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Microbial lipases constitute a class of biocatalysts with the ability to cleave ester linkages of long-chain triglycerides. This property makes them particularly attractive for industrial applications ranging from food processing to pharmaceutical preparation. Among such enzymes, Candida rugosa lipase (CRL) is one of the most frequently used in biotransformation. A notable feature of CRL, among many lipases, is its propensity for interfacial activation: these enzymes exhibit elevated catalytic rates when acting at the interface between aqueous and hydrophobic phases. Notably, this phenomenon can be attributed to the presence of a mobile lid domain, which in its closed state occludes the enzyme active site. To advance our understanding of interfacial activation, we explore the dynamics of CRL rotation at the octane–water interface in this work. To do so, we employ molecular dynamics and umbrella sampling to evaluate the free energy of rotation of the enzyme at the interface. We identify a global minimum in the rotational landscape that coincides with lid opening at the interface. Additionally, we investigate the role of surface residues outside the lid domain as they serve to instigate rotation of the lid toward the aqueous phase. In doing so, we identify a patch of leucine residues which when mutated to glycine impose a barrier to rotation that maintains the enzyme in the inactive (closed lid) state on the order of 1 μs. Importantly, this study presents a novel quantification of the rotational free energy corresponding to CRL lid opening at the octane–water interface. The accompanying mutagenesis study likewise clarifies the role of hydrophobic surface residues in the transition. As such, this work provides valuable insight into the phenomenon of interfacial activation that might open up new avenues for manipulating the microenvironment of industrially relevant lipases, affording enhanced control over the enzyme state. 

Thin metal particles with two-dimensional symmetry are attractive for multiple ap- plications, but are difficult to synthesize in a reproducible manner. Although molecules that selectively adsorb to facets have been used to control nanoparticle shape, there is still limited research into the temporal control of growth processes to control these structural outcomes. Moreover, much of the current research into the growth of thin two-dimensional particles lacks mechanistic details. In this work, we study why the substitution of isoleucine for methionine in a gold binding peptide (Z2, RMRMKMK) results in an increase in gold nanoparticle anisotropy. Nanoplatelet growth in the pres- ence of Z2M246I (RIRIKIK) is characterized using in situ small-angle X-ray scattering (SAXS) and UV-Vis spectroscopy. Fitting time-resolved SAXS profiles reveals that 10 nm thick particles with two-dimensional symmetry are formed within the first few min- utes of the reaction. Next, through a combination of electron diffraction and molecular dynamics simulations, we show that substitution of methionine for isoluecine increases the (111) facet selectivity in Z2M246I, and conclude that this is key to the growth of nanoplatelets. However, the potential application of nanoplatelets formed using Z2M246I is limited due to their uncontrolled lateral growth, aggregation, and rapid sedimentation. Therefore, we use a liquid handling robot to perform temporally con- trolled synthesis and dynamic intervention through the addition of Z2 to nanoplatelets growing in the presence of Z2M246I at different times. UV-Vis spectroscopy dynamic light scattering, and electron microscopy show that dynamic intervention results in control over the mean-size and stability of plate-like particles. Finally, we use in situ UV-Vis spectroscopy to study plate-like particle growth at different times of interven- tion. Our results demonstrate that both the selectivity and magnitude of binding free energy towards lattices is important for controlling nanoparticle growth pathways. 

Data science and machine learning are revolutionizing enzyme engineering; however, high-throughput simulations for screening large libraries of enzyme variants remain a challenge. Here, we present a novel but highly simple approach to comparing enzyme variants with fully atomistic classical molecular dynamics (MD) simulations on a tractable timescale. Our method greatly simplifies the problem by restricting sampling only to the reaction transition state, and we show that the resulting measurements of transition-state stability are well correlated with experimental activity measurements across two highly distinct enzymes, even for mutations with effects too small to resolve with free energy methods. This method will enable atomistic simulations to achieve sampling coverage for enzyme variant prescreening and machine learning model training on a scale that was previously not possible. 

Attention mechanisms have led to many breakthroughs in sequential data modeling but have yet to be incorporated into any generative algorithms for molecular design. Here we explore the impact of adding self-attention layers to generative β -VAE models and show that those with attention are able to learn a complex “molecular grammar” while improving performance on downstream tasks such as accurately sampling from the latent space (“model memory”) or exploring novel chemistries not present in the training data. There is a notable relationship between a model's architecture, the structure of its latent memory and its performance during inference. We demonstrate that there is an unavoidable tradeoff between model exploration and validity that is a function of the complexity of the latent memory. However, novel sampling schemes may be used that optimize this tradeoff. We anticipate that attention will play an important role in future molecular design algorithms that can make efficient use of the detailed molecular substructures learned by the transformer. 

Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science—the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.