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Determining structures of protein complexes is crucial for understanding cellular functions. Here, we describe an integrative structure determination approach that relies on in vivo measurements of genetic interactions. We construct phenotypic profiles for point mutations crossed against gene deletions or exposed to environmental perturbations, followed by converting similarities between two profiles into an upper bound on the distance between the mutated residues. We determine the structure of the yeast histone H3-H4 complex based on ~500,000 genetic interactions of 350 mutants. We then apply the method to subunits Rpb1-Rpb2 of yeast RNA polymerase II and subunits RpoB-RpoC of bacterial RNA polymerase. The accuracy is comparable to that based on chemical cross-links; using restraints from both genetic interactions and cross-links further improves model accuracy and precision. The approach provides an efficient means to augment integrative structure determination with in vivo observations.

Modeling of macromolecular structures involves structural sampling guided by a scoring function, resulting in an ensemble of good-scoring models. By necessity, the sampling is often stochastic, and must be exhaustive at a precision sufficient for accurate modeling and assessment of model uncertainty. Therefore, the very first step in analyzing the ensemble is an estimation of the highest precision at which the sampling is exhaustive. Here, we present an objective and automated method for this task. As a proxy for sampling exhaustiveness, we evaluate whether two independently and stochastically generated sets of models are sufficiently similar. The protocol includes testing 1) convergence of the model score, 2) whether model scores for the two samples were drawn from the same parent distribution, 3) whether each structural cluster includes models from each sample proportionally to its size, and 4) whether there is sufficient structural similarity between the two model samples in each cluster. The evaluation also provides the sampling precision, defined as the smallest clustering threshold that satisfies the third, most stringent test. We validate the protocol with the aid of enumerated good-scoring models for five illustrative cases of binary protein complexes. Passing the proposed four tests is necessary, but not sufficient for thorough sampling. The protocol is general in nature and can be applied to the stochastic sampling of any set of models, not just structural models. In addition, the tests can be used to stop stochastic sampling as soon as exhaustiveness at desired precision is reached, thereby improving sampling efficiency; they may also help in selecting a model representation that is sufficiently detailed to be informative, yet also sufficiently coarse for sampling to be exhaustive.

The immunoproteasome (iP) has been proposed to perform specialized roles in MHC class I antigen presentation, cytokine modulation, and T cell differentiation and has emerged as a promising therapeutic target for autoimmune disorders and cancer. However, divergence in function between the iP and the constitutive proteasome (cP) has been unclear. A global peptide library-based screening strategy revealed that the proteasomes have overlapping but distinct substrate specificities. Differing iP specificity alters the quantity of production of certain MHC I epitopes but does not appear to be preferentially suited for antigen presentation. Furthermore, iP specificity was found to have likely arisen through genetic drift from the ancestral cP. Specificity differences were exploited to develop isoform-selective substrates. Cellular profiling using these substrates revealed that divergence in regulation of the iP balances its relative contribution to proteasome capacity in immune cells, resulting in selective recovery from inhibition. These findings have implications for iP-targeted therapeutic development.

Oxidative stress has been implicated in multiple human neurological and other disorders. Proteasomes are multi-subunit proteases critical for the removal of oxidatively damaged proteins. To understand stress-associated human pathologies, it is important to uncover the molecular events underlying the regulation of proteasomes upon oxidative stress. To this end, we investigated H2O2 stress-induced molecular changes of the human 26S proteasome and determined that stress-induced 26S proteasome disassembly is conserved from yeast to human. Moreover, we developed and employed a new proteomic approach, XAP (in vivo cross-linking-assisted affinity purification), coupled with stable isotope labeling with amino acids in cell culture (SILAC)-based quantitative MS, to capture and quantify several weakly bound proteasome-interacting proteins and examine their roles in stress-mediated proteasomal remodeling. Our results indicate that the adapter protein Ecm29 is the main proteasome-interacting protein responsible for stress-triggered remodeling of the 26S proteasome in human cells. Importantly, using a disuccinimidyl sulfoxide-based cross-linking MS platform, we mapped the interactions of Ecm29 within itself and with proteasome subunits and determined the architecture of the Ecm29-proteasome complex with integrative structure modeling. These results enabled us to propose a structural model in which Ecm29 intrudes on the interaction between the 20S core particle and the 19S regulatory particle in the 26S proteasome, disrupting the proteasome structure in response to oxidative stress.

