Article
Pharmacology & Pharmacy
Tianduanyi Wang, Otto I. Pulkkinen, Tero Aittokallio
Summary: Most drug molecules have the ability to modulate multiple target proteins, which can lead to both therapeutic effects and unwanted side effects. Evaluating the selectivity of a compound is an important factor in drug development and repurposing efforts. Traditional methods for characterizing selectivity fall short in quantifying how selective a compound is against a particular target protein. In this study, we propose an optimization-based selectivity scoring method that allows for the identification of potent and selective compounds against given kinase targets. We demonstrate the effectiveness of this method in finding highly selective compounds in computational experiments using a large-scale kinase inhibitor dataset.
FRONTIERS IN PHARMACOLOGY
(2022)
Review
Biochemical Research Methods
Stephan Struckmann, Mathias Ernst, Sarah Fischer, Nancy Mah, Georg Fuellen, Steffen Moeller
Summary: Researchers are interested in finding new applications for known compounds by analyzing transcriptomics of biological samples from disease contexts. They found that matching a drug effect to the effect of the same drug at another concentration or in another cell line is a well-defined, reproducible challenge. By combining different similarity scores and heuristics, they were able to reduce the number of genes in the model and improve prediction accuracy.
BRIEFINGS IN BIOINFORMATICS
(2021)
Review
Biochemistry & Molecular Biology
Gabriela Bitencourt-Ferreira, Camila Rizzotto, Walter Filgueira de Azevedo Junior
Summary: This review discussed the development and application of machine learning scoring functions to predict binding affinity of protein-ligand complexes using the program SAnDReS. Results show superior predictive performance of SAnDReS-developed models compared to classical scoring functions available in docking programs.
CURRENT MEDICINAL CHEMISTRY
(2021)
Article
Biochemical Research Methods
Xujun Zhang, Chao Shen, Tianyue Wang, Yafeng Deng, Yu Kang, Dan Li, Tingjun Hou, Peichen Pan
Summary: Cracking the code of protein-ligand interaction is crucial for drug design and discovery. The ML-based PLI capturer (ML-PLIC) is a web platform that automatically characterizes PLI and generates machine learning-based scoring functions to identify potential binders. It outperforms traditional docking tools and performs competitively with deep learning-based methods. ML-PLIC integrates physical and biological knowledge to design a structure-based virtual screening pipeline.
BRIEFINGS IN BIOINFORMATICS
(2023)
Review
Chemistry, Multidisciplinary
Minsun Kim, Hyesung Jo, Gyoo Yeol Jung, Seung Soo Oh
Summary: As biomolecules essential for sustaining life, proteins are unique and highly valuable for biomedical and biocatalytic applications due to their molecular recognition functions. Proteomimetic materials, including peptides, supramolecules, and inorganic molecules, mimic proteins and perform molecular recognition, expanding their potential bio-applications. This review focuses on proteomimetic designs derived from various materials and their conformations, providing insights for the development of advanced protein mimicry.
ADVANCED MATERIALS
(2023)
Review
Pharmacology & Pharmacy
Yuyu Feng, Yumeng Yan, Jiahua He, Huanyu Tao, Qilong Wu, Sheng-You Huang
Summary: Nucleic acid-ligand interactions play crucial roles in cellular processes and have attracted significant interest in drug discovery. However, traditional docking algorithms and scoring functions for protein-ligand interactions may not be applicable to nucleic acid-ligand docking due to the differences in their properties. This review discusses the current status, challenges, and limitations of docking algorithms and scoring functions for DNA/RNA-ligand interactions.
