Global Subcellular Characterization of Protein Degradation Using Quantitative Proteomics

Protein degradation provides an important regulatory mechanism used to control cell cycle progression and many other cellular pathways. To comprehensively analyze the spatial control of protein degradation in U2OS osteosarcoma cells, we have combined drug treatment and SILAC-based quantitative mass spectrometry with subcellular and protein fractionation. The resulting data set analyzed more than 74,000 peptides, corresponding to ∼5000 proteins, from nuclear, cytosolic, membrane, and cytoskeletal compartments. These data identified rapidly degraded proteasome targets, such as PRR11 and highlighted a feedback mechanism resulting in translation inhibition, induced by blocking the proteasome. We show this is mediated by activation of the unfolded protein response. We observed compartment-specific differences in protein degradation, including proteins that would not have been characterized as rapidly degraded through analysis of whole cell lysates. Bioinformatic analysis of the entire data set is presented in the Encyclopedia of Proteome Dynamics, a web-based resource, with proteins annotated for stability and subcellular distribution.Targeted protein degradation is an important regulatory mechanism that allows co-ordination of cellular pathways in response to environmental and temporal stimuli (1). The control of diverse biochemical pathways, including cell cycle progression and the response to DNA damage, is mediated, at least in part, by dynamic alterations in protein degradation (2). Previous large scale proteomics studies in mammalian cells have shown that the rate of protein degradation can vary from the timescale of minutes, to essentially infinite stability for metastable proteins (3–8).Most intracellular proteins have similar degradation rates, with a half-life approximating the cell doubling rate. Under 5% of proteins display degradation rates more than threefold faster than the proteome average (3–5, 7). However, degradation rates for individual proteins can change, for example depending on either the cell cycle stage, or signaling events, and can also vary depending on subcellular localization. Disruption of such regulated protein stability underlies the disease mechanisms responsible for forms of cancer, e.g. p53 (9, 10) and the proto-oncogene c-Myc (11).Detection of rapidly degraded proteins can be difficult because of their low abundance. However, advances in mass spectrometry based proteomics have enabled in-depth quantitative analysis of cellular proteomes (12–14). Stable isotope labeling by amino acids in cell culture (SILAC)¹ (15), has been widely used to measure protein properties such as abundance, interactions, modifications, turnover, and subcellular localization under different conditions (16). Subcellular fractionation and protein size separation are also powerful techniques that enhance in-depth analysis of cellular proteomes. Not only do these fractionation techniques increase total proteome coverage, they also provide biological insight regarding how protein behavior differs between subcellular compartments. For example, subcellular fractionation has highlighted differences in the rate of ribosomal protein degradation between the nucleus and cytoplasm, (7, 17). Other studies have also demonstrated the benefit of in-depth subcellular fractionation and created methods for the characterization of how proteomes are localized in organelles (18–20).In this study we have used SILAC-based quantitative mass spectrometry combined with extensive subcellular and protein-level fractionation to identify rapidly degraded proteins in human U2OS cells. We provide a proteome level characterization of a major feedback mechanism involving inhibition of protein translation when the proteasome is inhibited. We also present the Encyclopedia of Proteome Dynamics, a user-friendly online resource providing access to the entire data set.     

Characterization of Native Protein Complexes and Protein Isoform Variation Using Size-fractionation-based Quantitative Proteomics

