Proteome Scale Research Articles

Exploiting conformational ensembles in modeling protein-protein interactions on the proteome scale.

Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than "blind" docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited an ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested "difficult" cases in a docking-benchmark data set, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from ~26 to 66%, and 57% of the interactions were successfully predicted in an "unbiased" scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome scale. We constructed the structural network of ERK interacting proteins as a case study.

LISE: a server using ligand-interacting and site-enriched protein triangles for prediction of ligand-binding sites

LISE is a web server for a novel method for predicting small molecule binding sites on proteins. It differs from a number of servers currently available for such predictions in two aspects. First, rather than relying on knowledge of similar protein structures, identification of surface cavities or estimation of binding energy, LISE computes a score by counting geometric motifs extracted from sub-structures of interaction networks connecting protein and ligand atoms. These network motifs take into account spatial and physicochemical properties of ligand-interacting protein surface atoms. Second, LISE has now been more thoroughly tested, as, in addition to the evaluation we previously reported using two commonly used small benchmark test sets and targets of two community-based experiments on ligand-binding site predictions, we now report an evaluation using a large non-redundant data set containing >2000 protein–ligand complexes. This unprecedented test, the largest ever reported to our knowledge, demonstrates LISE’s overall accuracy and robustness. Furthermore, we have identified some hard to predict protein classes and provided an estimate of the performance that can be expected from a state-of-the-art binding site prediction server, such as LISE, on a proteome scale. The server is freely available at http://lise.ibms.sinica.edu.tw.

Effect of posttranslational modifications on enzyme function and assembly

The detailed examination of enzyme molecules by mass spectrometry and other techniques continues to identify hundreds of distinct PTMs. Recently, global analyses of enzymes using methods of contemporary proteomics revealed widespread distribution of PTMs on many key enzymes distributed in all cellular compartments. Critically, patterns of multiple enzymatic and nonenzymatic PTMs within a single enzyme are now functionally evaluated providing a holistic picture of a macromolecule interacting with low molecular mass compounds, some of them being substrates, enzyme regulators, or activated precursors for enzymatic and nonenzymatic PTMs. Multiple PTMs within a single enzyme molecule and their mutual interplays are critical for the regulation of catalytic activity. Full understanding of this regulation will require detailed structural investigation of enzymes, their structural analogs, and their complexes. Further, proteomics is now integrated with molecular genetics, transcriptomics, and other areas leading to systems biology strategies. These allow the functional interrogation of complex enzymatic networks in their natural environment. In the future, one might envisage the use of robust high throughput analytical techniques that will be able to detect multiple PTMs on a global scale of individual proteomes from a number of carefully selected cells and cellular compartments. This article is part of a Special Issue entitled: Posttranslational Protein modifications in biology and Medicine.

Abstract 3392: New findings of kinase switch in gastrointestinal stromal tumor under imatinib using phosphoproteomic analysis.