The 26S proteasome is the macromolecular machine responsible for ATP/ubiquitin dependent degradation. As aberration in proteasomal degradation has been implicated in many human diseases, structural analysis of the human 26S proteasome complex is essential to advance our understanding of its action and regulation mechanisms. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for elucidating structural topologies of large protein assemblies, with its unique capability of studying protein complexes in cells. To facilitate the identification of cross-linked peptides, we have previously developed a robust amine reactive sulfoxide-containing MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). To better understand the structure and regulation of the human 26S proteasome, we have established new DSSO-based in vivo and in vitro XL-MS workflows by coupling with HB-tag based affinity purification to comprehensively examine protein-protein interactions within the 26S proteasome. In total, we have identified 447 unique lysine-to-lysine linkages delineating 67 interprotein and 26 intraprotein interactions, representing the largest cross-link dataset for proteasome complexes. In combination with EM maps and computational modeling, the architecture of the 26S proteasome was determined to infer its structural dynamics. In particular, three proteasome subunits Rpn1, Rpn6, and Rpt6 displayed multiple conformations that have not been previously reported. Additionally, cross-links between proteasome subunits and 15 proteasome interacting proteins including 9 known and 6 novel ones have been determined to demonstrate their physical interactions at the amino acid level. Our results have provided new insights on the dynamics of the 26S human proteasome and the methodologies presented here can be applied to study other protein complexes.

Many proteins have small-molecule binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially "druggable" human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite Web server is available at http://salilab.org/cryptosite.

TFIIH is essential for both RNA polymerase II transcription and DNA repair, and mutations in TFIIH can result in human disease. Here, we determine the molecular architecture of human and yeast TFIIH by an integrative approach using chemical crosslinking/mass spectrometry (CXMS) data, biochemical analyses, and previously published electron microscopy maps. We identified four new conserved "topological regions" that function as hubs for TFIIH assembly and more than 35 conserved topological features within TFIIH, illuminating a network of interactions involved in TFIIH assembly and regulation of its activities. We show that one of these conserved regions, the p62/Tfb1 Anchor region, directly interacts with the DNA helicase subunit XPD/Rad3 in native TFIIH and is required for the integrity and function of TFIIH. We also reveal the structural basis for defects in patients with xeroderma pigmentosum and trichothiodystrophy, with mutations found at the interface between the p62 Anchor region and the XPD subunit.

The TORC1 signaling pathway plays a major role in the control of cell growth and response to stress. Here we demonstrate that the SEA complex physically interacts with TORC1 and is an important regulator of its activity. During nitrogen starvation, deletions of SEA complex components lead to Tor1 kinase delocalization, defects in autophagy, and vacuolar fragmentation. TORC1 inactivation, via nitrogen deprivation or rapamycin treatment, changes cellular levels of SEA complex members. We used affinity purification and chemical cross-linking to generate the data for an integrative structure modeling approach, which produced a well-defined molecular architecture of the SEA complex and showed that the SEA complex comprises two regions that are structurally and functionally distinct. The SEA complex emerges as a platform that can coordinate both structural and enzymatic activities necessary for the effective functioning of the TORC1 pathway.

In the discovery of secondary metabolites (SMs), large-scale analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack ofrelevant computational resources. We present IMG-ABC (https://img.jgi.doe.gov/abc/) -- An Atlas of Biosynthetic gene Clusters within the Integrated Microbial Genomes(IMG) system1. IMG-ABC is a rich repository of both validated and predicted biosynthetic clusters (BCs) in cultured isolates, single-cells and metagenomes linked withthe SM chemicals they produce and enhanced with focused analysis tools within IMG. The underlying scalable framework enables traversal of phylogenetic dark matter and chemical structure space -- serving as a doorway to a new era in the discovery of novel molecules.

Several recent studies describe the influence of the gut microbiota on host brain and behavior. However, the mechanisms responsible for microbiota-nervous system interactions are largely unknown. Using a combination of genetics, biochemistry, and crystallography, we identify and characterize two phylogenetically distinct enzymes found in the human microbiome that decarboxylate tryptophan to form the β-arylamine neurotransmitter tryptamine. Although this enzymatic activity is exceedingly rare among bacteria more broadly, analysis of the Human Microbiome Project data demonstrate that at least 10% of the human population harbors at least one bacterium encoding a tryptophan decarboxylase in their gut community. Our results uncover a previously unrecognized enzymatic activity that can give rise to host-modulatory compounds and suggests a potential direct mechanism by which gut microbiota can influence host physiology, including behavior.

