This article delineates the nature of experimental data obtained post-IP-MS and the methodology for sieving through this data to pinpoint highly reliable interacting proteins.
In the aftermath of an IP-MS experiment, a multitude of data is generated, offering a snapshot of protein-protein interactions (PPIs) and post-translational modifications (PTMs). Navigating through this trove of information necessitates a rigorous approach to discerning meaningful interactions from noise.
1. Principles of Data Analysis
In the early stages of IP-MS experiments, the identification of interacting proteins often relies on the method of protein component identification. This involves obtaining separate lists of identified proteins from samples of the experimental and control groups through mass spectrometry detection. The proteins identified in the experimental group but not in the control group are then selected as target proteins for the interactome analysis. This method discerns interacting proteins by emphasizing differential protein identification between the experimental and control groups.
However, this approach to data analysis is currently less suitable for several reasons. On one hand, advancements in mass spectrometry instrument performance and detection sensitivity have reached a point where even trace amounts of non-specifically bound or residual background proteins, once considered negligible, are now detectable by state-of-the-art high-resolution mass spectrometers. This results in a dramatic increase in the information content of the IP-MS sample protein identification lists, rendering them challenging to effectively screen. On the other hand, in accordance with contemporary concepts in protein interaction studies, the interactions between proteins are predominantly characterized by weak, transient, and dynamic bindings. Employing experimental methods, such as more stringent washing conditions or increased washing cycles, to eliminate non-specific binding and residual background proteins may inadvertently remove this subset of weakly interacting proteins. Thus, the delicate balance between enhancing specificity and avoiding the loss of relevant interactions poses a significant challenge in current data analysis methodologies.
Therefore, in current IP-MS experiments, it is customary to adopt relatively mild washing conditions. This approach aims to preserve a broader spectrum of weakly interacting proteins, as well as those background and non-specifically bound proteins that are inherently unavoidable. Subsequently, leveraging quantitative proteomic data derived from mass spectrometry detection – namely, the protein abundance matrices – rigorous comparisons of protein quantification between the experimental and control groups are performed. This process facilitates the discernment of highly reliable protein interactions, effectively sieving out background proteins and non-specific binding entities. This strategy, rooted in the quantitative analysis of the proteome, serves as a nuanced and selective means to unravel genuine protein-protein interactions amidst the complex milieu of biological samples.
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2. Overview of Data Analysis Methods
In the field of mass spectrometry-based analytical science, recent years have seen the growth and diversification of numerous strategies aimed at deciphering complex protein-protein interaction networks. These innovative strategies, carefully engineered for deciphering the extensive and complex data generated from high-throughput protein-protein interaction experiments, are a testament to the scientific rigor and creativity of researchers around the world. However, these methods are not without their obstacles and concerns, frequently facing challenges in maintaining consistency and reliability when applied to complex datasets.
For example, early efforts to address these issues led to the development of platforms such as 'CRAPome,' a comprehensive repository that accumulates and catalogs non-specifically interacting proteins from various immunoprecipitation assays. The intention was to develop efficient and robust filtration mechanisms. However, compatibility of these methods and principles currently guiding the analysis of IP-MS data has proved challenging at times.
Likewise, a suite of tools including CompPASS, MiST, and the SAINT scoring algorithm have been designed to assign scores to proteins based on quantitative metrics across multiple experiments, identifying potential interactors that surpass specific thresholds. Nevertheless, these scoring methods have predominantly relied upon spectral counts from protein identification. With the growing prominence of label-free quantitative strategies, the reliance on spectral count-based quantitative algorithms has seen a parallel decline. Underlining the vigor and dynamism of the scientific endeavor, the task of refining and updating existing analytical strategies remains a key focus of this deeply complex and rapidly evolving field.
In the recent update of the SAINT algorithm (SAINTq), a notable transition has been made. The algorithm now utilizes quantitative values based on signal intensity for protein scoring and the subsequent selection of interacting proteins. This adjustment reflects the adaptability of methodologies to evolving quantitative strategies in the ever-changing landscape of proteomic research.
Hence, in contemporary IP-MS data analysis, the predominant focus revolves around the utilization of protein label-free quantification signal intensities (LFQ intensity) as the basis for protein quantification data. This approach involves comparing the quantitative differences in proteins between the experimental and control groups. The methodology aligns with conventional quantitative proteomic data analysis techniques, albeit typically involving the selection of higher thresholds for distinguishing differentially expressed proteins. This nuanced adaptation underscores the current emphasis on leveraging label-free quantification signal intensities to discern meaningful variations in protein abundance between experimental conditions, thus reflecting the meticulous considerations inherent in the contemporary analytical practices within the field of proteomics.