DRUG DISCOVERY TODAY
(2022)
Article
Biochemical Research Methods
Chao Shen, Gaoqi Weng, Xujun Zhang, Elaine Lai-Han Leung, Xiaojun Yao, Jinping Pang, Xin Chai, Dan Li, Ercheng Wang, Dongsheng Cao, Tingjun Hou
Summary: Machine-learning-based scoring functions have shown promising results in predicting protein-ligand binding affinity and virtual screening, outperforming classical scoring functions in some aspects. However, they still fall short compared to 2D fingerprint-based QSAR models. Integrating multiple types of protein-ligand interaction features can lead to improvements, but may not surpass MACCS-based QSAR models.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Chemistry, Medicinal
Hui Zhu, Jincai Yang, Niu Huang
Summary: This study investigates the scoring functions used in structure-based virtual screening, finding that machine-learning scoring functions (MLSFs) have unsatisfactory generalization capacity. By combining target-specific patterns with features shared among similar compounds, better performance can be achieved. Therefore, it is recommended to assess the generalization ability of MLSFs using the Pfam-cluster approach and to be cautious with the features learned by MLSFs.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Biochemistry & Molecular Biology
Francesco Pellicani, Diego Dal Ben, Andrea Perali, Sebastiano Pilati
Summary: Machine learning has shown potential as a strategy for accurate scoring functions in drug discovery through computational docking. However, recent studies suggest over-optimistic results due to correlations in the experimental databases. In this study, an artificial neural network is used to predict binding affinity using both experimental and computer-generated protein-ligand structures. Promising results are obtained, but there is a decrease in performance when testing on target proteins not included in the training data.
Article
Biochemical Research Methods
Yang Tian, Qiuyu Bao, Nian Wang, Ning Wan, Langlang Lv, Haiping Hao, Chaoyong He, Hui Ye
Summary: The study introduces an acetaldehyde labeling approach to complement formaldehyde labeling for probing ligand-protein interactions. The use of acetaldehyde allows for cleaner and more moderate reaction profiles compared to formaldehyde, enabling the identification of lysines involved in ligand-protein binding. Time-dependent changes in lysine accessibility profiles were detected using acetaldehyde labeling, further quantified by multiple reaction monitoring to sensitively determine ligand-binding sites and differentiate binding affinities among various ligands, which was confirmed by native mass spectrometry and molecular docking. Monitoring the chemical accessibility of these responsive peptides in cell lysates demonstrates the potential of these methods in characterizing ligand-protein interactions in complex cellular environments.
JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY
(2021)
Article
Pharmacology & Pharmacy
Peng Li, Chujie Bai, Lingmin Zhan, Haoran Zhang, Yuanyuan Zhang, Wuxia Zhang, Yingdong Wang, Jinzhong Zhao
Summary: Identifying the biological targets of a compound is crucial for understanding drug mechanisms and developing new drugs. The Connectivity Map concept connects genes, drugs, and diseases based on gene-expression signatures. However, existing methods are inefficient due to the need for reference drugs. In this study, we developed a procedure to extract target-induced gene modules and identified target gene clusters. Additionally, we proposed a gene module pair-based approach to predict novel compound-target interactions, leading to the discovery of new inhibitors for PI3K pathway proteins.
FRONTIERS IN PHARMACOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Dmitry A. A. Shulga, Arslan R. R. Shaimardanov, Nikita N. N. Ivanov, Vladimir A. A. Palyulin
Summary: Scoring functions are commonly used in drug discovery, but their accuracy needs improvement. Parameterizing interactions involving specific features can enhance the performance of SFs.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biochemical Research Methods
Yutai Hou, Yingce Xia, Lijun Wu, Shufang Xie, Yang Fan, Jinhua Zhu, Tao Qin, Tie-Yan Liu
Summary: This article introduces an end-to-end solution for automatically discovering drug-target interactions (DTI) in biomedical literature using a generative approach without the need for detailed annotations. Experimental results demonstrate that this method significantly outperforms traditional extractive approaches in DTI discovery.
Article
Multidisciplinary Sciences
Isabella A. Guedes, Andre M. S. Barreto, Diogo Marinho, Eduardo Krempser, Melaine A. Kuenemann, Olivier Sperandio, Laurent E. Dardenne, Maria A. Miteva
Summary: Scoring functions are crucial for in silico drug discovery, but accurate prediction of binding affinity remains a challenge. Developing scoring functions based on precise physics-based descriptors is necessary to improve prediction accuracy.
