Free Energy Calculations of Microcin J25 Variants Binding to the FhuA Receptor

The pearl jubilee of microcin J25: thirty years of research on an exceptional lasso peptide

Covering: 1992 up to 2023Since their discovery, lasso peptides went from peculiarities to be recognized as a major family of ribosomally synthesized and post-translationally modified peptide (RiPP) natural products that were shown to be spread throughout the bacterial kingdom. Microcin J25 was first described in 1992, making it one of the earliest known lasso peptides. No other lasso peptide has since then been studied to such an extent as microcin J25, yet, previous review articles merely skimmed over all the research done on this exceptional lasso peptide. Therefore, to commemorate the 30th anniversary of its first report, we give a comprehensive overview of all literature related to microcin J25. This review article spans the early work towards the discovery of microcin J25, its biosynthetic gene cluster, and the elucidation of its three-dimensional, threaded lasso structure. Furthermore, the current knowledge about the biosynthesis of microcin J25 and lasso peptides in general is summarized and a detailed overview is given on the biological activities associated with microcin J25, including means of self-immunity, uptake into target bacteria, inhibition of the Gram-negative RNA polymerase, and the effects of microcin J25 on mitochondria. The in vitro and in vivo models used to study the potential utility of microcin J25 in a (veterinary) medicine context are discussed and the efforts that went into employing the microcin J25 scaffold in bioengineering contexts are summed up.

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.

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

PepDRED: De Novo Peptide Design with Strong Binding Affinity for Target Protein

De novo design of peptides that bind specifically to functional proteins is beneficial for diagnostics and therapeutics. However, complex permutations and combinations of amino acids pose significant challenges to the rational design of peptides with desirable stability and affinity. Herein, we develop a computational-based evolution method, namely, peptidomimetics-driven recognition elements design (PepDRED), to derive hemoglobin-inspired peptidomimetics. PepDRED mimics the natural evolutionism pipeline to generate stable apovariant (AVs) structures for wild-type counterparts via automated point mutations and validates their efficiency through free binding energy analysis and per residue energy decomposition analysis. For application demonstration, we applied PepDRED to design de novo peptides to bind FhuA, a typical TonB-dependent transporter (TBDT). TBDTs are Gram-negative bacterial outer membrane proteins responsible for iron transport and vital for bacterial resistance. PepDRED generated a pool of AVs and proceeded to reach an optimized peptide, AV440, with a remarkable binding affinity of -21 kcal/mol. AV440 is ∼2.5-fold stronger than the existing FhuA inhibitor Microcin J25. Network energy analysis further unveils that incorporating methionine (M42) in the N-terminal region significantly enhances inter-residue contacts and binding affinity. PepDRED offers a prompt and efficient in silico approach to develop potent peptide candidates for target proteins.

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.

First Principles Calculation of Protein-Protein Dimer Affinities of ALS-Associated SOD1 Mutants

Cu,Zn superoxide dismutase (SOD1) is a 32 kDa homodimer that converts toxic oxygen radicals in neurons to less harmful species. The dimerization of SOD1 is essential to the stability of the protein. Monomerization increases the likelihood of SOD1 misfolding into conformations associated with aggregation, cellular toxicity, and neuronal death in familial amyotrophic lateral sclerosis (fALS). The ubiquity of disease-associated mutations throughout the primary sequence of SOD1 suggests an important role of physicochemical processes, including monomerization of SOD1, in the pathology of the disease. Herein, we use a first-principles statistical mechanics method to systematically calculate the free energy of dimer binding for SOD1 using molecular dynamics, which involves sequentially computing conformational, orientational, and separation distance contributions to the binding free energy. We consider the effects of two ALS-associated mutations in SOD1 protein on dimer stability, A4V and D101N, as well as the role of metal binding and disulfide bond formation. We find that the penalty for dimer formation arising from the conformational entropy of disordered loops in SOD1 is significantly larger than that for other protein-protein interactions previously considered. In the case of the disulfide-reduced protein, this leads to a bound complex whose formation is energetically disfavored. Somewhat surprisingly, the loop free energy penalty upon dimerization is still significant for the holoprotein, despite the increased structural order induced by the bound metal cations. This resulted in a surprisingly modest increase in dimer binding free energy of only about 1.5 kcal/mol upon metalation of the protein, suggesting that the most significant stabilizing effects of metalation are on folding stability rather than dimer binding stability. The mutant A4V has an unstable dimer due to weakened monomer-monomer interactions, which are manifested in the calculation by a separation free energy surface with a lower barrier. The mutant D101N has a stable dimer partially due to an unusually rigid β-barrel in the free monomer. D101N also exhibits anticooperativity in loop folding upon dimerization. These computational calculations are, to our knowledge, the most quantitatively accurate calculations of dimer binding stability in SOD1 to date.

Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters

High viscosity presents a challenge for manufacturing and drug delivery of therapeutic antibodies. The viscosity is determined by protein-protein interactions among many antibodies. Molecular simulation is a promising method to study protein-protein interactions; however, all-atom models do not allow the simulation of multiple molecules, which is necessary to compute viscosity directly. Coarse-grained models, on the other hand can do this. In this work, a 12-bead coarse-grained model based on Swan and coworkers (J. Phys. Chem. B 2018, 122, 2867-2880) was applied to study antibody interactions. Two adjustable parameters related to the short-range interactions on the variable and constant regions were determined by fitting experimental data of 20 IgG1 monoclonal antibodies at 150 mg/mL. The root-mean-square deviation improved from 1 to 0.68, and the correlation coefficient improved from 0.63 to 0.87 compared to that of a previous model that assumed the short-range interactions were the same for all the beads. Our model is also able to calculate the viscosity over a wide range of concentrations without additional parameters. A tabulated viscosity based on our model is provided to facilitate antibody screening in early-stage design.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Ib-M6 Antimicrobial Peptide: Antibacterial Activity against Clinical Isolates of Escherichia coli and Molecular Docking

The Ib-M6 peptide has antibacterial activity against non-pathogenic Escherichia coli K-12 strain. The first part of this study determines the antibacterial activity of Ib-M6 against fourteen pathogenic strains of E. coli O157:H7. Susceptibility assay showed that Ib-M6 had values of Minimum Inhibitory Concentration (MIC) lower than streptomycin, used as a reference antibiotic. Moreover, to predict the possible interaction between Ib-M6 and outer membrane components of E. coli, we used molecular docking simulations where FhuA protein and its complex with Lipopolysaccharide (LPS-FhuA) were used as targets of the peptide. FhuA/Ib-M6 complexes had energy values between -39.5 and -40.5 Rosetta Energy Units (REU) and only one hydrogen bond. In contrast, complexes between LPS-FhuA and Ib-M6 displayed energy values between -25.6 and -40.6 REU, and the presence of five possible hydrogen bonds. Hence, the antimicrobial activity of Ib-M6 peptide shown in the experimental assays could be caused by its interaction with the outer membrane of E. coli.

Exploring Gatekeeper Mutations in EGFR through Computer Simulations

The emergence of resistance against drugs that inhibit a particular protein is a major problem in targeted therapy. There is a clear need for rigorous methods to predict the likelihood of specific drug-resistance mutations arising in response to the binding of a drug. In this work we attempt to develop a robust computational protocol for predicting drug resistant mutations at the gatekeeper position (T790) in EGFR. We explore how mutations at this site affects interactions with ATP and three drugs that are currently used in clinics. We found, as expected, that certain mutations are not tolerated structurally, while some other mutations interfere with the natural substrate and hence are unlikely to be selected for. However, we found five possible mutations that are well tolerated structurally and energetically. Two of these mutations were predicted to have increased affinity for the drugs over ATP, as has been reported earlier. By reproducing the trends in the experimental binding affinities of the data, the methods chosen here are able to correctly predict the effects of these mutations on the binding affinities of the drugs. However, the increased affinity does not always translate into increased efficacy, because the efficacy is affected by several other factors such as binding kinetics, competition with ATP, and residence times. The computational methods used in the current study are able to reproduce or predict the effects of mutations on the binding affinities. However, a different set of methods is required to predict the kinetics of drug binding.

