A Blind Test of Computational Technique for Predicting the Likelihood of Peptide Sequences to Cyclize

Cheminformatics-Guided Cell-Free Exploration of Peptide Natural Products

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.

Computational Opportunities and Challenges in Finding Cyclic Peptide Modulators of Protein-Protein Interactions

Peptide cyclization can improve stability, conformational constraint, and compactness. However, apart from beta-turn structures, which are well incorporated into cyclic peptides (CPs), many primary peptide structures and functions are markedly altered by cyclization. Accordingly, to mimic linear peptide interfaces with cyclic peptides, it can be beneficial to screen combinatorial cyclic peptide libraries. Computational methods have been developed to screen CPs, but face a number of challenges. Here, we review methods to develop in silico computational libraries, and the potential for screening naturally occurring libraries of CPs. The simplest and most rapid computational pharmacophore methods that estimate peptide three-dimensional structures to be screened versus targets are relatively easy to implement, and while the constraint on structure imposed by cyclization makes them more effective than the same approaches with linear peptides, there are a large number of limiting assumptions. In contrast, full molecular dynamics simulations of cyclic peptide structures not only are costly to implement, but also require careful attention to interpretation, so that not only is the computation time rate limiting, but the interpretation time is also rate limiting due to the analysis of the typically complex underlying conformational space of CPs. A challenge for the field of computational cyclic peptide screening is to bridge this gap effectively. Natural compound libraries of short cyclic peptides, and short cyclized regions of proteins, encoded in the genomes of many organisms present a potential treasure trove of novel functionality which may be screened via combined computational and experimental screening approaches.

Peptide Cyclization Catalyzed by Cyanobactin Macrocyclases

Cyclic peptides are an emerging class of therapeutics that can modulate targets not amenable to traditional small molecule intervention (e.g., protein-protein interactions). However, N-to-C macrocyclization of peptides is a challenging and often a low yielding chemical transformation. Several macrocyclases from cyanobactin biosynthetic clusters have been used to catalyze this reaction.This chapter provides practical guidance to the processes of heterologous expression and purification of these enzymes as well as performing in vitro biochemical reactions. Finally, approaches to recover the final product from an enzymatic reaction mixture are also discussed.

The Biochemistry and Structural Biology of Cyanobactin Pathways: Enabling Combinatorial Biosynthesis

Cyanobactin biosynthetic enzymes have exceptional versatility in the synthesis of natural and unnatural products. Cyanobactins are ribosomally synthesized and posttranslationally modified peptides synthesized by multistep pathways involving a broad suite of enzymes, including heterocyclases/cyclodehydratases, macrocyclases, proteases, prenyltransferases, methyltransferases, and others. Here, we describe the enzymology and structural biology of cyanobactin biosynthetic enzymes, aiming at the twin goals of understanding biochemical mechanisms and biosynthetic plasticity. We highlight how this common suite of enzymes may be utilized to generate a large array or structurally and chemically diverse compounds.

Cyclic peptide production using a macrocyclase with enhanced substrate promiscuity and relaxed recognition determinants

The cyanobactin macrocyclase (OscG_mac) has high substrate promiscuity and can be used for making libraries of highly diverse cyclic peptides.