Article
Biotechnology & Applied Microbiology
Tesia Bobrowski, Lu Chen, Richard T. Eastman, Zina Itkin, Paul Shinn, Catherine Z. Chen, Hui Guo, Wei Zheng, Sam Michael, Anton Simeonov, Matthew D. Hall, Alexey Zakharov, Eugene N. Muratov
Summary: This study identified 16 synergistic drug combinations against SARS-CoV-2, with nitazoxanide combined with remdesivir, amodiaquine, or umifenovir showing significant synergy. However, the combination of remdesivir with lysosomotropic drugs like hydroxychloroquine exhibited strong antagonism. The results highlight the potential of drug repurposing and preclinical testing of drug combinations for treating COVID-19.
Review
Food Science & Technology
Xuan Chen, Hongyan Li, Bing Zhang, Zeyuan Deng
Summary: Frequent intake of whole foods and dietary food variety is recommended for health benefits, as it can help prevent various chronic diseases. The interactive effects of phytochemicals in whole foods are more effective for health than individual dietary supplements, but the specific mechanisms of interaction are still unclear.
CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION
(2022)
Article
Multidisciplinary Sciences
Jiaqi Li, Hongyan Xu, Richard A. McIndoe
Summary: Drug combination therapies play an important role in cancer treatment, but current methods to identify synergistic drug combinations are limited. This paper presents a computational algorithm that utilizes gene regulatory networks and single drug data to evaluate all possible drug pairs and find potential synergistic drug combinations.
Article
Pharmacology & Pharmacy
Shiqi Li, Fuhui Zhang, Xiuchan Xiao, Yanzhi Guo, Zhining Wen, Menglong Li, Xuemei Pu
Summary: Prostate cancer is a major cause of cancer-related deaths, and current monotherapies show limited efficacy. Transcriptomics-based and network-based prediction models can be used to quickly screen potential drug combinations and assess their effectiveness through in vitro experiments. For Prostate cancer, the transcriptomics-based method outperforms the network-based one, providing guidance for selecting computational methods in drug combination screening.
FRONTIERS IN PHARMACOLOGY
(2021)
Article
Multidisciplinary Sciences
Wengong Jin, Jonathan M. Stokes, Richard T. Eastman, Zina Itkin, Alexey Zakharov, James J. Collins, Tommi S. Jaakkola, Regina Barzilay
Summary: This study introduces a new neural network architecture for learning drug-target interactions and drug-drug synergy to aid in the discovery of drug combinations against COVID-19. By incorporating additional biological information, the model significantly outperforms previous methods in synergy prediction accuracy.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Biochemical Research Methods
Xiaowen Wang, Hongming Zhu, Yizhi Jiang, Yulong Li, Chen Tang, Xiaohan Chen, Yunjie Li, Qi Liu, Qin Liu
Summary: This study presents a novel deep learning method, PRODeepSyn, for predicting synergistic drug combinations in cancer treatment. By integrating protein-protein interaction network and omics data, PRODeepSyn constructs low-dimensional dense embeddings for cell lines and utilizes a deep neural network for prediction. The method achieves high accuracy and improves prediction results.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Chemistry, Organic
Astrid C. R. Larin, Michael C. Pfrunder, Kathleen M. Mullen, Sandra Wiedbrauk, Nathan R. Boase, Kathryn E. Fairfull-Smith
Summary: Neurodegenerative diseases impose a significant burden on global populations. Oxidative stress, caused by an imbalance in pro-oxidant/antioxidant levels, plays a role in these diseases. Manipulating reactive oxygen species (ROS) levels may be a promising treatment option, but effective treatments have not been achieved yet. By combining free radical nitroxide antioxidants with natural antioxidants called flavonoids, a potential multitargeted antioxidant was formed.