Proteins form a diverse array of complexes that mediate cellular function and regulation. A largely unexplored feature of such protein complexes is the selective participation of specific protein isoforms and/or post-translationally modified forms. In this study, we combined native size-exclusion chromatography (SEC) with high-throughput proteomic analysis to characterize soluble protein complexes isolated from human osteosarcoma (U2OS) cells. Using this approach, we have identified over 71,500 peptides and 1,600 phosphosites, corresponding to over 8,000 proteins, distributed across 40 SEC fractions. This represents >50% of the predicted U2OS cell proteome, identified with a mean peptide sequence coverage of 27% per protein. Three biological replicates were performed, allowing statistical evaluation of the data and demonstrating a high degree of reproducibility in the SEC fractionation procedure. Specific proteins were detected interacting with multiple independent complexes, as typified by the separation of distinct complexes for the MRFAP1-MORF4L1-MRGBP interaction network. The data also revealed protein isoforms and post-translational modifications that selectively associated with distinct subsets of protein complexes. Surprisingly, there was clear enrichment for specific Gene Ontology terms associated with differential size classes of protein complexes. This study demonstrates that combined SEC/MS analysis can be used for the system-wide annotation of protein complexes and to predict potential isoform-specific interactions. All of these SEC data on the native separation of protein complexes have been integrated within the Encyclopedia of Proteome Dynamics, an online, multidimensional data-sharing resource available to the community.The majority of proteins function as part of multiprotein complexes and not as isolated polypeptides. These protein complexes range from simple homodimers to large structures composed of many different polypeptides. Protein complexes vary in their size and shape from small globular dimers, such as 14-3-3 proteins, to large elongated filaments of variable length, such as microtubules. The wide variety of possible protein–protein interactions within multiprotein complexes contributes to the diversity of functions that are involved in cellular processes and regulatory mechanisms.Another important source of functional diversity and regulation is the large number of protein isoforms that may be generated from each gene. Functionally and structurally distinct isoforms can arise via multiple mechanisms, including alternative splicing, post-translational modification (PTM),¹ and proteolytic cleavage. Distinct isoforms can exhibit radically different properties. For example, including or excluding individual exons can either create or remove protein–protein interaction interfaces for binding specific interaction partners. Similarly, phosphorylation, and other PTMs, can either create or remove binding sites for interacting proteins, substrates, or ligands. PTMs can also promote structural changes in proteins and affect catalytic activity.The association of protein isoforms and post-translationally modified factors in multiprotein complexes can influence their subcellular location, activity, and substrate specificity. This can be dynamically regulated to modulate protein complex composition, and hence localization and function, to allow cells to respond to spatial and temporal stimuli. It is therefore important to characterize protein complexes at the level of the protein isoforms and post-translationally modified forms they contain in order to fully decipher the network of signaling and regulatory pathways within cells.Although many types of protein complexes have been studied in detail, in-depth analysis of the composition, dynamics, and isoform association of protein complexes formed in either human cells or model organisms is still not well documented at a system-wide level. The CORUM database, compiled using a variety of information from the literature describing protein interactions and assemblies, currently provides the largest public dataset of protein complexes (1). CORUM contains information relating to ∼1,970 protein complexes identified in human cells. However, these complexes are formed from proteins encoded by only ∼16% of the known human protein-coding genes, indicating that many forms of protein complexes still remain to be identified and characterized (1). Furthermore, the CORUM database does not describe how