Perspective: Emerging strategies for determining atomic-resolution structures of macromolecular complexes within cells
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59 BASIC BIOLOGICAL SCIENCES↗
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Active conformation of the p97-p47 unfoldase complex
The p97 AAA+ATPase is an essential and abundant regulator of protein homeostasis that plays a central role in unfolding ubiquitylated substrates. Here we report two cryo-EM structures of human p97 in complex with its p47 adaptor. One of the conformations is six-fold symmetric, corresponds to previously reported structures of p97, and lacks bound substrate. The other structure adopts a helical conformation, displays substrate running in an extended conformation through the pore of the p97 hexamer, and resembles structures reported for other AAA unfoldases. These findings support the model that p97 utilizes a “hand-over-hand” mechanism in which two residues of the substrate are translocated for hydrolysis of two ATPs, one in each of the two p97 AAA ATPase rings. Proteomics analysis supports the model that one p97 complex can bind multiple substrate adaptors or binding partners, and can process substrates with multiple types of ubiquitin modification.
59 BASIC BIOLOGICAL SCIENCES↗
Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses
Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.
59 BASIC BIOLOGICAL SCIENCES↗
Bioinformatic and experimental evidence for suicidal and catalytic plant THI4s
Like fungi and some prokaryotes, plants use a thiazole synthase (THI4) to make the thiazole precursor of thiamin. Fungal THI4s are suicide enzymes that destroy an essential active-site Cys residue to obtain the sulfur atom needed for thiazole formation. In contrast, certain prokaryotic THI4s have no active-site Cys, use sulfide as sulfur donor, and are truly catalytic. The presence of a conserved active-site Cys in plant THI4s and other indirect evidence implies that they are suicidal. To confirm this, we complemented the Arabidopsistz-1 mutant, which lacks THI4 activity, with a His-tagged Arabidopsis THI4 construct. LC–MS analysis of tryptic peptides of the THI4 extracted from leaves showed that the active-site Cys was predominantly in desulfurated form, consistent with THI4 having a suicide mechanism in planta. Unexpectedly, transcriptome data mining and deep proteome profiling showed that barley, wheat, and oat have both a widely expressed canonical THI4 with an active-site Cys, and a THI4-like paralog (non-Cys THI4) that has no active-site Cys and is the major type of THI4 in developing grains. Transcriptomic evidence also indicated that barley, wheat, and oat grains synthesize thiamin de novo, implying that their non-Cys THI4s synthesize thiazole. Structure modeling supported this inference, as did demonstration that non-Cys THI4s have significant capacity to complement thiazole auxotrophy in Escherichia coli. There is thus a prima facie case that non-Cys cereal THI4s, like their prokaryotic counterparts, are catalytic thiazole synthases. Bioenergetic calculations show that, relative to suicide THI4s, such enzymes could save substantial energy during the grain-filling period.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Phytoplankton exudates and lysates support distinct microbial consortia with specialized metabolic and ecophysiological traits
Marine dissolved organic matter, which originates from phytoplankton, holds as much carbon as Earth’s atmosphere; yet, the biological processes governing its fate are primarily studied under idealized laboratory conditions or through indirect measures such as genome sequencing. In this work, we used isotope labeling to directly quantify uptake of complex carbon pools from the two primary sources of marine organic carbon (diatoms and cyanobacteria) by a natural microbial community. Furthermore, our data show that carbon pools are partitioned into distinct microbial lineages whose physiological properties and resource acquisition strategies match the chemical nature of their preferred substrates. Our results provide ecological and functional insights into the patterns of microbial community structure changes that occur during marine phytoplankton blooms.