ORGANIC & BIOMOLECULAR CHEMISTRY
(2023)
Article
Biochemical Research Methods
Jinxian Wang, Xuejun Liu, Siyuan Shen, Lei Deng, Hui Liu
Summary: In this paper, we proposed a deep learning model based on graph neural network and attention mechanism to identify drug combinations that can effectively inhibit the viability of specific cancer cells. The model, called DeepDDS, achieved better performance than other methods in predicting drug synergy. Additionally, we explored the interpretability of the model and found important chemical substructures of drugs. DeepDDS is considered an effective tool for prioritizing synergistic drug combinations for further experimental validation.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Biology
Yichen Pan, Haotian Ren, Liang Lan, Yixue Li, Tao Huang
Summary: The prediction of drug combinations is clinically significant in reducing drug resistance and developing precision therapy. This review summarizes the latest methods and databases used for predicting the effects of drug combinations and introduces five algorithms applied to drug combinatorial prediction.
Article
Biochemical Research Methods
Pingjian Ding, Cheng Liang, Wenjue Ouyang, Guanghui Li, Qiu Xiao, Jiawei Luo
Summary: Combinatorial drug therapy shows promise in treating cancers by reducing side effects and improving efficacy, but identifying novel synergistic drug combinations is challenging due to the large combinatorial space. A computational method that fuses multi-source knowledge can efficiently infer synergistic drug combinations, but effectively combining multi-source information remains a challenge. The proposed ISDCSMP method outperforms existing methods in prioritizing synergistic drug combinations by integrating multi-source information in a novel drug-target network.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Jing Hu, Jie Gao, Xiaomin Fang, Zijing Liu, Fan Wang, Weili Huang, Hua Wu, Guodong Zhao
Summary: Drug combination therapies are superior to monotherapy in cancer treatment. Computational methods have been developed to predict drug pairs with potential synergistic functions. We propose a deep neural network model called DTSyn based on a multi-head attention mechanism to identify novel drug combinations. DTSyn achieved high performance and improved interpretability by capturing chemical-gene and gene-gene associations and extracting chemical-chemical and chemical-cell line interactions.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Peng Zhang, Shikui Tu, Wen Zhang, Lei Xu
Summary: This paper proposes a novel encoder-decoder network named SDCNet for predicting cell line-specific synergistic drug combinations (SDCs). SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. It achieves this by considering the SDC graphs of different cell lines as a relational graph and constructing a relational graph convolutional network (R-GCN) as the encoder. SDCNet outperforms state-of-the-art methods and is robust when generalized to new cell lines.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Virology
Aleksandr Ianevski, Eva Zusinaite, Tanel Tenson, Valentyn Oksenych, Wei Wang, Jan Egil Afset, Magnar Bjoras, Denis E. Kainov
Summary: The study tested several antiviral agents against enterovirus 1 in human cells, confirming the anti-enteroviral activities of some drugs and identifying synergistic effects of drug combinations against enterovirus infection.
Article
Biochemical Research Methods
Danyi Chen, Xiaowen Wang, Hongming Zhu, Yizhi Jiang, Yulong Li, Qi Liu, Qin Liu
Summary: In this paper, the authors propose MTLSynergy, a multi-task learning and deep neural network-based method, for predicting synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment and integrates classification and regression into the same model. The authors also employ autoencoders to reduce the dimensions of input features.
BMC BIOINFORMATICS
(2023)
Article
Multidisciplinary Sciences
Yasaman KalantarMotamedi, Ran Joo Choi, Siang-Boon Koh, Jo L. Bramhall, Tai-Ping Fan, Andreas Bender
Summary: Resistance to current therapies is common in pancreatic cancer, necessitating novel treatment options. A computational method was developed to select synergistic compound combinations based on transcriptomic profiles and pathway scoring system, successfully identifying effective compound combinations against pancreatic cancer cells.
