Predicting free energy changes(DDG)is essential for enhancing our understanding of protein evolution and plays a pivotal role in protein engineering and pharmaceutical development.While traditional methods offer valua...Predicting free energy changes(DDG)is essential for enhancing our understanding of protein evolution and plays a pivotal role in protein engineering and pharmaceutical development.While traditional methods offer valuable insights,they are often constrained by computational speed and reliance on biased training datasets.These constraints become particularly evident when aiming for accurate DDG predictions across a diverse array of protein sequences.Herein,we introduce Pythia,a self-supervised graph neural network specifically designed for zero-shot DDG predictions.Our comparative benchmarks demonstrate that Pythia outperforms other self-supervised pretraining models and force field-based approaches while also exhibiting competitive performance with fully supervised models.Notably,Pythia shows strong correlations and achieves a remarkable increase in computational speed of up to 105-fold.We further validated Pythia’s performance in predicting the thermostabilizing mutations of limonene epoxide hydrolase,leading to higher experimental success rates.This exceptional efficiency has enabled us to explore 26 million high-quality protein structures,marking a significant advancement in our ability to navigate the protein sequence space and enhance our understanding of the relationships between protein genotype and phenotype.In addition,we established a web server at https://pythia.wulab.xyz to allow users to easily perform such predictions.展开更多
Biotechnological strategies for plastic depolymerization and recycling have emerged as transformative approaches to combat the global plastic pollution crisis,aligning with the principles of a sustainable and circular...Biotechnological strategies for plastic depolymerization and recycling have emerged as transformative approaches to combat the global plastic pollution crisis,aligning with the principles of a sustainable and circular economy.Despite advances in engineering PET hydrolases,the degradation process is frequently compromised by product inhibition and the heterogeneity of final products,thereby obstructing subsequent PET recondensation and impeding the synthesis of high-value derivatives.In this work,we utilized previously devised computational strategies to redesign a thermostable DuraMHETase,achieving an apparent melting temperature of 72℃ in complex with MHET and a 6-fold higher in total turnover number(TTN)toward MHET than the wild-type enzyme at 60℃.The fused enzyme system composed of DuraMHETase and TurboPETase demonstrated higher efficiency than other PET hydrolases and the separated dual enzyme systems.Furthermore,we identified both exo-and endo-PETase activities in DuraMHETase,whereas the endo-activity was previously unobserved at ambient temperatures.These results expand the functional scope of MHETase beyond mere intermediate hydrolysis,and may provide guidance for the development of more synergistic approaches to plastic biodepolymerization and recycling.展开更多
Impact statement We have developed the GReedy Accumulated strategy for Protein Engineering(GRAPE)to improve enzyme stability across various applications,combining advanced computational methods with a unique clusterin...Impact statement We have developed the GReedy Accumulated strategy for Protein Engineering(GRAPE)to improve enzyme stability across various applications,combining advanced computational methods with a unique clustering and greedy accumulation approach to efficiently explore epistatic effects with minimal experimental effort.To make this strategy accessible to nonexperts,we introduced GRAPE‐WEB,an automated,user‐friendly web server that allows the design,inspection,and combination of stabilizing mutations without requiring extensive bioinformatics knowledge.展开更多
The combination of flow elasticity and inertia has emerged as a viable tool for focusing and manipulating particles using microfluidics.Although there is considerable interest in the field of elasto-inertial microflui...The combination of flow elasticity and inertia has emerged as a viable tool for focusing and manipulating particles using microfluidics.Although there is considerable interest in the field of elasto-inertial microfluidics owing to its potential applications,research on particle focusing has been mostly limited to low Reynolds numbers(Re<1),and particle migration toward equilibrium positions has not been extensively examined.In this work,we thoroughly studied particle focusing on the dynamic range of flow rates and particle migration using straight microchannels with a single inlet high aspect ratio.We initially explored several parameters that had an impact on particle focusing,such as the particle size,channel dimensions,concentration of viscoelastic fluid,and flow rate.Our experimental work covered a wide range of dimensionless numbers(0.05<Reynolds number<85,1.5<Weissenberg number<3800,5<Elasticity number<470)using 3,5,7,and 10μm particles.Our results showed that the particle size played a dominant role,and by tuning the parameters,particle focusing could be achieved at Reynolds numbers ranging from 0.2(1μL/min)to 85(250μL/min).Furthermore,we numerically and experimentally studied particle migration and reported differential particle migration for high-resolution separations of 5μm,7μm and 10μm particles in a sheathless flow at a throughput of 150μL/min.Our work elucidates the complex particle transport in elasto-inertial flows and has great potential for the development of high-throughput and high-resolution particle separation for biomedical and environmental applications.展开更多
基金supported by the National Key R&D Program of China(grant no.2023YFA0916000)the National Natural Science Foundation of China(32225002,32170033,and 32422001)+2 种基金the Key Research Program of Frontier Sciences(ZDBS-LYSM014)the Biological Resources Program(KFJ-BRP-009 and KFJ-BRP-017-58)from the Chinese Academy of Sciences,the Informatization Plan of Chinese Academy of Sciences(CAS-WX2021SF-0111)the Youth Innovation Promotion Association CAS(2022086).
