Structural transitions enable interleukin-18 maturation and signaling.

Several interleukin-1 (IL-1) family members, including IL-1ß and IL-18, require processing by inflammasome-associated caspases to unleash their activities. Here, we unveil, by cryoelectron microscopy (cryo-EM), two major conformations of the complex between caspase-1 and pro-IL-18. One conformation is similar to the complex of caspase-4 and pro-IL-18, with interactions at both the active site and an exosite (closed conformation), and the other only contains interactions at the active site (open conformation). Thus, pro-IL-18 recruitment and processing by caspase-1 is less dependent on the exosite than the active site, unlike caspase-4. Structure determination by nuclear magnetic resonance uncovers a compact fold of apo pro-IL-18, which is similar to caspase-1-bound pro-IL-18 but distinct from cleaved IL-18. Binding sites for IL-18 receptor and IL-18 binding protein are only formed upon conformational changes after pro-IL-18 cleavage. These studies show how pro-IL-18 is selected as a caspase-1 substrate, and why cleavage is necessary for its inflammatory activity.

The BioPlex Network: A Systematic Exploration of the Human Interactome.

Protein interactions form a network whose structure drives cellular function and whose organization informs biological inquiry. Using high-throughput affinity-purification mass spectrometry, we identify interacting partners for 2,594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7,668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biological process, and molecular function, enabling functional characterization of thousands of proteins. Network structure also reveals associations among thousands of protein domains, suggesting a basis for examining structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biological or clinical significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors.

Structural insights into cytokine cleavage by inflammatory caspase-4.

Inflammatory caspases are key enzymes in mammalian innate immunity that control the processing and release of interleukin-1 (IL-1)-family cytokines1,2. Despite the biological importance, the structural basis for inflammatory caspase-mediated cytokine processing has remained unclear. To date, catalytic cleavage of IL-1-family members, including pro-IL-1ß and pro-IL-18, has been attributed primarily to caspase-1 activities within canonical inflammasomes3. Here we demonstrate that the lipopolysaccharide receptor caspase-4 from humans and other mammalian species (except rodents) can cleave pro-IL-18 with an efficiency similar to pro-IL-1ß and pro-IL-18 cleavage by the prototypical IL-1-converting enzyme caspase-1. This ability of caspase-4 to cleave pro-IL-18, combined with its previously defined ability to cleave and activate the lytic pore-forming protein gasdermin D (GSDMD)4,5, enables human cells to bypass the need for canonical inflammasomes and caspase-1 for IL-18 release. The structure of the caspase-4-pro-IL-18 complex determined using cryogenic electron microscopy reveals that pro-lL-18 interacts with caspase-4 through two distinct interfaces: a protease exosite and an interface at the caspase-4 active site involving residues in the pro-domain of pro-IL-18, including the tetrapeptide caspase-recognition sequence6. The mechanisms revealed for cytokine substrate capture and cleavage differ from those observed for the caspase substrate GSDMD7,8. These findings provide a structural framework for the discussion of caspase activities in health and disease.

Homogeneous Oligomers of Pro-apoptotic BAX Reveal Structural Determinants of Mitochondrial Membrane Permeabilization.

BAX is a pro-apoptotic protein that transforms from a cytosolic monomer into a toxic oligomer that permeabilizes the mitochondrial outer membrane. How BAX monomers assemble into a higher-order conformation, and the structural determinants essential to membrane permeabilization, remain a mechanistic mystery. A key hurdle has been the inability to generate a homogeneous BAX oligomer (BAXO) for analysis. Here, we report the production and characterization of a full-length BAXO that recapitulates physiologic BAX activation. Multidisciplinary studies revealed striking conformational consequences of oligomerization and insight into the macromolecular structure of oligomeric BAX. Importantly, BAXO enabled the assignment of specific roles to particular residues and α helices that mediate individual steps of the BAX activation pathway, including unexpected functionalities of BAX α6 and α9 in driving membrane disruption. Our results provide the first glimpse of a full-length and functional BAXO, revealing structural requirements for the elusive execution phase of mitochondrial apoptosis.

DPP9 sequesters the C terminus of NLRP1 to repress inflammasome activation.

