Protein domains are independent structural and functional modules that can rearrange to create new proteins. While the evolution of multidomain proteins through the shuffling of different preexisting domains has been well documented, the evolution of domain repeat proteins and the origin of new domains are less understood. Metallothioneins (MTs) provide a good case study considering that they consist of metal-binding domain repeats, some of them with a likely de novo origin. In mollusks, for instance, most MTs are bidomain proteins that arose by lineage-specific rearrangements between six putative domains: α, β1, β2, β3, γ and δ. Some domains have been characterized in bivalves and gastropods, but nothing is known about the MTs and their domains of other Mollusca classes. To fill this gap, we investigated the metal-binding features of NpoMT1 of (Cephalopoda class) and FcaMT1 of (Caudofoveata class). Interestingly, whereas NpoMT1 consists of α and β1 domains and has a prototypical Cd preference, FcaMT1 has a singular preference for Zn ions and a distinct domain composition, including a new Caudofoveata-specific δ domain. Overall, our results suggest that the modular architecture of MTs has contributed to MT evolution during mollusk diversification, and exemplify how modularity increases MT evolvability.
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http://dx.doi.org/10.3390/ijms232415824 | DOI Listing |
Nat Commun
January 2025
TUM School of Natural Sciences, Department of Physics and Munich Center for Quantum Science and Technology (MCQST), Technical University of Munich, James-Franck-Str. 1, Garching, Germany.
Small registers of spin qubits in silicon can exhibit hour-long coherence times and exceeded error-correction thresholds. However, their connection to larger quantum processors is an outstanding challenge. To this end, spin qubits with optical interfaces offer key advantages: they can minimize the heat load and give access to modular quantum computing architectures that eliminate cross-talk and offer a large connectivity.
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January 2025
Imperial College London, London, UK.
The brain is structurally and functionally modular, although recent evidence has raised questions about the extent of both types of modularity. Using a simple, toy artificial neural network setup that allows for precise control, we find that structural modularity does not in general guarantee functional specialization (across multiple measures of specialization). Further, in this setup (1) specialization only emerges when features of the environment are meaningfully separable, (2) specialization preferentially emerges when the network is strongly resource-constrained, and (3) these findings are qualitatively similar across several different variations of network architectures.
View Article and Find Full Text PDFAdv Mater
January 2025
Department of Mechanical and Aerospace Engineering, Cornell University, 124 Hoy Road, Ithaca, NY, 14850, USA.
The adaptable, modular structure of muscles, combined with their confluent energy storage allows for numerous architectures found in nature: trunks, tongues, and tentacles to name some more complex ones. To provide an artificial analog to this biological soft muscle, a self-powered, soft hydrostat actuator is presented. As an example of how to use these modules, a worm robot is assembled where the near totality of the body stores electrochemical potential.
View Article and Find Full Text PDFSci Rep
December 2024
China 19T'' Metallurgical Group Corporation Limited, 610039, Chengdu, China.
As one of the primary precast components in prefabricated construction, composite slabs have increasingly attracted interest for their costs as well as carbon footprint in production and installation stages. Conventional methods for separating composite slabs can lead to a building project necessitating multiple specifications of composite slabs. Due to the requirement to customize molds for different modulus of composite slabs, the production process experiences a substantial rise in energy consumption and resource waste.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Department of Biomedical Engineering, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, VIC, Australia.
Connectome generative models, otherwise known as generative network models, provide insight into the wiring principles underpinning brain network organization. While these models can approximate numerous statistical properties of empirical networks, they typically fail to explicitly characterize an important contributor to brain organization-axonal growth. Emulating the chemoaffinity-guided axonal growth, we provide a novel generative model in which axons dynamically steer the direction of propagation based on distance-dependent chemoattractive forces acting on their growth cones.
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