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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.

Designing new ionic liquids (ILs) is of crucial importance for various industrial applications. However, this always leads to a daunting challenge, as the number of possible combinations of cation and anion are very high and it is impossible to experimentally propose and screen a wide pool of potential candidates. However, recent applications of machine learning (ML) models have greatly improved the overall chemical discovery pipeline. In this study, we compare different generative methods for producing ionic liquids. In this comparison, we show the following: (1) when training data is scarce, a transfer learning approach can be applied to variational autoencoders (VAEs) to generate molecular structures of the target molecule type; (2) in a VAE-like structure, separate latent spaces for the cationic and anionic moieties can result in meaningful representations for their combinative, macroscopic properties; (3) interpolating between ILs with desired properties can result in a new IL with attributes similar to the two structural end points.

High representation by ammonia-oxidizing archaea (AOA) in marine systems is consistent with their high affinity for ammonia, efficient carbon fixation, and copper (Cu)-centric respiratory system. However, little is known about their response to nutrient stress. We therefore used global transcriptional and proteomic analyses to characterize the response of a model AOA, Nitrosopumilus maritimusSCM1, to ammonia starvation, Cu limitation and Cu excess. Most predicted protein-coding genes were transcribed in exponentially growing cells, and of ~74% detected in the proteome, ~6% were modified by N-terminal acetylation. The general response to ammonia starvation and Cu stress was downregulation of genes for energy generation and biosynthesis. Cells rapidly depleted transcripts for the A and B subunits of ammonia monooxygenase (AMO) in response to ammonia starvation, yet retained relatively high levels of transcripts for the C subunit. Thus, similar to ammonia-oxidizing bacteria, selective retention of amoCtranscripts during starvation appears important for subsequent recovery, and also suggests that AMO subunit transcript ratios could be used to assess the physiological status of marine populations. Unexpectedly, cobalamin biosynthesis was upregulated in response to both ammonia starvation and Cu stress, indicating the importance of this cofactor in retaining functional integrity during times of stress.

Fats, oils and greases (FOG) are energy-dense wastes that can be added to anaerobic digesters to substantially increase biomethane recovery via their conversion through long-chain fatty acids (LCFAs). However, a better understanding of the ecophysiology of syntrophic LCFA-degrading microbial communities in anaerobic digesters is needed to develop operating strategies that mitigate inhibitory LCFA accumulation from FOG. In this research, DNA stable isotope probing (SIP) was coupled with metagenomic sequencing for a genome-centric comparison of oleate (C18:1)-degrading populations in two anaerobic codigesters operated with either a pulse feeding or continuous-feeding strategy. The pulse-fed codigester microcosms converted oleate into methane at over 20% higher rates than the continuous-fed codigester microcosms. Differential coverage binning was demonstrated for the first time to recover population genome bins (GBs) from DNA-SIP metagenomes. About 70% of the 13C-enriched GBs were taxonomically assigned to the Syntrophomonas genus, thus substantiating the importance of Syntrophomonas species to LCFA degradation in anaerobic digesters. Phylogenetic comparisons of 13C-enriched GBs showed that phylogenetically distinct Syntrophomonas GBs were unique to each codigester. Overall, these results suggest that syntrophic populations in anaerobic digesters can have different adaptive capacities, and that selection for divergent populations may be achieved by adjusting reactor operating conditions to maximize biomethane recovery.

Fats, oils and greases (FOG) are energy-dense wastes that can be added to anaerobic digesters to substantially increase biomethane recovery via their conversion through long-chain fatty acids (LCFAs). However, a better understanding of the ecophysiology of syntrophic LCFA-degrading microbial communities in anaerobic digesters is needed to develop operating strategies that mitigate inhibitory LCFA accumulation from FOG. In this research, DNA stable isotope probing (SIP) was coupled with metagenomic sequencing for a genome-centric comparison of oleate (C18:1)-degrading populations in two anaerobic codigesters operated with either a pulse feeding or continuous-feeding strategy. The pulse-fed codigester microcosms converted oleate into methane at over 20% higher rates than the continuous-fed codigester microcosms. Differential coverage binning was demonstrated for the first time to recover population genome bins (GBs) from DNA-SIP metagenomes. About 70% of the 13C-enriched GBs were taxonomically assigned to the Syntrophomonasgenus, thus substantiating the importance of Syntrophomonasspecies to LCFA degradation in anaerobic digesters. Phylogenetic comparisons of 13C-enriched GBs showed that phylogenetically distinct SyntrophomonasGBs were unique to each codigester. Overall, these results suggest that syntrophic populations in anaerobic digesters can have different adaptive capacities, and that selection for divergent populations may be achieved by adjusting reactor operating conditions to maximize biomethane recovery.

