Proteomic Barcoding Platform for Macromolecular Screening and Delivery.

Engineered macromolecules offer compelling means for the therapy of conventionally undruggable interactions in human disease. However, their efficacy is limited by barriers to tissue and intracellular delivery. Inspired by recent advances in molecular barcoding and evolution, we developed BarcodeBabel, a generalized method for the design of libraries of peptide barcodes suitable for high-throughput mass spectrometry proteomics. Combined with PeptideBabel, a Monte Carlo sampling algorithm for the design of peptides with evolvable physicochemical properties and sequence complexity, we developed a barcoded library of cell penetrating peptides (CPPs) with distinct physicochemical features. Using quantitative targeted mass spectrometry, we identified CPPS with improved nuclear and cytoplasmic delivery exceeding hundreds of millions of molecules per human cell while maintaining minimal membrane disruption and negligible toxicity in vitro. These studies provide a proof of concept for peptide barcoding as a homogeneous high-throughput method for macromolecular screening and delivery. BarcodeBabel and PeptideBabel are available open-source from https://github.com/kentsisresearchgroup/.

Bridging structural and cell biology with cryo-electron microscopy.

Most life scientists would agree that understanding how cellular processes work requires structural knowledge about the macromolecules involved. For example, deciphering the double-helical nature of DNA revealed essential aspects of how genetic information is stored, copied and repaired. Yet, being reductionist in nature, structural biology requires the purification of large amounts of macromolecules, often trimmed off larger functional units. The advent of cryogenic electron microscopy (cryo-EM) greatly facilitated the study of large, functional complexes and generally of samples that are hard to express, purify and/or crystallize. Nevertheless, cryo-EM still requires purification and thus visualization outside of the natural context in which macromolecules operate and coexist. Conversely, cell biologists have been imaging cells using a number of fast-evolving techniques that keep expanding their spatial and temporal reach, but always far from the resolution at which chemistry can be understood. Thus, structural and cell biology provide complementary, yet unconnected visions of the inner workings of cells. Here we discuss how the interplay between cryo-EM and cryo-electron tomography, as a connecting bridge to visualize macromolecules in situ, holds great promise to create comprehensive structural depictions of macromolecules as they interact in complex mixtures or, ultimately, inside the cell itself.

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal.

OBJECTIVE: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. METHODS: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen in training compared to the end-to-end deep learning method. RESULTS: Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. CONCLUSION: This study provides an intelligent, reliable and robust MRS quantification. SIGNIFICANCE: QNet is the first LLS quantification aided by deep learning.

Physicochemical homeostasis in bacteria.

In living cells, the biochemical processes such as energy provision, molecule synthesis, gene expression, and cell division take place in a confined space where the internal chemical and physical conditions are different from those in dilute solutions. The concentrations of specific molecules and the specific reactions and interactions vary for different types of cells, but a number of factors are universal and kept within limits, which we refer to as physicochemical homeostasis. For instance, the internal pH of many cell types is kept within the range of 7.0 to 7.5, the fraction of macromolecules occupies 15%-20% of the cell volume (also known as macromolecular crowding) and the ionic strength is kept within limits to prevent salting-in or salting-out effects. In this article we summarize the generic physicochemical properties of the cytoplasm of bacteria, how they are connected to the energy status of the cell, and how they affect biological processes (Fig. 1). We describe how the internal pH and proton motive force are regulated, how the internal ionic strength is kept within limits, what the impact of macromolecular crowding is on the function of enzymes and the interaction between molecules, how cells regulate their volume (and turgor), and how the cytoplasm is structured. Physicochemical homeostasis is best understood in Escherichia coli, but pioneering studies have also been performed in lactic acid bacteria.

Measuring in vivo protein turnover and exchange in yeast macromolecular assemblies.

We present a comprehensive and robust protocol to track the dynamics of all proteins in a complex in yeast cells. A single member of the protein assembly is tagged and conditionally expressed, minimizing the perturbations to the protein complex. Then, SILAC labeling and affinity purification are used for the assessment of the whole protein complex dynamics. This method can determine and distinguish both subunit turnover and exchange specifically in an assembly to provide a comprehensive picture of assembly dynamics. For complete details on the use and execution of this protocol, please refer to Hakhverdyan et al. (2021).