文摘Predicting free energy changes(DDG)is essential for enhancing our understanding of protein evolution and plays a pivotal role in protein engineering and pharmaceutical development.While traditional methods offer valuable insights,they are often constrained by computational speed and reliance on biased training datasets.These constraints become particularly evident when aiming for accurate DDG predictions across a diverse array of protein sequences.Herein,we introduce Pythia,a self-supervised graph neural network specifically designed for zero-shot DDG predictions.Our comparative benchmarks demonstrate that Pythia outperforms other self-supervised pretraining models and force field-based approaches while also exhibiting competitive performance with fully supervised models.Notably,Pythia shows strong correlations and achieves a remarkable increase in computational speed of up to 105-fold.We further validated Pythia’s performance in predicting the thermostabilizing mutations of limonene epoxide hydrolase,leading to higher experimental success rates.This exceptional efficiency has enabled us to explore 26 million high-quality protein structures,marking a significant advancement in our ability to navigate the protein sequence space and enhance our understanding of the relationships between protein genotype and phenotype.In addition,we established a web server at https://pythia.wulab.xyz to allow users to easily perform such predictions.
文摘Biotechnological strategies for plastic depolymerization and recycling have emerged as transformative approaches to combat the global plastic pollution crisis,aligning with the principles of a sustainable and circular economy.Despite advances in engineering PET hydrolases,the degradation process is frequently compromised by product inhibition and the heterogeneity of final products,thereby obstructing subsequent PET recondensation and impeding the synthesis of high-value derivatives.In this work,we utilized previously devised computational strategies to redesign a thermostable DuraMHETase,achieving an apparent melting temperature of 72℃ in complex with MHET and a 6-fold higher in total turnover number(TTN)toward MHET than the wild-type enzyme at 60℃.The fused enzyme system composed of DuraMHETase and TurboPETase demonstrated higher efficiency than other PET hydrolases and the separated dual enzyme systems.Furthermore,we identified both exo-and endo-PETase activities in DuraMHETase,whereas the endo-activity was previously unobserved at ambient temperatures.These results expand the functional scope of MHETase beyond mere intermediate hydrolysis,and may provide guidance for the development of more synergistic approaches to plastic biodepolymerization and recycling.
基金supported by the National Key R&D Program of China(grant no.2021YFC2103600)the National Natural Science Foundation of China(31822002,32170033,and 32422001)+2 种基金the Key Research Program of Frontier Sciences(ZDBS‐LY‐SM014)the Biological Resources Program(KFJBRP‐009 and KFJ‐BRP‐017‐58)from the Chinese Academy of Sciences,the Informatization Plan of Chinese Academy of Sciences(CAS‐WX2021SF‐0111)the Youth Innovation Promotion Association CAS(2022086).
文摘Impact statement We have developed the GReedy Accumulated strategy for Protein Engineering(GRAPE)to improve enzyme stability across various applications,combining advanced computational methods with a unique clustering and greedy accumulation approach to efficiently explore epistatic effects with minimal experimental effort.To make this strategy accessible to nonexperts,we introduced GRAPE‐WEB,an automated,user‐friendly web server that allows the design,inspection,and combination of stabilizing mutations without requiring extensive bioinformatics knowledge.
基金funding from the European Union’s Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie Grant Agreement No.860775the European Union’s Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie Grant Agreement No.955605+1 种基金the Swedish Research Council(VR 2021-05861)supported by the European Research Council through project StG-852529(MUCUS)。
文摘The combination of flow elasticity and inertia has emerged as a viable tool for focusing and manipulating particles using microfluidics.Although there is considerable interest in the field of elasto-inertial microfluidics owing to its potential applications,research on particle focusing has been mostly limited to low Reynolds numbers(Re<1),and particle migration toward equilibrium positions has not been extensively examined.In this work,we thoroughly studied particle focusing on the dynamic range of flow rates and particle migration using straight microchannels with a single inlet high aspect ratio.We initially explored several parameters that had an impact on particle focusing,such as the particle size,channel dimensions,concentration of viscoelastic fluid,and flow rate.Our experimental work covered a wide range of dimensionless numbers(0.05<Reynolds number<85,1.5<Weissenberg number<3800,5<Elasticity number<470)using 3,5,7,and 10μm particles.Our results showed that the particle size played a dominant role,and by tuning the parameters,particle focusing could be achieved at Reynolds numbers ranging from 0.2(1μL/min)to 85(250μL/min).Furthermore,we numerically and experimentally studied particle migration and reported differential particle migration for high-resolution separations of 5μm,7μm and 10μm particles in a sheathless flow at a throughput of 150μL/min.Our work elucidates the complex particle transport in elasto-inertial flows and has great potential for the development of high-throughput and high-resolution particle separation for biomedical and environmental applications.