SCIENTIFIC REPORTS
(2021)
Article
Biochemistry & Molecular Biology
Zehong Zhang, Lifan Chen, Feisheng Zhong, Dingyan Wang, Jiaxin Jiang, Sulin Zhang, Hualiang Jiang, Mingyue Zheng, Xutong Li
Summary: This article provides an overview of the application of deep neural networks and graph neural networks in drug-target interaction (DTI) prediction. The use of graph neural networks has proven effective in predicting DTIs, finding repositioning drugs, and accelerating drug discovery. The article also highlights current challenges and future directions for further development in this field.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2022)
Article
Chemistry, Multidisciplinary
Jing-yi Zhou, Rui-rui Yang, Jie Chang, Jia Song, Zi-sheng Fan, Ying-hui Zhang, Cheng-hao Lu, Hua-liang Jiang, Ming-yue Zheng, Su-lin Zhang
Summary: This study describes the discovery and identification of a novel BCL-2 inhibitor, DC-B01, which targets the BH4 domain of BCL-2. The results indicate that DC-B01 induces cell apoptosis, inhibits tumor growth, and suppresses the transcriptional activity of c-Myc by disrupting the interaction between BCL-2 and c-Myc.
ACTA PHARMACOLOGICA SINICA
(2023)
Article
Chemistry, Multidisciplinary
Jia Song, Rui-rui Yang, Jie Chang, Ya-dan Liu, Cheng-hao Lu, Li-fan Chen, Hao Guo, Ying-hui Zhang, Zi-sheng Fan, Jing-yi Zhou, Gui-zhen Zhou, Ke-ke Zhang, Xiao-min Luo, Kai-xian Chen, Hua-liang Jiang, Su-lin Zhang, Ming-yue Zheng
Summary: Several novel types of cGAS inhibitors have been discovered, among which compound 3 displayed the highest potency and selectivity at the cellular level. It exhibited better anti-inflammatory effects than existing inhibitors and has the potential to be used for the treatment of inflammatory diseases.
ACTA PHARMACOLOGICA SINICA
(2023)
Article
Chemistry, Multidisciplinary
Lan-song Xu, Su-xin Zheng, Liang-he Mei, Ke-xin Yang, Ya-fang Wang, Qiang Zhou, Xiang-tai Kong, Ming-yue Zheng, Hua-liang Jiang, Cheng-ying Xie
Summary: A novel and highly potent KRAS(G12C) inhibitor, 143D, was identified through a structure-based and focused chemical library analysis. It showed comparable antitumor efficacy to AMG510 and MRTX849 and selectively inhibited cell proliferation by downregulating KRAS(G12C)-dependent signal transduction, inducing cell cycle arrest and apoptosis.
ACTA PHARMACOLOGICA SINICA
(2023)
Article
Engineering, Electrical & Electronic
Wangyong Chen, Mingyue Zheng, Yaoyang Lyu, Linlin Cai
Summary: The temperature issue is a critical concern in integrated circuit design, especially for advanced technology. This study presents a new insight into the zero-temperature-coefficient (ZTC) based design strategy to minimize temperature-induced delay variation in digital circuits. An effective current-based method is developed to determine the ZTC point (V-ZTC) for standard cells, which is demonstrated on advanced gate-all-around (GAA) nanosheet (NS) FETs using calibrated TCAD simulation. The results show that operating near V-ZTC can significantly reduce frequency variation and achieve power-performance trade-offs.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2023)
Review
Nursing
Yunting Luo, Xianqiong Feng, Dandan Wang, Xu Qiao, Xujia Xiao, Shiqi Jia, Mingyue Zheng, Jan D. Reinhardt
Summary: This qualitative review summarizes the subjective experiences of clinical nurses caring for patients with COVID-19, highlighting the difficulties and challenges they face. The findings emphasize the need for support from organizations, families, and society, as well as the importance of exploring positive coping strategies and paying attention to nurses' experiences and voices.
