Structure Prediction and Protein Engineering Yield New Insights into Microcin J25 Precursor Recognition.

Microcin J25 (MccJ25), a lasso peptide antibiotic with a unique structure that resembles the lariat knot, has been a topic of intense interest since its discovery in 1992. The precursor (McjA) contains a leader and a core segment. McjB is a protease activated upon binding to the leader, and McjC converts the core segment into the mature MccJ25. Previous studies suggested that these biosynthetic steps likely proceed in a (nearly) concerted fashion; however, there is only limited information regarding the structural and molecular intricacies of MccJ25 biosynthesis. To close this knowledge gap, we used AlphaFold2 to predict the structure of the precursor (McjA) in complex with its biosynthetic enzymes (McjB and McjC) and queried the critical predicted features by protein engineering. Based on the predicted structure, we designed protein variants to show that McjB can still be functional and form a proficient biosynthetic complex with McjC when its recognition and protease domains were circularly permutated or split into separate proteins. Specific residues important for McjA recognition were also identified, which permitted us to pinpoint a compensatory mutation (McjBM108T) to restore McjA/McjB interaction that rescued an otherwise nearly nonproductive precursor variant (McjAT-2M). Studies of McjA, McjB, and McjC have long been mired by them being extremely difficult to handle experimentally, and our results suggest that the AF2 predicted ternary complex structure may serve as a reasonable starting point for understanding MccJ25 biosynthesis. The prediction-validation workflow presented herein combined artificial intelligence and laboratory experiments constructively to gain new insights.

Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides.

The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms.

ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization.

Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user's configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at https://github.com/xlxlxlx/ARGem.