Currently, no clinically approved therapeutic drugs specifically target dengue virus infections. This study aims to evaluate the potential of antiviral drugs originally developed for other purposes as viable candidate...Currently, no clinically approved therapeutic drugs specifically target dengue virus infections. This study aims to evaluate the potential of antiviral drugs originally developed for other purposes as viable candidates for combating dengue virus. The RNA-elongating NS5-NS3 complex is a critical molecular structure responsible for dengue virus replication. Using the cryo-electron microscopy (Cryo-EM) structures available in the Protein Data Bank and AlphaFold 3 predictions, this study simulated the replication complexes of dengue virus serotypes 1, 2, 3, and 4. The RNA-dependent RNA polymerase (RdRp) domain of the NS5 protein within the NS5-NS3 complex was selected as the molecular docking template. Molecular docking simulations were conducted using AutoDock4. Seven small molecules—AT-9010, RK-0404678, Oseltamivir, Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin—were assessed for binding affinity by calculating their binding energies, where lower values indicate stronger molecular interactions. Based on published data, antiviral replication assays were conducted for the four dengue virus serotypes. AT-9010 and RK-0404678 were used as benchmarks for antiviral replication efficacy, while Oseltamivir served as the control group. The Mann-Whitney U test was employed to classify the clinical antiviral candidates—Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin. Results demonstrated that among the four small molecules, Favipiravir-RTP exhibited the highest binding affinity with the RdRp domain of the NS5-NS3 complex across all four dengue virus serotypes. Statistical classification revealed that in five simulated scenarios—including the four virus serotypes and Cryo-EM structural data—Favipiravir-RTP shared three classifications with the benchmark molecule AT-9010. Based on these findings, Favipiravir-RTP, a broad-spectrum antiviral agent, shows potential as a therapeutic option for inhibiting dengue virus replication. However, further clinical trials are necessary to validate their efficacy in humans.展开更多
The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which li...The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobodyeantigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.展开更多
This study utilizes the enzyme-substrate complex theory to predict the clinical efficacy of COVID-19 treatments at the biological systems level, using molecular docking stability indicators. Experimental data from the...This study utilizes the enzyme-substrate complex theory to predict the clinical efficacy of COVID-19 treatments at the biological systems level, using molecular docking stability indicators. Experimental data from the Protein Data Bank and molecular structures generated by AlphaFold 3 were used to create macromolecular complex templates. Six templates were developed, including the holo nsp7-nsp8-nsp12 (RNA-dependent RNA polymerase) complex with dsRNA primers (holo-RdRp-RNA). The study evaluated several ligands—Favipiravir-RTP, Remdesivir, Abacavir, Ribavirin, and Oseltamivir—as potential viral RNA polymerase inhibitors. Notably, the first four of these ligands have been clinically employed in the treatment of COVID-19, allowing for comparative analysis. Molecular docking simulations were performed using AutoDock 4, and statistical differences were assessed through t-tests and Mann-Whitney U tests. A review of the literature on COVID-19 treatment outcomes and inhibitors targeting RNA polymerase enzymes was conducted, and the inhibitors were ranked according to their clinical efficacy: Remdesivir > Favipiravir-RTP > Oseltamivir. Docking results obtained from the second and third templates aligned with clinical observations. Furthermore, Abacavir demonstrated a predicted efficacy comparable to Favipiravir-RTP, while Ribavirin exhibited a predicted efficacy similar to that of Remdesivir. This research, focused on inhibitors of SARS-CoV-2 RNA-dependent RNA polymerase, establishes a framework for screening AI-generated drug templates based on clinical outcomes. Additionally, it develops a drug screening platform based on molecular docking binding energy, enabling the evaluation of novel or repurposed drugs and potentially accelerating the drug development process.展开更多
AlphaFold3(AF3),as the latest generation of artificial intelligence model jointly developed by Google DeepMind and Isomorphic Labs,has been widely heralded in the scientific research community since its launch.With un...AlphaFold3(AF3),as the latest generation of artificial intelligence model jointly developed by Google DeepMind and Isomorphic Labs,has been widely heralded in the scientific research community since its launch.With unprecedented accuracy,the AF3 model may successfully predict the structure and interactions of virtually all biomolecules,including proteins,ligands,nucleic acids,ions,etc.By accurately simulating the structural information and interactions of biomacromolecules,it has shown great potential in many aspects of structural prediction,mechanism research,drug design,protein engineering,vaccine development,and precision therapy.In order to further understand the characteristics of AF3 and accelerate its promotion,this article sets out to address the development process,working principle,and application in drugs and biomedicine,especially focusing on the intricate differences and some potential pitfalls compared to other deep learning models.We explain how a structure-prediction tool can impact many research fields,and in particular revolutionize the strategies for designing of effective next generation vaccines and chemical and biological drugs.展开更多
