Crystal structures of Magnaporthe oryzae trehalose-6-phosphate synthase (MoTps1) suggest a model for catalytic process of Tps1.

Trehalose-6-phosphate (T6P) synthase (Tps1) catalyzes the formation of T6P from UDP-glucose (UDPG) (or GDPG, etc.) and glucose-6-phosphate (G6P), and structural basis of this process has not been well studied. MoTps1 (Magnaporthe oryzae Tps1) plays a critical role in carbon and nitrogen metabolism, but its structural information is unknown. Here we present the crystal structures of MoTps1 apo, binary (with UDPG) and ternary (with UDPG/G6P or UDP/T6P) complexes. MoTps1 consists of two modified Rossmann-fold domains and a catalytic center in-between. Unlike Escherichia coli OtsA (EcOtsA, the Tps1 of E. coli), MoTps1 exists as a mixture of monomer, dimer, and oligomer in solution. Inter-chain salt bridges, which are not fully conserved in EcOtsA, play primary roles in MoTps1 oligomerization. Binding of UDPG by MoTps1 C-terminal domain modifies the substrate pocket of MoTps1. In the MoTps1 ternary complex structure, UDP and T6P, the products of UDPG and G6P, are detected, and substantial conformational rearrangements of N-terminal domain, including structural reshuffling (ß3-ß4 loop to α0 helix) and movement of a 'shift region' towards the catalytic centre, are observed. These conformational changes render MoTps1 to a 'closed' state compared with its 'open' state in apo or UDPG complex structures. By solving the EcOtsA apo structure, we confirmed that similar ligand binding induced conformational changes also exist in EcOtsA, although no structural reshuffling involved. Based on our research and previous studies, we present a model for the catalytic process of Tps1. Our research provides novel information on MoTps1, Tps1 family, and structure-based antifungal drug design.

MTC-CSNet: Marrying Transformer and Convolution for Image Compressed Sensing.

Image compressed sensing (ICS) has been extensively applied in various imaging domains due to its capability to sample and reconstruct images at subNyquist sampling rates. The current predominant approaches in ICS, specifically pure convolutional networks (ConvNets)-based ICS methods, have demonstrated their effectiveness in capturing local features for image recovery. Simultaneously, the Transformer architecture has gained significant attention due to its capability to model global correlations among image features. Motivated by these insights, we propose a novel hybrid network for ICS, named MTC-CSNet, which effectively combines the strengths of both ConvNets and Transformer architectures in capturing local and global image features to achieve high-quality image recovery. Particularly, MTC-CSNet is a dual-path framework that consists of a ConvNets-based recovery branch and a Transformer-based recovery branch. Along the ConvNets-based recovery branch, we design a lightweight scheme to capture the local features in natural images. Meanwhile, we implement a Transformer-based recovery branch to iteratively model the global dependencies among image patches. Ultimately, the ConvNets-based and Transformer-based recovery branches collaborate through a bridging unit, facilitating the adaptive transmission and fusion of informative features for image reconstruction. Extensive experimental results demonstrate that our proposed MTC-CSNet surpasses the state-of-the-art methods on various public datasets. The code and models are publicly available at MTC-CSNet.

TransCS: A Transformer-Based Hybrid Architecture for Image Compressed Sensing.

Well-known compressed sensing (CS) is widely used in image acquisition and reconstruction. However, accurately reconstructing images from measurements at low sampling rates remains a considerable challenge. In this paper, we propose a novel Transformer-based hybrid architecture (dubbed TransCS) to achieve high-quality image CS. In the sampling module, TransCS adopts a trainable sensing matrix strategy that gains better image reconstruction by learning the structural information from the training images. In the reconstruction module, inspired by the powerful long-distance dependence modelling capacity of the Transformer, a customized iterative shrinkage-thresholding algorithm (ISTA)-based Transformer backbone that iteratively works with gradient descent and soft threshold operation is designed to model the global dependency among image subblocks. Moreover, the auxiliary convolutional neural network (CNN) is introduced to capture the local features of images. Therefore, the proposed hybrid architecture that integrates the customized ISTA-based Transformer backbone with CNN can gain high-performance reconstruction for image compressed sensing. The experimental results demonstrate that our proposed TransCS obtains superior reconstruction quality and noise robustness on several public benchmark datasets compared with other state-of-the-art methods. Our code is available on TransCS.