Publications

• Discovery and characterization of a marine bacterial SAM-dependent chlorinase.

  Eustaquio A.S., Pojer F., Noel J.P., Moore B.S.

  Halogen atom incorporation into a scaffold of bioactive compounds often amplifies biological activity, as is the case for the anticancer agent salinosporamide A (1), a chlorinated natural product from the marine bacterium Salinispora tropica. Significant effort in understanding enzymatic chlorinat ... >> More

  Halogen atom incorporation into a scaffold of bioactive compounds often amplifies biological activity, as is the case for the anticancer agent salinosporamide A (1), a chlorinated natural product from the marine bacterium Salinispora tropica. Significant effort in understanding enzymatic chlorination shows that oxidative routes predominate to form reactive electrophilic or radical chlorine species. Here we report the genetic, biochemical and structural characterization of the chlorinase SalL, which halogenates S-adenosyl-L-methionine (2) with chloride to generate 5'-chloro-5'-deoxyadenosine (3) and L-methionine (4) in a rarely observed nucleophilic substitution strategy analogous to that of Streptomyces cattleya fluorinase. Further metabolic tailoring produces a halogenated polyketide synthase substrate specific for salinosporamide A biosynthesis. SalL also accepts bromide and iodide as substrates, but not fluoride. High-resolution crystal structures of SalL and active site mutants complexed with substrates and products support the S(N)2 nucleophilic substitution mechanism and further illuminate halide specificity in this newly discovered halogenase family. << Less

  Nat. Chem. Biol. 4:69-74(2008) [PubMed] [EuropePMC]

• Enzyme function prediction using contrastive learning.

  Yu T., Cui H., Li J.C., Luo Y., Jiang G., Zhao H.

  Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied proteins or those with previously uncharacterized func ... >> More

  Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied proteins or those with previously uncharacterized functions or multiple activities. We present a machine learning algorithm named CLEAN (contrastive learning-enabled enzyme annotation) to assign EC numbers to enzymes with better accuracy, reliability, and sensitivity compared with the state-of-the-art tool BLASTp. The contrastive learning framework empowers CLEAN to confidently (i) annotate understudied enzymes, (ii) correct mislabeled enzymes, and (iii) identify promiscuous enzymes with two or more EC numbers-functions that we demonstrate by systematic in silico and in vitro experiments. We anticipate that this tool will be widely used for predicting the functions of uncharacterized enzymes, thereby advancing many fields, such as genomics, synthetic biology, and biocatalysis. << Less

  Science 379:1358-1363(2023) [PubMed] [EuropePMC]

  This publication is cited by 2 other entries.