De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis

Chen, Xu; Chen, Zhidong; Xu, Daiyun; Lyu, Yonghui; Li, Yongxiao; Li, Shengbin; Wang, Junqing; Wang, Zhe

doi:10.3389/fbioe.2021.694100

Cited by 7 publications

(11 citation statements)

References 42 publications

(44 reference statements)

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“…The convolution neural network (CNN), as a neural network based on the principle of biological visual neural perception, includes convolution computations and a kind of feedforward neural network with a deep structure ( Chen et al., 2021 ; Li et al., 2021 ). Object detection methods based on deep learning are developing rapidly and can be roughly divided into two types: the two-stage detection method based on candidate region and the one-stage detection method based on regression ( Huang et al., 2021 ; Yun et al., 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Real-time determination of flowering period for field wheat based on improved YOLOv5s model

et al. 2023

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The flowering period is one of the important indexes of wheat breeding. The early or late flowering affects the final yield and character stability of wheat. In order to solve the problem that it is difficult to accurately and quickly detect the flowering period of a large number of wheat breeding materials, a determination method of flowering period for field wheat based on the improved You Only Look Once (YOLO) v5s model was proposed. Firstly, a feature fusion (FF) method combing RGB images and corresponding comprehensive color features was proposed to highlight more texture features and reduce the distortion caused by light on the extracted feature images. Second, the YOLOv5s model was selected as a base version of the improved model and the convolutional block attention model (CBAM) was adopted into the feature fusion layer of YOLOV5s model. Florets and spikelets were given greater weight along the channel and spatial dimensions to further refine their effective feature information. At the same time, an integrated Transformer small-target detection head (TSDH) was added to solve the high miss rate of small targets in wheat population images. The accurate and rapid detection of florets and spikelets was realized, and the flowering period was determined according to the proportion of florets and spikelets. The experimental results showed that the average computing time of the proposed method was 11.5ms, and the average recognition accuracy of florets and spikelets was 88.9% and 96.8%, respectively. The average difference between the estimated flowering rate and the actual flowering rate was within 5%, and the determination accuracy of the flowering period reached 100%, which met the basic requirements of the flowering period determination of wheat population in the field.

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Section: Methodsmentioning

confidence: 99%

Real-time determination of flowering period for field wheat based on improved YOLOv5s model

et al. 2023

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“…In addition, the benzene ring of TAK-875 was found to interact with Val84 through π-H interaction. 31 In another investigation, 27 2-(6-(substituted phenylethynyl)-2,3-dihydrobenzofuran-3-yl) acetic acids (11a-j) were revealed as unique chiral aryl propionic acid derivatives. They created the compounds through a multistep reaction that began with the Pechmann condensation of resorcinol and 4-chloroethyl acetoacetate.…”

Section: Benzofuranmentioning

confidence: 99%

“…In another study, the interactions of TAK‐875 with the GPCR40 receptor were also assessed using the GBVI method (through molecular docking) and it corroborated the binding of the carboxylic acid group of TAK‐875 with Arg183 and Arg258 through a salt bridge. In addition, the benzene ring of TAK‐875 was found to interact with Val84 through π–H interaction 31 …”

Section: Five‐membered Ring Natural Heterocyclicsmentioning

confidence: 99%

Advancement in chiral heterocycles for the antidiabetic activity

Gupta,

Rani,

Nainwal

et al. 2024

Chirality

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For the synthesis and development of pharmaceuticals, chirality is an important structural component. Chiral heterocyclic compounds have annoyed the interest of synthetic chemists who are working to create useful and efficient techniques for these molecules. As indicated by the expanding number of chiral drugs created in the last two decades, the link between chirality and pharmacological activity has become more important in the pharmaceutical and biopharmaceutical industries. Approximately 65% of currently used drugs are chiral, and many of them are promoted as racemates in many circumstances. There are a growing number of new chiral heterocyclic compounds with important biological properties and intriguing uses in medical chemistry and drug discovery. In this study, we review current breakthroughs in chiral heterocycles and their different physiological activities that have been published in the last year (from 2010 to early 2023). This study focuses on the current trends in the use of chiral heterocycles in drug design and the creation of several powerful and competent candidates for diabetic illnesses.

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“…Data-driven approaches to de novo protein design (including structural data and massive protein sequences) have also emerged in recent years [32][33][34][35][36][37][38][39][40][41]95] along with the wave of big data and artificial intelligence development, the development of high-throughput data collection methods, and the accumulation of available data. Liu and co-authors made a sig-nificant contribution to the development of data-driven protein design methods [96].…”

Section: De Novo Protein Designmentioning

confidence: 99%

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

Wang,

Chen,

Yao

et al. 2023

Molecules

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In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

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De novo Design of G Protein-Coupled Receptor 40 Peptide Agonists for Type 2 Diabetes Mellitus Based on Artificial Intelligence and Site-Directed Mutagenesis

Cited by 7 publications

References 42 publications

Real-time determination of flowering period for field wheat based on improved YOLOv5s model

Real-time determination of flowering period for field wheat based on improved YOLOv5s model

Advancement in chiral heterocycles for the antidiabetic activity

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

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