A new lysine biosynthetic enzyme from a bacterial endosymbiont shaped by genetic drift and genome reduction.

The effect of population bottlenecks and genome reduction on enzyme function is poorly understood. Candidatus Liberibacter solanacearum is a bacterium with a reduced genome that is transmitted vertically to the egg of an infected psyllid-a population bottleneck that imposes genetic drift and is predicted to affect protein structure and function. Here, we define the function of Ca.

Predicting Long-Time-Scale Kinetics under Variable Experimental Conditions with Kinetica.jl.

Predicting the degradation processes of molecules over long time scales is a key aspect of industrial materials design. However, it is made computationally challenging by the need to construct large networks of chemical reactions that are relevant to the experimental conditions that kinetic models must mirror, with every reaction requiring accurate kinetic data. Here, we showcase , a new software package for constructing large-scale chemical reaction networks in a fully automated fashion by exploring chemical reaction space with a kinetics-driven algorithm; coupled to efficient machine-learning models of activation energies for sampled elementary reactions, we show how this approach readily enables generation and kinetic characterization of networks containing ∼10 chemical species and ≃10-10 reactions.

High-throughput property-driven generative design of functional organic molecules.

The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measure or compute. While molecular structures produced through generative deep learning will satisfy these patterns, they often only possess specific target properties by chance and not by design, which makes molecular discovery via this route inefficient. In this work, we predict molecules with (Pareto-)optimal properties by combining a generative deep learning model that predicts three-dimensional conformations of molecules with a supervised deep learning model that takes these as inputs and predicts their electronic structure.

On the utility of microfluidic systems to study protein interactions: advantages, challenges, and applications.

Within the complex milieu of a cell, which comprises a large number of different biomolecules, interactions are critical for function. In this post-reductionist era of biochemical research, the 'holy grail' for studying biomolecular interactions is to be able to characterize them in native environments. While there are a limited number of in situ experimental techniques currently available, there is a continuing need to develop new methods for the analysis of biomolecular complexes that can cope with the additional complexities introduced by native-like solutions.