Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites.

Direct sampling of building wastewater has the potential to enable "precision public health" observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic "features", but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features-providing orthogonal information to physicochemical properties-illustrate that many possess different feature temporal dynamics (fTDs) across buildings, with large and unpredictable single day deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features found to possess unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.

Xenoprotein engineering via synthetic libraries.

Chemical methods have enabled the total synthesis of protein molecules of ever-increasing size and complexity. However, methods to engineer synthetic proteins comprising noncanonical amino acids have not kept pace, even though this capability would be a distinct advantage of the total synthesis approach to protein science. In this work, we report a platform for protein engineering based on the screening of synthetic one-bead one-compound protein libraries. Screening throughput approaching that of cell surface display was achieved by a combination of magnetic bead enrichment, flow cytometry analysis of on-bead screens, and high-throughput MS/MS-based sequencing of identified active compounds. Direct screening of a synthetic protein library by these methods resulted in the de novo discovery of mirror-image miniprotein-based binders to a ∼150-kDa protein target, a task that would be difficult or impossible by other means.

Conformational Stabilization and Rapid Labeling of a 29-Residue Peptide by a Small Molecule Reaction Partner.

A 29-residue peptide (MP01), identified by in vitro selection for reactivity with a small molecule perfluoroaromatic, was modified and characterized using experimental and computational techniques, with the goal of understanding the molecular basis of its reactivity. These studies identified a six-amino acid point mutant (MP01-Gen4) that exhibited a reaction rate constant of 25.8 ± 1.8 M-1 s-1 at pH 7.4 and room temperature, approximately 2 orders of magnitude greater than that of its progenitor sequence and 3 orders of magnitude greater than background cysteine reactivity. MP01-Gen4 appeared to be conformationally dynamic and exhibited several properties reminiscent of larger protein molecules, including denaturant-sensitive structure and reactivity. We believe the majority of the reaction rate enhancement can be attributed to interaction of MP01-Gen4 with the perfluoroaromatic probe, which was found to stabilize a helical conformation of both MP01-Gen4 and nonreactive Cys-to-Ser or Cys-to-Ala variants. These findings demonstrate the ability of dynamic peptides to access proteinlike reaction mechanisms and the potential of perfluoroaromatic functionality to stabilize small peptide folds.

Studies on a landscape of perfluoroaromatic-reactive peptides.

We investigated 26 midsized peptides (∼30 amino acids in length) selected using mRNA display to perform a nucleophilic aromatic substitution reaction (SNAr). Analysis suggested a diverse set of reactive sequences with significant differences in primary sequence, secondary structure and even predicted tertiary structural features. Several of the sequences displayed rapid kinetics allowing for near complete labeling in under one hour. Rosetta ab initio structure prediction of these sequences suggested a landscape of structural features, ranging from beta-sheet-based sequences to those possessing more alpha-helical-like character. Circular dichroism spectroscopy confirmed elements of the structure predictions for the majority of peptides. This analysis additionally uncovered that several peptides underwent secondary structure alterations upon reaction. These results suggest a broad sequence and structural landscape of SNAr active peptides along with a potentially important feature of these biopolymers.

Balanced Permeability Index: A Multiparameter Index for Improved In Vitro Permeability.

The optimization of passive permeability is a key objective for orally available small molecule drug candidates. For drugs targeting the central nervous system (CNS), minimizing P-gp-mediated efflux is an additional important target for optimization. The physicochemical properties most strongly associated with high passive permeability and lower P-gp efflux are size, polarity, and lipophilicity. In this study, a new metric called the Balanced Permeability Index (BPI) was developed that combines these three properties. The BPI was found to be more effective than any single property in classifying molecules based on their permeability and efflux across a diverse range of chemicals and assays. BPI is easy to understand, allowing researchers to make decisions about which properties to prioritize during the drug development process.

High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome.

Characterizing complex microbial communities with single-cell resolution has been a long-standing goal of microbiology. We present Microbe-seq, a high-throughput method that yields the genomes of individual microbes from complex microbial communities. We encapsulate individual microbes in droplets with microfluidics and liberate their DNA, which we then amplify, tag with droplet-specific barcodes, and sequence. We explore the human gut microbiome, sequencing more than 20,000 microbial single-amplified genomes (SAGs) from a single human donor and coassembling genomes of almost 100 bacterial species, including several with multiple subspecies strains. We use these genomes to probe microbial interactions, reconstructing the horizontal gene transfer (HGT) network and observing HGT between 92 species pairs; we also identify a significant in vivo host-phage association between crAssphage and one strain of Bacteroides vulgatus. Microbe-seq contributes high-throughput culture-free capabilities to investigate genomic blueprints of complex microbial communities with single-microbe resolution.

Predicting human health from biofluid-based metabolomics using machine learning.

Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified-where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.

Quantifying residue-specific conformational dynamics of a highly reactive 29-mer peptide.

Understanding structural transitions within macromolecules remains an important challenge in biochemistry, with important implications for drug development and medicine. Insight into molecular behavior often requires residue-specific dynamics measurement at micromolar concentrations. We studied MP01-Gen4, a library peptide selected to rapidly undergo bioconjugation, by using electron paramagnetic resonance (EPR) to measure conformational dynamics. We mapped the dynamics of MP01-Gen4 with residue-specificity and identified the regions involved in a structural transformation related to the conjugation reaction. Upon reaction, the conformational dynamics of residues near the termini slow significantly more than central residues, indicating that the reaction induces a structural transition far from the reaction site. Arrhenius analysis demonstrates a nearly threefold decrease in the activation energy of conformational diffusion upon reaction (8.0 kBT to 3.4 kBT), which occurs across the entire peptide, independently of residue position. This novel approach to EPR spectral analysis provides insight into the positional extent of disorder and the nature of the energy landscape of a highly reactive, intrinsically disordered library peptide before and after conjugation.

Convolutional Neural Network Analysis of Two-Dimensional Hyperfine Sublevel Correlation Electron Paramagnetic Resonance Spectra.

A machine learning approach is presented for analyzing complex two-dimensional hyperfine sublevel correlation electron paramagnetic resonance (HYSCORE EPR) spectra with the proficiency of an expert spectroscopist. The computer vision algorithm requires no training on experimental data; rather, all of the spin physics required to interpret the spectra are learned from simulations alone. This approach is therefore applicable even when insufficient experimental data exist to train the algorithm. The neural network is demonstrated to be capable of utilizing the full information content of two-dimensional 14N HYSCORE spectra to predict the magnetic coupling parameters and their underlying probability distributions that were previously inaccessible. The predicted hyperfine ( a, T) and 14N quadrupole ( K, η) coupling constants deviate from the previous manual analyses of the experimental spectra on average by 0.11 MHz, 0.09 MHz, 0.19 MHz, and 0.09, respectively.

Chemiresistive Sensor Array and Machine Learning Classification of Food.

Successful identification of complex odors by sensor arrays remains a challenging problem. Herein, we report robust, category-specific multiclass-time series classification using an array of 20 carbon nanotube-based chemical sensors. We differentiate between samples of cheese, liquor, and edible oil based on their odor. In a two-stage machine-learning approach, we first obtain an optimal subset of sensors specific to each category and then validate this subset using an independent and expanded data set. We determined the optimal selectors via independent selector classification accuracy, as well as a combinatorial scan of all 4845 possible four selector combinations. We performed sample classification using two models-a k-nearest neighbors model and a random forest model trained on extracted features. This protocol led to high classification accuracy in the independent test sets for five cheese and five liquor samples (accuracies of 91% and 78%, respectively) and only a slightly lower (73%) accuracy on a five edible oil data set.

Discovery of a 29-Amino-Acid Reactive Abiotic Peptide for Selective Cysteine Arylation.

The regio- and chemoselective modification of proteins or peptides with chemical reagents is often challenging. One approach to overcome this problem involves identifying abiotic polypeptide sequences that react with specific small molecules. Toward this goal, we profiled ∼5 × 1013 randomized 30-mer peptides using mRNA display and high-throughput sequencing in search of polypeptides that can undergo cysteine arylation with a water-soluble perfluoroarene. Within this vast chemical space, we discovered a cysteine-containing sequence with a second-order rate constant of 0.29 M-1 s-1 for arylation. An N- and C-terminal truncation reduced the reaction rate, as did the addition of denaturants. When the reactive peptide was covalently fused to the enzyme Sortase A, we observed regiospecific arylation at a single cysteine site, leaving the enzyme's active site cysteine unchanged. Taken together, these results demonstrate that long polypeptides of defined sequence, when matched with the appropriate reactive group, can be used for selective arylation of cysteine in water.

Total synthesis and biochemical characterization of mirror image barnase.

In this study we synthesized and characterized mirror image barnase (B. amyloliquefaciens ribonuclease). d-Barnase was identical to l-barnase, when analyzed by liquid chromatography and mass-spectrometry. Proteolysis of the mirror image enzyme revealed that in contrast to its native counterpart, d-barnase was completely stable to digestive proteases. In enzymatic assays, d-barnase had the reciprocal chiral specificity and was fully active towards mirror image substrates. Interestingly, d-barnase also hydrolyzed the substrate of the native chirality, albeit 4000 times less efficiently. This effect was further confirmed by digesting a native 112-mer RNA with the enzyme. Additional studies revealed that barnase accommodates a range of substrates with various chiralities, but the prime requirement for guanosine remains. These studies point toward using mirror image enzymes as modern agents in biotechnology.