An Enzyme-Cleavable Solubilizing-Tag Facilitates the Chemical Synthesis of Mirror-Image Proteins.

Mirror-image proteins (D-proteins) are useful in biomedical research for purposes such as mirror-image screening for D-peptide drug discovery, but the chemical synthesis of many D-proteins is often low yielding due to the poor solubility or aggregation of their constituent peptide segments. Here, we report a Lys-C protease-cleavable solubilizing tag and its use to synthesize difficult-to-obtain D-proteins. Our tag is easily installed onto multiple amino acids such as DLys, DSer, DThr, and/or the N-terminal amino acid of hydrophobic D-peptides, is impervious to various reaction conditions, such as peptide synthesis, ligation, desulfurization, and transition metal-mediated deprotection, and yet can be completely removed by Lys-C protease under denaturing conditions to give the desired D-protein. The efficacy and practicality of the new method were exemplified in the synthesis of two challenging D-proteins: D-enantiomers of programmed cell death protein 1 IgV domain and SARS-CoV-2 envelope protein, in high yield. This work demonstrates that the enzymatic cleavage of solubilizing tags under denaturing conditions is feasible, thus paving the way for the production of more D-proteins.

Backbone-Installed Split Intein-Assisted Ligation for the Chemical Synthesis of Mirror-Image Proteins.

Membrane-associated D-proteins are an important class of synthetic molecules needed for D-peptide drug discovery, but their chemical synthesis using canonical ligation methods such as native chemical ligation is often hampered by the poor solubility of their constituent peptide segments. Here, we describe a Backbone-Installed Split Intein-Assisted Ligation (BISIAL) method for the synthesis of these proteins, wherein the native L-forms of the N- and C-intein fragments of the unique consensus-fast (Cfa) (i.e. L-CfaN and L-CfaC ) are separately installed onto the two D-peptide segments to be ligated via a removable backbone modification. The ligation proceeds smoothly at micromolar (µM) concentrations under strongly chaotropic conditions (8.0 M urea), and the subsequent removal of the backbone modification groups affords the desired D-proteins without leaving any "ligation scar" on the products. The effectiveness and practicality of the BISIAL method are exemplified by the synthesis of the D-enantiomers of the extracellular domains of T cell immunoglobulin and ITIM domain (TIGIT) and tropomyosin receptor kinase C (TrkC). The BISIAL method further expands the chemical protein synthesis ligation toolkit and provides practical access to challenging D-protein targets.

Clinical value and molecular mechanism of AQGPs in different tumors.

Aquaglyceroporins (AQGPs), including AQP3, AQP7, AQP9, and AQP10, are transmembrane channels that allow small solutes across biological membranes, such as water, glycerol, H2O2, and so on. Increasing evidence suggests that they play critical roles in cancer. Overexpression or knockdown of AQGPs can promote or inhibit cancer cell proliferation, migration, invasion, apoptosis, epithelial-mesenchymal transition and metastasis, and the expression levels of AQGPs are closely linked to the prognosis of cancer patients. Here, we provide a comprehensive and detailed review to discuss the expression patterns of AQGPs in different cancers as well as the relationship between the expression patterns and prognosis. Then, we elaborate the relevance between AQGPs and malignant behaviors in cancer as well as the latent upstream regulators and downstream targets or signaling pathways of AQGPs. Finally, we summarize the potential clinical value in cancer treatment. This review will provide us with new ideas and thoughts for subsequent cancer therapy specifically targeting AQGPs.

A Survey on Human Cancer Categorization Based on Deep Learning.

In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.

Self-Supervised monocular depth and ego-Motion estimation in endoscopy: Appearance flow to the rescue.

Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios. One widely adopted assumption of depth and ego-motion self-supervised learning is that the image brightness remains constant within nearby frames. Unfortunately, the endoscopic scene does not meet this assumption because there are severe brightness fluctuations induced by illumination variations, non-Lambertian reflections and interreflections during data collection, and these brightness fluctuations inevitably deteriorate the depth and ego-motion estimation accuracy. In this work, we introduce a novel concept referred to as appearance flow to address the brightness inconsistency problem. The appearance flow takes into consideration any variations in the brightness pattern and enables us to develop a generalized dynamic image constraint. Furthermore, we build a unified self-supervised framework to estimate monocular depth and ego-motion simultaneously in endoscopic scenes, which comprises a structure module, a motion module, an appearance module and a correspondence module, to accurately reconstruct the appearance and calibrate the image brightness. Extensive experiments are conducted on the SCARED dataset and EndoSLAM dataset, and the proposed unified framework exceeds other self-supervised approaches by a large margin. To validate our framework's generalization ability on different patients and cameras, we train our model on SCARED but test it on the SERV-CT and Hamlyn datasets without any fine-tuning, and the superior results reveal its strong generalization ability. Code is available at: https://github.com/ShuweiShao/AF-SfMLearner.

A multi-scale unsupervised learning for deformable image registration.

PURPOSE: Image registration is a fundamental task in the area of image processing, and it is critical to many clinical applications, e.g., computer-assisted surgery. In this work, we attempt to design an effective framework that gains higher accuracy at a minimal cost of the invertibility of registration field. METHODS: A hierarchically aggregated transformation (HAT) module is proposed. Within each HAT module, we connect multiple convolutions in a hierarchical manner to capture the multi-scale context, enabling small and large displacements between a pair of images to be taken into account simultaneously during the registration process. Besides, an adaptive feature scaling (AFS) mechanism is presented to refine the multi-scale feature maps derived from the HAT module by rescaling channel-wise features in the global receptive field. Based on the HAT module and AFS mechanism, we establish an efficacious and efficient unsupervised deformable registration framework. RESULTS: The devised framework is validated on the dataset of SCARED and MICCAI Instrument Segmentation and Tracking Challenge 2015, and the experimental results demonstrate that our method achieves better registration accuracy with fewer number of folding pixels than three widely used baseline approaches of SyN, NiftyReg and VoxelMorph. CONCLUSION: We develop a novel method for unsupervised deformable image registration by incorporating the HAT module and AFS mechanism into the framework, which provides a new way to obtain a desirable registration field between a pair of images.