3. Data Preprocessing
The raw spectral data obtained through mass spectrometry analysis, when parsed by database search software, provides relative quantitative information for proteins in each sample. Given the sensitivity of current mass spectrometry instruments and the intricacies of protein interaction study designs, IP-MS samples originating from human cells typically yield quantification for 1000-2000 proteins, with the majority being nonspecifically bound and residual background proteins.
Similar to conventional quantitative proteomic data, once a protein abundance matrix is obtained, preprocessing steps are crucial before undertaking protein quantification comparisons between different groups. These steps generally include:
(1) Logarithmic transformation of quantitative values: The original quantitative values of proteins are commonly log2-transformed for subsequent statistical analysis and presentation.
(2) Removal of invalid data: This involves the elimination of common contaminants, reverse database proteins, and low-frequency quantitative data. Common contaminant proteins include those difficult to avoid during sample preparation, such as keratins from skin flakes of experimental operators, bovine serum proteins from cell culture media, and porcine trypsin from the protein digestion process. Reverse database proteins are virtual proteins with sequences opposite to true protein sequences, incorporated during database retrieval for false discovery rate (FDR) filtering of protein identification results. Low-frequency quantitative data are considered less reliable due to their infrequent detection across different samples. For instance, in experiments with three replicates per group, proteins quantified in at least two replicate experiments are typically retained.
(3) Imputation of missing data: To facilitate statistical analysis, missing data in the data matrix are filled with numerical values following specific rules, a common practice in omics data analysis. In proteomics data, missing values are often imputed by fitting a minimal value to a normal distribution. In essence, a distribution resembling the detection limit of mass spectrometry is established based on the original data's normal distribution. Data points are then randomly generated from this distribution to fill the missing portions of the original data.
4. Interacting Protein Selection
Based on IP-MS quantitative data, proteins significantly elevated in the IP experimental group compared to the IP control group are identified as high-confidence interacting proteins with the bait protein. Due to the enrichment principle of IP experiments, the selection criteria for significantly different proteins in IP-MS are typically more stringent compared to conventional proteomic analyses. A common standard for selecting interacting proteins might involve a fold change >10 and a P-value <0.01. These criteria underscore the meticulous considerations applied in establishing stringent thresholds for the identification of high-confidence interacting proteins in the context of IP-MS studies.
5. Common Issues and Data Result Assessment
In current optimal IP-MS results, each sample typically yields quantification for over 1000 distinct proteins. The majority of these proteins show no significant differences in abundance across different samples. Within the experimental group, target proteins exhibit detectable signals of high intensity and typically manifest the most significant differences between the experimental and control groups. Proteins, other than the target proteins, that are significantly more abundant in the experimental group are identified as interacting proteins with the target proteins.
If the IP-MS result data deviates from expectations, potential reasons can be investigated in the following aspects:
(1) Insufficient Identification of Proteins in the Experimental Group Sample Possible reasons:
Insufficient sample quantity, commonly arising from an inadequately low starting cell count for IP.
Inappropriate washing conditions, such as using high-salt buffer for bead washing, leading to protein loss before elution or mass spectrometry analysis.
(2) Non-identification of Target Proteins in the Experimental Group Possible reasons:
Insufficient efficacy of antibody enrichment resulting in a low yield of target proteins during IP.
Antibody specificity issues, where the antibody recognizes proteins other than the intended target, potentially proteins with molecular weights close to the target protein.
(3) Excessive Interacting Proteins Identified through Differential Analysis Possible reason:
Incomplete concordance between experimental conditions in the control and experimental groups, such as variations in cell quantity and antibody dosage. These discrepancies may result in a significantly lower quantification of proteins in the control group compared to the experimental group.
References
- CHOI H, LARSEN B, LIN Z Y, et al. 2011. SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat Methods, 8: 70-73.
- JäGER S, CIMERMANCIC P, GULBAHCE N, et al. 2011. Global landscape of HIV-human protein complexes. Nature, 481: 365-370.
- KEILHAUER E C, HEIN M Y, MANN M 2015. Accurate protein complex retrieval by affinity enrichment mass spectrometry (AE-MS) rather than affinity purification mass spectrometry (AP-MS). Mol Cell Proteomics, 14: 120-135.
- MELLACHERUVU D, WRIGHT Z, COUZENS A L, et al. 2013. The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nature Methods, 10: 730-736.
- SOWA M E, BENNETT E J, GYGI S P, et al. 2009. Defining the human deubiquitinating enzyme interaction landscape. Cell , 138: 389-403.
- TEO G, KOH H, FERMIN D, et al. 2016. SAINTq: Scoring protein-protein interactions in affinity purification - mass spectrometry experiments with fragment or peptide intensity data. Proteomics, 16: 2238-2245.
- SHANG J, XIA T, HAN Q Q, et al. 2018. Quantitative Proteomics Identified TTC4 as a TBK1 Interactor and a Positive Regulator of SeV-Induced Innate Immunity. Proteomics, 18.
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