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Neural network was trained on the amino acid sequences of known two-component systems (2CS) pairs. The recommender algorithm uses learned model to select the most likely 2CS pair from an arbitrary set of protein pairs whose amino acid sequences are known.
Proteins can perform ideal therapeutic functions. However, their large size and significant surface hydrophilicity and charge prohibit them from reaching intracellular targets. These chemical features also render them poorly encapsulated by nanoparticles used for intracellular delivery. In this work, a novel combination of protein vesicles and hydrophobic ion pairing (HIP) was used to load protein cargo and achieve cytosolic delivery to overcome the limitations of previous protein vesicle properties. Protein vesicles are thermally self-assembling nanoparticles made from elastin-like polypeptide (ELP) fused to an arginine-rich leucine zipper and a globular protein fused to a glutamate-rich leucine zipper. To impart stimuli-responsive disassembly, physiological stability, and small size, the ELP sequence was modified to include histidine and tyrosine residues. HIP was used to load and release protein cargo requiring endosomal escape for cytosolic function. HIP vesicles enabled delivery of cytochrome c, a cytosolically active protein, and a significant reduction in viability in both a traditional two-dimensional (2D) human cancer cell line culture and a biomimetic three-dimensional (3D) organoid model of acute myeloid leukemia. By examining the uptake of positively and negatively charged fluorescent protein cargos loaded by HIP, this work revealed the necessity of HIP for cytosolic cargo delivery and how HIP loading influences protein vesicle self-assembly and disassembly using microscopy, small-angle X-ray scattering, and nanoparticle tracking analysis. HIP protein vesicles have the potential to broaden the use of intracellular proteins as therapeutics for various diseases and extend protein vesicles to deliver other biomacromolecules, as the strategy developed here resulted in the first cytosolic protein cargo delivery using protein vesicles.
Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared mRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchymal cells from normal human lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40% of RNA-protein pairs were coherently expressed. While the correlation between RNA and their protein products was relatively low (approximately 0.4), cell specific signature genes involved in functional processes characteristic of each cell type, were more highly correlated with their protein products. Consistency of cell specific RNA-protein signatures indicated an essential framework for the function of each cell type. Visualization and reutilization of the protein and RNA profiles are supported by a new web application, "LungProteomics," which is incorporated into the LGEA web portal and freely accessible at (https://research.cchmc.org/pbge/lunggens/lungProteomics/human_sorted_profiles.html).
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Researchers often measure changes in gene expression across conditions to better understand the shared functional roles and regulatory mechanisms of different genes. Analogous to this is comparing gene expression across species, which can improve our understanding of the evolutionary processes shaping the evolution of both individual genes and functional pathways. One area of interest is determining genes showing signals of coevolution, which can also indicate potential functional similarity, analogous to co-expression analysis often performed across conditions for a single species. However, as with any trait, comparing gene expression across species can be confounded by the non-independence of species due to shared ancestry, making standard hypothesis testing inappropriate. We compared RNA-Seq data across 18 fungal species using a multivariate Brownian Motion phylogenetic comparative method (PCM), which allowed us to quantify coevolution between protein pairs while directly accounting for the shared ancestry of the species. Our work indicates proteins which physically-interact show stronger signals of coevolution than randomly-generated pairs. Interactions with stronger empirical and computational evidence also showing stronger signals of coevolution. We examined the effects of number of protein interactions and gene expression levels on coevolution, finding both factors are overall poor predictors of the strength of coevolution between a protein pair. Simulations further demonstrate the potential issues of analyzing gene expression coevolution without accounting for shared ancestry in a standard hypothesis testing framework. Furthermore, our simulations indicate the use of a randomly-generated null distribution as a means of determining statistical significance for detecting coevolving genes with phylogenetically-uncorrected correlations, as has previously been done, is less accurate than PCMs, although is a significant improvement over standard hypothesis testing. These methods are further improved by using a phylogenetically-corrected correlation metric. Our work highlights potential benefits of using PCMs to detect gene expression coevolution from high-throughput omics scale data. This framework can be built upon to investigate other evolutionary hypotheses, such as changes in transcription regulatory mechanisms across species.