Supramolecular Bioimaging through Signal Amplification by Combining Indicator Displacement Assay with Förster Resonance Energy Transfer.

Fluorescent chemosensors are powerful imaging tools in the fields of life sciences and engineering. Based on the principle of supramolecular chemistry, indicator displacement assay (IDA) provides an alternative approach for constructing and optimizing chemosensors, which has the advantages of simplicity, tunability, and modularity. However, the application of IDA in bioimaging continues to face a series of challenges, including interfering signals, background noise, and inconsistent spatial location. Accordingly, we herein report a supramolecular bioimaging strategy of Förster resonance energy transfer (FRET)-assisted IDA by employing macrocyclic amphiphiles as the operating platform. By merging FRET with IDA, the limitations of IDA in bioimaging were addressed. As a proof of concept, the study achieved mitochondria-targeted imaging of adenosine triphosphate in live cells with signal amplification. This study opens a non-covalent avenue for bioimaging with advancements in tunability, generality, and simplicity, apart from the covalent approach.

NP Navigator: A New Look at the Natural Product Chemical Space.

Natural products (NPs), being evolutionary selected over millions of years to bind to biological macromolecules, remained an important source of inspiration for medicinal chemists even after the advent of efficient drug discovery technologies such as combinatorial chemistry and high-throughput screening. Thus, there is a strong demand for efficient and user-friendly computational tools that allow to analyze large libraries of NPs. In this context, we introduce NP Navigator - a freely available intuitive online tool for visualization and navigation through the chemical space of NPs and NP-like molecules. It is based on the hierarchical ensemble of generative topographic maps, featuring NPs from the COlleCtion of Open NatUral producTs (COCONUT), bioactive compounds from ChEMBL and commercially available molecules from ZINC. NP Navigator allows to efficiently analyze different aspects of NPs - chemotype distribution, physicochemical properties, biological activity and commercial availability of NPs. The latter concerns not only purchasable NPs but also their close analogs that can be considered as synthetic mimetics of NPs or pseudo-NPs.

Macromolecular interactions in vitro, comparing classical and novel approaches.

Biophysical quantification of protein interactions is central to unveil the molecular mechanisms of cellular processes. Researchers can choose from a wide panel of biophysical methods that quantify molecular interactions in different ways, including both classical and more novel techniques. We report the outcome of an ARBRE-MOBIEU training school held in June 2019 in Gif-sur-Yvette, France ( https://mosbio.sciencesconf.org/ ). Twenty European students benefited from a week's training with theoretical and practical sessions in six complementary approaches: (1) analytical ultracentrifugation with or without a fluorescence detector system (AUC-FDS), (2) isothermal titration calorimetry (ITC), (3) size exclusion chromatography coupled to multi-angle light scattering (SEC-MALS), (4) bio-layer interferometry (BLI), (5) microscale thermophoresis (MST) and, (6) switchSENSE. They implemented all these methods on two examples of macromolecular interactions with nanomolar affinity: first, a protein-protein interaction between an artificial alphaRep binder, and its target protein, also an alphaRep; second, a protein-DNA interaction between a DNA repair complex, Ku70/Ku80 (hereafter called Ku), and its cognate DNA ligand. We report the approaches used to analyze the two systems under study and thereby showcase application of each of the six techniques. The workshop provided students with improved understanding of the advantages and limitations of different methods, enabling future choices concerning approaches that are most relevant or informative for specific kinds of sample and interaction.

Material-assisted mass spectrometric analysis of low molecular weight compounds for biomedical applications.

Low molecular weight compounds play an important role in encoding the current physiological state of an individual. Laser desorption/ionization mass spectrometry (LDI MS) offers high sensitivity with low cost for molecular detection, but it is not able to cover small molecules due to the drawbacks of the conventional matrix. Advanced materials are better alternatives, showing little background interference and high LDI efficiency. Herein, we first classify the current materials with a summary of compositions and structures. Matrix preparation protocols are then reviewed, to enhance the selectivity and reproducibility of MS data better. Finally, we highlight the biomedical applications of material-assisted LDI MS, at the tissue, bio-fluid, and cellular levels. We foresee that the advanced materials will bring far-reaching implications in LDI MS towards real-case applications, especially in clinical settings.