Although biosynthetic gene clusters (BGCs) have been discovered for hundreds of bacterial metabolites, our knowledge of their diversity remains limited. Here, we used a novel algorithm to systematically identify BGCs in the extensive extant microbial sequencing data. Network analysis of the predicted BGCs revealed large gene cluster families, the vast majority uncharacterized. We experimentally characterized the most prominent family, consisting of two subfamilies of hundreds of BGCs distributed throughout the Proteobacteria; their products are aryl polyenes, lipids with an aryl head group conjugated to a polyene tail. We identified a distant relationship to a third subfamily of aryl polyene BGCs, and together the three subfamilies represent the largest known family of biosynthetic gene clusters, with more than 1,000 members. Although these clusters are widely divergent in sequence, their small molecule products are remarkably conserved, indicating for the first time the important roles these compounds play in Gram-negative cell biology.

© 2014 Medema et al.Bacterial secondary metabolites are widely used as antibiotics, anticancer drugs, insecticides and food additives. Attempts to engineer their biosynthetic gene clusters (BGCs) to produce unnatural metabolites with improved properties ar

Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host’s cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry1-3 to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV–human protein–protein interactions involving 435 individual human proteins, with ~40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.

Peptide natural products show broad biological properties and are commonly produced by orthogonal ribosomal and nonribosomal pathways in prokaryotes and eukaryotes. To harvest this large and diverse resource of bioactive molecules, we introduce here natural product peptidogenomics (NPP), a new MS-guided genome-mining method that connects the chemotypes of peptide natural products to their biosynthetic gene clusters by iteratively matching de novo tandem MS (MS(n)) structures to genomics-based structures following biosynthetic logic. In this study, we show that NPP enabled the rapid characterization of over ten chemically diverse ribosomal and nonribosomal peptide natural products of previously unidentified composition from Streptomycete bacteria as a proof of concept to begin automating the genome-mining process. We show the identification of lantipeptides, lasso peptides, linardins, formylated peptides and lipopeptides, many of which are from well-characterized model Streptomycetes, highlighting the power of NPP in the discovery of new peptide natural products from even intensely studied organisms.

Endocytic sorting of signalling receptors between recycling and degradative pathways is a key cellular process controlling the surface complement of receptors and, accordingly, the cell's ability to respond to specific extracellular stimuli. The β2 adrenergic receptor (β2AR) is a prototypical seven-transmembrane signalling receptor that recycles rapidly and efficiently to the plasma membrane after ligand-induced endocytosis. β2AR recycling is dependent on the receptor's carboxy-terminal PDZ ligand and Rab4. This active sorting process is required for functional resensitization of β2AR-mediated signalling. Here we show that sequence-directed sorting occurs at the level of entry into retromer tubules and that retromer tubules are associated with Rab4. Furthermore, we show that sorting nexin 27 (SNX27) serves as an essential adaptor protein linking β2ARs to the retromer tubule. SNX27 does not seem to directly interact with the retromer core complex, but does interact with the retromer-associated Wiskott-Aldrich syndrome protein and SCAR homologue (WASH) complex. The present results identify a role for retromer in endocytic trafficking of signalling receptors, in regulating a receptor-linked signalling pathway, and in mediating direct endosome-to-plasma membrane traffic.

Significance: We extend the capabilities of virtual staining from AF to a different disease and stain modality. Our work includes newly developed virtual stains for H&E and a multiplex immunofluorescence panel (DAPI, PanCK, PD-L1, CD3, and CD8) for non-small cell lung cancer, which reproduce the key features of real stains., (©2024 The Authors; Published by the American Association for Cancer Research.)

