This study assesses the ability of 10 Earth System Models(ESMs)that participated in the Coupled Model Intercomparison Project Phase 6(CMIP6)to reproduce the present-day inhalable particles with diameters less than 2.5...This study assesses the ability of 10 Earth System Models(ESMs)that participated in the Coupled Model Intercomparison Project Phase 6(CMIP6)to reproduce the present-day inhalable particles with diameters less than 2.5 micrometers(PM_(2.5))over Asia and discusses the uncertainty.PM_(2.5)accounts for more than 30%of the surface total aerosol(fine and coarse)concentration over Asia,except for central Asia.The simulated spatial distributions of PM_(2.5)and its components,averaged from 2005 to 2020,are consistent with the Modern-Era Retrospective Analysis for Research and Applications version 2(MERRA-2)reanalysis.They are characterized by the high PM_(2.5)concentrations in eastern China and northern India where anthropogenic components such as sulfate and organic aerosol dominate,and in northwestern China where the mineral dust in PM_(2.5)fine particles(PM_(2.5)DU)dominates.The present-day multimodel mean(MME)PM_(2.5)concentrations slightly underestimate ground-based observations in the same period of 2014–2019,although observations are affected by the limited coverage of observation sites and the urban areas.Those model biases partly come from other aerosols(such as nitrate and ammonium)not involved in our analyses,and also are contributed by large uncertainty in PM_(2.5)simulations on local scale among ESMs.The model uncertainties over East Asia are mainly attributed to sulfate and PM_(2.5)DU;over South Asia,they are attributed to sulfate,organic aerosol,and PM_(2.5)DU;over Southeast Asia,they are attributed to sea salt in PM_(2.5)fine particles(PM_(2.5)SS);and over central Asia,they are attributed to PM_(2.5)DU.They are mainly caused by the different representations of aerosols within individual ESMs including the representation of aerosol size distributions,dynamic transport,and physical and chemistry mechanisms.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
基金Supported by the National Key Research and Development Program of China(2016YFA0602100)UK–China Research&Innovation Partnership Fund through the Met Office Climate Science for Service Partnership(CSSP)China as part of the Newton Fund.
文摘This study assesses the ability of 10 Earth System Models(ESMs)that participated in the Coupled Model Intercomparison Project Phase 6(CMIP6)to reproduce the present-day inhalable particles with diameters less than 2.5 micrometers(PM_(2.5))over Asia and discusses the uncertainty.PM_(2.5)accounts for more than 30%of the surface total aerosol(fine and coarse)concentration over Asia,except for central Asia.The simulated spatial distributions of PM_(2.5)and its components,averaged from 2005 to 2020,are consistent with the Modern-Era Retrospective Analysis for Research and Applications version 2(MERRA-2)reanalysis.They are characterized by the high PM_(2.5)concentrations in eastern China and northern India where anthropogenic components such as sulfate and organic aerosol dominate,and in northwestern China where the mineral dust in PM_(2.5)fine particles(PM_(2.5)DU)dominates.The present-day multimodel mean(MME)PM_(2.5)concentrations slightly underestimate ground-based observations in the same period of 2014–2019,although observations are affected by the limited coverage of observation sites and the urban areas.Those model biases partly come from other aerosols(such as nitrate and ammonium)not involved in our analyses,and also are contributed by large uncertainty in PM_(2.5)simulations on local scale among ESMs.The model uncertainties over East Asia are mainly attributed to sulfate and PM_(2.5)DU;over South Asia,they are attributed to sulfate,organic aerosol,and PM_(2.5)DU;over Southeast Asia,they are attributed to sea salt in PM_(2.5)fine particles(PM_(2.5)SS);and over central Asia,they are attributed to PM_(2.5)DU.They are mainly caused by the different representations of aerosols within individual ESMs including the representation of aerosol size distributions,dynamic transport,and physical and chemistry mechanisms.
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.
