Cheminformatics-Guided Cell-Free Exploration of Peptide Natural Products

Abstract

There have been significant advances in the flexibility and power of in vitro cell-free translation systems. The increasing ability to incorporate noncanonical amino acids and complement translation with recombinant enzymes has enabled cell-free production of peptide-based natural products (NPs) and NP-like molecules. We anticipate that many more such compounds and analogs might be accessed in this way. To assess the peptide NP space that is directly accessible to current cell-free technologies, we developed a peptide parsing algorithm that breaks down peptide NPs into building blocks based on ribosomal translation logic. Using the resultant data set, we broadly analyze the biophysical properties of these privileged compounds and perform a retrobiosynthetic analysis to predict which peptide NPs could be directly synthesized in augmented cell-free translation reactions. We then tested these predictions by preparing a library of highly modified peptide NPs. Two macrocyclases, PatG and PCY1, were used to effect the head-to-tail macrocyclization of candidate NPs. This retrobiosynthetic analysis identified a collection of high-priority building blocks that are enriched throughout peptide NPs, yet they had not previously been tested in cell-free translation. To expand the cell-free toolbox into this space, we established, optimized, and characterized the flexizyme-enabled ribosomal incorporation of piperazic acids. Overall, these results demonstrate the feasibility of cell-free translation for peptide NP total synthesis while expanding the limits of the technology. This work provides a novel computational tool for exploration of peptide NP chemical space, that could be expanded in the future to allow design of ribosomal biosynthetic pathways for NPs and NP-like molecules.

Figure 1.

(a) Overview of workflow for the cheminformatics-guided cell free biosynthesis of macrocyclic peptide natural products. (b) Development of macrocyclic peptide natural product (NP) parsing algorithm. The algorithm proceeds through five key steps beginning with (1) the collection of NP structures from the Lotus, Supernatural II, and MiBiG online datasets. (2) These structures are searched to identify component amino acid building blocks matching the patterns of α-, β- and γ-amino acids. (3) These building blocks are combined using an optimized scoring function described in the SI to identify the longest and simplest peptide backbone. (4) The side chains of each individual building block are parsed to capture information on macrocycle composition. (5) Sites of macrocyclization are identified by connections between the peptide backbone. (c) The NPs are classified based on the type of macrocyclization.

Figure 2.

Computational analysis of peptide natural products. (a) TreeMap visualization of peptide natural product chemical space colored by macrocycle classification. (b) Comparison of the size of macrocycles (volume) to the calculated partition coefficient. (c) Occurrence frequency of the canonical amino acids in peptide natural products. (d) Heatmap of the abundance of non-canonical amino acids (ncAAs) compared to the total number of amino acids in a peptide natural product. (e-f) Box-and-whisker plots of the (e) number of ncAAs and (f) longest consecutive ncAA run in a peptide natural product split up per class. (g-k) Occurrence frequency of (e) ncAA categories, (f) modified canonical amino acids, (g) common non-canonical amino acids, (h) side chain-to-backbone amine (N) or carboxyl (C) macrocyclization linkages, and (i) side chain-to-side chain macrocyclization linkages in peptide natural products.

Figure 3.

Retrobiosynthetic analysis of macrocycle accessibility for cell free biosynthesis. (a) Pie chart showing the predicted building block accessibility of each peptide natural product class. Predictions are based on the collection of building blocks previously demonstrated to be incorporated using CFB. (b-c) Occurrence frequency of (b) inaccessible ncAA categories and (c) individual inaccessible building blocks. (d) PatG and PCY1 macrocyclization scheme and pie chart showing the predicted macrocyclization accessibility for the Class 1 backbone macrocycles using these enzymes.

Figure 4.

Expanding cell free biosynthesis capabilities into natural product chemical space by the ribosomal translation of piperazic acid. (a) Structure of piperazic acid (piz) and examples of natural products containing the different regioisomers of piz. (b) MALDI mass spectra showing the optimized incorporation of a single and four consecutive L- and D-piz residues. The optimized translation reaction utilized altered concentrations of translation factors (EF-P, EF-G, EF-Tu, EF-Ts, IF-2). (c) nano-LCMS characterization of the regiochemistry of ribosomal piz translation comparing synthetic standards of both regioisomers to a translation sample (IVT) for three standard peptides. (d) MALDI mass spectrum for the CFB of a Hytramycin piz-natural product analog. This substrate was unable to be modified at all by a backbone macrocyclase enzyme when trying to access the natural backbone macrocyclic structure. (e-g) nano-LCMS mass spectra for the CFB of piz-containing natural product analogs (e) Svetamycin, (f) L-156373, and (g) Monamycin. Macrocyclization with PCY1 led strictly to hydrolysis in all three cases.