The enzymatic depolymerization of polyethylene terephthalate(PET)offers a sustainable approach for the recycling of PET waste.Great efforts have been devoted to engineering PET depolymerases on the substrate binding c...The enzymatic depolymerization of polyethylene terephthalate(PET)offers a sustainable approach for the recycling of PET waste.Great efforts have been devoted to engineering PET depolymerases on the substrate binding cleft and the surrounding loops/α-helices on the surface.Here,we report the systematic engineering of whole β-sheet regions in the core of IsPETase(a PETase from Ideonella sakaiensis)via a fluorescent high-throughput screening assay.Twenty-one beneficial substitutions were obtained and iteratively recombined.The best variant,DepoPETase β,with an increase in the melting temperatures(T_(m))of 22.9℃,exhibited superior depolymerization performance and enabled complete depolymerization of100.5 g of untreated post-consumer PET(pc-PET;0.26% W_(enzyme)/W_(PET) enzyme loading)in liter-scale bioreactor at 50℃within 4 d.Crystallization and molecular dynamics simulations revealed that the improved activity and thermostability of DepoPETase β were due to enhanced hydrogen bonds and salt bridges in the β-sheet region,a more tightly packed structure of the core sheets and the surrounding helix,and improved binding of PET to the active sites.This study not only demonstrates the importance of engineering strategy in theβ-sheet region of PET hydrolases but also provides a potential PET depolymerase for large-scale PET recycling.展开更多
Protein-protein complexes play an important role in the physiology and the pathology of cellular functions, and therefore are attractive therapeutic targets. A small subset of residues known as “hot spots”, accounts...Protein-protein complexes play an important role in the physiology and the pathology of cellular functions, and therefore are attractive therapeutic targets. A small subset of residues known as “hot spots”, accounts for most of the protein-protein binding free energy. Computational methods play a critical role in identifying the hotspots on the proteinprotein interface. In this paper, we use a computational alanine scanning method with all-atom force fields for predicting hotspots for 313 mutations in 16 protein complexes of known structures. We studied the effect of force fields, solvation models, and conformational sampling on the hotspot predictions. We compared the calculated change in the protein-protein interaction energies upon mutation of the residues in and near the protein-protein interface, to the experimental change in free energies. The AMBER force field (FF) predicted 86% of the hotspots among the three commonly used FF for proteins, namely, AMBER FF, Charmm27 FF, and OPLS-2005 FF. However, AMBER FF also showed a high rate of false positives, while the Charmm27 FF yielded 74% correct predictions of the hotspot residues with low false positives. Van der Waals and hydrogen bonding energy show the largest energy contribution with a high rate of prediction accuracy, while the desolvation energy was found to contribute little to improve the hot spot prediction. Using a conformational ensemble including limited backbone movement instead of one static structure leads to better predicttion of hotpsots.展开更多
Although cytochrome P450 enzymes are the most versatile biocatalysts in nature,there is insufficient comprehension of the molecular mechanism underlying their functional innovation process.Here,by combining ancestral ...Although cytochrome P450 enzymes are the most versatile biocatalysts in nature,there is insufficient comprehension of the molecular mechanism underlying their functional innovation process.Here,by combining ancestral sequence reconstruction,reverse mutation assay,and progressive forward accumulation,we identified 5 founder residues in the catalytic pocket of flavone 6-hydroxylase(F6H)and proposed a"3-point fixation"model to elucidate the functional innovation mechanisms of P450s in nature.According to this design principle of catalytic pocket,we further developed a de novo diffusion model(P450Diffusion)to generate artificial P450s.Ultimately,among the 17 non-natural P450s we generated,10 designs exhibited significant F6H activity and 6 exhibited a 1.3-to 3.5-fold increase in catalytic capacity compared to the natural CYP706X1.This work not only explores the design principle of catalytic pockets of P450s,but also provides an insight into the artificial design of P450 enzymes with desired functions.展开更多
Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a widerange of scientific disciplines, including the development of artificial cell factories for bio...Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a widerange of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, wereview the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. Wefirst briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis fordata-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are alsopresented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factorydevelopment using examples from recent studies, including the prediction of protein function, improvement of metabolicmodels, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization.In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methodsshould be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains forindustrial biomanufacturing.展开更多
基金funded by the National Key Research and Development Program of China(2023YFC3903300)the Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project(TSBICIPIJCP-003,TSBICIP-KJGG-009-0203,and TSBICIP-BRFI-005)the Innovation Fund of Haihe Laboratory of Synthetic Biology(22HHSWSS00018)。
文摘The enzymatic depolymerization of polyethylene terephthalate(PET)offers a sustainable approach for the recycling of PET waste.Great efforts have been devoted to engineering PET depolymerases on the substrate binding cleft and the surrounding loops/α-helices on the surface.Here,we report the systematic engineering of whole β-sheet regions in the core of IsPETase(a PETase from Ideonella sakaiensis)via a fluorescent high-throughput screening assay.Twenty-one beneficial substitutions were obtained and iteratively recombined.The best variant,DepoPETase β,with an increase in the melting temperatures(T_(m))of 22.9℃,exhibited superior depolymerization performance and enabled complete depolymerization of100.5 g of untreated post-consumer PET(pc-PET;0.26% W_(enzyme)/W_(PET) enzyme loading)in liter-scale bioreactor at 50℃within 4 d.Crystallization and molecular dynamics simulations revealed that the improved activity and thermostability of DepoPETase β were due to enhanced hydrogen bonds and salt bridges in the β-sheet region,a more tightly packed structure of the core sheets and the surrounding helix,and improved binding of PET to the active sites.This study not only demonstrates the importance of engineering strategy in theβ-sheet region of PET hydrolases but also provides a potential PET depolymerase for large-scale PET recycling.