文摘Currently, no clinically approved therapeutic drugs specifically target dengue virus infections. This study aims to evaluate the potential of antiviral drugs originally developed for other purposes as viable candidates for combating dengue virus. The RNA-elongating NS5-NS3 complex is a critical molecular structure responsible for dengue virus replication. Using the cryo-electron microscopy (Cryo-EM) structures available in the Protein Data Bank and AlphaFold 3 predictions, this study simulated the replication complexes of dengue virus serotypes 1, 2, 3, and 4. The RNA-dependent RNA polymerase (RdRp) domain of the NS5 protein within the NS5-NS3 complex was selected as the molecular docking template. Molecular docking simulations were conducted using AutoDock4. Seven small molecules—AT-9010, RK-0404678, Oseltamivir, Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin—were assessed for binding affinity by calculating their binding energies, where lower values indicate stronger molecular interactions. Based on published data, antiviral replication assays were conducted for the four dengue virus serotypes. AT-9010 and RK-0404678 were used as benchmarks for antiviral replication efficacy, while Oseltamivir served as the control group. The Mann-Whitney U test was employed to classify the clinical antiviral candidates—Remdesivir, Favipiravir-RTP, Abacavir, and Ribavirin. Results demonstrated that among the four small molecules, Favipiravir-RTP exhibited the highest binding affinity with the RdRp domain of the NS5-NS3 complex across all four dengue virus serotypes. Statistical classification revealed that in five simulated scenarios—including the four virus serotypes and Cryo-EM structural data—Favipiravir-RTP shared three classifications with the benchmark molecule AT-9010. Based on these findings, Favipiravir-RTP, a broad-spectrum antiviral agent, shows potential as a therapeutic option for inhibiting dengue virus replication. However, further clinical trials are necessary to validate their efficacy in humans.
基金supported by the National Natural Science Foundation of China(Grant Nos.:92477103,22273023,12474285 and 22373116)the National Key R&D Program of China(Grant No.:2019YFA0905200)+5 种基金Shanghai Municipal Natural Science Foundation(Grant No.:23ZR1418200)Natural Science Foundation of Chongqing,China(Grant No.:CSTB2023NSCQ-MSX0616)Shanghai Frontiers Science Center of Molecule Intelligent SynthesesShanghai Future Discipline Program(Quantum Science and Tech-nology)Shanghai Municipal Education Commission’s“Artificial Intelligence-Driven Research Paradigm Reform and Discipline Advancement Program”the Fundamental Research Funds for the Central Universities.
文摘The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobodyeantigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.
文摘This study utilizes the enzyme-substrate complex theory to predict the clinical efficacy of COVID-19 treatments at the biological systems level, using molecular docking stability indicators. Experimental data from the Protein Data Bank and molecular structures generated by AlphaFold 3 were used to create macromolecular complex templates. Six templates were developed, including the holo nsp7-nsp8-nsp12 (RNA-dependent RNA polymerase) complex with dsRNA primers (holo-RdRp-RNA). The study evaluated several ligands—Favipiravir-RTP, Remdesivir, Abacavir, Ribavirin, and Oseltamivir—as potential viral RNA polymerase inhibitors. Notably, the first four of these ligands have been clinically employed in the treatment of COVID-19, allowing for comparative analysis. Molecular docking simulations were performed using AutoDock 4, and statistical differences were assessed through t-tests and Mann-Whitney U tests. A review of the literature on COVID-19 treatment outcomes and inhibitors targeting RNA polymerase enzymes was conducted, and the inhibitors were ranked according to their clinical efficacy: Remdesivir > Favipiravir-RTP > Oseltamivir. Docking results obtained from the second and third templates aligned with clinical observations. Furthermore, Abacavir demonstrated a predicted efficacy comparable to Favipiravir-RTP, while Ribavirin exhibited a predicted efficacy similar to that of Remdesivir. This research, focused on inhibitors of SARS-CoV-2 RNA-dependent RNA polymerase, establishes a framework for screening AI-generated drug templates based on clinical outcomes. Additionally, it develops a drug screening platform based on molecular docking binding energy, enabling the evaluation of novel or repurposed drugs and potentially accelerating the drug development process.
基金supported by funds of the Key Research and Development Grant of MOST(grant No.2023YFA095000)the Affiliated Xiangshan Hospital of Wenzhou Medical University,the National Science Foundation of China(grant No.82470005)Wenzhou Institute University of Chinese Academy of Sciences.
文摘AlphaFold3(AF3),as the latest generation of artificial intelligence model jointly developed by Google DeepMind and Isomorphic Labs,has been widely heralded in the scientific research community since its launch.With unprecedented accuracy,the AF3 model may successfully predict the structure and interactions of virtually all biomolecules,including proteins,ligands,nucleic acids,ions,etc.By accurately simulating the structural information and interactions of biomacromolecules,it has shown great potential in many aspects of structural prediction,mechanism research,drug design,protein engineering,vaccine development,and precision therapy.In order to further understand the characteristics of AF3 and accelerate its promotion,this article sets out to address the development process,working principle,and application in drugs and biomedicine,especially focusing on the intricate differences and some potential pitfalls compared to other deep learning models.We explain how a structure-prediction tool can impact many research fields,and in particular revolutionize the strategies for designing of effective next generation vaccines and chemical and biological drugs.
