Background:The FadR subfamily of regulators plays essential roles in the regulation of diverse metabolic pathways in bacteria.LldR,an FadR-type regulator,regulates lactate utilization in Pseudomonas aeruginosa.Results...Background:The FadR subfamily of regulators plays essential roles in the regulation of diverse metabolic pathways in bacteria.LldR,an FadR-type regulator,regulates lactate utilization in Pseudomonas aeruginosa.Results:Sequence network analysis of the LldR proteins from different bacterial species showed that LldR proteins from Pseudomonas sp.and Escherichia coli were separated into different clusters,suggesting that LldRs are derived from two ancestors that functionally diverged.Then,the recombinant PLldR protein(LldR of P.aeruginosa)was expressed,purified,and crystallized.Preliminary X-ray diffraction analysis of LldR protein crystals was performed.The PLldR crystal diffracted to 2.55A resolution and belonged to the trigonal space group P3,with unit-cell parameters a=68.5A b=68.5A and c=237.0AConclusion:These results will facilitate further understanding of the regulatory mechanism and the adaptation to sensing of both l-lactate and d-lactate of LldR proteins from Pseudomonas sp.in lactate metabolism.展开更多
In this paper the relation between the σ-images of metrical spaces and spaces with σ-locally finite cs-network, or spaces with σ-locally finite cs^*-network, or spaces with σ-locally finite sequence neighborhood n...In this paper the relation between the σ-images of metrical spaces and spaces with σ-locally finite cs-network, or spaces with σ-locally finite cs^*-network, or spaces with σ-locally finite sequence neighborhood network, or spaces with σ-locally finite sequence open network are established by use of σ-mapping.展开更多
Recent advancements in data technology offer immense opportunities for the discovery and development of new enzymes for the green synthesis of chemicals.Current protein databases predominantly prioritize overall seque...Recent advancements in data technology offer immense opportunities for the discovery and development of new enzymes for the green synthesis of chemicals.Current protein databases predominantly prioritize overall sequence matches.The multi-scale features underpinning catalytic mechanisms and processes,which are scat-tered across various data sources,have not been sufficiently integrated to be effectively utilized in enzyme mining.In this study,we developed a sequence-and taxonomic-feature evaluation driven workflow to discover enzymes that can be expressed in E.coli and catalyze chemical reactions in vitro,using alcohol oxidase(AOX)for demonstration,which catalyzes the conversion of methanol to formaldehyde.A dataset of 21 reported AOXs was used to construct sequence scoring rules based on features,including sequence length,structural motifs,catalytic-related residues,binding residues,and overall structure.These scoring rules were applied to filter the results from HMM-based searches,yielding 357 candidate sequences of eukaryotic origin,which were catego-rized into six classes at 85%sequence similarity.Experimental validation was conducted in two rounds on 31 selected sequences representing all classes.Among these selected sequences,19 were expressed as soluble proteins in E.coli,and 18 of these soluble proteins exhibited AOX activity,as predicted.Notably,the most active recombinant AOX exhibited an activity of 8.65±0.29 U/mg,approaching the highest activity of native eukaryotic enzymes.Compared to the established UniProt-annotation-based workflow,this feature-evaluation-based approach yielded a higher probability of highly active recombinant AOX(from 8.3%to 19.4%),demonstrating the efficiency and potential of this multi-dimensional feature evaluation method in accelerating the discovery of active enzymes.展开更多
基金supported by Grants from the National Natural Science Foundation of China(31270090).
文摘Background:The FadR subfamily of regulators plays essential roles in the regulation of diverse metabolic pathways in bacteria.LldR,an FadR-type regulator,regulates lactate utilization in Pseudomonas aeruginosa.Results:Sequence network analysis of the LldR proteins from different bacterial species showed that LldR proteins from Pseudomonas sp.and Escherichia coli were separated into different clusters,suggesting that LldRs are derived from two ancestors that functionally diverged.Then,the recombinant PLldR protein(LldR of P.aeruginosa)was expressed,purified,and crystallized.Preliminary X-ray diffraction analysis of LldR protein crystals was performed.The PLldR crystal diffracted to 2.55A resolution and belonged to the trigonal space group P3,with unit-cell parameters a=68.5A b=68.5A and c=237.0AConclusion:These results will facilitate further understanding of the regulatory mechanism and the adaptation to sensing of both l-lactate and d-lactate of LldR proteins from Pseudomonas sp.in lactate metabolism.
基金Supported by Financial Aid Program of the Young Core Teacher of Higher Institution of Henan Province(2003100)
文摘In this paper the relation between the σ-images of metrical spaces and spaces with σ-locally finite cs-network, or spaces with σ-locally finite cs^*-network, or spaces with σ-locally finite sequence neighborhood network, or spaces with σ-locally finite sequence open network are established by use of σ-mapping.
基金supported by National Key Research and Development Program of China(no.2022YFC2105900).
文摘Recent advancements in data technology offer immense opportunities for the discovery and development of new enzymes for the green synthesis of chemicals.Current protein databases predominantly prioritize overall sequence matches.The multi-scale features underpinning catalytic mechanisms and processes,which are scat-tered across various data sources,have not been sufficiently integrated to be effectively utilized in enzyme mining.In this study,we developed a sequence-and taxonomic-feature evaluation driven workflow to discover enzymes that can be expressed in E.coli and catalyze chemical reactions in vitro,using alcohol oxidase(AOX)for demonstration,which catalyzes the conversion of methanol to formaldehyde.A dataset of 21 reported AOXs was used to construct sequence scoring rules based on features,including sequence length,structural motifs,catalytic-related residues,binding residues,and overall structure.These scoring rules were applied to filter the results from HMM-based searches,yielding 357 candidate sequences of eukaryotic origin,which were catego-rized into six classes at 85%sequence similarity.Experimental validation was conducted in two rounds on 31 selected sequences representing all classes.Among these selected sequences,19 were expressed as soluble proteins in E.coli,and 18 of these soluble proteins exhibited AOX activity,as predicted.Notably,the most active recombinant AOX exhibited an activity of 8.65±0.29 U/mg,approaching the highest activity of native eukaryotic enzymes.Compared to the established UniProt-annotation-based workflow,this feature-evaluation-based approach yielded a higher probability of highly active recombinant AOX(from 8.3%to 19.4%),demonstrating the efficiency and potential of this multi-dimensional feature evaluation method in accelerating the discovery of active enzymes.
