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A comprehensive assessment of process design, cost efficiency, critical mineral recovery, and CO 2 storage in mine tailings.
Methods for patterning highly sensitive materials, such as organic materials, organic semiconductors, biomolecular materials, and the like, with photolithographic resolution are disclosed. In some embodiments, a germanium mask (304) is formed on the surface of the sensitive material (302), thereby protecting it from subsequent processes that employ harsh chemicals that would otherwise destroy the sensitive material (302). A microlithography mask (306) is patterned on the germanium mask layer (304), after which the germanium exposed by the microlithography mask (306) is removed by dissolving it in water. After transferring the pattern of the germanium mask (304) into the sensitive material (302), the germanium and microlithography masks (304, 306) are completely removed by immersing the substrate in water, which dissolves the remaining germanium and lifts off the microlithography mask material. As a result, the only chemical to which the sensitive material (302) is exposed during the patterning process is water, thereby mitigating or avoiding damage to the material (302).
Rhodotorula toruloides is a non-model, oleaginous yeast uniquely suited to produce acetyl-CoA-derived chemicals. However, the lack of well-characterized genomic integration sites has impeded the metabolic engineering of this organism. Here we report a set of computationally predicted and experimentally validated chromosomal integration sites in R. toruloides . We first implemented an in silico platform by integrating essential gene information and transcriptomic data to identify candidate sites that meet stringent criteria. We then conducted a full experimental characterization of these sites, assessing integration efficiency, gene expression levels, impact on cell growth, and long-term expression stability. Among the identified sites, 12 exhibited integration efficiencies of 50% or higher, making them sufficient for most metabolic engineering applications. Using selected high-efficiency sites, we achieved simultaneous double and triple integrations and efficiently integrated long functional pathways (up to 14.7 kb). Additionally, we developed a new inducible marker recycling system that allows multiple rounds of integration at our characterized sites. We validated this system by performing five sequential rounds of GFP integration and three sequential rounds of MaFAR integration for fatty alcohol production, demonstrating, for the first time, precise gene copy number tuning in R. toruloides . These characterized integration sites should significantly advance metabolic engineering efforts and future genetic tool development in R. toruloides .
Light is a critical environmental stimulus for plants, serving as an energy source via photosynthesis and a signal for developmental programming. Plants perceive light through various light-responsive proteins, termed photoreceptors. Phytochromes are red-light photoreceptors that are highly conserved across kingdoms. In the model plant Arabidopsis thaliana , phytochrome B serves as a light and thermal sensor, mediating physiological processes such as seedling germination and establishment, hypocotyl growth, chlorophyll biogenesis, and flowering. In response to red light, phytochromes convert to a biologically active form, translocating from the cytoplasm into the nucleus and further compartmentalizes into subnuclear compartments termed photobodies. PhyB photobodies regulate phytochrome-mediated signaling and physiological outputs. However, photobody function, composition, and biogenesis remain undefined since their discovery. Based on photobody cellular dynamics and the properties of internal components, photobodies have been suggested to undergo liquid-liquid phase separation, a process by which some membraneless compartments form. Here, we explore photobodies as environmental sensors, examine the role of their protein constituents, and outline the biophysical perspective that photobodies may be undergoing liquid-liquid phase separation. Understanding the molecular, cellular, and biophysical processes that shape how plants perceive light will help in engineering improved sunlight capture and fitness of important crops.
Abstract This tutorial review provides a comprehensive overview of machine learning (ML)-based model predictive control (MPC) methods, covering both theoretical and practical aspects. It provides a theoretical analysis of closed-loop stability based on the generalization error of ML models and addresses practical challenges such as data scarcity, data quality, the curse of dimensionality, model uncertainty, computational efficiency, and safety from both modeling and control perspectives. The application of these methods is demonstrated using a nonlinear chemical process example, with open-source code available on GitHub. The paper concludes with a discussion on future research directions in ML-based MPC.