Nucleotide-binding domain and leucine-rich repeat pyrin-domain containing protein 1 (NLRP1) is an inflammasome sensor that mediates the activation of caspase-1 to induce cytokine maturation and pyroptosis1-4. Gain-of-function mutations of NLRP1 cause severe inflammatory diseases of the skin4-6. NLRP1 contains a function-to-find domain that auto-proteolyses into noncovalently associated subdomains7-9, and proteasomal degradation of the repressive N-terminal fragment of NLRP1 releases its inflammatory C-terminal fragment (NLRP1 CT)10,11. Cytosolic dipeptidyl peptidases 8 and 9 (hereafter, DPP8/DPP9) both interact with NLRP1, and small-molecule inhibitors of DPP8/DPP9 activate NLRP1 by mechanisms that are currently unclear10,12-14. Here we report cryo-electron microscopy structures of the human NLRP1-DPP9 complex alone and with Val-boroPro (VbP), an inhibitor of DPP8/DPP9. The structures reveal a ternary complex that comprises DPP9, full-length NLRP1 and the NLRPT CT. The binding of the NLRP1 CT to DPP9 requires full-length NLRP1, which suggests that NLRP1 activation is regulated by the ratio of NLRP1 CT to full-length NLRP1. Activation of the inflammasome by ectopic expression of the NLRP1 CT is consistently rescued by co-expression of autoproteolysis-deficient full-length NLRP1. The N terminus of the NLRP1 CT inserts into the DPP9 active site, and VbP disrupts this interaction. Thus, VbP weakens the NLRP1-DPP9 interaction and accelerates degradation of the N-terminal fragment10 to induce inflammasome activation. Overall, these data demonstrate that DPP9 quenches low levels of NLRP1 CT and thus serves as a checkpoint for activation of the NLRP1 inflammasome.

A protein complex network of Drosophila melanogaster.

Determining the composition of protein complexes is an essential step toward understanding the cell as an integrated system. Using coaffinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly 5,000 individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a statistical framework designed to define individual protein-protein interactions, led to the generation of a Drosophila protein interaction map (DPiM) encompassing 556 protein complexes. The high quality of the DPiM and its usefulness as a paradigm for metazoan proteomes are apparent from the recovery of many known complexes, significant enrichment for shared functional attributes, and validation in human cells. The DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. The DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution.

A specialized integrin-binding motif enables proTGF-ß2 activation by integrin αVß6 but not αVß8.

Activation of latent transforming growth factor (TGF)-ß2 is incompletely understood. Unlike TGF-ß1 and ß3, the TGF-ß2 prodomain lacks a seven-residue RGDLXX (L/I) integrin-recognition motif and is thought not to be activated by integrins. Here, we report the surprising finding that TGF-ß2 contains a related but divergent 13-residue integrin-recognition motif (YTSGDQKTIKSTR) that specializes it for activation by integrin αVß6 but not αVß8. Both classes of motifs compete for the same binding site in αVß6. Multiple changes in the longer motif underlie its specificity. ProTGF-ß2 structures define interesting differences from proTGF-ß1 and the structural context for activation by αVß6. Some integrin-independent activation is also seen for proTGF-ß2 and even more so for proTGF-ß3. Our findings have important implications for therapeutics to αVß6 in clinical trials for fibrosis, in which inhibition of TGF-ß2 activation has not been anticipated.

A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics.

Mass-spectrometry-based phosphoproteomics has become indispensable for understanding cellular signaling in complex biological systems. Despite the central role of protein phosphorylation, the field still lacks inexpensive, regenerable, and diverse phosphopeptides with ground-truth phosphorylation positions. Here, we present Iterative Synthetically Phosphorylated Isomers (iSPI), a proteome-scale library of human-derived phosphoserine-containing phosphopeptides that is inexpensive, regenerable, and diverse, with precisely known positions of phosphorylation. We demonstrate possible uses of iSPI, including use as a phosphopeptide standard, a tool to evaluate and optimize phosphorylation-site localization algorithms, and a benchmark to compare performance across data analysis pipelines. We also present AScorePro, an updated version of the AScore algorithm specifically optimized for phosphorylation-site localization in higher energy fragmentation spectra, and the FLR viewer, a web tool for phosphorylation-site localization, to enable community use of the iSPI resource. iSPI and its associated data constitute a useful, multi-purpose resource for the phosphoproteomics community.

The relaxin receptor RXFP1 signals through a mechanism of autoinhibition.

The relaxin family peptide receptor 1 (RXFP1) is the receptor for relaxin-2, an important regulator of reproductive and cardiovascular physiology. RXFP1 is a multi-domain G protein-coupled receptor (GPCR) with an ectodomain consisting of a low-density lipoprotein receptor class A (LDLa) module and leucine-rich repeats. The mechanism of RXFP1 signal transduction is clearly distinct from that of other GPCRs, but remains very poorly understood. In the present study, we determine the cryo-electron microscopy structure of active-state human RXFP1, bound to a single-chain version of the endogenous agonist relaxin-2 and the heterotrimeric Gs protein. Evolutionary coupling analysis and structure-guided functional experiments reveal that RXFP1 signals through a mechanism of autoinhibition. Our results explain how an unusual GPCR family functions, providing a path to rational drug development targeting the relaxin receptors.

