Antimicrobial Lasso Peptide Cloacaenodin Utilizes a Unique TonB-Dependent Transporter to Access Susceptible Bacteria.

The development of new antimicrobial agents effective against Gram-negative bacteria remains a major challenge in drug discovery. The lasso peptide cloacaenodin has potent antimicrobial activity against multiple strains in the Enterobacter genus, one of the ESKAPE pathogens. Here, we show that cloacaenodin uses a previously uncharacterized TonB-dependent transporter, which we name CloU, to cross the outer membrane (OM) of susceptible bacteria. Inner membrane transport is mediated by the protein SbmA. CloU is distinct from the known OM transporters (FhuA and PupB) utilized by other antimicrobial lasso peptides and thus offers important insight into the spectrum of activity of cloacaenodin. Using knowledge of the transport pathway to predict other cloacaenodin-susceptible strains, we demonstrate the activity of cloacaenodin against clinical isolates of Enterobacter and of a Kluyvera strain. Further, we use molecular dynamics simulations and mutagenesis of CloU to explain the variation in cloacaenodin susceptibility observed across different strains of Enterobacter. This work expands the currently limited understanding of lasso peptide uptake and advances the potential of cloacaenodin as an antibiotic.

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.

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

Lasso peptides are a subclass of ribosomally synthesized and post-translationally modified peptides with a slipknot conformation. With superior thermal stability, protease resistance, and antimicrobial activity, lasso peptides are promising candidates for bioengineering and pharmaceutical applications. To enable high-throughput computational prediction and design of lasso peptides, we developed a software, LassoHTP, for automatic lasso peptide structure construction and modeling. LassoHTP consists of three modules, including the scaffold constructor, mutant generator, and molecular dynamics (MD) simulator. With a user-provided sequence and conformational annotation, LassoHTP can either generate the structure and conformational ensemble as is or conduct random mutagenesis. We used LassoHTP to construct eight known lasso peptide structures de novo and to simulate their conformational ensembles for 100 ns MD simulations. For benchmarking, we calculated the root mean square deviation (RMSD) of these ensembles with reference to their experimental crystal or NMR PDB structures; we also compared these RMSD values against those of the MD ensembles that are initiated from the PDB structures. Dihedral principal component analysis was also conducted. The results show that the LassoHTP-initiated ensembles are similar to those of the PDB-initiated ensembles. LassoHTP offers a computational platform to develop strategies for lasso peptide prediction and design.

Molecular Dynamics-Derived Descriptor Informs the Impact of Mutation on the Catalytic Turnover Number in Lactonase Across Substrates.

Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers remain largely unexplored. In this work, we report the identification of molecular modeling-derived descriptors to predict mutation effect on the turnover number of lactonase SsoPox with both native and non-native substrates. The study consists of 10 enzyme-substrate complexes resulting from a combination of five enzyme variants with two substrates. For each complex, we derived 15 descriptors from molecular dynamics simulations and applied principal component analysis to rank the predictive capability of the descriptors. A top-ranked descriptor was identified, which is the solvent-accessible surface area (SASA) ratio of the substrate to the active site pocket. A uniform volcano-shaped plot was observed in the distribution of experimental activation free energy against the SASA ratio. To achieve efficient lactonase hydrolysis, a non-native substrate-bound enzyme variant needs to involve a similar range of the SASA ratio to the native substrate-bound wild-type enzyme. The descriptor reflects how well the enzyme active site pocket accommodates a substrate for reaction, which has the potential of guiding optimization of enzyme reaction turnover for non-native chemical transformations.

Identification of C10 nitrogen-containing aporphines with dopamine D1 versus D5 receptor selectivity.

New aporphines containing C10 nitrogen substituents (viz. nitro, aniline or amide moieties), were synthesized and evaluated for affinity at human serotonin 5-HT1A and 5-HT2A receptors and at human dopamine D1, D2 and D5 receptors. Two series of analogs were investigated: series A which contain a sole C10 nitrogen substituent on the tetracyclic aporphine core and series B which are 1,2,10-trisubstituted aporphines. Remarkably, compounds from both series lacked affinity for the D5 receptor, thus attaining D1 versus D5 selectivity. Compound 20c was the most potent D1 ligand identified. Docking studies at D1 and D5 receptors indicate that the binding mode of 20c at the D1 receptor allows for stronger hydrophobic contacts, (primarily with Phe residues) as compared to the D5 receptor, accounting for its D1 versus D5 selectivity. Considering the lack of affinity for the D5 receptor (and low affinity at other receptors tested), compound 20c represents an interesting starting point for further structural diversification of aporphines as sub-type selective D1 receptor tools.