Molecules are essential building blocks of life and their different conformations (i.e., shapes) crucially determine the functional role that they play in living organisms. Cryogenic Electron Microscopy (cryo-EM) allows for acquisition of large image datasets of individual molecules. Recent advances in computational cryo-EM have made it possible to learn latent variable models of conformation landscapes. However, interpreting these latent spaces remains a challenge as their individual dimensions are often arbitrary. The key message of our work is that this interpretation challenge can be viewed as an Independent Component Analysis (ICA) problem where we seek models that have the property of identifiability. That means, they have an essentially unique solution, representing a conformational latent space that separates the different degrees of freedom a molecule is equipped with in nature. Thus, we aim to advance the computational field of cryo-EM beyond visualizations as we connect it with the theoretical framework of (nonlinear) ICA and discuss the need for identifiable models, improved metrics, and benchmarks. Moving forward, we propose future directions for enhancing the disentanglement of latent spaces in cryo-EM, refining evaluation metrics and exploring techniques that leverage physics-based decoders of biomolecular systems. Moreover, we discuss how future technological developments in time-resolved single particle imaging may enable the application of nonlinear ICA models that can discover the true conformation changes of molecules in nature. The pursuit of interpretable conformational latent spaces will empower researchers to unravel complex biological processes and facilitate targeted interventions. This has significant implications for drug discovery and structural biology more broadly. More generally, latent variable models are deployed widely across many scientific disciplines. Thus, the argument we present in this work has much broader applications in AI for science if we want to move from impressive nonlinear neural network models to mathematically grounded methods that can help us learn something new about nature.
To advance our quest to understand the role of low energy electrons in biomolecular systems, we performed investigations on dissociative electron attachment (DEA) to gas-phase N-ethylformamide (NEF) and N-ethylacetamide (NEA) molecules. Both molecules contain the amide bond, which is the linkage between two consecutive amino acid residues in proteins. Thus, their electron-induced dissociation can imitate the resonant behavior of the DEA process in more complex biostructures. Our experimental results indicate that in these two molecules, the dissociation of the amide bond results in a double resonant structure with peaks at ~5 eV and 9 eV. We also determined the energy position of resonant states for several negative ions, i.e., the other dissociation products from NEF and NEA. Our predictions of dissociation channels were supported by density functional theory calculations of the corresponding threshold energies. Our results and those previously reported for small amides and peptides imply the fundamental nature for breakage of the amide bond through the DEA process.
Herein, we have synthesized high-performance, low-cost ionomer biocomposites comprised of sulfonated poly(ether ether ketone) (SPEEK) and softwood Kraft lignin with proton selectivities three- to ten-fold higher than that of neat SPEEK.
High-resolution investigation of cell wall materials has emerged as an important application of biomolecular solid-state NMR (ssNMR). Multidimensional correlation experiments have become a standard method for obtaining sufficient spectral resolution to determine the polymorphic structure of carbohydrates and address biochemical questions regarding the supramolecular organization of cell walls. Here, using plant cellulose and matrix polysaccharides as examples, we will review how the multifaceted complexity of polysaccharide structure is impeding the resonance assignment process and assess the available biochemical and spectroscopic approaches that could circumvent this barrier. We will emphasize the ineffectiveness of the current methods in reconciling the ever-growing dataset and deriving structural information. We will evaluate the protocols for achieving efficient and homogeneous hyperpolarization across the cell wall material using magic-angle spinning dynamic nuclear polarization (MAS-DNP). Critical questions regarding the line-broadening effects of cell wall molecules at cryogenic temperature and by paramagnetic biradicals will be considered. Finally, the MAS-DNP method will be placed into a broader context with other structural characterization techniques, such as cryo-electron microscopy, to advance ssNMR research in carbohydrate and cell wall biomaterials.