This work introduces a three-dimensional (3D) invariant graph-to-string transformer variational autoencoders (VAE) (Vagrant) for generating molecules with accurate density functional theory (DFT)-level properties. Vagrant learns to model the joint probability distribution of a 3D molecular structure and its properties by encoding molecular structures into a 3D-aware latent space. Directed navigation through this latent space implicitly optimizes the 3D structure of a molecule, and the latent embedding can be used to condition a generative transformer to predict the candidate structure as a one-dimensional (1D) sequence. Additionally, we introduce two novel sampling methods that exploit the latent characteristics of a VAE to improve performance. We show that our method outperforms comparable 3D autoregressive and diffusion methods for predicting quantum chemical property values of novel molecules in terms of both sample quality and computational efficiency.

Germ-free mice are used to examine questions about the role of the gut microbiota in development of diseases. Generally these animals are maintained in semi-rigid or flexible-film isolators to ensure their continued sterility or, if colonized with specific microbiota, to ensure that no new species are introduced. Here, we describe the use of a caging system in which individual cages are hermetically sealed and have their own filtered positive airflow. This isopositive caging system requires less space and reduces animal housing costs. By using strict sterile techniques, we kept mice germ-free in this caging system for 12 weeks. We also used this caging system and approach to conduct studies evaluating a) the stability of the microbiome in germ-free mice receiving a fecal transplant and b) the stability of dietary-induced microbiota changes in fecal-transplanted mice. As has been shown in fecal transfer studies in isolators, we found that the transferred microbiota stabilizes as early as 2 weeks post transfer although recipient microbiota did not completely recapitulate those of the donors. Interestingly, we also noted some sex effects in these studies indicating that the sex of recipients or donors may play a role in colonization of microbiota. However, a larger study will be needed to determine what role, if any, sex plays in colonization of microbiota. Based on our studies, an isopositive caging system may be utilized to test multiple donor samples for their effects on phenotypes of mice in both normal and disease states even with limited available space for housing.

Halide perovskites have the potential to disrupt the photovoltaics market based on their high performance and low cost. However, the decomposition of perovskites under moisture, oxygen, and light raises concerns about service lifetime, especially because degradation mechanisms and the corresponding rate laws that fit the observed data have thus far eluded researchers. Here, we report a water-accelerated photooxidation mechanism dominating the degradation kinetics of archetypal perovskite CH3NH3PbI3in air under >1% relative humidity at 25 °C. From this mechanism, we develop a kinetic model that quantitatively predicts the degradation rate as a function of temperature, ambient O2and H2O levels, and illumination. Because water is a possible product of dry photooxidation, these results highlight the need for encapsulation schemes that rigorously block oxygen ingress, as product water may accumulate beneath the encapsulant and initiate the more rapid water-accelerated photooxidative decomposition.

One-carbon compounds such as methane and methanol are of increasing interest as sustainable substrates for biological production of fuels and industrial chemicals. The bacteria that carry out these conversions have been studied for many decades, but gaps exist in our knowledge of their metabolic pathways. One such gap is the difference between growth on methane and growth on methanol. Understanding such metabolism is important, since each has advantages and disadvantages as a feedstock for production of chemicals and fuels. The significance of our research is in the demonstration that the metabolic network is substantially altered in each case and in the delineation of these changes. The resulting new insights into the core metabolism of this bacterium now provide an improved basis for future strain design.

Data science is playing a transformational role in all aspects of science, engineering, commerce, and society. Here we raise the questions, “what is data science, and why is it relevant to ECS?” We tackle these questions from several different perspectives: using definitions provided by thought leaders and early adopters; summarizing the four papers contributed by leading electrochemical and solid state researchers; and adopting a realpolitikunderstanding of the world we live in.