Systematic Mutagenesis of Oncocin Reveals Enhanced Activity and Insights into the Mechanisms of Antimicrobial Activity

Oncocin is a proline-rich antimicrobial peptide that inhibits protein synthesis by binding to the bacterial ribosome. In this work, the antimicrobial activity of oncocin was improved by systematic peptide mutagenesis and activity evaluation. We found that a pair of cationic substitutions (P4K and L7K/R) improves the activity by 2-4 fold (p<0.05) against multiple Gram-negative bacteria. An in vitro transcription / translation assay indicated that the increased activity was not because of stronger ribosome binding. Rather a cellular internalization assay revealed a higher internalization rate for the optimized analogs thereby suggesting a mechanism to increase potency. In addition, we found that the optimized peptides' benefit is dependent upon nutrient-depleted media conditions. The molecular design and characterization strategies have broad potential for development of antimicrobial peptides.

Natural Products from Mammalian Gut Microbiota

The mammalian gut has a remarkable abundance of microbes. These microbes have strong potential to biosynthesize distinct metabolites that are promising drugs, and many more bioactive compounds have yet to be explored as potential drug candidates. These small bioactive molecules often mediate important host-microbe and microbe-microbe interactions. In this review, we provide perspectives on and challenges associated with three mining strategies - culture-based, (meta)genomics-based, and metabolomics-based mining approaches - for discovering natural products derived from biosynthetic gene clusters (BGCs) in mammalian gut microbiota. In addition, we comprehensively summarize the structures, biological functions, and BGCs of these compounds. Improving these techniques, including by using combinatorial approaches, may accelerate drug discovery from gut microbes.

Multispecies activity screening of microcin J25 mutants yields antimicrobials with increased specificity toward pathogenic Salmonella species relative to human commensal Escherichia coli

Modern large-scale agricultural practices that incorporate high density farming with subtherapeutic antibiotic dosing are considered a major contributor to the rise of antibiotic-resistant bacterial infections of humans with species of Salmonella being a leading agriculture-based bacterial infection. Microcin J25, a potent and highly stable antimicrobial peptide active against Enterobacteriaceae, is a candidate antimicrobial against multiple Salmonella species. Emerging evidence supports the hypothesis that the composition of the microbiota of the gastrointestinal tract prevents a variety of diseases by preventing infectious agents from proliferating. Reducing clearance of off-target bacteria may decrease susceptibility to secondary infection. Of the Enterobacteriaceae susceptible to microcin J25, Escherichia coli are the most abundant within the human gut. To explore the modulation of specificity, a collection of 207 mutants encompassing 12 positions in both the ring and loop of microcin J25 was built and tested for activity against Salmonella and E. coli strains. As has been found previously, mutational tolerance of ring residues was lower than loop residues, with 22% and 51% of mutations, respectively, retaining activity toward at least one target within the target organism test panel. The multitarget screening elucidated increased mutational tolerance at position G2, G3, and G14 than previously identified in panels composed of single targets. Multiple mutations conferred differential response between the different targets. Examination of specificity differences between mutants found that 30% showed significant improvements to specificity toward any of the targets. Generation and testing of a combinatorial library designed from the point-mutant study revealed that microcin J25^I13T reduces off-target activity toward commensal human-derived E. coli isolates by 81% relative to Salmonella enterica serovar Enteritidis. These in vitro specificity improvements are likely to improve in vivo treatment efficacy by reducing clearance of commensal bacteria in the gastrointestinal tract of hosts.