Generative ß-hairpin design using a residue-based physicochemical property landscape.

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the ß-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of ß hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial ß-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

N-terminal intrinsic disorder is an ancestral feature of Gγ subunits that influences the balance between different Gßγ signaling axes in yeast.

Activated G protein-coupled receptors promote the dissociation of heterotrimeric G proteins into Gα and Gßγ subunits that bind to effector proteins to drive intracellular signaling responses. In yeast, Gßγ subunits coordinate the simultaneous activation of multiple signaling axes in response to mating pheromones, including MAP kinase (MAPK)-dependent transcription, cell polarization, and cell cycle arrest responses. The Gγ subunit in this complex contains an N-terminal intrinsically disordered region that governs Gßγ-dependent signal transduction in yeast and mammals. Here, we demonstrate that N-terminal intrinsic disorder is likely an ancestral feature that has been conserved across different Gγ subtypes and organisms. To understand the functional contribution of structural disorder in this region, we introduced precise point mutations that produce a stepwise disorder-to-order transition in the N-terminal tail of the canonical yeast Gγ subunit, Ste18. Mutant tail structures were confirmed using circular dichroism and molecular dynamics and then substituted for the wildtype gene in yeast. We find that increasing the number of helix-stabilizing mutations, but not isometric mutation controls, has a negative and proteasome-independent effect on Ste18 protein levels as well as a differential effect on pheromone-induced levels of active MAPK/Fus3, but not MAPK/Kss1. When expressed at wildtype levels, we further show that mutants with an alpha-helical N terminus exhibit a counterintuitive shift in Gßγ signaling that reduces active MAPK/Fus3 levels whilst increasing cell polarization and cell cycle arrest. These data reveal a role for Gγ subunit intrinsically disordered regions in governing the balance between multiple Gßγ signaling axes.

Broadening access to small-molecule parameterization with the force field toolkit.

Access to accurate force-field parameters for small molecules is crucial for computational studies of their interactions with proteins. Although a number of general force fields for small molecules exist, e.g., CGenFF, GAFF, and OPLS, they do not cover all common chemical groups and their combinations. The Force Field Toolkit (ffTK) provides a comprehensive graphical interface that streamlines the development of classical parameters for small molecules directly from quantum mechanical (QM) calculations, allowing for force-field generation for almost any chemical group and validation of the fit relative to the target data. ffTK relies on supported external software for the QM calculations, but it can generate the necessary QM input files and parse and analyze the QM output. In previous ffTK versions, support for Gaussian and ORCA QM packages was implemented. Here, we add support for Psi4, an open-source QM package free for all users, thereby broadening user access to ffTK. We also compare the parameter sets obtained with the new ffTK version using Gaussian, ORCA, and Psi4 for three molecules: pyrrolidine, n-propylammonium cation, and chlorobenzene. Despite minor differences between the resulting parameter sets for each compound, most prominently in the dihedral and improper terms, we show that conformational distributions sampled in molecular dynamics simulations using these parameter sets are quite comparable.

Uncovering the folding mechanism of pertactin: A comparative study of isolated and vectorial folding.

Autotransporters are a large family of virulence factors found in Gram-negative bacteria that play important roles in their pathogenesis. The passenger domain of autotransporters is almost always composed of a large ß-helix, with only a small portion of it being relevant to its virulence function. This has led to the hypothesis that the folding of the ß-helical structure aids the secretion of the passenger domain across the Gram-negative outer membrane. In this study, we used molecular dynamics simulations and enhanced sampling methods to investigate the stability and folding of the passenger domain of pertactin, an autotransporter from Bordetella pertussis. Specifically, we employed steered molecular dynamics to simulate the unfolding of the entire passenger domain as well as self-learning adaptive umbrella sampling to compare the energetics of folding rungs of the ß-helix independently ("isolated folding") versus folding rungs on top of a previously folded rung ("vectorial folding"). Our results showed that vectorial folding is highly favorable compared with isolated folding; moreover, our simulations showed that the C-terminal rung of the ß-helix is the most resistant to unfolding, in agreement with previous studies that found the C-terminal half of the passenger domain to be more stable than the N-terminal one. Overall, this study provides new insights into the folding process of an autotransporter passenger domain and its potential role in secretion across the outer membrane.