Many-Body eXpansion (MBX) is a C++ library that implements many-body potential energy functions (PEFs) within the “many-body energy” (MB-nrg) formalism. MB-nrg PEFs integrate an underlying polarizable model with explicit machine-learned representations of many-body interactions to achieve chemical accuracy from the gas to the condensed phases. MBX can be employed either as a stand-alone package or as an energy/force engine that can be integrated with generic software for molecular dynamics and Monte Carlo simulations. MBX is parallelized internally using Open Multi-Processing and can utilize Message Passing Interface when available in interfaced molecular simulation software. In this study, MBX enables classical and quantum molecular simulations with MB-nrg PEFs, as well as hybrid simulations that combine conventional force fields and MB-nrg PEFs, for diverse systems ranging from small gas-phase clusters to aqueous solutions and molecular fluids to biomolecular systems and metal-organic frameworks.
As part of the 2021 Biological Systems Science Division (BSSD) Principal Investigator (PI) Meeting, the Bioimaging Science program (BSP), within BSSD's Biomolecular Characterization and Imaging Science portfolio, held its annual PI meeting virtually February 22–23. BSP's mission is to understand the translation of genomic information into the mechanisms that power living cells, communities of cells, and whole organisms. The goal of BSP is to develop new imaging and measurement technologies to visualize the spatial and temporal relationships of key metabolic processes governing phenotypic expression in plants and microbes. BSP convenes annual PI meetings to bring together its contributing investigators to review progress and current state-of-the-art bioimaging research. Holding the BSP meeting as part of the broader BSSD PI meeting allowed researchers to interact with the extended Genomic Science program community. This convergence provided a platform for networking and exchange of ideas, helping to forge new multidisciplinary collaborations among investigators from the two sister programs. An important highlight of the BSP meeting was the keynote presentation “Enhancing Fluorescence Microscopy with Computation” by Dr. Hari Shroff of the NIH National Institute of Biomedical Imaging and Bioengineering. All the BSP PIs made presentations describing their research focus and progress, and these were followed by roundtable discussions of each project. The meeting’s proceedings provide an outline of the program’s current state and potential future directions and opportunities.
Protein structure prediction has become increasingly popular and successful in recent years. An essential step for fragment-free, template-free methods is the generation of a final three-dimensional protein model from a set of predicted amino acid contacts that are often described by interresidue pairwise atomic distances. Here we explore the use of modern, open-source molecular dynamics (MD) engines, which have been continually developed over the last three decades with all-atom Hamiltonians to model biomolecular structure and dynamics, to generate accurate protein structures starting from a set of inferred pairwise distances. Additionally, the ability of MD empirical physical potentials to correct inaccuracies in the predicted geometries is tested. We rigorously characterize the effect of modeling parameters on results, the effect of different amounts of error in the predicted distances on the final structures, and test the ability of post-processing analysis to sort the best models out of a set of statistical replicas. We find that with exact distances and with noisy distances, the method can produce excellent structural models, and that the molecular dynamics force field seems to help correct errors in distance predictions, resisting the effects of applied noise.
Chemical, physical, biological and materials engineering disciplines use a variety of chiroptical spectroscopies to probe geometrical and optical asymmetry in molecules and particles. Electronic (ECD) and vibrational (VCD) circular dichroism are the most common of these techniques and collectively enable the studies of electronic and vibronic transitions with energies between 0.1 and 5.0 eV. The vibrational states with characteristic energies in the range of 0.001–0.01 eV carry valuable information about concerted intermolecular motions in molecules and crystals involving multiple atoms. These vibronic transitions located in the terahertz (THz) part of the spectrum become increasingly more important for the chemistry, physics, and biology of complex molecules and materials However, the methodology and hardware of THz circular dichroism (TCD) are much less developed than the chiroptical spectroscopies for ultraviolet, visible, near- and mid infrared photons. In this report we provide theoretical foundations, practical implementations, comparative assessments, and exemplary applications of TCD spectroscopy. We show that the sign, intensity, and position of TCD peaks are highly sensitive to the three-dimensional structure and long-range organization of molecular crystals, which offer unique capabilities to study (bio) molecules, their crystals, and nanoscale assemblies and apply the novel data processing methodologies. TCD also offers a convenient toolbox to identify new physical phenomena, such as chiral phonons and their propagation in nanostructured matter. We also discuss the major challenges, emerging opportunities and promising research directions, including broad investigation of chiral phonons observed in chiral (nano) crystals and emerging machine learning methodologies for TCD in biological and nanoscale structures. Ubiquity of low-frequency vibrations with rotational components in biomolecular structures, combined with sharpness of peaks in TCD spectra, enables a variety of technological translations.