Effector-triggered immunity (ETI) is activated when plant disease resistance (R) proteins recognize the presence of pathogen effector proteins delivered into host cells. The ETI response generally encompasses a defensive ‘hypersensitive response’ (HR) that involves programmed cell death at the site of pathogen recognition. While many R protein and effector protein pairs are known to trigger HR, other components of the ETI signaling pathway remain elusive. Effector genes regulated by inducible promoters cause background HR due to leaky protein expression, preventing the generation of relevant transgenic plant lines. By employing the HyP5SM suicide exon, we have developed a strategy to tightly regulate effector proteins such that HR is chemically inducible and non-leaky. This alternative splicing-based gene regulation system was shown to successfully control Bs2/AvrBs2-dependent and RPP1/ATR1Δ51-dependent HR in Nicotiana benthamiana and Nicotiana tabacum, respectively. It was also used to generate viable and healthy transgenic Arabidopsis thaliana plants that inducibly initiate HR. In conclusion, beyond enabling studies on the ETI pathway, our regulatory strategy is generally applicable to reduce or eliminate undesired background expression of transgenes.
To survive in highly competitive environments, bacteria use specialized secretion systems to deliver antibacterial toxins into neighboring cells, thereby inhibiting their growth. In many Gram-positive bacteria, the export of such toxins requires a membrane-bound molecular apparatus known as the type VIIb secretion system (T7SSb). Recently, it was shown that toxin recruitment to the T7SSb requires a physical interaction between a toxin and two or more so-called targeting factors, which harbor key residues required for T7SS-dependent protein export. However, in addition to these targeting factors, some toxins additionally require a protein belonging to the DUF4176 protein family. Here, by examining two toxin–DUF4176 protein pairs, we demonstrate that DUF4176 constitutes a family of toxin-specific molecular chaperones. In addition to being required for toxin stability in producing cells, we find that DUF4176 proteins facilitate toxin export by specifically interacting with a previously uncharacterized intrinsically disordered region found in many T7SS toxins. Using X-ray crystallography, we determine structures of several DUF4176 chaperones in their unbound state, and of a DUF4176 chaperone in complex with the binding site of its cognate toxin. These structures reveal that this binding site consists of a disordered amphipathic α-helix that requires interaction with its cognate chaperone for proper folding. Overall, we have identified a family of secretion system associated molecular chaperones found throughout T7SSb-containing Gram-positive bacteria.
Marine algae perform approximately half of global carbon fixation, but their growth is often limited by availability of phosphate or other nutrients1,2. As oceans warm, the area of phosphate-limited surface waters is predicted to increase, resulting in ocean desertification3,4. Understanding the responses of key eukaryotic phytoplankton to nutrient limitation is therefore critical5,6. We used advanced photo-bioreactors to investigate how the widespread green alga Micromonas commoda grows under transitions from replete nutrients to chronic phosphate limitation and subsequent relief, analyzing photosystem changes and broad cellular responses using proteomics, transcriptomics and biophysical measurements. We find that physiological and protein expression responses previously attributed to stress are critical to supporting stable exponential growth when phosphate is limiting. Unexpectedly, the abundance of most proteins involved in light-harvesting does not change, but an ancient light-harvesting related protein, LHCSR, is induced and dissipates damaging excess absorbed light as heat during P-limitation. Concurrently, a suite of uncharacterized proteins with narrow phylogenetic distributions increase multifold. Notably, of the proteins that exhibit significant changes, 70% are not differentially expressed at the mRNA transcript level, highlighting the importance of post-transcriptional processes in microbial eukaryotes. Nevertheless, transcript-protein pairs with concordant changes were identified that will enable more robust interpretation of eukaryotic phytoplankton responses in the field from metatranscriptomic studies. Our results show P-limited Micromonas responds quickly to a fresh pulse of phosphate by rapidly increasing replication and that the protein network associated with this ability is composed of both conserved and phylogenetically recent proteome systems that promote dynamic phosphate homeostasis. That an ancient mechanism for mitigating light-stress is central to sustaining growth throughout extended periods of phosphate limitation highlights possible interactive effects, arising from multiple stressors under ocean change, which could reduce the efficacy of algal strategies for optimizing marine photosynthesis.
A challenging task in computational biophysics is to ascertain the solvent effect on the electronic structure and interatomic bonding at the atomistic level. Simulations must be carried out on reasonably large biomolecules for accurate calculations to yield valid results. We report the results of a calculation on collagen model in the form of a peptide under three different environments: vacuum, solvated and with neutral and charged sites. Quantitative results and analysis of the partial charge (PC) distribution on each amino acid are discussed. A significant charge transfer of more than 1 electron from protein to water molecules is found with similar results when the model contains charged sites. The main contributions to the interatomic bonding are from hydrogen bonds (HBs) between water‐water and water‐protein pairs. A connection between PC and HBs can be established since the nonpolar amino acids form no HBs and have the smallest PC and vice versa. The ab initio PC obtained are used in the NAMD simulation showing significant improvement over the default values as reflected in the root mean square deviation of atomic positions in the MD steps and the total free energy in energy minimization. These results could facilitate the interpretation of data on interaction of various ligands in charged proteins in relation to isoelectric points. © 2016 Wiley Periodicals, Inc.