the protein compositions of the complexes may vary, either dynamically or in different subcellular locations, or how this relates to protein isoforms and PTMs. This illustrates that there is still a major deficit in our knowledge of the structure and functions of cellular protein complexes and how they contribute to biological regulatory mechanisms.The technique that is now most widely used to identify the components of protein complexes is affinity purification of an individual “bait” protein and subsequent analysis of the co-isolated proteins, usually via mass spectrometry (2). Affinity purification can use antibodies specific for an endogenous target protein (3, 4), if available, or, alternatively, can utilize a genetically constructed, epitope-tagged bait protein. The latter procedure is now widely used and is advantageous in that many different complexes can be compared using an identical antibody, or other affinity-purification method, targeted to the tag on the bait (for examples, see Refs. 5–7; for reviews, see Refs. 8 and 9). In contrast, it is harder to directly compare the results from immunoprecipitation of different endogenous protein complexes because each specific antibody that is used has different affinities and properties. Nonetheless, although epitope-tag “pull-down” techniques are now commonly used, they also have limitations. Not least, the addition of epitope tags to the bait can affect protein function and interactions (10, 11).To help determine whether co-purifying proteins detected using pull-down strategies represent specific partner proteins in bona fide complexes or are nonspecific contaminants, we and others have developed quantitative approaches, for example, based on variations of the stable isotope labeling of amino acids in cell culture (12–16). Additional data analysis procedures, including the use of a “super experiment” database that predicts the likelihood of nonspecific protein interactions based on the frequency with which any given protein is co-purified across many separate experiments, can also help to define the composition of protein complexes (17). Nonetheless, affinity purification strategies have a limited ability to distinguish multiple related complexes that may differ with respect to isoforms and PTMs. They are also costly and difficult to implement for large-scale studies to survey cellular complexes, and thus not well suited to study variations in complexes under different cellular growth conditions and responses.For system-wide studies of the composition and dynamics of protein complexes, alternative methods, in addition to immune-affinity purification, are required for convenient separation, characterization, and comparison of cellular protein complexes. To address this, a number of studies have utilized various forms of either column chromatography or native gel electrophoresis in combination with mass-spectrometry-based proteomics. For example, protein complexes have been separated using techniques including blue native polyacrylamide gel electrophoresis (18, 19), ion-exchange chromatography (20), and size-exclusion chromatography (21, 22) prior to MS analysis of proteins in the fractionated complexes. Size-exclusion chromatography (SEC) is a well-established technique used to separate proteins and protein complexes in solution on the basis of their shape/size (rotational cross-section) (23). SEC has been extensively used as an intermediate step in conventional multistep biochemical protein purification strategies. In contrast, SEC has been less commonly combined with mass-spectrometry-based proteomics for the high-throughput characterization of protein complexes. However, this has been demonstrated in previous studies that analyzed native protein complexes in plant chloroplasts (22) or large cytosolic complexes in mammalian cells (21).In this study, we combined native SEC with high-throughput mass-spectrometry-based proteomic analysis to characterize soluble protein complexes isolated from human osteosarcoma cells. Herein we demonstrate the utility and reproducibility of this approach for the system-wide characterization of endogenous, untagged protein complexes and show how it can be used to identify specific protein isoforms and PTMs associated with distinct protein complexes. The resulting data are available to the community in a convenient format in the Encyclopedia of Proteome Dynamics (EPD) (www.peptracker.com/encyclopediaInformation/), a user-friendly, searchable online database.     