59 BASIC BIOLOGICAL SCIENCES↗
Functional role of myosin-binding protein H in thick filaments of developing vertebrate fast-twitch skeletal muscle
Myosin-binding protein H (MyBP-H) is a component of the vertebrate skeletal muscle sarcomere with sequence and domain homology to myosin-binding protein C (MyBP-C). Whereas skeletal muscle isoforms of MyBP-C (fMyBP-C, sMyBP-C) modulate muscle contractility via interactions with actin thin filaments and myosin motors within the muscle sarcomere “C-zone,” MyBP-H has no known function. This is in part due to MyBP-H having limited expression in adult fast-twitch muscle and no known involvement in muscle disease. Quantitative proteomics reported here reveal that MyBP-H is highly expressed in prenatal rat fast-twitch muscles and larval zebrafish, suggesting a conserved role in muscle development and prompting studies to define its function. We take advantage of the genetic control of the zebrafish model and a combination of structural, functional, and biophysical techniques to interrogate the role of MyBP-H. Transgenic, FLAG-tagged MyBP-H or fMyBP-C both localize to the C-zones in larval myofibers, whereas genetic depletion of endogenous MyBP-H or fMyBP-C leads to increased accumulation of the other, suggesting competition for C-zone binding sites. Does MyBP-H modulate contractility in the C-zone? Globular domains critical to MyBP-C’s modulatory functions are absent from MyBP-H, suggesting that MyBP-H may be functionally silent. However, our results suggest an active role. In vitro motility experiments indicate MyBP-H shares MyBP-C’s capacity as a molecular “brake.” These results provide new insights and raise questions about the role of the C-zone during muscle development.
59 BASIC BIOLOGICAL SCIENCES↗
Single-Cell Metabolomics with Rapid Determination of Chemical Formulas from Isotopic Fine Structures
Metabolomic measurements can provide functional readouts of cellular states and phenotypes. Here, we present a protocol for single-cell metabolomics that permits direct untargeted detection of a broad number of metabolites under ambient conditions, without the need for sample processing, and with high confidence in the discovery and identification of the molecular formulas for detected metabolites. This protocol describes combining fiber-based laser ablation electrospray ionization (f-LAESI) with a 21 Tesla Fourier transform ion cyclotron resonance mass spectrometer (21T-FTICR-MS) to obtain high confidence molecular formula information about detected metabolites. The f-LAESI source utilizes mid-infrared laser ablation through a sharp optical fiber tip, affording direct ambient analysis of cells without the need for sample processing. Using the 21T-FTICR-MS as a mass analyzer enabled measurement of the isotopic fine structure (IFS) for numerous metabolites simultaneously from single cells, and the IFSs were in turn computationally processed to rapidly determine the corresponding elemental compositions. This metabolomics technique complements other single cell omics measurement methods, helping to resolve complex molecular interactions that take place within cells unattainable from single cell transcriptomic and proteomics methods.
mass spectrometry, FTICR, FTMS, ultrahigh mass res↗
The microbiologist's guide to metaproteomics
Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.
bioinformatics↗
Bacterial Origin and Reductive Evolution of the CPR Group
The candidate phyla radiation (CPR) is a proposed subdivision within the bacterial domain comprising several candidate phyla. CPR organisms are united by small genome and physical sizes, lack several metabolic enzymes, and populate deep branches within the bacterial subtree of life. Here, these features raise intriguing questions regarding their origin and mode of evolution. In this study, we performed a comparative and phylogenomic analysis to investigate CPR origin and evolution. Unlike previous gene/protein sequence-based reports of CPR evolution, we used protein domain superfamilies classified by protein structure databases to resolve the evolutionary relationships of CPR with non-CPR bacteria, Archaea, Eukarya, and viruses. Across all supergroups, CPR shared maximum superfamilies with non-CPR bacteria and were placed as deep branching bacteria in most phylogenomic trees. CPR contributed 1.22% of new superfamilies to bacteria including the ribosomal protein L19e and encoded four core superfamilies that are likely involved in cell-to-cell interaction and establishing episymbiotic lifestyles. Although CPR and non-CPR bacterial proteomes gained common superfamilies over the course of evolution, CPR and Archaea had more common losses. These losses mostly involved metabolic superfamilies. In fact, phylogenies built from only metabolic protein superfamilies separated CPR and non-CPR bacteria. These findings indicate that CPR are bacterial organisms that have probably evolved in an Archaea-like manner via the early loss of metabolic functions. We also discovered that phylogenies built from metabolic and informational superfamilies gave contrasting views of the groupings among Archaea, Bacteria, and Eukarya, which add to the current debate on the evolutionary relationships among superkingdoms.