Recent development and application of cyclometalated iridium(III) complexes as chemical and biological probes.

Iridium complexes have been widely applied as molecular sensors because of their rich photophysical properties, including large Stokes shifts, long emission lifetimes, environment-sensitive emissions, and high luminescence quantum yields. In this paper, we review the recent development and application of iridium complexes as probes for ions, anions, gaseous species, organic molecules, small biomolecules, biomacromolecules, and subcellular organelles. Our outlook for iridium-based probes is also discussed.

Quantitative density gradient analysis by mass spectrometry (qDGMS) and complexome profiling analysis (ComPrAn) R package for the study of macromolecular complexes.

Many cellular processes involve the participation of large macromolecular assemblies. Understanding their function requires methods allowing to study their dynamic and mechanistic properties. Here we present a method for quantitative analysis of native protein or ribonucleoprotein complexes by mass spectrometry following their separation by density - qDGMS. Mass spectrometric quantitation is enabled through stable isotope labelling with amino acids in cell culture (SILAC). We provide a complete guide, from experimental design to preparation of publication-ready figures, using a purposely-developed R package - ComPrAn. As specific examples, we present the use of sucrose density gradients to inspect the assembly and dynamics of the human mitochondrial ribosome (mitoribosome), its interacting proteins, the small subunit of the cytoplasmic ribosome, cytoplasmic aminoacyl-tRNA synthetase complex and the mitochondrial PDH complex. ComPrAn provides tools for analysis of peptide-level data as well as normalization and clustering tools for protein-level data, dedicated visualization functions and graphical user interface. Although, it has been developed for the analysis of qDGMS samples, it can also be used for other proteomics experiments that involve 2-state labelled samples separated into fractions. We show that qDGMS and ComPrAn can be used to study macromolecular complexes in their native state, accounting for the dynamics inherent to biological systems and benefiting from its proteome-wide quantitative and qualitative capability.

Low-Noise Solid-State Nanopore Enhancing Direct Label-Free Analysis for Small Dimensional Assemblies Induced by Specific Molecular Binding.

Solid-state nanopores show special potential as a new single-molecular characterization for nucleic acid assemblies and molecular machines. However, direct recognition of small dimensional species is still quite difficult due the lower resolution compared with biological pores. We recently reported a very efficient noise-reduction and resolution-enhancement mechanism via introducing high-dielectric additives (e.g., formamide) into conical glass nanopore (CGN) test buffer. Based on this advance, here, for the first time, we apply a bare CGN to directly recognize small dimensional assemblies induced by small molecules. Cocaine and its split aptamer (Capt assembly) are chosen as the model set. By introducing 20% formamide into CGN test buffer, high cocaine-specific distinguishing of the 113 nt Capt assembly has been realized without any covalent label or additional signaling strategies. The signal-to-background discrimination is much enhanced compared with control characterizations such as gel electrophoresis and fluorescence resonance energy transfer (FRET). As a further innovation, we verify that low-noise CGN can also enhance the resolution of small conformational/size changes happening on the side chain of large dimensional substrates. Long duplex concatamers generated from the hybridization chain reaction (HCR) are selected as the model substrates. In the presence of cocaine, low-noise CGN has sensitively captured the current changes when the 26 nt aptamer segment is assembled on the side chain of HCR duplexes. This paper proves that the introduction of the low-noise mechanism has significantly improved the resolution of the solid-state nanopore at smaller and finer scales and thus may direct extensive and deeper research in the field of CGN-based analysis at both single-molecular and statistical levels, such as molecular recognition, assembly characterization, structure identification, information storage, and target index.

Enzyme-Instructed Self-Assembly Enabled Monomer-Excimer Transition to Construct Higher Ordered Luminescent Supramolecular Assembly for Activity-based Bioimaging.