Article
Chemistry, Medicinal
Qiaojing Huang, Luhua Lai, Zhirong Liu
Summary: Dynamic allosteric regulation refers to a type of regulation that does not involve noticeable conformational changes. By developing a unified anisotropic elastic network model, the contribution of pure dynamic allosteric regulation was quantitatively estimated. The study found that pure dynamic allosteric regulation has a generally small contribution, which decays exponentially with the distance between the substrate and the allosteric ligand. Toy models were also used to analyze the factors determining dynamic allosteric regulation in proteins.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Medicine, General & Internal
Shuaishi Gao, Ziwei Dai, Hanyu Xu, Luhua Lai
Summary: In this study, we analyzed the metabolic tasks of different cancer types and established cancer type-specific metabolic models, providing new insights into the development of specific anti-cancer drugs and treatment plans for specific cancer subtypes.
FRONTIERS IN MEDICINE
(2022)
Article
Biochemistry & Molecular Biology
Shiwei Wang, Haoyu Lin, Zhixian Huang, Yufeng He, Xiaobing Deng, Youjun Xu, Jianfeng Pei, Luhua Lai
Summary: This study constructed the CavitySpace database, the first pocket library for all proteins in the human proteome. By analyzing known ligand binding sites, it was found that these sites can be well recovered. The predicted binding sites were grouped according to their similarity, which can be used in protein function prediction and drug repurposing studies. Novel binding sites in highly reliable predicted structure regions provide new opportunities for drug discovery.
Article
Cell Biology
Rong Hu, Umar Farook Shahul Hameed, Xiang Sun, Balakrishnan Shenbaga Moorthy, Wen Zhang, Philip D. Jeffrey, Li Zhou, Xin Ma, Fangjin Chen, Jianfeng Pei, Pankaj K. Giri, Yonggao Mou, Kunchithapadam Swaminathan, Ping Yuan
Summary: Peptide LSD1-197-211 can repress BTICs by interfering the synergistic function of NR2E1 and LSD1 and may be a promising lead peptide for brain tumour therapy in future.
CELL PROLIFERATION
(2023)
Article
Biochemistry & Molecular Biology
Chenjing Cai, Haoyu Lin, Hongyi Wang, Youjun Xu, Qi Ouyang, Luhua Lai, Jianfeng Pei
Summary: The drug development pipeline consists of in vitro assays, in vivo assays, and clinical trials. We developed three subdivisional models using graph neural networks to predict the compound's capacity to pass through the in vivo testing, clinical trials, and market approval stages. By combining active learning and ensemble learning, we improved the quality of the models and achieved satisfactory performance in internal and external test datasets. Our model system, miDruglikeness, also showed a powerful ability in virtual screening tasks, providing a comprehensive drug-likeness prediction tool for drug discovery and development.
Article
Chemistry, Physical
Juan Xie, Gaoxiang Pan, Yibo Li, Luhua Lai
Summary: This study demonstrates the importance of protein topology in allosteric regulation, showing that allosteric proteins mainly have multiple domains or subunits, and allosteric sites are often located between domains of the same fold type. A novel method, called TopoAlloSite, was developed to predict the location of allosteric sites on protein topology, successfully identifying cryptic allosteric sites in several proteins. This research provides insights for finding new druggable targets and designing allosteric drugs.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Virology
Zhao-Xi Wang, Bing Liu, Tian Yang, Daqi Yu, Chu Zhang, Liming Zheng, Jin Xie, Bin Liu, Mengxi Liu, Hailin Peng, Luhua Lai, Qi Ouyang, Songying Ouyang, Yong-An Zhang
Summary: Aquaculture accounts for 70% of global aquatic products, but viral diseases are a major problem. SVCV causes a highly contagious disease in common carp, but there are no vaccines or effective treatments. This study resolved the structure of SVCV ribonucleoprotein complex (RNP) and screened antiviral drugs based on this structure, finding that suramin has good antiviral effects.