59 BASIC BIOLOGICAL SCIENCES↗
Algorithms and file structures to extend and enhance liquid chromatography and ion mobility mass spectrometry workflows (CRADA Final Report)
The purpose of this project was to continue supporting customizations of algorithms and raw data file structures to enhance software workflows for liquid chromatography (LC), mass spectrometry (MS) and ion mobility mass spectrometry (IM-MS)-based protein and metabolite characterization. PNNL worked with Agilent to design, implement, evaluate, and demonstrate new algorithms and integrated them as functionalities into the PNNL-PreProcessor software. The project augmented PNNL’s capabilities to analyze complex proteomics and metabolomics samples. These capabilities are directly beneficial to DOE and PNNL efforts to characterize and analyze these compounds in microbial and plant communities. The project assisted Agilent in further developing improved instrument-software solutions combining liquid chromatography and ion mobility with mass spectrometry for widespread applications in life sciences and other fields.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Hydrazinoacetic acid is a biosynthetic precursor of the bacterially produced nitramine, N -nitroglycine
Nitramines [R(R′)N–NO 2 ; R,R′=H or alkyl] are valuable synthetic products, but knowledge of the biosynthetic processes that generate these compounds is limited. This work sought to elucidate the biosynthesis of a nitramine natural product, N-nitroglycine (NNG) by Streptomyces noursei . Stable isotope studies showed that S. noursei cells supplemented with L-(ε- 15 N)lysine, ( 15 N)glycine, or ( 13 C)hydrazinoacetic acid (HAA) incorporated 67%, 88%, and 67% of the isotope label into NNG, respectively, indicating that these compounds are biosynthetic precursors of NNG. Liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) of 15 N-Lys-labeled NNG confirmed that the nitro nitrogen of NNG originates from Lys. Bioinformatics analysis of the S. noursei genome showed evidence for a biosynthetic gene cluster (BGC) that contained machinery for HAA biosynthesis ( nngKLM ), consistent with the results of the isotope labeling. In vitro reconstitution of the gene products produced HAA. The borders of this BGC were defined by cross-referencing the predicted BGC with previously published differential proteomics data. Furthermore, we show that azaserine is produced alongside NNG in S. noursei cultures, linking the two biosynthetic pathways via a proposed nitrosamine biosynthetic intermediate. Finally, the oxygen balance for NNG is −20.2% for the formation of carbon dioxide (CO 2 ), which is comparable to that of hexahydro-1,3,5- trinitro-1,3,5-triazene (common name: RDX; −21.6%). Crystal structure data of NNG indicate that the unit crystalizes as a pure material, not a hydrate, suggesting a favorable energetic crystallization phase. The combined results suggest a route that, with further development, could lead to sustainable production of energetic nitramines via synthetic biology or biocatalytic approaches.
biosynthesis↗
A microphysiological model of human trophoblast invasion during implantation
Abstract Successful establishment of pregnancy requires adhesion of an embryo to the endometrium and subsequent invasion into the maternal tissue. Abnormalities in this critical process of implantation and placentation lead to many pregnancy complications. Here we present a microenigneered system to model a complex sequence of orchestrated multicellular events that plays an essential role in early pregnancy. Our implantation-on-a-chip is capable of reconstructing the three-dimensional structural organization of the maternal-fetal interface to model the invasion of specialized fetal extravillous trophoblasts into the maternal uterus. Using primary human cells isolated from clinical specimens, we demonstrate in vivo-like directional migration of extravillous trophoblasts towards a microengineered maternal vessel and their interactions with the endothelium necessary for vascular remodeling. Through parametric variation of the cellular microenvironment and proteomic analysis of microengineered tissues, we show the important role of decidualized stromal cells as a regulator of extravillous trophoblast migration. Furthermore, our study reveals previously unknown effects of pre-implantation maternal immune cells on extravillous trophoblast invasion. This work represents a significant advance in our ability to model early human pregnancy, and may enable the development of advanced in vitro platforms for basic and clinical research of human reproduction.
60 APPLIED LIFE SCIENCES↗
NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes
Abstract The Novel Metagenome Protein Families Database (NMPFamsDB) is a database of metagenome- and metatranscriptome-derived protein families, whose members have no hits to proteins of reference genomes or Pfam domains. Each protein family is accompanied by multiple sequence alignments, Hidden Markov Models, taxonomic information, ecosystem and geolocation metadata, sequence and structure predictions, as well as 3D structure models predicted with AlphaFold2. In its current version, NMPFamsDB hosts over 100 000 protein families, each with at least 100 members. The reported protein families significantly expand (more than double) the number of known protein sequence clusters from reference genomes and reveal new insights into their habitat distribution, origins, functions and taxonomy. We expect NMPFamsDB to be a valuable resource for microbial proteome-wide analyses and for further discovery and characterization of novel functions. NMPFamsDB is publicly available in http://www.nmpfamsdb.org/ or https://bib.fleming.gr/NMPFamsDB.