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Quantitative proteomics combined with immuno-affinity purification, SILAC immunoprecipitation, represent a powerful means for the discovery of novel protein:protein interactions. By allowing the accurate relative quantification of protein abundance in both control and test samples, true interactions may be easily distinguished from experimental contaminants. Low affinity interactions can be preserved through the use of less-stringent buffer conditions and remain readily identifiable. This protocol discusses the labeling of tissue culture cells with stable isotope labeled amino acids, transfection and immunoprecipitation of an affinity tagged protein of interest, followed by the preparation for submission to a mass spectrometry facility. This protocol then discusses how to analyze and interpret the data returned from the mass spectrometer in order to identify cellular partners interacting with a protein of interest. As an example this technique is applied to identify proteins binding to the eukaryotic translation initiation factors: eIF4AI and eIF4AII.     

Using a Label Free Quantitative Proteomics Approach to Identify Changes in Protein Abundance in Multidrug-Resistant Mycobacterium tuberculosis

Reports in recent years indicate that the increasing emergence of resistance to drugs be using to TB treatment. The resistance to them severely affects to options for effective treatment. The emergence of multidrug-resistant tuberculosis has increased interest in understanding the mechanism of drug resistance in M. tuberculosis and the development of new therapeutics, diagnostics and vaccines. In this study, a label-free quantitative proteomics approach has been used to analyze proteome of multidrug-resistant and susceptible clinical isolates of M. tuberculosis and identify differences in protein abundance between the two groups. With this approach, we were able to identify a total of 1,583 proteins. The majority of identified proteins have predicted roles in lipid metabolism, intermediary metabolism, cell wall and cell processes. Comparative analysis revealed that 68 proteins identified by at least two peptides showed significant differences of at least twofolds in relative abundance between two groups. In all protein differences, the increase of some considering proteins such as NADH dehydrogenase, probable aldehyde dehydrogenase, cyclopropane mycolic acid synthase 3, probable arabinosyltransferase A, putative lipoprotein, uncharacterized oxidoreductase and six membrane proteins in resistant isolates might be involved in the drug resistance and to be potential diagnostic protein targets. The decrease in abundance of proteins related to secretion system and immunogenicity (ESAT-6-like proteins, ESX-1 secretion system associated proteins, O-antigen export system and MPT63) in the multidrug-resistant strains can be a defensive mechanism undertaken by the resistant cell.

Electronic supplementary material

The online version of this article (doi:10.1007/s12088-015-0511-2) contains supplementary material, which is available to authorized users.     

A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments

Abstract A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.     

Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory

Modeling of signal transduction pathways is instrumental for understanding cells’ function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells’ biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways’ logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein–protein interaction networks and to provide meaningful biological insights.     

稳定同位素标签技术在定量蛋白质组研究的应用

高通量的从蛋白质组水平进行整体的蛋白质鉴定和精确定量比较分析,在阐述生物功能以及疾病发生发展机制等方面非常重要。稳定同位素标签技术在过去的几年中获得了很大的发展,并形成了代谢引入类、化学合成类以及酶解引入类等三大类型。该文对稳定同位素标签技术的技术特点以及应用进行了简述。     

定量蛋白质组同位素标记技术及应用

定量蛋白质组学是对蛋白质组进行精确的定量和鉴定的学科,突破了传统蛋白质组研究集中于对蛋白质的分离和鉴定,着重于定性定量解析细胞蛋白质的动态变化信息,更真实地反映了细胞功能、过程机制等综合信息。以同位素为内标的质谱分析新技术的提出,显示出可同时自动鉴定和精确定量的能力,代表了目前定量蛋白质组研究的主要发展方向。对近年来定量蛋白质组学同位素标记技术和应用研究所取得的重要进展以及最新的发展动态进行了综述。     

Cloning of a Heavy-Metal-Binding Protein Derived from Activated-Sludge Microorganisms

A gene of the heavy-metal-binding protein (HMBP) was newly isolated from a genetic DNA library of activated-sludge microorganisms. HMBP was produced by transformed Escherichia coli, and the copper-binding ability of HMBP was confirmed. HMBP derived from activated sludge could be available as heavy metal adsorbents in water and wastewater treatments.     

Probabilistic Approach to Determining Unbiased Random-coil Carbon-13 Chemical Shift Values from the Protein Chemical Shift Database

We describe a probabilistic model for deriving, from the database of assigned chemical shifts, a set of random coil chemical shift values that are “unbiased” insofar as contributions from detectable secondary structure have been minimized (RCCS_u). We have used this approach to derive a set of RCCS_u values for ¹³C^α and ¹³C^β for 17 of the 20 standard amino acid residue types by taking advantage of the known opposite conformational dependence of these parameters. We present a second probabilistic approach that utilizes the maximum entropy principle to analyze the database of ¹³C^α and ¹³C^β chemical shifts considered separately; this approach yielded a second set of random coil chemical shifts (RCCS). Both new approaches analyze the chemical shift database without reference to known structure. Prior approaches have used either the chemical shifts of small peptides assumed to model the random coil state (RCCS_peptide) or statistical analysis of chemical shifts associated with structure not in helical or strand conformation (RCCS). We show that the RCCS values are strikingly similar to published RCCS_peptide and RCCS values. By contrast, the RCCS_u values differ significantly from both published types of random coil chemical shift values. The differences (RCCS_peptide−RCCS_u) for individual residue types show a correlation with known intrinsic conformational propensities. These results suggest that random coil chemical shift values from both prior approaches are biased by conformational preferences. RCCS_u values appear to be consistent with the current concept of the “random coil” as the state in which the geometry of the polypeptide ensemble samples the allowed region of (ϕ,ψ)-space in the absence of any dominant stabilizing interactions and thus represent an improved basis for the detection of secondary structure. Coupled with the growing database of chemical shifts, this probabilistic approach makes it possible to refine relationships among chemical shifts, their conformational propensities, and their dependence on pH, temperature, or neighboring residue type.Electronic Supplementary Material Supplementary material is available to authorised users in the online version of this article at .     