JOURNAL OF CLINICAL NURSING
(2023)
Article
Chemistry, Medicinal
Meiying Liu, Guizhen Zhou, Wenhong Su, Yuejiao Gu, Mingshan Gao, Kun Wang, Ruifeng Huo, Yupeng Li, Zehui Zhou, Kaixian Chen, Mingyue Zheng, Sulin Zhang, Tianfeng Xu
Summary: The study developed a series of new SOS1 inhibitors with the pyrido[2,3-d]pyrimidin-7-one scaffold, among which compound 8u showed comparable activities to the reported SOS1 inhibitor BI-3406 in both biochemical and cell growth inhibition assays. Compound 8u exhibited good cellular activities against a panel of KRAS G12-mutated cancer cell lines and inhibited downstream ERK and AKT activation. Additionally, it displayed synergistic antiproliferative effects when used in combination with KRAS G12C or G12D inhibitors. Further modifications may lead to a promising SOS1 inhibitor for the treatment of KRAS-mutated patients.
ACS MEDICINAL CHEMISTRY LETTERS
(2023)
Article
Engineering, Manufacturing
Junfeng Xiao, Qiuquan Guo, Yang Bai, Mingyue Zheng, Yong Sun, Liwen Zhang, Dongxing Zhang, Jun Yang
Summary: In this study, a simple method of 3D printing functional devices using plant-inspired polyphenols is reported. The compatibility of different polyphenols with 3D printing resin is assessed and two polyphenols are selected. The polyphenols can be distributed anywhere on the 3D printed objects, allowing for versatile secondary reactions and fabrication of functional objects with novel surface properties.
ADDITIVE MANUFACTURING
(2023)
Article
Chemistry, Medicinal
Zehui Zhou, Guizhen Zhou, Chuan Zhou, Zisheng Fan, Rongrong Cui, Yupeng Li, Rui Li, Yuejiao Gu, Huajie Li, Zhiming Ge, Xiaojia Cai, Bing Jiang, Dan Wang, Mingyue Zheng, Tianfeng Xu, Sulin Zhang
Summary: The linker moiety of a PROTAC molecule is crucial for its degradation activity, target selectivity, and physico-chemical properties. Understanding the underlying mechanisms of linker modification is important for enhancing PROTAC degradation and developing novel chemotherapies.
JOURNAL OF MEDICINAL CHEMISTRY
(2023)
Article
Chemistry, Medicinal
Chunpu Li, Yang Dai, Xiangtai Kong, Bao Wang, Xia Peng, Hengbo Wu, Yanyan Shen, Yanchen Yang, Yinchun Ji, Danyi Wang, Shuangjie Li, Xutong Li, Yuqiang Shi, Meiyu Geng, Mingyue Zheng, Jing Ai, Hong Liu
Summary: By optimizing the lead compound 1 with the help of molecular docking, we obtained a series of novel covalent FGFR inhibitors. Among them, compound 2e showed potent FGFR inhibitory activity and significant antiproliferative effects in FGFR-aberrant cancer cell lines. Moreover, oral administration of 2e demonstrated potent antitumor efficacy in FGFR-amplified tumor xenograft models.