Abstract Despite the initial effectiveness of imatinib for gastrointestinal stromal tumors (GISTs), patients must continue lifelong imatinib therapy, and may eventually relapse owing to acquired drug resistance. Pathology reveals the persistence of viable cells during imatinib therapy, even in the disappearance region upon radiological examination. These cells are thought to cause acquired drug resistance and relapse. To uncover the mechanisms underlying these clinical issues, we used GIST-T1 cells to investigate the phosphoproteomic alterations induced by imatinib treatment. Because some tyrosine kinases might be activated after imatinib exposure, it is important to reveal the increased phosphorylation of proteins on a proteomic scale, and elucidate drug-resistant proteins. Proteins extracted from imatinib-treated or untreated GIST-T1 cells were digested with trypsin. The resulting tyrosine-phosphorylated peptides were purified using an immunoaffinity-enriched and iTRAQ-labeled reagent, and analyzed using mass spectroscopy. Using this phosphoproteomic approach, we identified 171 tyrosine phosphorylation sites spanning 134 proteins. We also detected increased tyrosine phosphorylation of Fyn (Y420) and focal adhesion kinase (FAK) (Y576). Increased phosphorylation was also confirmed by Western blot analysis, and knockdown of Fyn or FAK increased cells’ sensitivity to imatinib. Furthermore, the anti-proliferative effect of imatinib significantly enhanced imatinib-resistant GIST-T1 cells, which exhibited constitutive phosphorylation of FAK when treated with a FAK-selective kinase inhibitor. Moreover, FYN was proved to be a signal of FAK of upper reaches. These results indicate that an iTRAQ-based quantitative phospho-tyrosine-focused phosphoproteomic approach is a powerful method for screening phosphoproteins associated with drug resistance. Fyn or FAK might be potential therapeutic targets to overcome acquired resistance to imatinib in GISTs. Citation Format: Maiko Ako, Tsuyoshi Takahashi, Satoshi Serada, Minoru Fujimoto, Yasuaki Miyazaki, Kazuki Shimada, Takayuki Ikezoe, Takahiro Taguchi, Yukinori Kurokawa, Makoto Yamasaki, Hiroshi Miyata, Kiyokazu Nakajima, Shuji Takiguchi, Masaki Mori, Yuichirou Doki, Tetsuji Naka, Toshirou Nishida. New findings of kinase switch in gastrointestinal stromal tumor under imatinib using phosphoproteomic analysis. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3392. doi:10.1158/1538-7445.AM2013-3392

Proteome-wide profiling of activated transcription factors with a concatenated tandem array of transcription factor response elements

Transcription factors (TFs) are families of proteins that bind to specific DNA sequences, or TF response elements (TFREs), and function as regulators of many cellular processes. Because of the low abundance of TFs, direct quantitative measurement of TFs on a proteome scale remains a challenge. In this study, we report the development of an affinity reagent that permits identification of endogenous TFs at the proteome scale. The affinity reagent is composed of a synthetic DNA containing a concatenated tandem array of the consensus TFREs (catTFRE) for the majority of TF families. By using catTFRE to enrich TFs from cells, we were able to identify as many as 400 TFs from a single cell line and a total of 878 TFs from 11 cell types, covering more than 50% of the gene products that code for the DNA-binding TFs in the genome. We further demonstrated that catTFRE pull-downs could quantitatively measure proteome-wide changes in DNA binding activity of TFs in response to exogenous stimulation by using a label-free MS-based quantification approach. Applying catTFRE on the evaluation of drug effects, we described a panoramic view of TF activations and provided candidates for the elucidation of molecular mechanisms of drug actions. We anticipate that the catTFRE affinity strategy will find widespread applications in biomedical research.

Protein Microarrays for Identification of Novel Extracellular Protein‐Protein Interactions

Functional protein microarrays offer the capability for high-throughput protein interaction analysis and have long promised to be a powerful tool for understanding protein interactions at the proteome scale. Although popular techniques for protein-protein interaction mapping like yeast-two-hybrid and affinity-purification mass spectrometry have performed well for identifying intracellular protein-protein interactions, the study of interactions between extracellular proteins has remained challenging for these methods. Instead, the use of protein microarrays appears to be a robust and efficient method for the identification of interactions among the members of this class of protein. This unit describes methods for extracellular protein microarray production, screening, and analysis. A protocol is described for enhanced detection of low-affinity interactions by generating multivalent complexes using Fc-fusion bait proteins and protein A microbeads, along with a statistical method for hit scoring and identification of nonspecific interactions.

Stable isotope labeling of mammals (SILAM) for in vivo quantitative proteomic analysis

Metabolic labeling of rodent proteins with 15N, a heavy stable isotope of nitrogen, provides an efficient way for relative quantitation of differentially expressed proteins. Here we describe a protocol for metabolic labeling of rats with an 15N-enriched spirulina diet. As a case study, we also demonstrate the application of 15N-enriched tissue as a common internal standard in quantitative analysis of differentially expressed proteins in neurodevelopment in rats at two different time points, postnatal day 1 and 45. We briefly discuss the bioinformatics tools, ProLucid and Census, which can easily be used in a sequential manner to identify and quantitate relative protein levels on a proteomic scale.

