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Phosphotriesterases (PTEs) represent a class of enzymes capable of efficient neutralization of organophosphates (OPs), a dangerous class of neurotoxic chemicals. PTEs suffer from low catalytic activity, particularly at higher temperatures, due to low thermostability and low solubility. Supercharging, a protein engineering approach via selective mutation of surface residues to charged residues, has been successfully employed to generate proteins with increased solubility and thermostability by promoting charge–charge repulsion between proteins. We set out to overcome the challenges in improving PTE activity against OPs by employing a computational protein supercharging algorithm in Rosetta. Here, we discover two supercharged PTE variants, one negatively supercharged (with −14 net charge) and one positively supercharged (with +12 net charge) and characterize them for their thermodynamic stability and catalytic activity. We find that positively supercharged PTE possesses slight but significant losses in thermostability, which correlates to losses in catalytic efficiency at all temperatures, whereas negatively supercharged PTE possesses increased catalytic activity across 25°C–55°C while offering similar thermostability characteristic to the parent PTE. The impact of supercharging on catalytic efficiency will inform the design of shelf-stable PTE and criteria for enzyme engineering.

 

Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields such as non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy. In this work, we present a new deep learning method called GRACE, which stands for General, Rapid, And Comprehensive whole-hEad tissue segmentation. GRACE is trained and validated on a novel dataset that consists of 177 manually corrected MR-derived reference segmentations that have undergone meticulous manual review. Each T1-weighted MRI volume is segmented into 11 tissue types, including white matter, grey matter, eyes, cerebrospinal fluid, air, blood vessel, cancellous bone, cortical bone, skin, fat, and muscle. To the best of our knowledge, this work contains the largest manually corrected dataset to date in terms of number of MRIs and segmented tissues. GRACE outperforms five freely available software tools and a traditional 3D U-Net on a five-tissue segmentation task. On this task, GRACE achieves an average Hausdorff Distance of 0.21, which exceeds the runner-up at an average Hausdorff Distance of 0.36. GRACE can segment a whole-head MRI in about 3 seconds, while the fastest software tool takes about 3 minutes. In summary, GRACE segments a spectrum of tissue types from older adults’ T1-MRI scans at favorable accuracy and speed. The trained GRACE model is optimized on older adult heads to enable high-precision modeling in age-related brain disorders. To support open science, the GRACE code and trained weights are made available online and open to the research community at https://github.com/lab-smile/GRACE.

 

Synopsis Animal communication is inherently spatial. Both signal transmission and signal reception have spatial biases—involving direction, distance, and position—that interact to determine signaling efficacy. Signals, be they visual, acoustic, or chemical, are often highly directional. Likewise, receivers may only be able to detect signals if they arrive from certain directions. Alignment between these directional biases is therefore critical for effective communication, with even slight misalignments disrupting perception of signaled information. In addition, signals often degrade as they travel from signaler to receiver, and environmental conditions that impact transmission can vary over even small spatiotemporal scales. Thus, how animals position themselves during communication is likely to be under strong selection. Despite this, our knowledge regarding the spatial arrangements of signalers and receivers during communication remains surprisingly coarse for most systems. We know even less about how signaler and receiver behaviors contribute to effective signaling alignment over time, or how signals themselves may have evolved to influence and/or respond to these aspects of animal communication. Here, we first describe why researchers should adopt a more explicitly geometric view of animal signaling, including issues of location, direction, and distance. We then describe how environmental and social influences introduce further complexities to the geometry of signaling. We discuss how multimodality offers new challenges and opportunities for signalers and receivers. We conclude with recommendations and future directions made visible by attention to the geometry of signaling. 

The catalytic enantioselective synthesis of α‐chiral alkenes and alkynes represents a powerful strategy for rapid generation of molecular complexity. Herein, we report a transient directing group (TDG) strategy to facilitate site‐selective palladium‐catalyzed reductive Heck‐type hydroalkenylation and hydroalkynylation of alkenylaldehyes using alkenyl and alkynyl bromides, respectively, allowing for construction of a stereocenter at the δ‐position with respect to the aldehyde. Computational studies reveal the dual beneficial roles of rigid TDGs, such as L‐tert‐leucine, in promoting TDG binding and inducing high levels of enantioselectivity in alkene insertion with a variety of migrating groups.