Protein Structure Validation and Refinement Using Amide Proton Chemical Shifts Derived from Quantum Mechanics

We present the ProCS method for the rapid and accurate prediction of protein backbone amide proton chemical shifts - sensitive probes of the geometry of key hydrogen bonds that determine protein structure. ProCS is parameterized against quantum mechanical (QM) calculations and reproduces high level QM results obtained for a small protein with an RMSD of 0.25 ppm (r = 0.94). ProCS is interfaced with the PHAISTOS protein simulation program and is used to infer statistical protein ensembles that reflect experimentally measured amide proton chemical shift values. Such chemical shift-based structural refinements, starting from high-resolution X-ray structures of Protein G, ubiquitin, and SMN Tudor Domain, result in average chemical shifts, hydrogen bond geometries, and trans-hydrogen bond (^h3 J_NC'') spin-spin coupling constants that are in excellent agreement with experiment. We show that the structural sensitivity of the QM-based amide proton chemical shift predictions is needed to obtain this agreement. The ProCS method thus offers a powerful new tool for refining the structures of hydrogen bonding networks to high accuracy with many potential applications such as protein flexibility in ligand binding.     

基于复杂网络的脑疾病功能网络研究进展

脑功能连接能够反映脑区间的相互联系状况,目前已成为脑功能研究的主要方法。复杂网络方法源于图论分析,可以对功能连接所构网络进行量化分析,提供多种量化指标。本文介绍了当前复杂网络的多种基本概念,及其在几种典型脑部疾病上的应用情况。     

Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations

Despite recent progress in proteomics most protein complexes are still unknown. Identification of these complexes will help us understand cellular regulatory mechanisms and support development of new drugs. Therefore it is really important to establish detailed information about the composition and the abundance of protein complexes but existing algorithms can only give qualitative predictions. Herein, we propose a new approach based on stochastic simulations of protein complex formation that integrates multi-source data—such as protein abundances, domain-domain interactions and functional annotations—to predict alternative forms of protein complexes together with their abundances. This method, called SiComPre (Simulation based Complex Prediction), achieves better qualitative prediction of yeast and human protein complexes than existing methods and is the first to predict protein complex abundances. Furthermore, we show that SiComPre can be used to predict complexome changes upon drug treatment with the example of bortezomib. SiComPre is the first method to produce quantitative predictions on the abundance of molecular complexes while performing the best qualitative predictions. With new data on tissue specific protein complexes becoming available SiComPre will be able to predict qualitative and quantitative differences in the complexome in various tissue types and under various conditions.     

Quantitative Proteomics Reveal Distinct Protein Regulations Caused by Aggregatibacter actinomycetemcomitans within Subgingival Biofilms