Microbial natural products constitute a wide variety of chemical compounds, many which can have antibiotic, antiviral, or anticancer properties that make them interesting for clinical purposes. Natural product classes include polyketides (PKs), nonribosomal peptides (NRPs), and ribosomally synthesized and post-translationally modified peptides (RiPPs). While variants of biosynthetic gene clusters (BGCs) for known classes of natural products are easy to identify in genome sequences, BGCs for new compound classes escape attention. In particular, evidence is accumulating that for RiPPs, subclasses known thus far may only represent the tip of an iceberg. Here, we present decRiPPter (Data-driven Exploratory Class-independent RiPP TrackER), a RiPP genome mining algorithm aimed at the discovery of novel RiPP classes. DecRiPPter combines a Support Vector Machine (SVM) that identifies candidate RiPP precursors with pan-genomic analyses to identify which of these are encoded within operon-like structures that are part of the accessory genome of a genus. Subsequently, it prioritizes such regions based on the presence of new enzymology and based on patterns of gene cluster and precursor peptide conservation across species. We then applied decRiPPter to mine 1,295 Streptomyces genomes, which led to the identification of 42 new candidate RiPP families that could not be found by existing programs. One of these was studied further and elucidated as a representative of a novel subfamily of lanthipeptides, which we designate class V. The 2D structure of the new RiPP, which we name pristinin A3 (1), was solved using nuclear magnetic resonance (NMR), tandem mass spectrometry (MS/MS) data, and chemical labeling. Two previously unidentified modifying enzymes are proposed to create the hallmark lanthionine bridges. Taken together, our work highlights how novel natural product families can be discovered by methods going beyond sequence similarity searches to integrate multiple pathway discovery criteria., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: P.C. is currently an employee of Verily Life Sciences. M.H. is currently an employee of LifeMine Therapeutics. M.S.D. is a member of the Scientific Advisory Board of DeepBiome Therapeutics. M.A.F. is a cofounder and director of Federation Bio. M.H.M. is on the scientific advisory board of Hexagon Bio and co-founder of Design Pharmaceuticals.

The immunoproteasome (iP) has been proposed to perform specialized roles in MHC class I antigen presentation, cytokine modulation, and T cell differentiation and has emerged as a promising therapeutic target for autoimmune disorders and cancer. However, divergence in function between the iP and the constitutive proteasome (cP) has been unclear. A global peptide library-based screening strategy revealed that the proteasomes have overlapping but distinct substrate specificities. Differing iP specificity alters the quantity of production of certain MHC I epitopes but does not appear to be preferentially suited for antigen presentation. Furthermore, iP specificity was found to have likely arisen through genetic drift from the ancestral cP. Specificity differences were exploited to develop isoform-selective substrates. Cellular profiling using these substrates revealed that divergence in regulation of the iP balances its relative contribution to proteasome capacity in immune cells, resulting in selective recovery from inhibition. These findings have implications for iP-targeted therapeutic development.

Bacterial secondary metabolites are widely used as antibiotics, anticancer drugs, insecticides and food additives. Attempts to engineer their biosynthetic gene clusters (BGCs) to produce unnatural metabolites with improved properties are often frustrated by the unpredictability and complexity of the enzymes that synthesize these molecules, suggesting that genetic changes within BGCs are limited by specific constraints. Here, by performing a systematic computational analysis of BGC evolution, we derive evidence for three findings that shed light on the ways in which, despite these constraints, nature successfully invents new molecules: 1) BGCs for complex molecules often evolve through the successive merger of smaller sub-clusters, which function as independent evolutionary entities. 2) An important subset of polyketide synthases and nonribosomal peptide synthetases evolve by concerted evolution, which generates sets of sequence-homogenized domains that may hold promise for engineering efforts since they exhibit a high degree of functional interoperability, 3) Individual BGC families evolve in distinct ways, suggesting that design strategies should take into account family-specific functional constraints. These findings suggest novel strategies for using synthetic biology to rationally engineer biosynthetic pathways.

In complex biological systems, small molecules often mediate microbe-microbe and microbe-host interactions. Using a systematic approach, we identified 3,118 small-molecule biosynthetic gene clusters (BGCs) in genomes of human-associated bacteria and studied their representation in 752 metagenomic samples from the NIH Human Microbiome Project. Remarkably, we discovered that BGCs for a class of antibiotics in clinical trials, thiopeptides, are widely distributed in genomes and metagenomes of the human microbiota. We purified and solved the structure of a thiopeptide antibiotic, lactocillin, from a prominent member of the vaginal microbiota. We demonstrate that lactocillin has potent antibacterial activity against a range of Gram-positive vaginal pathogens, and we show that lactocillin and other thiopeptide BGCs are expressed in vivo by analyzing human metatranscriptomic sequencing data. Our findings illustrate the widespread distribution of small-molecule-encoding BGCs in the human microbiome, and they demonstrate the bacterial production of drug-like molecules in humans. PAPERCLIP:, (Copyright © 2014 Elsevier Inc. All rights reserved.)