Chemical Synthesis of a Full-Length G-Protein-Coupled Receptor ß2-Adrenergic Receptor with Defined Modification Patterns at the C-Terminus.

The ß2-adrenergic receptor (ß2AR) is a G-protein-coupled receptor (GPCR) that responds to the hormone adrenaline and is an important drug target in the context of respiratory diseases, including asthma. ß2AR function can be regulated by post-translational modifications such as phosphorylation and ubiquitination at the C-terminus, but access to the full-length ß2AR with well-defined and homogeneous modification patterns critical for biochemical and biophysical studies remains challenging. Here, we report a practical synthesis of differentially modified, full-length ß2AR based on a combined native chemical ligation (NCL) and sortase ligation strategy. An array of homogeneous samples of full-length ß2ARs with distinct modification patterns, including a full-length ß2AR bearing both monoubiquitination and octaphosphorylation modifications, were successfully prepared for the first time. Using these homogeneously modified full-length ß2AR receptors, we found that different phosphorylation patterns mediate different interactions with ß-arrestin1 as reflected in different agonist binding affinities. Our experiments also indicated that ubiquitination can further modulate interactions between ß2AR and ß-arrestin1. Access to full-length ß2AR with well-defined and homogeneous modification patterns at the C-terminus opens a door to further in-depth mechanistic studies into the structure and dynamics of ß2AR complexes with downstream transducer proteins, including G proteins, arrestins, and GPCR kinases.

Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks.

Pathological examination is the gold standard for the diagnosis of cancer. Common pathological examinations include hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). In some cases, it is hard to make accurate diagnoses of cancer by referring only to H&E staining images. Whereas, the IHC examination can further provide enough evidence for the diagnosis process. Hence, the generation of virtual IHC images from H&E-stained images will be a good solution for current IHC examination hard accessibility issue, especially for some low-resource regions. However, existing approaches have limitations in microscopic structural preservation and the consistency of pathology properties. In addition, pixel-level paired data is hard available. In our work, we propose a novel adversarial learning method for effective Ki-67-stained image generation from corresponding H&E-stained image. Our method takes fully advantage of structural similarity constraint and skip connection to improve structural details preservation; and pathology consistency constraint and pathological representation network are first proposed to enforce the generated and source images hold the same pathological properties in different staining domains. We empirically demonstrate the effectiveness of our approach on two different unpaired histopathological datasets. Extensive experiments indicate the superior performance of our method that surpasses the state-of-the-art approaches by a significant margin. In addition, our approach also achieves a stable and good performance on unbalanced datasets, which shows our method has strong robustness. We believe that our method has significant potential in clinical virtual staining and advance the progress of computer-aided multi-staining histology image analysis.

Ligation of Soluble but Unreactive Peptide Segments in the Chemical Synthesis of Haemophilus Influenzae DNA Ligase.

During the total chemical synthesis of the water-soluble globular Haemophilus Influenzae DNA ligase (Hin-Lig), we observed the surprising phenomenon of a soluble peptide segment that failed to undergo native chemical ligation. Based on dynamic light scattering and transmission electron microscopy experiments, we determined that the peptide formed soluble colloidal particles in a homogeneous solution containing 6 m guanidine hydrochloride. Conventional peptide performance-improving strategies, such as installation of a terminal/side-chain Arg tag or O-acyl isopeptide, failed to enable the reaction, presumably because of their inability to disrupt the formation of soluble colloidal particles. However, a removable backbone modification strategy recently developed for the synthesis of membrane proteins did disrupt the formation of the colloids, and the desired ligation of this soluble but unreactive system was eventually accomplished. This work demonstrates that an appropriate solution dispersion state, in addition to good peptide solubility, is a prerequisite for successful peptide ligation.

One-pot multi-segment condensation strategies for chemical protein synthesis.

With the growing requirement for otherwise-difficult-to-obtain proteins, it is necessary to develop more efficient chemical protein synthesis methods for rapid access to designed protein samples. In particular, a one-pot multi-segment condensation method, with only one purification step to obtain the final product, is expected to demonstrate unique benefits in chemical protein synthesis, such as the requirement of fewer handling procedures and the higher efficiency in obtaining aimed protein samples. The utilization of the one-pot multi-segment condensation strategy is demonstrated via the synthesis of a series of post-translational modification (PTM) or disease-associated peptides or proteins for basic and advanced scientific research. This review summarizes the recent one-pot multi-segment condensation methods utilized in chemical protein synthesis, in which two aspects of drive-strategies will be mainly included: a kinetically controlled strategy and a protecting group-removal strategy, respectively. On one hand, the activities of peptides in N-terminal thiol amino acids or C-terminal acyl donors can be largely different based on the differences in properties, such as steric hindrance, migration rates, electrophilicity, and introduction of active elements such as selenium, etc. Using the different activities, regio-selective peptide ligation can be performed in a kinetically controlled manner. On the other hand, the protecting group-removal strategy involves various moieties, which can block the activity of functional groups arising from N-terminal thiol amino acids or C-terminal acyl donors, and they can be removed by using additives, and pH- or photo-stimulation conditions with further achievement of chemical protein synthesis by the one-pot strategy.