Proteome-Wide Discovery and Characterizations of Nucleotide-Binding Proteins with Affinity-Labeled Chemical Probes

Nucleotide-binding proteins play pivotal roles in many cellular processes including cell signaling. However, targeted studies of the subproteome of nucleotide-binding proteins, especially protein kinases and GTP-binding proteins, remain challenging. Here, we report a general strategy in using affinity-labeled chemical probes to enrich, identify, and quantify ATP- and GTP-binding proteins in the entire human proteome. Our results revealed that the ATP/GTP affinity probes facilitated the identification of 100 GTP-binding proteins and 206 kinases with the use of low milligram quantities of lysate of HL-60 cells. In combination with the use of the stable isotope labeling by amino acids in cell culture-based quantitative proteomics method, we assessed the ATP/GTP binding selectivities of nucleotide-binding proteins at the global proteome scale. Our results confirmed known and, more importantly, unveiled new ATP/GTP-binding preferences of hundreds of nucleotide-binding proteins. Additionally, our strategy led to the identification of three and one unique nucleotide-binding motifs for kinases and GTP-binding proteins, respectively, and the characterizations of the nucleotide-binding selectivities of individual motifs. Our strategy for capturing and characterizing ATP/GTP-binding proteins should be generally applicable for those proteins that can interact with other nucleotides.

Structural dynamics flexibility informs function and evolution at a proteome scale

Protein structures are dynamic entities with a myriad of atomic fluctuations, side-chain rotations, and collective domain movements. Although the importance of these dynamics to proper functioning of proteins is emerging in the studies of many protein families, there is a lack of broad evidence for the critical role of protein dynamics in shaping the biological functions of a substantial fraction of residues for a large number of proteins in the human proteome. Here, we propose a novel dynamic flexibility index (dfi) to quantify the dynamic properties of individual residues in any protein and use it to assess the importance of protein dynamics in 100 human proteins. Our analyses involving functionally critical positions, disease-associated and putatively neutral population variations, and the rate of interspecific substitutions per residue produce concordant patterns at a proteome scale. They establish that the preservation of dynamic properties of residues in a protein structure is critical for maintaining the protein/biological function. Therefore, structural dynamics needs to become a major component of the analysis of protein function and evolution. Such analyses will be facilitated by the dfi, which will also enable the integrative use of structural dynamics with evolutionary conservation in genomic medicine as well as functional genomics investigations.

Structural Dynamics Flexibility Informs Function and Evolution at a Proteome Scale

Structural Dynamics Flexibility Informs Function and Evolution at a Proteome Scale

Discrimination of Differentially Inhibited Cysteine Proteases by Activity-Based Profiling Using Cystatin Variants with Tailored Specificities

Recent research has shown the possibility of tailoring the inhibitory specificity of plant cystatins toward cysteine (Cys) proteases by single mutations at positively selected amino acid sites. Here we devised a cystatin activity-based profiling approach to assess the impact of such mutations at the proteome scale using single variants of tomato cystatin SlCYS8 and digestive Cys proteases of the herbivorous insect, Colorado potato beetle, as a model. Biotinylated forms of SlCYS8 and SlCYS8 variants were used to capture susceptible Cys proteases in insect midgut protein extracts by biotin immobilization on avidin-embedded beads. A quantitative LC-MS/MS analysis of the captured proteins was performed to compare the inhibitory profile of different SlCYS8 variants. The approach confirmed the relevance of phylogenetic inferences categorizing the insect digestive Cys proteases into six functionally distinct families. It also revealed significant variation in protease family profiles captured with N-terminal variants of SlCYS8, in line with in silico structural models for Cys protease-SlCYS8 interactions suggesting a functional role for the N-terminal region. Our data confirm overall the usefulness of cystatin activity-based protease profiling for the monitoring of Cys protease-inhibitor interactions in complex biological systems. They also illustrate the potential of biotinylated cystatins to identify recombinant cystatin candidates for the inactivation of specific Cys protease targets.