JOURNAL OF MEDICINAL CHEMISTRY
(2023)
Article
Biochemistry & Molecular Biology
Yunxia Wang, Zhen Chen, Ziqi Pan, Shijie Huang, Jin Liu, Weiqi Xia, Hongning Zhang, Mingyue Zheng, Honglin Li, Tingjun Hou, Feng Zhu
Summary: RNAincoder is a deep learning-based encoder that provides comprehensive RNA encoding features and enables representation of any RNA-associated interactions. It identifies the optimal performance in RNA-associated interaction prediction by scanning all possible feature combinations.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Biology
Lian Zhang, Jing Yu, Mingyue Zheng, Hui Zhen, Qingqiang Xie, Chundong Zhang, Zhongjun Zhou, Guoxiang Jin
Summary: The lysosome protein RAGA interacts with and promotes lysosome localization and degradation of the macrophage-specific immune checkpoint protein CD47, with possible relevance to lung adenocarcinoma. RAGA deficiency leads to CD47 accumulation, increased membrane/intracellular CD47 expression ratio, and reduced phagocytic clearance of cancer cells, promoting tumor growth. Clinical analysis shows a negative correlation between RAGA and CD47 proteins in lung adenocarcinoma patient samples, with high RAGA protein level associated with longer patient survival. RAGA(high)CD47(low) patients exhibit the longest overall survival, suggesting RAGA as a potential diagnostic biomarker of lung adenocarcinoma.
COMMUNICATIONS BIOLOGY
(2023)
Article
Medicine, Research & Experimental
Yu-Pei Zhuang, Hong-Li Zhou, Hai-Bin Chen, Ming-Yue Zheng, Yu-Wei Liang, Yu-Tian Gu, Wen-Ting Li, Wen -Li Qiu, Hong-Guang Zhou
Summary: Colorectal cancer (CRC) has suboptimal response to immunotherapy, possibly due to the influence of gut microbiota on immune responses. Understanding how gut microbiota modulates immune responses is crucial to improve outcomes for CRC patients and overcome resistance.
BIOMEDICINE & PHARMACOTHERAPY
(2023)
Article
Nursing
Yunting Luo, Xianqiong Feng, Dandan Wang, Mingyue Zheng, Jan D. Reinhardt
Summary: This study aimed to explore the early experiences of frontline nurses in China during the COVID-19 pandemic through social media posts. It used an explanatory sequential mixed-method design, employing text mining for sentiment analysis, the chi-square test to compare sentiment classification ratios across different months, statistical analysis of word frequency, and thematic analysis. The primary sentiments expressed in the posts were positive and neutral. The number of posts with positive emotions was lowest in January, peaked in March, and gradually declined in April 2020. Nurse-oriented narrative themes emerged, such as seeing and being seen, moving forward amid adversity and support, and returning to everyday life and constructing meaning. The study found that the sentiments of Chinese nurses towards the pandemic fluctuated, starting with positive emotions but not sustained. The study recommends encouraging nurses to engage in expressive writing while adhering to ethical guidelines.
NURSING & HEALTH SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Yaoyang Lyu, Wangyong Chen, Mingyue Zheng, Binyu Yin, Jinning Li, Linlin Cai
Summary: This study proposes a machine learning-based approach for device modeling with process variations, which is verified on advanced Nanosheet FETs. The artificial neural network algorithm accurately captures the impacts of global and local variations on circuit performance, improving modeling efficiency.
IEEE JOURNAL OF THE ELECTRON DEVICES SOCIETY
(2023)
Article
Chemistry, Medicinal
Meiying Liu, Guizhen Zhou, Wenhong Su, Yuejiao Gu, Mingshan Gao, Kun Wang, Ruifeng Huo, Yupeng Li, Zehui Zhou, Kaixian Chen, Mingyue Zheng, Sulin Zhang, Tianfeng Xu
Summary: The study designed and synthesized a series of new SOS1 inhibitors, and one representative compound 8u showed comparable activities to the reported SOS1 inhibitor BI-3406 in both the biochemical assay and the 3-D cell growth inhibition assay. Compound 8u exhibited good cellular activities against a panel of KRAS G12-mutated cancer cell lines, inhibited downstream ERK and AKT activation in MIA PaCa-2 and AsPC-1 cells, and demonstrated synergistic antiproliferative effects when used in combination with KRAS G12C or G12D inhibitors. Further modifications of these new compounds may lead to a promising SOS1 inhibitor with favorable druglike properties for the treatment of KRAS-mutated patients.
ACS MEDICINAL CHEMISTRY LETTERS
(2023)