It is challenging to construct high-performing excimer-based luminescent analytic tools at low molecular concentrations. We report that enzyme-instructed self-assembly (EISA) enables the monomer-excimer transition of a coumarin dye (Cou) at low molecular concentrations, and the resulting higher ordered luminescent supramolecular assemblies (i.e., nanofibers) efficiently record the spatiotemporal details of alkaline phosphatase (ALP) activity in vitro and in vivo. Cou was conjugated to short self-assembly peptides with a hydrophilic ALP-responsive group. By ALP triggering, EISA actuated a nanoparticles-nanofibers transition at low peptide concentrations followed by monomer-excimer transition of Cou. Analysis of structure-property relationships revealed that the self-assembly motif was a prerequisite for peptides to induce the monomer-excimer transition of Cou. Luminescent supramolecular nanofibers of pYD (LSN-pYD) illuminated the intercellular bridge of cancer cells and distinguished cancer cells (tissues) from normal cells (tissues) efficiently and rapidly, promising potential use for the early diagnosis of cancer. This work extends the functions of EISA and provides a new application of supramolecular chemistry.

MEGA-PRESS of GABA+: Influences of acquisition parameters.

γ-aminobutyric acid (GABA) was the first molecule that was edited with MEGA-PRESS. GABA edited spectroscopy is challenged by limited selectivity of editing pulses. Coediting of resonances from macromolecules (MM) is the greatest single limitation of GABA edited spectroscopy. In this contribution, relative signal contributions from GABA, MM and homocarnosine to the total MEGA-PRESS edited signal at ~3 ppm, i.e., GABA+, are simulated at 3 tesla using several acquisition schemes. The base scheme is modeled after those currently supplied by vendors: it uses typical pulse shapes and lengths, it minimizes the first echo time (TE), and the delay between the editing pulses is kept at TE/2. Edited spectra are simulated for imperfect acquisition parameters such as incorrect frequency, larger chemical shift displacement, incorrect transmit B1 -field calibration for localization and editing pulses, and longer TE. An alternative timing scheme and longer editing pulses are also considered. Additional simulations are performed for symmetric editing around the MM frequency to suppress the MM signal. The relative influences of these acquisition parameters on the constituents of GABA+ are examined from the perspective of modern experimental designs for investigating brain GABA concentration differences in healthy and diseased humans. Other factors that influence signal contributions, such as T1 and T2 relaxation times are also considered.

Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts' consensus recommendations.

Once an MRS dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we review these three main steps in the post-acquisition workflow of a single-voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step.

Click-ExM enables expansion microscopy for all biomolecules.

Expansion microscopy (ExM) allows super-resolution imaging on conventional fluorescence microscopes, but has been limited to proteins and nucleic acids. Here we develop click-ExM, which integrates click labeling into ExM to enable a 'one-stop-shop' method for nanoscale imaging of various types of biomolecule. By click labeling with biotin and staining with fluorescently labeled streptavidin, a large range of biomolecules can be imaged by the standard ExM procedure normally used for proteins. Using 18 clickable labels, we demonstrate click-ExM on lipids, glycans, proteins, DNA, RNA and small molecules. We demonstrate that click-ExM is applicable in cell culture systems and for tissue imaging. We further show that click-ExM is compatible with signal-amplification techniques and two-color imaging. Click-ExM thus provides a convenient and versatile method for super-resolution imaging, which may be routinely used for cell and tissue samples.

Chemometric modeling of palate fullness in lager beers.

Palate fullness and mouthfeel of beer are key attributes of sensory beer quality. Non-volatile substances and molar mass fractions influence sensory perceptions of palate fullness and mouthfeel. However, systematic correlations between sensory attributes and native beer compounds have not been evaluated within the concentration range found in lager beer. This article reports a chemometric analysis of 41 lager beers by evaluating analytical data of beer compositions, palate fullness, and mouthfeel descriptors. AF4-MALS-dRI indicated high variability in the macromolecular compositions of classical lager beers. Screened beers were clustered into groups differing significantly in palate fullness intensity and macromolecular distribution. Significant correlations were found between palate fullness and macromolecular fractions and beer composition parameters: original gravity, viscosity, indices of macromolecular distribution, total nitrogen (p < 0.001), and ß-glucan (p < 0.01). Thus, a model was built using partial least square regression (PLS) analysis to predict the palate fullness intensity in beers (R2C = 0.7993). This model can be used as a guideline by brewers to control palate fullness and mouthfeel.

