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Covering: up to the beginning of 2023 

A bstract We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression — a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature. 

Abstract We explore sequence determinants of enzyme activity and specificity in a major enzyme family of terpene synthases. Most enzymes in this family catalyze reactions that produce cyclic terpenes—complex hydrocarbons widely used by plants and insects in diverse biological processes such as defense, communication, and symbiosis. To analyze the molecular mechanisms of emergence of terpene cyclization, we have carried out in-depth examination of mutational space around (E)-β-farnesene synthase, an Artemisia annua enzyme which catalyzes production of a linear hydrocarbon chain. Each mutant enzyme in our synthetic libraries was characterized biochemically, and the resulting reaction rate data were used as input to the Michaelis–Menten model of enzyme kinetics, in which free energies were represented as sums of one-amino-acid contributions and two-amino-acid couplings. Our model predicts measured reaction rates with high accuracy and yields free energy landscapes characterized by relatively few coupling terms. As a result, the Michaelis–Menten free energy landscapes have simple, interpretable structure and exhibit little epistasis. We have also developed biophysical fitness models based on the assumption that highly fit enzymes have evolved to maximize the output of correct products, such as cyclic products or a specific product of interest, while minimizing the output of byproducts. This approach results in nonlinear fitness landscapes that are considerably more epistatic. Overall, our experimental and computational framework provides focused characterization of evolutionary emergence of novel enzymatic functions in the context of microevolutionary exploration of sequence space around naturally occurring enzymes. 

Abstract Insects have evolved a chemical communication system using terpenoids, a structurally diverse class of specialized metabolites, previously thought to be exclusively produced by plants and microbes. Gene discovery, bioinformatics, and biochemical characterization of multiple insect terpene synthases (TPSs) revealed that isopentenyl diphosphate synthases (IDS), enzymes from primary isoprenoid metabolism, are their likely evolutionary progenitors. However, the mutations underlying the emergence of the TPS function remain a mystery. To address this gap, we present the first structural and mechanistic model for the evolutionary emergence of TPS function in insects. Through identifying key mechanistic differences between IDS and TPS enzymes, we hypothesize that the loss of isopentenyl diphosphate (IPP) binding motifs strongly correlates with the gain of the TPS function. Based on this premise, we have elaborated the first explicit structural definition of isopentenyl diphosphate‐binding motifs (IBMs) and used the IBM definitions to examine previously characterized insect IDSs and TPSs and to predict the functions of as yet uncharacterized insect IDSs. Consistent with our hypothesis, we observed a clear pattern of disruptive substitutions to IBMs in characterized insect TPSs. In contrast, insect IDSs maintain essential consensus residues for binding IPP. Extending our analysis, we constructed the most comprehensive phylogeny of insect IDS sequences (430 full length sequences from eight insect orders) and used IBMs to predict the function of TPSs. Based on our analysis, we infer multiple, independent TPS emergence events across the class of insects, paving the way for future gene discovery efforts.