Structural Mechanism of Smad4 Recognition by the Nuclear Oncoprotein Ski Insights on Ski-Mediated Repression of TGF-β Signaling

AbstractThe Ski family of nuclear oncoproteins represses TGF-β signaling through interactions with the Smad proteins. The crystal structure of the Smad4 binding domain of human c-Ski in complex with the MH2 domain of Smad4 reveals specific recognition of the Smad4 L3 loop region by a highly conserved interaction loop (I loop) from Ski. The Ski binding surface on Smad4 significantly overlaps with that required for binding of the R-Smads. Indeed, Ski disrupts the formation of a functional complex between the Co- and R-Smads, explaining how it could lead to repression of TGF-β, activin, and BMP responses. Intriguingly, the structure of the Ski fragment, stabilized by a bound zinc atom, resembles the SAND domain, in which the corresponding I loop is responsible for DNA binding

Discovery of diverse and functional antibodies from large human repertoire antibody libraries

AbstractPhage display antibody libraries have a proven track record for the discovery of therapeutic human antibodies, increasing the demand for large and diverse phage antibody libraries for the discovery of new therapeutics. We have constructed naïve antibody phage display libraries in both Fab and scFv formats, with each library having more than 250billion clones that encompass the human antibody repertoire. These libraries show high fidelity in open reading frame and expression percentages, and their V-gene family distribution, VH-CDR3 length and amino acid usage mirror the natural diversity of human antibodies. Both the Fab and scFv libraries show robust sequence diversity in target-specific binders and differential V-gene usage for each target tested, supporting the use of libraries that utilize multiple display formats and V-gene utilization to maximize antibody-binding diversity. For each of the targets, clones with picomolar affinities were identified from at least one of the libraries and for the two targets assessed for activity, functional antibodies were identified from both libraries

Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons

Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic devices used to construct SNNs persistently result in considerable energy consumption owing to the absence of sufficient biological parallels. Drawing inspiration from the transport nature of Na+ and K+ in synapses, here, a Li-based memristor (LixAlOy) was proposed to emulate the biological synapse, leveraging the similarity of Li as a homologous main group element to Na and K. The Li-based memristor exhibits ∼8 ns ultrafast operating speed, 1.91 and 0.72 linearity conductance modulation, and reproducible switching behavior, enabled by lithium vacancies forming a conductive filament mechanism. Moreover, these memristors are capable of simulating fundamental behaviors of a biological synapse, including long-term potentiation and long-term depression behaviors. Most importantly, a threshold-tunable leaky integrate-and-fire (TT-LIF) neuron is built using LixAlOy memristors, successfully integrating synaptic signals from both temporal and spatial levels and achieving an optimal threshold of SNNs. A computationally efficient TT-LIF-based SNN algorithm is also implemented for image recognition schemes, featuring a high recognition rate of 90.1% and an ultralow firing rate of 0.335%, which is 4 times lower than those of other memristor-based SNNs. Our studies reveal the ion dynamics mechanism of the LixAlOy memristor and confirm its potential in rapid switching and the construction of SNNs