High-density chemical cross-linking for modeling protein interactions.

Detailed mechanistic understanding of protein complex function is greatly enhanced by insights from its 3-dimensional structure. Traditional methods of protein structure elucidation remain expensive and labor-intensive and require highly purified starting material. Chemical cross-linking coupled with mass spectrometry offers an alternative that has seen increased use, especially in combination with other experimental approaches like cryo-electron microscopy. Here we report advances in method development, combining several orthogonal cross-linking chemistries as well as improvements in search algorithms, statistical analysis, and computational cost to achieve coverage of 1 unique cross-linked position pair for every 7 amino acids at a 1% false discovery rate. This is accomplished without any peptide-level fractionation or enrichment. We apply our methods to model the complex between a carbonic anhydrase (CA) and its protein inhibitor, showing that the cross-links are self-consistent and define the interaction interface at high resolution. The resulting model suggests a scaffold for development of a class of protein-based inhibitors of the CA family of enzymes. We next cross-link the yeast proteasome, identifying 3,893 unique cross-linked peptides in 3 mass spectrometry runs. The dataset includes 1,704 unique cross-linked position pairs for the proteasome subunits, more than half of them intersubunit. Using multiple recently solved cryo-EM structures, we show that observed cross-links reflect the conformational dynamics and disorder of some proteasome subunits. We further demonstrate that this level of cross-linking density is sufficient to model the architecture of the 19-subunit regulatory particle de novo.

Improved Monoisotopic Mass Estimation for Deeper Proteome Coverage.

Accurate assignment of monoisotopic peaks is essential for the identification of peptides in bottom-up proteomics. Misassignment or inaccurate attribution of peptidic ions leads to lower sensitivity and fewer total peptide identifications. In the present work, we present a performant, open-source, cross-platform algorithm, Monocle, for the rapid reassignment of instrument-assigned precursor peaks to monoisotopic peptide assignments. We demonstrate that the present algorithm can be integrated into many common proteomic pipelines and provides rapid conversion from multiple data source types. Finally, we show that our monoisotopic peak assignment results in up to a twofold increase in total peptide identifications compared to analyses lacking monoisotopic correction and a 44% improvement over previous monoisotopic peak correction algorithms.

Active Instrument Engagement Combined with a Real-Time Database Search for Improved Performance of Sample Multiplexing Workflows.

Quantitative proteomics employing isobaric reagents has been established as a powerful tool for biological discovery. Current workflows often utilize a dedicated quantitative spectrum to improve quantitative accuracy and precision. A consequence of this approach is a dramatic reduction in the spectral acquisition rate, which necessitates the use of additional instrument time to achieve comprehensive proteomic depth. This work assesses the performance and benefits of online and real-time spectral identification in quantitative multiplexed workflows. A Real-Time Search (RTS) algorithm was implemented to identify fragment spectra within milliseconds as they are acquired using a probabilistic score and to trigger quantitative spectra only upon confident peptide identification. The RTS-MS3 was benchmarked against standard workflows using a complex two-proteome model of interference and a targeted 10-plex comparison of kinase abundance profiles. Applying the RTS-MS3 method provided the comprehensive characterization of a 10-plex proteome in 50% less acquisition time. These data indicate that the RTS-MS3 approach provides dramatic performance improvements for quantitative multiplexed experiments.

Large-scale characterization of drug mechanism of action using proteome-wide thermal shift assays.

In response to an ever-increasing demand of new small molecules therapeutics, numerous chemical and genetic tools have been developed to interrogate compound mechanism of action. Owing to its ability to characterize compound-dependent changes in thermal stability, the proteome-wide thermal shift assay has emerged as a powerful tool in this arsenal. The most recent iterations have drastically improved the overall efficiency of these assays, providing an opportunity to screen compounds at a previously unprecedented rate. Taking advantage of this advance, we quantified 1.498 million thermal stability measurements in response to multiple classes of therapeutic and tool compounds (96 compounds in living cells and 70 compounds in lysates). When interrogating the dataset as a whole, approximately 80% of compounds (with quantifiable targets) caused a significant change in the thermal stability of an annotated target. There was also a wealth of evidence portending off-target engagement despite the extensive use of the compounds in the laboratory and/or clinic. Finally, the combined application of cell- and lysate-based assays, aided in the classification of primary (direct ligand binding) and secondary (indirect) changes in thermal stability. Overall, this study highlights the value of these assays in the drug development process by affording an unbiased and reliable assessment of compound mechanism of action.

Mycobacterium tuberculosis prokaryotic ubiquitin-like protein-deconjugating enzyme is an unusual aspartate amidase.

