Identification of Structure–Activity Relationships from Screening a Structurally Compact DNA‐Encoded Chemical Library

Methods for the rapid and inexpensive discovery of hit compounds are essential for pharmaceutical research and DNA‐encoded chemical libraries represent promising tools for this purpose. We here report on the design and synthesis of DAL‐100K, a DNA‐encoded chemical library containing 103 200 structurally compact compounds. Affinity screening experiments and DNA‐sequencing analysis provided ligands with nanomolar affinities to several proteins, including prostate‐specific membrane antigen and tankyrase 1. Correlations of sequence counts with binding affinities and potencies of enzyme inhibition were observed and enabled the identification of structural features critical for activity. These results indicate that libraries of this type represent a useful source of small‐molecule binders for target proteins of pharmaceutical interest and information on structural features important for binding.     

Extending the biocatalytic scope of regiocomplementary flavin-dependent halogenase enzymes

Flavin-dependent halogenases are potentially valuable biocatalysts for the regioselective halogenation of aromatic compounds. These enzymes, utilising benign inorganic halides, offer potential advantages over traditional non-enzymatic halogenation chemistry that often lacks regiocontrol and requires deleterious reagents. Here we extend the biocatalytic repertoire of the tryptophan halogenases, demonstrating how these enzymes can halogenate a range of alternative aryl substrates. Using structure guided mutagenesis we also show that it is possible to alter the regioselectivity as well as increase the activity of the halogenases with non-native substrates including anthranilic acid; an important intermediate in the synthesis and biosynthesis of pharmaceuticals and other valuable products.     

Polymerization of a Photocleavable Monomer Using Visible Light

The polymerization of the photocleavable monomer, o‐nitrobenzyl methacrylate (NBMA), is investigated using photoinduced electron/energy transfer reversible addition‐fragmentation chain transfer polymerization. The polymerizations under visible red (λ _max = 635 nm, 0.7 mW cm⁻²) and yellow (λ _max = 560 nm, 9.7 mW cm⁻²) light are performed and demonstrate rational evidence of a controlled/living radical polymerization process. Well‐defined poly(o‐nitrobenzyl methacrylate) (PNBMA) homopolymers with good control over the molecular weight and polymer dispersity are successfully synthesized by varying the irradiation time and/or targeted degree of polymerization. Chain extension of a poly(oligo(ethylene glycol) methyl ether methacrylate) macro‐chain transfer agent with NBMA is carried out to fabricate photocleavable amphiphilic block copolymers (BCP). Finally, these self‐assembled BCP rapidly dissemble under UV light suggesting the photoresponsive character of NBMA is not altered during the polymerization under yellow or red light. Such photoresponsive polymers can be potentially used for the remote‐controlled delivery of therapeutic compounds.

     

Mass spectrometric evidence for the modification of small molecules in a cobalt‐60‐irradiated rodent diet

The purpose of this study was to investigate the effect of radiation on the content of animal diet constituents using global metabolomics. Aqueous methanolic extracts of control and cobalt‐60‐irradiated Teklad 7001 diets were comprehensively analyzed using nano‐liquid chromatography–MS/MS. Among the over 2000 ions revealed by XCMS followed by data preprocessing, 94 positive and 143 negative metabolite ions had greater than 1.5‐fold changes and p‐values <0.01. Use of MetaboAnalyst statistical software demonstrated complete separation of the irradiated and non‐radiated diets in unsupervised principal components analysis and supervised partial least squares discriminant analysis. Irradiation led to an increase in the content of phytochemicals such as glucosinolates and oxidized lipids in the diet. Twenty‐eight metabolites that were significantly changed in the irradiated samples were putatively identified at the level of molecular formulae by MS/MS. MS/MS^ALL analysis of chloroform–methanol extracts of the irradiated diet showed increased levels of a number of unique linoleic acid‐derived branched fatty acid esters of hydroxy fatty acids. These data imply that gamma irradiation of animal diets causes chemical changes to dietary components, which in turn may influence the risk of mammary cancer. Copyright © 2017 John Wiley & Sons, Ltd.     

Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data

Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogenous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied to a broad class of epidemic models with only minimal modifications to the model dynamics and/or emission distribution. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school. Supplementary material for this article is available online.     

Efficient Computation of the Joint Sample Frequency Spectra for Multiple Populations

A wide range of studies in population genetics have employed the sample frequency spectrum (SFS), a summary statistic which describes the distribution of mutant alleles at a polymorphic site in a sample of DNA sequences and provides a highly efficient dimensional reduction of large-scale population genomic variation data. Recently, there has been much interest in analyzing the joint SFS data from multiple populations to infer parameters of complex demographic histories, including variable population sizes, population split times, migration rates, admixture proportions, and so on. SFS-based inference methods require accurate computation of the expected SFS under a given demographic model. Although much methodological progress has been made, existing methods suffer from numerical instability and high computational complexity when multiple populations are involved and the sample size is large. In this article, we present new analytic formulas and algorithms that enable accurate, efficient computation of the expected joint SFS for thousands of individuals sampled from hundreds of populations related by a complex demographic model with arbitrary population size histories (including piecewise-exponential growth). Our results are implemented in a new software package called momi (MOran Models for Inference). Through an empirical study, we demonstrate our improvements to numerical stability and computational complexity.