LassoHTP: A High-Throughput Computational Tool for Lasso Peptide Structure Construction and Modeling

Merging Flow Synthesis and Enzymatic Maturation to Expand the Chemical Space of Lasso Peptides

Many peptidic natural products, such as lasso peptides, cyclic peptides, and cyclotides, are conformationally constrained and show biological stability, making them attractive scaffolds for drug development. Although many peptides can be synthesized and modified through chemical methods, knot-like lasso peptides such as microcin J25 (MccJ25) and their analogues remain elusive. As the chemical space of MccJ25 analogues accessible through purely biological methods is also limited, we proposed a hybrid approach: flow-based chemical synthesis of non-natural precursor peptides, followed by in vitro transformation with recombinant maturation enzymes, to yield a more diverse array of lasso peptides. Herein, we established the rapid, flow-based synthesis of chemically modified MccJ25 precursor peptides (57 amino acids). Heterologous expression of enzymes McjB and McjC was extensively optimized to improve yields and facilitate the synthesis of multiple analogues of MccJ25, including the incorporation of non-canonical tyrosine and histidine derivatives into the lasso scaffold. Finally, using our chemoenzymatic strategy, we produced a biologically active analogue containing three d-amino acids in the loop region and incorporated backbone N-methylations. Our method provides rapid access to chemically modified lasso peptides that could be used to investigate structure-activity relationships, epitope grafting, and the improvement of therapeutic properties.

One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates

Identifying optimal reaction coordinates for complex conformational changes and protein folding remains an outstanding challenge. This study combines collective variable (CV) discovery based on chemical intuition and machine learning with enhanced sampling to converge the folding free energy landscape of lasso peptides, a unique class of natural products with knot-like tertiary structures. This knotted scaffold imparts remarkable stability, making lasso peptides resistant to proteolytic degradation, thermal denaturation, and extreme pH conditions. Although their direct synthesis would enable therapeutic design, it has not yet been possible due to the improbable occurrence of spontaneous lasso folding. Thus, simulations characterizing the folding propensity are needed to identify strategies for increasing access to the lasso architecture by stabilizing the pre-lasso ensemble before isopeptide bond formation. Herein, harmonic linear discriminant analysis (HLDA) is combined with metadynamics-enhanced sampling to discover CVs capable of distinguishing the pre-lasso fold and converging the folding propensity. Intuitive CVs are compared to iterative rounds of HLDA to identify CVs that not only accomplish these goals for one lasso peptide but also seem to be transferable to others, establishing a protocol for the identification of folding reaction coordinates for lasso peptides.

Lasso Peptides: Exploring the Folding Landscape of Nature's Smallest Interlocked Motifs

Lasso peptides make up a class of natural products characterized by a threaded structure. Given their small size and stability, chemical synthesis would offer tremendous potential for the development of novel therapeutics. However, the accessibility of the pre-folded lasso architecture has limited this advance. To better understand the folding process de novo, simulations are used herein to characterize the folding propensity of microcin J25 (MccJ25), a lasso peptide known for its antimicrobial properties. New algorithms are developed to unambiguously distinguish threaded from nonthreaded precursors and determine handedness, a key feature in natural lasso peptides. We find that MccJ25 indeed forms right-handed pre-lassos, in contrast to past predictions but consistent with all natural lasso peptides. Additionally, the native pre-lasso structure is shown to be metastable prior to ring formation but to readily transition to entropically favored unfolded and nonthreaded structures, suggesting that de novo lasso folding is rare. However, by altering the ring forming residues and appending thiol and thioester functionalities, we are able to increase the stability of pre-lasso conformations. Furthermore, conditions leading to protonation of a histidine imidazole side chain further stabilize the modified pre-lasso ensemble. This work highlights the use of computational methods to characterize lasso folding and demonstrates that de novo access to lasso structures can be facilitated by optimizing sequence, unnatural modifications, and reaction conditions like pH.

Bioinformatics-guided discovery of biaryl-linked lasso peptides

Lasso peptides are a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) that feature an isopeptide bond and a distinct lariat fold. A growing number of secondary modifications have been described that further decorate lasso peptide scaffolds. Using genome mining, we have discovered a pair of lasso peptide biosynthetic gene clusters (BGCs) that include cytochrome P450 genes. Using mass spectrometry, stable isotope incorporation, and extensive 2D-NMR spectrometry, we report the structural characterization of two unique examples of (C-N) biaryl-linked lasso peptides. Nocapeptin A, from Nocardia terpenica, is tailored with a Trp-Tyr crosslink, while longipepetin A, from Longimycelium tulufanense, features a Trp-Trp linkage. Besides the unusual bicyclic frame, a Met of longipepetin A undergoes S-methylation to yield a trivalent sulfonium, a heretofore unprecedented RiPP modification. A bioinformatic survey revealed additional lasso peptide BGCs containing P450 enzymes which await future characterization. Lastly, nocapeptin A bioactivity was assessed against a panel of human and bacterial cell lines with modest growth-suppression activity detected towards Micrococcus luteus.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.