Finding Protein Targets for Small Biologically Relevant Ligands across Fold Space Using Inverse Ligand Binding Predictions

Inverse ligand binding prediction utilizes a few protein-ligand (drug) complexes to predict other secondary therapeutic and off-targets of a given drug molecule on a proteomic scale. We adapt twobinding site predictors, FINDSITE and SMAP, to perform the inverse predictions and evaluate them on over 30 representative ligands. Use of justone complex allows the identification of other protein targets; the availability of additional complexes improves the results. Both methods offer comparable quality when using three complexes with diverse proteins. SMAP is better when fewer complexes are available, while FINDSITE provides stronger predictions for smaller ligands. We propose a consensus that combines (and outperforms)the two complementary approaches implemented by FINDSITE and SMAP. Most importantly, we demonstrate that these methods successfully find distant targets that belong to structurally different folds compared to the proteins in the input complexes.

Proteomics Strategy to Identify Substrates of LNX, a PDZ Domain-containing E3 Ubiquitin Ligase

Ubiquitin ligases (E3s) confer specificity to ubiquitination by recognizing target substrates. However, the substrates of most E3s have not been extensively discovered, and new methods are needed to efficiently and comprehensively identify these substrates. Mostly, E3s specifically recognize substrates via their protein interaction domains. We developed a novel integrated strategy to identify substrates of E3s containing protein interaction domains on a proteomic scale. The binding properties of the protein interaction domains were characterized by screening a random peptide library using a yeast two-hybrid system. Artificial degrons, consisting of a preferential ubiquitination sequence and particular interaction domain-binding motifs, were tested as potential substrates by in vitro ubiquitination assays. Using this strategy, not only substrates but also nonsubstrate regulators can be discovered. The detailed substrate recognition mechanisms, which are useful for drug discovery, can also be characterized. We used the Ligand of Numb protein X (LNX) family of E3s, a group of PDZ domain-containing RING-type E3 ubiquitin ligases, to demonstrate the feasibility of this strategy. Many potential substrates of LNX E3s were identified. Eight of the nine selected candidates were ubiquitinated in vitro, and two novel endogenous substrates, PDZ-binding kinase (PBK) and breakpoint cluster region protein (BCR), were confirmed in vivo. We further revealed that the LNX1-mediated ubiquitination and degradation of PBK inhibited cell proliferation and enhanced sensitivity to doxorubicin-induced apoptosis. The substrate recognition mechanism of LNX E3s was also characterized; this process involves the recognition of substrates via their specific PDZ domains by binding to the C-termini of the target proteins. This strategy can potentially be extended to a variety of E3s that contain protein interaction domain(s), thereby serving as a powerful tool for the comprehensive identification of their substrates on a proteomic scale.

Tearing the Top Off ‘Top-Down’ Proteomics

Tearing the Top Off ‘Top-Down’ Proteomics

Simplified proteomics approach to discover protein–ligand interactions

Identifying targets of biologically active small molecules is an essential but still challenging task in drug research and chemical genetics. Energetics-based target identification is an approach that utilizes the change in the conformational stabilities of proteins upon ligand binding in order to identify target proteins. Different from traditional affinity-based capture approaches, energetics-based methods do not require any labeling or immobilization of the test molecule. Here, we report a surprisingly simple version of energetics-based target identification, which only requires ion exchange chromatography, SDS PAGE, and minimal use of mass spectrometry. The complexity of a proteome is reduced through fractionation by ion exchange chromatography. Urea-induced unfolding of proteins in each fraction is then monitored by the significant increase in proteolytic susceptibility upon unfolding in the presence and the absence of a ligand. Proteins showing a different degree of unfolding with the ligand are identified by SDS PAGE followed by mass spectrometry. Using this approach, we identified ATP-binding proteins in the Escherichia coli proteome. In addition to known ATP-binding proteins, we also identified a number of proteins that were not previously known to interact with ATP. To validate one such finding, we cloned and purified phosphoglyceromutase, which was not previously known to bind ATP, and confirmed that ATP indeed stabilizes this protein. The combination of fractionation and pulse proteolysis offers an opportunity to investigate protein-drug or protein-metabolite interactions on a proteomic scale with minimal instrumentation and without modification of a molecule of interest.

