CRAPome for Proteomics: A Repository of Common Contaminants

Proteomics research often encounters background noise from proteins that appear in experiments but are not biologically relevant. These contaminants stem from lab environments, reagents, or sample handling and can lead to misleading conclusions if not properly accounted for.

To address this issue, CRAPome serves as a specialized repository cataloging common contaminants in proteomic analyses.

Resource Functions In Proteomics

Proteomics relies on high-throughput techniques to analyze protein expression, interactions, and modifications, but non-specific proteins can obscure meaningful results. CRAPome helps researchers distinguish genuine protein interactions from background noise by cataloging frequently detected contaminants. This resource allows scientists to refine datasets, improving the accuracy of protein-protein interaction studies and mass spectrometry-based analyses. Without such a repository, distinguishing true biological signals from experimental artifacts would be significantly more challenging.

One primary application of CRAPome is in affinity purification-mass spectrometry (AP-MS), a widely used technique for studying protein complexes. In AP-MS experiments, proteins are isolated using tagged bait proteins, but co-purified contaminants often appear alongside true interactors. By cross-referencing data with CRAPome, researchers can filter out proteins frequently detected in negative controls, reducing false positives. This approach is particularly useful in large-scale interactome mapping projects, where distinguishing specific from non-specific interactions is essential.

Beyond AP-MS, CRAPome supports data interpretation in shotgun proteomics, where complex protein mixtures are digested and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS). Contaminants such as keratins from human skin or serum proteins from sample handling can introduce misleading signals. Researchers use CRAPome’s database to apply exclusion criteria, refining datasets for more precise biological insights. This is especially relevant in clinical proteomics, where distinguishing disease biomarkers from sample preparation artifacts is crucial.

Data Sources In The Repository

CRAPome compiles data from diverse proteomic studies, ensuring comprehensive coverage of common contaminants across different experimental conditions. The repository aggregates results from AP-MS experiments, where negative controls are systematically analyzed to identify proteins that frequently co-purify with bait proteins despite lacking specific interactions. These datasets, contributed by multiple research groups, enhance the reliability of contaminant profiles by allowing cross-laboratory validation.

Beyond AP-MS, CRAPome integrates data from shotgun proteomics experiments, where whole proteomes are digested into peptides and analyzed via LC-MS/MS. Contaminants in these studies arise from sample handling, reagents, and laboratory environments, requiring rigorous filtering to separate biologically relevant proteins from background noise. By curating data from shotgun proteomics workflows, CRAPome aids researchers conducting global proteomic analyses.

To maintain data quality, CRAPome relies on well-characterized negative controls, which differentiate contaminants from genuine interactors. These controls include experiments using empty affinity purification matrices, mock purifications without bait proteins, or samples processed under identical conditions but lacking the biological variable of interest. By systematically analyzing proteins detected in these negative controls, the repository establishes a reference set of frequently observed background proteins, minimizing false positives.

Common Contaminants In The Database

CRAPome catalogs proteins frequently appearing as contaminants in proteomic experiments, often originating from laboratory environments, reagents, or biological sample handling. Among the most common are keratins, serum albumin, heat shock proteins, and cytoskeletal proteins.

Keratins

Keratins are prevalent contaminants, primarily introduced through human skin, hair, and dust particles. These structural proteins shed into laboratory environments, contaminating samples during handling, pipetting, or exposure to ambient air. In mass spectrometry-based proteomics, keratins frequently appear in negative controls, making them a well-documented source of background noise. Their presence is particularly problematic in low-abundance protein studies, where even trace contamination can obscure meaningful signals.

To mitigate keratin contamination, laboratories implement strict protocols such as wearing gloves, using laminar flow hoods, and regularly cleaning workspaces with ethanol or specialized detergents. Researchers also compare experimental datasets against keratin reference lists, such as those in CRAPome, to systematically exclude these proteins from analyses. Despite these precautions, keratins remain a persistent challenge, requiring rigorous quality control measures.

Serum Albumin

Serum albumin, a frequent contaminant, is introduced through biological samples such as blood, plasma, or cell culture media. As the most abundant protein in human and animal serum, albumin can persist in samples even after extensive washing, leading to its unintended detection in proteomic analyses. Its high concentration in biological fluids makes it a dominant signal in mass spectrometry, potentially masking lower-abundance proteins.

In AP-MS experiments, albumin contamination is common when working with serum-containing culture media, as residual proteins can non-specifically bind to affinity matrices. To reduce its impact, researchers use depletion strategies such as albumin removal kits or affinity-based depletion columns. CRAPome provides reference data on albumin’s occurrence in negative controls, allowing researchers to filter out its presence when interpreting protein interaction networks. Despite these efforts, complete removal of albumin remains challenging.

Heat Shock Proteins

Heat shock proteins (HSPs) frequently appear as contaminants due to their ubiquitous expression and strong affinity for other proteins. These molecular chaperones play a crucial role in protein folding, stabilization, and stress responses, making them highly abundant in both prokaryotic and eukaryotic cells. Their tendency to bind misfolded or denatured proteins increases the likelihood of their co-purification in affinity-based experiments.

In AP-MS workflows, HSPs often associate with bait proteins through non-specific interactions, complicating the identification of true protein-protein interactions. CRAPome helps researchers recognize and exclude HSPs by providing frequency-based occurrence data across multiple studies. While some HSP interactions may be biologically relevant, their frequent detection as contaminants requires careful validation through complementary experimental approaches.

Cytoskeletal Proteins

Cytoskeletal proteins, including actin, tubulin, and vimentin, are common contaminants due to their high cellular abundance and strong interactions with other proteins. These structural components are integral to maintaining cell shape, intracellular transport, and mechanical stability but often appear in proteomic datasets unrelated to the study’s focus. Their tendency to co-purify with affinity matrices or adhere to sample preparation surfaces makes them a persistent source of background noise.

In mass spectrometry-based proteomics, cytoskeletal proteins frequently appear in both AP-MS and shotgun proteomics experiments, particularly when working with lysed cells or tissue samples. Their strong interactions with other cellular components can lead to non-specific binding, complicating the identification of true interactors. CRAPome provides reference data on cytoskeletal protein contamination, enabling researchers to apply exclusion criteria. While some cytoskeletal interactions may be biologically meaningful, their frequent detection as contaminants necessitates careful interpretation.

Typical Data Fields

The CRAPome repository organizes its database using structured data fields that provide researchers with detailed information on common contaminants. Each entry includes a protein’s name and corresponding gene symbol, ensuring standardized identification across different datasets. This consistency allows seamless integration with bioinformatics tools used in proteomic data analysis.

A key attribute in the database is contamination frequency, which quantifies how often a protein appears in negative controls across multiple studies. Expressed as a percentage or occurrence rate, this metric helps researchers determine whether a detected protein is an artifact or a true interactor. Higher frequency values indicate recurrent contaminants that should be excluded from analyses. Additionally, metadata such as experimental conditions, sample types, and purification methods provide context, allowing for more nuanced interpretation of contamination sources.