysis (PCA) were also performed through NIA microarray analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/). The input file for NIA microarray analysis tool is available at http://lgsun.grc.nia.nih.gov/microarray/data.html

Dataset Information

Bait correlation improves interactor identification by TMT-AP-MS

Publications

Bait Correlation Improves Interactor Identification by Tandem Mass Tag-Affinity Purification-Mass Spectrometry.

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