5-Aza-2′-deoxycytidine Induced Growth Inhibition of Leukemia Cells through Modulating Endogenous Cholesterol Biosynthesis

5-Aza-2'-deoxycytidine (5-Aza-CdR), a nucleoside analog that can inhibit DNA cytosine methylation, possesses potent antitumorigenic activities for myeloid disorders. Although 5-Aza-CdR is known to be incorporated into DNA and inhibit DNA (cytosine-5)-methyltransferases, the precise mechanisms underlying the drug's antineoplastic activity remain unclear. Here we utilized a mass spectrometry-based quantitative proteomic method to analyze the 5-Aza-CdR-induced perturbation of protein expression in Jurkat-T cells at the global proteome scale. Among the ≈ 2780 quantified proteins, 188 exhibited significant alteration in expression levels upon a 24-hr treatment with 5 μm 5-Aza-CdR. In particular, we found that drug treatment led to substantially reduced expression of farnesyl diphosphate synthase (FDPS) and farnesyl diphosphate farnesyltransferase (FDFT1), two important enzymes involved in de novo cholesterol synthesis. Consistent with this finding, 5-Aza-CdR treatment of leukemia (Jurkat-T, K562 and HL60) and melanoma (WM-266-4) cells led to a marked decrease in cellular cholesterol content and pronounced growth inhibition, which could be rescued by externally added cholesterol. Exposure of these cells to 5-Aza-CdR also led to epigenetic reactivation of dipeptidyl peptidase 4 (DPP4) gene. Additionally, suppression of DPP4 expression with siRNA induced elevated protein levels of FDPS and FDFT1, and increased cholesterol biosynthesis in WM-266-4 cells. Together, the results from the present study revealed, for the first time, that 5-Aza-CdR exerts its cytotoxic effects in leukemia and melanoma cells through epigenetic reactivation of DPP4 gene and the resultant inhibition of cholesterol biosynthesis in these cells.

Constructing structural networks of signaling pathways on the proteome scale

Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces.

Compartment Modeling for Mammalian Protein Turnover Studies by Stable Isotope Metabolic Labeling

Protein turnover studies on a proteome scale based on metabolic isotopic labeling can provide a systematic understanding of mechanisms for regulation of protein abundances and their transient behaviors. At this time, these large-scale studies typically utilize a simple kinetic model to extract protein dynamic information. Although many high-quality, protein isotope incorporation data are available from those experiments, accurate and additionally useful protein dynamic information cannot be extracted from the experimental data by use of the simple kinetic models. In this paper, we describe a formal connection between data obtained from elemental isotope labeling experiments and the well-known compartment modeling, and we demonstrate that an appropriate application of a compartment model to turnover of proteins from mammalian tissues can indeed lead to a better fitting of the experimental data.

Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models.

Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein-protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue-residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain-peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein-protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain-peptide interactions.

Fast and accurate modeling of protein-protein interactions by combining template-interface-based docking with flexible refinement.

The similarity between folding and binding led us to posit the concept that the number of protein-protein interface motifs in nature is limited, and interacting protein pairs can use similar interface architectures repeatedly, even if their global folds completely vary. Thus, known protein-protein interface architectures can be used to model the complexes between two target proteins on the proteome scale, even if their global structures differ. This powerful concept is combined with a flexible refinement and global energy assessment tool. The accuracy of the method is highly dependent on the structural diversity of the interface architectures in the template dataset. Here, we validate this knowledge-based combinatorial method on the Docking Benchmark and show that it efficiently finds high-quality models for benchmark complexes and their binding regions even in the absence of template interfaces having sequence similarity to the targets. Compared to "classical" docking, it is computationally faster; as the number of target proteins increases, the difference becomes more dramatic. Further, it is able to distinguish binders from nonbinders. These features allow performing large-scale network modeling. The results on an independent target set (proteins in the p53 molecular interaction map) show that current method can be used to predict whether a given protein pair interacts. Overall, while constrained by the diversity of the template set, this approach efficiently produces high-quality models of protein-protein complexes. We expect that with the growing number of known interface architectures, this type of knowledge-based methods will be increasingly used by the broad proteomics community.