Periodontitis is an infectious disease that causes the inflammatory destruction of the tooth-supporting (periodontal) tissues, caused by polymicrobial biofilm communities growing on the tooth surface. Aggressive periodontitis is strongly associated with the presence of Aggregatibacter actinomycetemcomitans in the subgingival biofilms. Nevertheless, whether and how A. actinomycetemcomitans orchestrates molecular changes within the biofilm is unclear. The aim of this work was to decipher the interactions between A. actinomycetemcomitans and other bacterial species in a multi-species biofilm using proteomic analysis. An in vitro 10-species “subgingival” biofilm model, or its derivative that included additionally A. actinomycetemcomitans, were anaerobically cultivated on hydroxyapatite discs for 64 h. When present, A. actinomycetemcomitans formed dense intra-species clumps within the biofilm mass, and did not affect the numbers of the other species in the biofilm. Liquid chromatography-tandem mass spectrometry was used to identify the proteomic content of the biofilm lysate. A total of 3225 and 3352 proteins were identified in the biofilm, in presence or absence of A. actinomycetemcomitans, respectively. Label-free quantitative proteomics revealed that 483 out of the 728 quantified bacterial proteins (excluding those of A. actinomycetemcomitans) were accordingly regulated. Interestingly, all quantified proteins from Prevotella intermedia were up-regulated, and most quantified proteins from Campylobacter rectus, Streptococcus anginosus, and Porphyromonas gingivalis were down-regulated in presence of A. actinomycetemcomitans. Enrichment of Gene Ontology pathway analysis showed that the regulated groups of proteins were responsible primarily for changes in the metabolic rate, the ferric iron-binding, and the 5S RNA binding capacities, on the universal biofilm level. While the presence of A. actinomycetemcomitans did not affect the numeric composition or absolute protein numbers of the other biofilm species, it caused qualitative changes in their overall protein expression profile. These molecular shifts within the biofilm warrant further investigation on their potential impact on its virulence properties, and association with periodontal pathogenesis.     

A Guided Materials Screening Approach for Developing Quantitative Sol-gel Derived Protein Microarrays

Microarrays have found use in the development of high-throughput assays for new materials and discovery of small-molecule drug leads. Herein we describe a guided material screening approach to identify sol-gel based materials that are suitable for producing three-dimensional protein microarrays. The approach first identifies materials that can be printed as microarrays, narrows down the number of materials by identifying those that are compatible with a given enzyme assay, and then hones in on optimal materials based on retention of maximum enzyme activity. This approach is applied to develop microarrays suitable for two different enzyme assays, one using acetylcholinesterase and the other using a set of four key kinases involved in cancer. In each case, it was possible to produce microarrays that could be used for quantitative small-molecule screening assays and production of dose-dependent inhibitor response curves. Importantly, the ability to screen many materials produced information on the types of materials that best suited both microarray production and retention of enzyme activity. The materials data provide insight into basic material requirements necessary for tailoring optimal, high-density sol-gel derived microarrays.     

Inference of a Probabilistic Boolean Network from a Single Observed Temporal Sequence

The inference of gene regulatory networks is a key issue for genomic signal processing. This paper addresses the inference of probabilistic Boolean networks (PBNs) from observed temporal sequences of network states. Since a PBN is composed of a finite number of Boolean networks, a basic observation is that the characteristics of a single Boolean network without perturbation may be determined by its pairwise transitions. Because the network function is fixed and there are no perturbations, a given state will always be followed by a unique state at the succeeding time point. Thus, a transition counting matrix compiled over a data sequence will be sparse and contain only one entry per line. If the network also has perturbations, with small perturbation probability, then the transition counting matrix would have some insignificant nonzero entries replacing some (or all) of the zeros. If a data sequence is sufficiently long to adequately populate the matrix, then determination of the functions and inputs underlying the model is straightforward. The difficulty comes when the transition counting matrix consists of data derived from more than one Boolean network. We address the PBN inference procedure in several steps: (1) separate the data sequence into "pure" subsequences corresponding to constituent Boolean networks; (2) given a subsequence, infer a Boolean network; and (3) infer the probabilities of perturbation, the probability of there being a switch between constituent Boolean networks, and the selection probabilities governing which network is to be selected given a switch. Capturing the full dynamic behavior of probabilistic Boolean networks, be they binary or multivalued, will require the use of temporal data, and a great deal of it. This should not be surprising given the complexity of the model and the number of parameters, both transitional and static, that must be estimated. In addition to providing an inference algorithm, this paper demonstrates that the data requirement is much smaller if one does not wish to infer the switching, perturbation, and selection probabilities, and that constituent-network connectivity can be discovered with decent accuracy for relatively small time-course sequences.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]