The fabrication of complex assemblies with interesting collective properties from plasmonic nanoparticles (NPs) is often challenging. While DNA-directed self-assembly has emerged as one of the most promising approaches to forming such complex assemblies, the resulting structures tend to have large variability in gap sizes and shapes, as the DNA strands used to organize these particles are flexible, and the polydispersity of the NPs leads to variability in these critical structural features. Here, we use a new strategy termed docking to DNA origami cages (D-DOC) to organize spherical NPs into a linear heterotrimer with a precisely defined geometrical arrangement. Instead of binding NPs to the exterior of the DNA templates, D-DOC binds the NPs to either the interior or the opening of a 3D cage, which significantly reduces the variability of critical structural features by incorporating multiple diametrically arranged capture strands to tether NPs. Additionally, such a spatial arrangement of the capture strand can work synergistically with shape complementarity to achieve tighter confinement. To assemble NPs via D-DOC, we developed a multistep assembly process that first encapsulates an NP inside a cage and then binds two other NPs to the openings. Microscopic characterization shows low variability in the bond angles and gap sizes. Both UV–vis absorption and surface-enhanced Raman scattering (SERS) measurements showed strong plasmonic coupling that aligned with predictions by electrodynamic simulations, further confirming the precision of the assembly. These results suggest D-DOC could open new opportunities in biomolecular sensing, SERS and fluorescence spectroscopies, and energy harvesting through the self-assembly of NPs into more complex 3D assemblies.
The discovery of the molecular candidates for application in drug targets, biomolecular systems, catalysts, photovoltaics, organic electronics, and batteries necessitates the development of machine learning algorithms capable of rapid exploration of chemical spaces targeting the desired functionalities. Here, we introduce a novel approach for active learning over the chemical spaces based on hypothesis learning. We construct the hypotheses on the possible relationships between structures and functionalities of interest based on a small subset of data followed by introducing them as (probabilistic) mean functions for the Gaussian process. This approach combines the elements from the symbolic regression methods, such as SISSO and active learning, into a single framework. The primary focus of constructing this framework is to approximate physical laws in an active learning regime toward a more robust predictive performance, as traditional evaluation on hold-out sets in machine learning does not account for out-of-distribution effects which may lead to a complete failure on unseen chemical space. Here, we demonstrate it for the QM9 dataset, but it can be applied more broadly to datasets from both domains of molecular and solid-state materials sciences.
We demonstrate a strategy that allows for the spontaneous reconfiguration of self-assembled DNA polymers exploiting RNA as chemical fuel. To do this, we have rationally designed orthogonally addressable DNA building blocks that can be transiently deactivated by RNA fuels and subtracted temporarily from participation in the self-assembly process. Through a fine modulation of the rate at which the building blocks are reactivated we can carefully control the final composition of the polymer and convert a disordered polymer in a higher order polymer, which is disfavored from a thermodynamic point of view. We measure the dynamic reconfiguration via fluorescent signals and confocal microscopy, and we derive a kinetic model that captures the experimental results. Our approach suggests a novel route toward the development of biomolecular materials in which engineered chemical reactions support the autonomous spatial reorganization of multiple components.
Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.