Project description:Advances in bioconjugation and native protein modification are appearing at a blistering pace, making it increasingly time consuming for practitioners to identify the best chemical method for modifying a specific amino acid residue in a complex setting. The purpose of this perspective is to provide an informative, graphically rich manual highlighting significant advances in the field over the past decade. This guide will help triage candidate methods for peptide alteration and will serve as a starting point for those seeking to solve long-standing challenges.
Project description:Laccases are oxidases that only require O2 as a terminal oxidant. Thus, they provide an attractive green alternative to established alcohol oxidation protocols. However, laccases typically require catalytic amounts of mediator molecules to serve as electron shuttles between the enzyme and desired substrate. Consequently, laccase-mediator systems are defined by a multitude of parameters such as, e. g., the choice of laccase and mediator, the respective concentrations, pH, and the oxygen source. This complexity and a perceived lack of comparable data throughout literature represent an entry burden into this field. To provide a solid starting point, particularly for organic chemists, we herein provide a time-resolved, quantitative laccase and mediator screening based on the oxidation of anis alcohol as model reaction. We measured the redox potentials of mediators under the reaction conditions to relate them to their performance. Lastly, for particularly efficient laccase-mediator pairs, we screened important reaction parameters, resulting in an optimized setup for mediator-assisted laccase catalyzed oxidations.
Project description:The physiological and pharmacological role of nucleic acids structures folded into the non canonical G-quadruplex conformation have recently emerged. Their activities are targeted at vital cellular processes including telomere maintenance, regulation of transcription and processing of the pre-messenger or telomeric RNA. In addition, severe conditions like cancer, fragile X syndrome, Bloom syndrome, Werner syndrome and Fanconi anemia J are related to genomic defects that involve G-quadruplex forming sequences. In this connection G-quadruplex recognition and processing by nucleic acid directed proteins and enzymes represents a key event to activate or deactivate physiological or pathological pathways. In this review we examine protein-G-quadruplex recognition in physiologically significant conditions and discuss how to possibly exploit the interactions' selectivity for targeted therapeutic intervention.
Project description:A main challenge in drug discovery is finding molecules with a desirable balance of multiple properties. Here, we focus on the task of molecular optimization, where the goal is to optimize a given starting molecule towards desirable properties. This task can be framed as a machine translation problem in natural language processing, where in our case, a molecule is translated into a molecule with optimized properties based on the SMILES representation. Typically, chemists would use their intuition to suggest chemical transformations for the starting molecule being optimized. A widely used strategy is the concept of matched molecular pairs where two molecules differ by a single transformation. We seek to capture the chemist's intuition from matched molecular pairs using machine translation models. Specifically, the sequence-to-sequence model with attention mechanism, and the Transformer model are employed to generate molecules with desirable properties. As a proof of concept, three ADMET properties are optimized simultaneously: logD, solubility, and clearance, which are important properties of a drug. Since desirable properties often vary from project to project, the user-specified desirable property changes are incorporated into the input as an additional condition together with the starting molecules being optimized. Thus, the models can be guided to generate molecules satisfying the desirable properties. Additionally, we compare the two machine translation models based on the SMILES representation, with a graph-to-graph translation model HierG2G, which has shown the state-of-the-art performance in molecular optimization. Our results show that the Transformer can generate more molecules with desirable properties by making small modifications to the given starting molecules, which can be intuitive to chemists. A further enrichment of diverse molecules can be achieved by using an ensemble of models.
Project description:Miniaturised high-throughput experimentation (HTE) is widely employed in industrial and academic laboratories for rapid reaction optimisation using material-limited, multifactorial reaction condition screening. In fragment-based drug discovery (FBDD), common toolbox reactions such as the Suzuki-Miyaura and Buchwald-Hartwig cross couplings can be hampered by the fragment's intrinsic heteroatom-rich pharmacophore which is required for ligand-protein binding. At Astex, we are using microscale HTE to speed up reaction optimisation and prevent target down-prioritisation. By identifying catalyst/base/solvent combinations which tolerate unprotected heteroatoms we can rapidly optimise key cross-couplings and expedite route design by avoiding superfluous protecting group manipulations. However, HTE requires extensive upfront training, and this modern automated synthesis technique largely differs to the way organic chemists are traditionally trained. To make HTE accessible to all our synthetic chemists we have developed a semi-automated workflow enabled by pre-made 96-well screening kits, rapid analytical methods and in-house software development, which is empowering chemists at Astex to run HTE screens independently with minimal training.