JOURNAL OF VIROLOGY
(2023)
Article
Chemistry, Multidisciplinary
Shuai Liu, Xiangyu Sun, Huan Lu, Daiqin Chen, Xue Li, Lei Li, Shenge Su, Zhongpu Zhao, Xinran Cao, Libing Liu, Luhua Lai, Xueguang Lu, Chunying Shu, Chunru Wang, Chunli Bai
Summary: A fullerene-based nanocomplex was designed for the inhalation delivery of siRNA, which protects siRNA against degradation, improves cellular uptake and gene knockdown efficiency, and prolongs pulmonary retention. By packaging PD-L1 siRNA, this nanocomplex significantly inhibits the progression of metastatic lung cancer in a mouse model without obvious adverse events and toxicity. This fullerene-based nanocomplex opens up the possibility of siRNA for treating a diverse range of pulmonary diseases.
Article
Biology
He-Wei Jiang, Hong Chen, Yun-Xiao Zheng, Xue-Ning Wang, Qingfeng Meng, Jin Xie, Jiong Zhang, ChangSheng Zhang, Zhao-Wei Xu, Zi-Qing Chen, Lei Wang, Wei-Sha Kong, Kuan Zhou, Ming-Liang Ma, Hai-Nan Zhang, Shu-Juan Guo, Jun-Biao Xue, Jing-Li Hou, Zhe-Yi Liu, Wen-Xue Niu, Fang-Jun Wang, Tao Wang, Wei Li, Rui-Na Wang, Yong-Jun Dang, Daniel M. Czajkowsky, JianFeng Pei, Jia-Jia Dong, Sheng-Ce Tao
Summary: Protein-biomolecule interactions are important in biological processes, and the identification of interacting proteins is crucial. The SPIDER method, combining proximity labeling activity and streptavidin-biotin system, successfully validated known protein-DNA, protein-RNA, and protein-small molecule interactions. It was also used to construct global protein interactomes for m(6)A and mRNA, identify unknown m(6)A binding proteins, and validate SRSF7 as a potential m(6)A reader. Furthermore, SPIDER identified binding proteins for lenalidomide and CobB, as well as SARS-CoV-2-specific receptors on cell membranes.
SCIENCE CHINA-LIFE SCIENCES
(2023)
Article
Endocrinology & Metabolism
Jianfeng Pei, Yanyun Li, Yihui Yang, Minna Cheng, Yan Shi, Wang Hong Xu
Summary: The prevalence of diabetes and prediabetes among Chinese adults in Shanghai has been increasing over the years, leading to a heavier disease burden. The study emphasizes the urgent need to strengthen the community healthcare system in China to ensure comprehensive management of diabetes and prediabetes.
JOURNAL OF DIABETES
(2023)
Article
Biology
Juan Xie, Weilin Zhang, Xiaolei Zhu, Minghua Deng, Luhua Lai, Shozeb Haider
Summary: Allostery plays a fundamental role in biological processes, but predicting the impact of allosteric mutations, modifications, and effector binding on protein function is challenging. We developed a novel computational method, KeyAlloSite, to predict allosteric sites and identify key allosteric residues based on the evolutionary coupling model. Our predictions are consistent with previous experimental studies and key cancer mutations. KeyAlloSite can be applied in studying the evolution of protein allosteric regulation, designing and optimizing allosteric drugs, and performing functional protein design and enzyme engineering.
Editorial Material
Chemistry, Multidisciplinary
Jintao Zhu, Luhua Lai, Jianfeng Pei
ACS CENTRAL SCIENCE
(2023)
Article
Biochemistry & Molecular Biology
Xiaoyu Qing, Qian Wang, Hanyu Xu, Pei Liu, Luhua Lai
Summary: Loop epitopes at protein-protein binding interfaces are underexplored as drug targets due to their high flexibility, relatively few hot spots, and solvent accessibility. However, this study demonstrates that cyclic peptides derived from loop epitopes can inhibit protein activity by directly binding to the dimer interface and disrupting the oligomer formation. These findings suggest that rationally designed cyclic peptides can modulate protein oligomers and be used to design inhibitors for other seemingly intractable oligomeric proteins.