Analytical combinations to evaluate the macromolecular composition of extracellular substances (ECS) from Lactobacillus plantarum cell culture media.

Sugar-enriched media are used to produce extracellular substances (ECS) by Lactobacillus plantarum WCSF1, with a focus on growing stages and carbon source substrates. Combination of size exclusion chromatography and ATR-FTIR spectroscopy provides physicochemical patterns of bulk ECS produced along culture growing time. Secreted biopolymers present polydisperse and high molecular weight distributions, with significant amounts of carbohydrates and proteins. Results, supported by a multivariate statistical analysis, enable to differentiate the macromolecular content of bacterial ECS along the growing stages regardless of the growing media, highlighting a higher production of proteinaceous materials compared to polysaccharides. At the end of the exponential phase, common exoproteins were present in all the tested sugar-enriched media such as transglycosylases between 20 and 35 kDa, a muropeptidase at 36.9 kDa and a cell wall hydrolase. Additionally, L. plantarum WCFS1 secretes ECS with a greater diversity of proteins, when growing in the sucrose-enriched media. Graphical abstract.

How the Supramolecular Nature of Lignohumate Affects Its Diffusion in Agarose Hydrogel.

Lignohumate, as an industrially produced analog of natural humic substances, is studied from the point of view of its diffusion properties. This work focuses on its permeation ability, important in agricultural and horticultural applications, connected with its penetration into plant organs as leaves and roots. The hydrogel based on agarose was used as a model material for the diffusion of lignohumate. Two types of experiments were realized: the diffusion of lignohumate in the hydrogel diffusion couple and the diffusion of lignohumate from its solution into hydrogel. The diffusion coefficient of lignohumate in the hydrogel was determined and used for the modelling of the time development of concentration profiles. It was found that the model agrees with experimental data for short times but an accumulation of lignohumate in front of the interface between donor and acceptor hydrogels was observed after several days. The particle size distribution of lignohumate and changes in the E4/E6 ratio used as an indicator of molecular weight of humic substances were determined. The results showed that the supramolecular structure of lignohumate can react sensitively to actual changes in its environs and thus affect their mobility and permeability into different materials. A filtration effect at the interface can be observed as an accompanying phenomenon of the re-arrangement in the lignohumate secondary structure.

Analytical Ultracentrifugation (AUC): An Overview of the Application of Fluorescence and Absorbance AUC to the Study of Biological Macromolecules.

The biochemical and biophysical investigation of proteins, nucleic acids, and the assemblies that they form yields essential information to understand complex systems. Analytical ultracentrifugation (AUC) represents a broadly applicable and information-rich method for investigating macromolecular characteristics such as size, shape, stoichiometry, and binding properties, all in the true solution-state environment that is lacking in most orthogonal methods. Despite this, AUC remains underutilized relative to its capabilities and potential in the fields of biochemistry and molecular biology. Although there has been a rapid development of computing power and AUC analysis tools in this millennium, fewer advancements have occurred in development of new applications of the technique, leaving these powerful instruments underappreciated and underused in many research institutes. With AUC previously limited to absorbance and Rayleigh interference optics, the addition of fluorescence detection systems has greatly enhanced the applicability of AUC to macromolecular systems that are traditionally difficult to characterize. This overview provides a resource for novices, highlighting the potential of AUC and encouraging its use in their research, as well as for current users, who may benefit from our experience. We discuss the strengths of fluorescence-detected AUC and demonstrate the power of even simple AUC experiments to answer practical and fundamental questions about biophysical properties of macromolecular assemblies. We address the development and utility of AUC, explore experimental design considerations, present case studies investigating properties of biological macromolecules that are of common interest to researchers, and review popular analysis approaches. © 2020 The Authors.