文摘Protein-protein complexes play an important role in the physiology and the pathology of cellular functions, and therefore are attractive therapeutic targets. A small subset of residues known as “hot spots”, accounts for most of the protein-protein binding free energy. Computational methods play a critical role in identifying the hotspots on the proteinprotein interface. In this paper, we use a computational alanine scanning method with all-atom force fields for predicting hotspots for 313 mutations in 16 protein complexes of known structures. We studied the effect of force fields, solvation models, and conformational sampling on the hotspot predictions. We compared the calculated change in the protein-protein interaction energies upon mutation of the residues in and near the protein-protein interface, to the experimental change in free energies. The AMBER force field (FF) predicted 86% of the hotspots among the three commonly used FF for proteins, namely, AMBER FF, Charmm27 FF, and OPLS-2005 FF. However, AMBER FF also showed a high rate of false positives, while the Charmm27 FF yielded 74% correct predictions of the hotspot residues with low false positives. Van der Waals and hydrogen bonding energy show the largest energy contribution with a high rate of prediction accuracy, while the desolvation energy was found to contribute little to improve the hot spot prediction. Using a conformational ensemble including limited backbone movement instead of one static structure leads to better predicttion of hotpsots.
基金funding from the National Key R&D Program of China(grant no.2019YFA0905700 and 2021YFC2103500)the National Natural Science Foundation of China(no.32371499)+3 种基金the China Postdoctoral Science Foundation(grant no.2019M661032)the National Natural Science Foundation of China(NSFC,grant nos.31901026 and 32171418)the Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project(nos.TSBICIP-KJGG-002-02 and TSBICIP-CXRC-015)the Tianjin Science Fund for Distinguished Young Scholars(no.18JCJQJC48300).
文摘Although cytochrome P450 enzymes are the most versatile biocatalysts in nature,there is insufficient comprehension of the molecular mechanism underlying their functional innovation process.Here,by combining ancestral sequence reconstruction,reverse mutation assay,and progressive forward accumulation,we identified 5 founder residues in the catalytic pocket of flavone 6-hydroxylase(F6H)and proposed a"3-point fixation"model to elucidate the functional innovation mechanisms of P450s in nature.According to this design principle of catalytic pocket,we further developed a de novo diffusion model(P450Diffusion)to generate artificial P450s.Ultimately,among the 17 non-natural P450s we generated,10 designs exhibited significant F6H activity and 6 exhibited a 1.3-to 3.5-fold increase in catalytic capacity compared to the natural CYP706X1.This work not only explores the design principle of catalytic pockets of P450s,but also provides an insight into the artificial design of P450 enzymes with desired functions.
基金the National Key Research and Development Program of China(grant number 2018YFA0900300)the International Partnership Program of Chinese Academy of Sciences(grant number 153D31KYSB20170121)Youth Innovation Promotion Association CAS,and the Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project(grant numbers TSBICIP-PTJS-001 and TSBICIP-CXRC-018).
文摘Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a widerange of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, wereview the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. Wefirst briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis fordata-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are alsopresented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factorydevelopment using examples from recent studies, including the prediction of protein function, improvement of metabolicmodels, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization.In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methodsshould be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains forindustrial biomanufacturing.