Deamidase of Pup (Dop), the prokaryotic ubiquitin-like protein (Pup)-deconjugating enzyme, is critical for the full virulence of Mycobacterium tuberculosis and is unique to bacteria, providing an ideal target for the development of selective chemotherapies. We used a combination of genetics and chemical biology to characterize the mechanism of depupylation. We identified an aspartate as a potential nucleophile in the active site of Dop, suggesting a novel protease activity to target for inhibitor development.

Protein-protein docking benchmark version 3.0.

We present version 3.0 of our publicly available protein-protein docking benchmark. This update includes 40 new test cases, representing a 48% increase from Benchmark 2.0. For all of the new cases, the crystal structures of both binding partners are available. As with Benchmark 2.0, Structural Classification of Proteins (Murzin et al., J Mol Biol 1995;247:536-540) was used to remove redundant test cases. The 124 unbound-unbound test cases in Benchmark 3.0 are classified into 88 rigid-body cases, 19 medium-difficulty cases, and 17 difficult cases, based on the degree of conformational change at the interface upon complex formation. In addition to providing the community with more test cases for evaluating docking methods, the expansion of Benchmark 3.0 will facilitate the development of new algorithms that require a large number of training examples. Benchmark 3.0 is available to the public at http://zlab.bu.edu/benchmark.

Protein-protein docking: overview and performance analysis.

Protein-protein docking is the computational prediction of protein complex structure given the individually solved component protein structures. It is an important means for understanding the physicochemical forces that underlie macromolecular interactions and a valuable tool for modeling protein complex structures. Here, we report an overview of protein-protein docking with specific emphasis on our Fast Fourier Transform-based rigid-body docking program ZDOCK, which is consistently rated as one of the most accurate docking programs in the Critical Assessment of Predicted Interactions (CAPRI), a series of community-wide blind tests. We also investigate ZDOCK's performance on a non-redundant protein complex benchmark. Finally, we perform regression analysis to better understand the strengths and weaknesses of ZDOCK and to suggest areas of future development for protein-docking algorithms in general.

The performance of ZDOCK and ZRANK in rounds 6-11 of CAPRI.

We present an evaluation of our protein-protein docking approach using the ZDOCK and ZRANK algorithms, in combination with structural clustering and filtering, utilizing biological data in Rounds 6-11 of the CAPRI docking experiment. We achieved at least one prediction of acceptable accuracy for five of six targets submitted. In addition, two targets resulted in medium-accuracy predictions. In the new scoring portion of the CAPRI exercise, we were able to attain at least one acceptable prediction for the three targets submitted and achieved three medium-accuracy predictions for Target 26. Scoring was performed using ZRANK, a new algorithm for reranking initial-stage docking predictions using a weighted energy function and no structural refinement. Here we outline a practical and successful docking strategy, given limited prior biological knowledge of the complex to be predicted.

Integrating statistical pair potentials into protein complex prediction.

The biophysical study of protein-protein interactions and docking has important implications in our understanding of most complex cellular signaling processes. Most computational approaches to protein docking involve a tradeoff between the level of detail incorporated into the model and computational power required to properly handle that level of detail. In this work, we seek to optimize that balance by showing that we can reduce the complexity of model representation and thus make the computation tractable with minimal loss of predictive performance. We also introduce a pair-wise statistical potential suitable for docking that builds on previous work and show that this potential can be incorporated into our fast fourier transform-based docking algorithm ZDOCK. We use the Protein Docking Benchmark to illustrate the improved performance of this potential compared with less detailed other scoring functions. Furthermore, we show that the new potential performs well on antibody-antigen complexes, with most predictions clustering around the Complementarity Determining Regions of antibodies without any manual intervention.

Design of a combinatorial DNA microarray for protein-DNA interaction studies.

BACKGROUND: Discovery of precise specificity of transcription factors is an important step on the way to understanding the complex mechanisms of gene regulation in eukaryotes. Recently, double-stranded protein-binding microarrays were developed as a potentially scalable approach to tackle transcription factor binding site identification. RESULTS: Here we present an algorithmic approach to experimental design of a microarray that allows for testing full specificity of a transcription factor binding to all possible DNA binding sites of a given length, with optimally efficient use of the array. This design is universal, works for any factor that binds a sequence motif and is not species-specific. Furthermore, simulation results show that data produced with the designed arrays is easier to analyze and would result in more precise identification of binding sites. CONCLUSION: In this study, we present a design of a double stranded DNA microarray for protein-DNA interaction studies and show that our algorithm allows optimally efficient use of the arrays for this purpose. We believe such a design will prove useful for transcription factor binding site identification and other biological problems.