59 BASIC BIOLOGICAL SCIENCES↗
Proteomic Profiling of the Substantia Nigra to Identify Determinants of Lewy Body Pathology and Dopaminergic Neuronal Loss
Proteinaceous aggregates containing ?-synuclein protein called Lewy bodies in the substantia nigra is a hallmark of Parkinson’s disease. The molecular mechanisms of Lewy body formation and associated neuronal loss remain largely unknown. To gain insights on proteins and pathways associated with Lewy body pathology, we performed quantitative profiling of the proteome. We analyzed substantia nigra tissue from 51 subjects arranged into three groups: cases with Lewy body pathology, Lewy body-negative controls with matching neuronal loss and controls with no neuronal loss. Using label-free LC-MS/MS approach we quantified the 2,963 most abundant proteins. Statistical testing for differential protein abundance, followed by pathway enrichment and Bayesian learning of the causal network structure were performed to identify likely drivers of Lewy body formation and dopaminergic neuronal loss. The identified pathways include (1) Arp2/3 complex-mediated actin nucleation; (2) synaptic function; (3) poly(A) RNA binding; (4) basement membrane and endothelium; and (5) hydrogen peroxide metabolic process. According to the data, the endothelial/basement membrane pathway is tightly connected with both pathologies and likely to be one of the drivers of the neuronal loss. The poly(A) RNA-binding proteins, including the ones relevant to other neurodegenerative disorders (e.g. TDP-43 and FUS) have strong inverse correlation with Lewy bodies and may reflect alternative mechanism of nigral neurodegeneration.
Petyuk, Vladislav A.↗
Data Summarization and Inference at Scale
This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.
97 MATHEMATICS AND COMPUTING↗
Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome
In every omics experiment, genes or their products are identified for which even state of the art tools are unable to assign a function. In the biotechnology chassis organism Pseudomonas putida, these proteins of unknown function make up 14% of the proteome. This missing information can bias analyses since these proteins can carry out functions which impact the engineering of organisms. As a consequence of predicting protein function across all organisms, function prediction tools generally fail to use all of the types of data available for any specific organism, including protein and transcript expression information. Additionally, the release of Alphafold predictions for all Uniprot proteins provides a novel opportunity for leveraging structural information. We constructed a bespoke machine learning model to predict the function of recalcitrant proteins of unknown function in Pseudomonas putida based on these sources of data, which annotated 1079 terms to 213 proteins. Among the predicted functions supplied by the model, we found evidence for a significant overrepresentation of nitrogen metabolism and macromolecule processing proteins. These findings were corroborated by manual analyses of selected proteins which identified, among others, a functionally unannotated operon that likely encodes a branch of the shikimate pathway.
60 APPLIED LIFE SCIENCES↗
Response of Pseudomonas putida to Complex, Aromatic-Rich Fractions from Biomass
There is strong interest in the valorization of lignin to produce valuable products; however, its structural complexity has been a conversion bottleneck. Chemical pretreatment liberates lignin-derived soluble fractions that may be upgraded by bioconversion. Here, cholinium ionic liquid pretreatment of sorghum produced soluble, aromatic-rich fractions that were converted by Pseudomonas putida (P. putida), a promising host for aromatic bioconversion. Growth studies and mutational analysis demonstrated that P. putida growth on these fractions was dependent on aromatic monomers but unknown factors also contributed. Proteomic and metabolomic analyses indicated that these unknown factors were amino acids and residual ionic liquid; the oligomeric aromatic fraction derived from lignin was not converted. A cholinium catabolic pathway was identified, and the deletion of the pathway stopped the ability of P. putida to grow on cholinium ionic liquid. This work demonstrates that aromatic-rich fractions obtained through pretreatment contain multiple substrates; conversion strategies should account for this complexity.
09 BIOMASS FUELS↗
SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 1 (Simulation Initial Conditions and Parameters)
A set of 24 protein structures/complexes from the SARS CoV-2 proteome and inputs prepared for simulation using the CHARMM36m forcefield in PDB and gromacs formats. Each system contains a pdb and gromacs top and related input files necessary for running a temperature replica-exchange simulation. In addition, we also include: charmm PSF files (generated from the gromacs topology), a list of temperatures at which replica-exchange simulations were done (tempRamp), example gromacs run input mdp files, and initial minimized structures where available (minimized.pdb).
60 APPLIED LIFE SCIENCES↗