To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across 9 growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a 3-fold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.
In photosynthesis, absorbed light energy transfers through a network of antenna proteins with near-unity quantum efficiency to reach the reaction center, which initiates the downstream biochemical reactions. While the energy transfer dynamics within individual antenna proteins have been extensively studied over the past decades, the dynamics between the proteins are poorly understood due to the heterogeneous organization of the network. Previously reported timescales averaged over such heterogeneity, obscuring individual interprotein energy transfer steps. Here, we isolated and interrogated interprotein energy transfer by embedding two variants of the primary antenna protein from purple bacteria, light-harvesting complex 2 (LH2), together into a near-native membrane disc, known as a nanodisc. We integrated ultrafast transient absorption spectroscopy, quantum dynamics simulations, and cryogenic electron microscopy to determine interprotein energy transfer timescales. By varying the diameter of the nanodiscs, we replicated a range of distances between the proteins. The closest distance possible between neighboring LH2, which is the most common in native membranes, is 25 Å and resulted in a timescale of 5.7 ps. Larger distances of 28 to 31 Å resulted in timescales of 10 to 14 ps. Corresponding simulations showed that the fast energy transfer steps between closely spaced LH2 increase transport distances by ∼15%. Overall, our results introduce a framework for well-controlled studies of interprotein energy transfer dynamics and suggest that protein pairs serve as the primary pathway for the efficient transport of solar energy.
The Exabiome project seeks to improve the understanding of microbiomes through the development of methods for accelerating metagenomic science using exascale computing. This article gives an overview of scientific impact of the three components of the project: metagenome assembly, protein family detection, and comparative analysis of metagenomes. Exabiome developed MetaHipMer, the only metagenome assembler capable of scaling to full exascale systems. MetaHipMer has enabled ground-breaking assemblies on the Frontier supercomputer, with many scientific benefits, such as the discovery of rare species and viral genomes. To investigate protein families, Exabiome developed two exascale tools, PASTIS and HipMCL. Together, these can utilize exascale resources to understand the functional diversity of billions of dark matter proteins and novel protein families. For comparative analysis, Exabiome developed kmerprof, a tool that can be used to compare huge metagenomes for many different scientific purposes, for example, grouping human microbiomes according to body location.
The genus Marinomonas comprises Gram negative bacteria which are widespread in the marine environment and there is no report on the genomic analysis of SXT/R391 ICEs derived from this group of bacteria. This study describes the genomic features of three new SXT/R391 integrating conjugating elements (ICEs) identified in the genome of Marinomonas fungiae JCM 18476 T (ICE Mfu Ind1a and ICE Mfu Ind1b) and in Marinomonas profundimaris strain D104 (ICE Mpr Chn1). Structural organizations of the three ICEs were similar to the typical SXT/R391 family of ICEs and showed high degree of conservation in the core genes. Sequence analysis revealed ICE Mfu Ind1b and ICE Mpr Chn1 were inserted into the genome at 5'-end of an typical host prfC gene, while ICE Mfu Ind1a was inserted at 5'-end of an atypical hipA -like gene. Despite their coexistence, the ICE Mfu Ind1a and ICE Mfu Ind1b were not present in a tandem fashion in the genome of M. fungiae . Phylogenetic analyses revealed the three ICEs either evolved independently or high degrees of recombination events had masked their evolution from a common SXT ancestor. Further, we found that the typical entry exclusion mechanism mediated by the TraG/EeX protein pair was likely defective in preventing the conjugative transfer of a second copy of the same S (SXT) group ICE into the M. fungiae genome due to mutations. Our analysis showed the presence of 16, 25, and 27 variable genes in the hotspots of ICE Mfu Ind1a, ICE Mfu Ind1b, and ICE Mpr Chn1, respectively, many of which were not reported earlier for SXT/R391 ICEs. Sequence analysis predicted these hot spot regions were shaped by acquisition of genes through homologous recombination between the SXT and R391 related ICEs or mobile genetic elements present in disparate marine bacteria. Multidrug resistance genes which are hallmark feature of SXT/R391 ICEs were not present in either of the two ICEs from M. fungiae but were present within a transposon cassette in the HS-1 of the ICE Mpr Chn1 from M. profundimaris . Lastly, our data provided information on the genetic diversity and predicted functions encoded by variable genes present in the hotspot regions of these new ICEs.