Restriction factors, such as the retroviral complementary DNA deaminase APOBEC3G, are cellular proteins that dominantly block virus replication. The AIDS virus, human immunodeficiency virus type 1 (HIV-1), produces the accessory factor Vif, which counteracts the host's antiviral defence by hijacking a ubiquitin ligase complex, containing CUL5, ELOC, ELOB and a RING-box protein, and targeting APOBEC3G for degradation. Here we reveal, using an affinity tag/purification mass spectrometry approach, that Vif additionally recruits the transcription cofactor CBF-β to this ubiquitin ligase complex. CBF-β, which normally functions in concert with RUNX DNA binding proteins, allows the reconstitution of a recombinant six-protein assembly that elicits specific polyubiquitination activity with APOBEC3G, but not the related deaminase APOBEC3A. Using RNA knockdown and genetic complementation studies, we also demonstrate that CBF-β is required for Vif-mediated degradation of APOBEC3G and therefore for preserving HIV-1 infectivity. Finally, simian immunodeficiency virus (SIV) Vif also binds to and requires CBF-β to degrade rhesus macaque APOBEC3G, indicating functional conservation. Methods of disrupting the CBF-β-Vif interaction might enable HIV-1 restriction and provide a supplement to current antiviral therapies that primarily target viral proteins.

Various cyclin-dependent kinase (Cdk) complexes have been implicated in the regulation of transcription. In this study, we identified a 70-kDa Cyclin K (CycK) that binds Cdk12 and Cdk13 to form two different complexes (CycK/Cdk12 or CycK/Cdk13) in human cells. The CycK/Cdk12 complex regulates phosphorylation of Ser2 in the C-terminal domain of RNA polymerase II and expression of a small subset of human genes, as revealed in expression microarrays. Depletion of CycK/Cdk12 results in decreased expression of predominantly long genes with high numbers of exons. The most prominent group of down-regulated genes are the DNA damage response genes, including the critical regulators of genomic stability: BRCA1 (breast and ovarian cancer type 1 susceptibility protein 1), ATR (ataxia telangiectasia and Rad3-related), FANCI, and FANCD2. We show that CycK/Cdk12, rather than CycK/Cdk13, is necessary for their expression. Nuclear run-on assays and chromatin immunoprecipitations with RNA polymerase II on the BRCA1 and FANCI genes suggest a transcriptional defect in the absence of CycK/Cdk12. Consistent with these findings, cells without CycK/Cdk12 induce spontaneous DNA damage and are sensitive to a variety of DNA damage agents. We conclude that through regulation of expression of DNA damage response genes, CycK/Cdk12 protects cells from genomic instability. The essential role of CycK for organisms in vivo is further supported by the result that genetic inactivation of CycK in mice causes early embryonic lethality.

Bacterial and fungal secondary metabolism is a rich source of novel bioactive compounds with potential pharmaceutical applications as antibiotics, anti-tumor drugs or cholesterol-lowering drugs. To find new drug candidates, microbiologists are increasingly relying on sequencing genomes of a wide variety of microbes. However, rapidly and reliably pinpointing all the potential gene clusters for secondary metabolites in dozens of newly sequenced genomes has been extremely challenging, due to their biochemical heterogeneity, the presence of unknown enzymes and the dispersed nature of the necessary specialized bioinformatics tools and resources. Here, we present antiSMASH (antibiotics & Secondary Metabolite Analysis Shell), the first comprehensive pipeline capable of identifying biosynthetic loci covering the whole range of known secondary metabolite compound classes (polyketides, non-ribosomal peptides, terpenes, aminoglycosides, aminocoumarins, indolocarbazoles, lantibiotics, bacteriocins, nucleosides, beta-lactams, butyrolactones, siderophores, melanins and others). It aligns the identified regions at the gene cluster level to their nearest relatives from a database containing all other known gene clusters, and integrates or cross-links all previously available secondary-metabolite specific gene analysis methods in one interactive view. antiSMASH is available at http://antismash.secondarymetabolites.org.

To fully understand how pathogens infect their host and hijack key biological processes, systematic mapping of intra-pathogenic and pathogen-host protein-protein interactions (PPIs) is crucial. Due to the relatively small size of viral genomes (usually around 10-100 proteins), generation of comprehensive host-virus PPI maps using different experimental platforms, including affinity tag purification-mass spectrometry (AP-MS) and yeast two-hybrid (Y2H) approaches, can be achieved. Global maps such as these provide unbiased insight into the molecular mechanisms of viral entry, replication and assembly. However, to date, only two-hybrid methodology has been used in a systematic fashion to characterize viral-host protein-protein interactions, although a deluge of data exists in databases that manually curate from the literature individual host-pathogen PPIs. We will summarize this work and also describe an AP-MS platform that can be used to characterize viral-human protein complexes and discuss its application for the HIV genome., (Copyright © 2010. Published by Elsevier Inc.)