Project description:Reductions play a key role in organic synthesis, producing chiral products with new functionalities. Enzymes can catalyse such reactions with exquisite stereo-, regio- and chemoselectivity, leading the way to alternative shorter classical synthetic routes towards not only high-added-value compounds but also bulk chemicals. In this review we describe the synthetic state-of-the-art and potential of enzymes that catalyse reductions, ranging from carbonyl, enone and aromatic reductions to reductive aminations.
Project description:Diffusion at the molecular level involves random collisions between particles, the structure of local microscopic environments, and interactions among the molecules involved. Sampling all of these aspects, along with correcting for finite-size effects, can make the calculation of infinitely dilute diffusion coefficients computationally difficult. We present a new approach for estimating the translational diffusion coefficient of biomolecular structures by encapsulating these driving forces of diffusion through piecewise assembly of the component residues of the protein structure. By linking the local chemistry of a solvent-exposed patch of a molecule to its contribution to the overall hydrodynamic radius, an accurate prediction of the computationally and experimentally comparable diffusion coefficients can be constructed following a solvent-excluded surface area calculation. We demonstrate that the resulting predictions for diffusion coefficients from peptides through to protein structures are comparable to explicit molecular simulations and improve on statistical mass-based predictions, which tend to rely on limited training data. As this approach uses the chemical identity of molecular structures, we find that it is able to predict and identify differences in diffusivity for structures that would be indistinguishable by mass information alone.
Project description:Contemporary efforts for empirically-unbiased modeling of protein-ligand interactions entail a painful tradeoff - as reliable information on both noncovalent binding factors and the dynamic behavior of a protein-ligand complex is often beyond practical limits. We demonstrate that information drawn exclusively from static molecular structures can be used for reproducing and predicting experimentally-measured binding affinities for protein-ligand complexes. In particular, inhibition constants (Ki) were calculated for seven different competitive inhibitors of Torpedo californica acetylcholinesterase using a multiple-linear-regression-based model. The latter, incorporating five independent variables - drawn from QM cluster, DLPNO-CCSD(T) calculations and LED analyses on the seven complexes, each containing active amino-acid residues found within interacting distance (3.5 Å) from the corresponding ligand - is shown to recover 99.9% of the sum of squares for measured Ki values, while having no statistically-significant residual errors. Despite being fitted to a small number of data points, leave-one-out cross-validation statistics suggest that it possesses surprising predictive value (Q2LOO=0.78, or 0.91 upon removal of a single outlier). This thus challenges ligand-invariant definitions of active sites, such as implied in the lock-key binding theory, as well as in alternatives highlighting shape-complementarity without taking electronic effects into account. Broader implications of the current work are discussed in dedicated appendices.
Project description:Essential oils produced by medicinal plants possess important bioactive properties (antibacterial, antioxidant) of high value for human society. Pollination and herbivory can modify the chemical defences of plants and therefore they may influence the bioactivity of essential oils. However, the effect of ecological interactions on plant bioactivity has not yet been evaluated. We tested the hypothesis that cross-pollination and simulated herbivory modify the chemical composition of essential oils, improving the bioactive properties of the medicinal plant Lepechinia floribunda (Lamiaceae). Through controlled experiments, we showed that essential oils from the outcrossed plant progeny had a higher relative abundance of oxygenated terpenes and it almost doubled the bacteriostatic effect on Staphylococcus aureus, compared to inbred progeny (i.e., progeny produced in absence of pollinators). Herbivory affected negatively and positively the production of rare compounds in inbred and outcrossed plants, respectively, but its effects on bioactivity still remain unknown. We show for the first time that by mediating cross-pollination (indirect ecosystem service), pollinators can improve ecosystem services linked to the biological activity of plant's essential oils. We stress the importance of the qualitative component of pollination (self, cross); an aspect usually neglected in studies of pollination services.
Project description:The CRISPR/Cas system uses guide RNAs (gRNAs) to direct sequence-specific DNA cleavage. Not every gRNA elicits cleavage and the mechanisms that govern gRNA activity have not been resolved. Low activity could result from either failure to form a functional Cas9-gRNA complex or inability to recognize targets in vivo. Here we show that both phenomena influence Cas9 activity by comparing mutagenesis rates in zebrafish embryos with in vitro cleavage assays. In vivo, our results suggest that genomic factors such as CTCF inhibit mutagenesis. Comparing near-identical gRNA sequences with different in vitro activities reveals that internal gRNA interactions reduce cleavage. Even though gRNAs containing these structures do not yield cleavage-competent complexes, they can compete with active gRNAs for binding to Cas9. These results reveal that both genomic context and internal gRNA interactions can interfere with Cas9-mediated cleavage and illuminate previously uncharacterized features of Cas9-gRNA complex formation.