Biological electron transfer is challenging to directly regulate using environmental conditions. To enable dynamic, protein level control over energy flow in metabolic systems for synthetic biology and bioelectronics, we created ferredoxin logic gates that utilize transcriptional and post-translational inputs to control energy flow through a synthetic electron transfer pathway that is required for bacterial growth. These logic gates were created by subjecting a thermostable, plant-type ferredoxin to backbone fission and fusing the resulting fragments to a pair of proteins that self-associate, a pair of proteins whose association is stabilized by a small molecule, and to the termini of a ligand-binding domain. In our work we show that the latter domain insertion design strategy yields an allosteric ferredoxin switch that acquires an oxygen-tolerant [2Fe–2S] cluster and can use different chemicals, including a therapeutic drug and an environmental pollutant, to control the production of a reduced metabolite in Escherichia coli and cell lysates.
Abstract not provided
Natural photosystems couple light harvesting to charge separation using a ‘special pair’ of chlorophyll molecules that accepts excitation energy from the antenna and initiates an electron-transfer cascade. To investigate the photophysics of special pairs independently of the complexities of native photosynthetic proteins, and as a first step toward creating synthetic photosystems for new energy conversion technologies, we designed C 2 -symmetric proteins that hold two chlorophyll molecules in closely juxtaposed arrangements. X-ray crystallography confirmed that one designed protein binds two chlorophylls in the same orientation as native special pairs, whereas a second designed protein positions them in a previously unseen geometry. Spectroscopy revealed that the chlorophylls are excitonically coupled, and fluorescence lifetime imaging demonstrated energy transfer. The cryo-electron microscopy structure of a designed 24-chlorophyll octahedral nanocage with a special pair on each edge closely matched the design model. The results suggest that the de novo design of artificial photosynthetic systems is within reach of current computational methods.
Interactions between chromosomes and LINC (linker of nucleoskeleton and cytoskeleton) complexes in the nuclear envelope (NE) promote homolog pairing and synapsis during meiosis. By tethering chromosomes to cytoskeletal motors, these connections lead to processive chromosome movements along the NE. This activity is usually mediated by telomeres, but in the nematode Caenorhabditis elegans, special chromosome regions called “pairing centers” (PCs) have acquired this meiotic function. Here, we identify a previously uncharacterized meiosis-specific NE protein, MJL-1 (MAJIN-Like-1), that is essential for interactions between PCs and LINC complexes in C. elegans. Mutations in MJL-1 eliminate active chromosome movements during meiosis, resulting in nonhomologous synapsis and impaired homolog pairing. Fission yeast and mice also require NE proteins to connect chromosomes to LINC complexes. Extensive similarities in the molecular architecture of meiotic chromosome-NE attachments across eukaryotes suggest a common origin and/or functions of this architecture during meiosis.
Deep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain contacts between proteins. However, these methods require multiple sequence alignments (MSAs) of a pair of interacting proteins (dimers) as input, which are often difficult to obtain because there are not many known protein complexes available to generate MSAs of sufficient depth for a pair of proteins. In recognizing that multiple sequence alignments of a monomer that forms homomultimers contain the co-evolutionary signals of both intrachain and interchain residue pairs in contact, we applied DNCON2 (a deep learning-based protein intrachain residue-residue contact predictor) to predict both intrachain and interchain contacts for homomultimers using multiple sequence alignment (MSA) and other co-evolutionary features of a single monomer followed by discrimination of interchain and intrachain contacts according to the tertiary structure of the monomer. We name this tool DNCON2_Inter. Allowing true-positive predictions within two residue shifts, the best average precision was obtained for the Top-L/10 predictions of 22.9% for homodimers and 17.0% for higher-order homomultimers. In some instances, especially where interchain contact densities are high, DNCON2_Inter predicted interchain contacts with 100% precision. We also developed Con_Complex, a complex structure reconstruction tool that uses predicted contacts to produce the structure of the complex. Using Con_Complex, we show that the predicted contacts can be used to accurately construct the structure of some complexes. Our experiment demonstrates that monomeric multiple sequence alignments can be used with deep learning to predict interchain contacts of homomeric proteins.