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
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
With the rapid advancement of Artificial Intelligence(AI)technology,cutting-edge protein structure prediction methods have emerged,exemplified by AlphaFold2 and RoseTTAFold[1].These methods have revolutionized our und...With the rapid advancement of Artificial Intelligence(AI)technology,cutting-edge protein structure prediction methods have emerged,exemplified by AlphaFold2 and RoseTTAFold[1].These methods have revolutionized our understanding and utilization of protein structures in biological research.While their primary focus lies in predicting the 3D structures of separate protein molecules,the potential of native proteins to perform essential functions through the formation of various"biomolecular assemblies"is overlooked.These assemblies involve interactions with other biomolecules including nucleic acids,polysaccharides,metals,small ligands,etc.The complex bonding mechanisms—ranging from covalent and noncovalent bonding to metal chelation,etc.—between different molecular units play critical roles in maintaining biological activities of these assemblies.Thus,effectively predicting binding interactions between proteins and other(bio)molecules within one biomolecular assembly remains an outstanding challenge.Recently,one groundbreaking work published in Science by Baker et al.released two upgraded deep learning tools:RoseTTAFold All Atom(RFAA)and RFdiffusion All-Atom(RFdiffusionAA)[2],which significantly broaden the in silico construction scope for biomolecule assemblies,enabling researchers to explore complex interactions beyond individual protein structures(Fig.1).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
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.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With the rapid advancement of Artificial Intelligence(AI)technology,cutting-edge protein structure prediction methods have emerged,exemplified by AlphaFold2 and RoseTTAFold[1].These methods have revolutionized our understanding and utilization of protein structures in biological research.While their primary focus lies in predicting the 3D structures of separate protein molecules,the potential of native proteins to perform essential functions through the formation of various"biomolecular assemblies"is overlooked.These assemblies involve interactions with other biomolecules including nucleic acids,polysaccharides,metals,small ligands,etc.The complex bonding mechanisms—ranging from covalent and noncovalent bonding to metal chelation,etc.—between different molecular units play critical roles in maintaining biological activities of these assemblies.Thus,effectively predicting binding interactions between proteins and other(bio)molecules within one biomolecular assembly remains an outstanding challenge.Recently,one groundbreaking work published in Science by Baker et al.released two upgraded deep learning tools:RoseTTAFold All Atom(RFAA)and RFdiffusion All-Atom(RFdiffusionAA)[2],which significantly broaden the in silico construction scope for biomolecule assemblies,enabling researchers to explore complex interactions beyond individual protein structures(Fig.1).
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.