Parameterization of a drug molecule with a halogen σ-hole particle using ffTK: Implementation, testing, and comparison.

Halogen atoms are widely used in drug molecules to improve their binding affinity for the receptor proteins. Many of the examples involve "halogen bonding" between the molecule and the binding site, which is a directional interaction between a halogen atom and a nucleophilic atom. Such an interaction is induced by an electron cloud shift of the halogen atom toward its covalently bonded neighbor to form the σ-bond, leaving a small electrostatic positive region opposite to the bond called the "σ-hole." To mimic the effect of the σ-hole in the CHARMM non-polarizable force field, recently CGenFF added a positively charged massless particle to halogen atoms, positioned at the opposite side of the carbon-halogen bond. This particle is referred to as a lone pair (LP) particle because it uses the lone pair implementation in the CHARMM force field. Here, we have added support for LP particles to ffTK, an automated force field parameterization toolkit widely distributed as a plugin to the molecular visualization software VMD. We demonstrate the updated optimization process using an example halogenated drug molecule, AT130, which is a capsid assembly modulator targeting the hepatitis B virus. Our results indicate that parameterization with the LP particle significantly improves the accuracy of the electrostatic response of the molecule, especially around the halogen atom. Although the inclusion of the LP particle does not produce a prominent effect on the interactions between the molecule and its target protein, the protein-ligand binding performance is greatly improved by optimization of the parameters.

The role of intramolecular nonbonded interaction and angle sampling in single-step free energy perturbation.

Single-step free energy perturbation (sFEP) has often been proposed as an efficient tool for a quick free energy scan due to its straightforward protocol and the ability to recycle an existing molecular dynamics trajectory for free energy calculations. Although sFEP is expected to fail when the sampling of a system is inefficient, it is often expected to hold for an alchemical transformation between ligands with a moderate difference in their sizes, e.g., transforming a benzene into an ethylbenzene. Yet, exceptions were observed in calculations for anisole and methylaniline, which have similar physical sizes as ethylbenzene. In this study, we show that such exceptions arise from the sampling inefficiency on an unexpected rigid degree of freedom, namely, the bond angle θ. The distributions of θ differ dramatically between two end states of a sFEP calculation, i.e., the conformation of the ligand changes significantly during the alchemical transformation process. Our investigation also reveals the interrelation between the ligand conformation and the intramolecular nonbonded interactions. This knowledge suggests a best combination of the ghost ligand potential and the dual topology setting, which improves the accuracy in a single reference sFEP calculation by bringing down its error from around 5kBT to kBT.

From simple to complex: Reconstructing all-atom structures from coarse-grained models using cg2all.

In this issue of Structure, Heo and Feig present cg2all, a novel deep-learning model capable of efficiently predicting all-atom protein structures from coarse-grained (CG) representations. The model maintains high accuracy, even when the CG model is simplified to a single bead per residue, and has a number of promising applications.

SARS-CoV-2 spike opening dynamics and energetics reveal the individual roles of glycans and their collective impact.

The trimeric spike (S) glycoprotein, which protrudes from the SARS-CoV-2 viral envelope, binds to human ACE2, initiated by at least one protomer's receptor binding domain (RBD) switching from a "down" (closed) to an "up" (open) state. Here, we used large-scale molecular dynamics simulations and two-dimensional replica exchange umbrella sampling calculations with more than a thousand windows and an aggregate total of 160 µs of simulation to investigate this transition with and without glycans. We find that the glycosylated spike has a higher barrier to opening and also energetically favors the down state over the up state. Analysis of the S-protein opening pathway reveals that glycans at N165 and N122 interfere with hydrogen bonds between the RBD and the N-terminal domain in the up state, while glycans at N165 and N343 can stabilize both the down and up states. Finally, we estimate how epitope exposure for several known antibodies changes along the opening path. We find that the BD-368-2 antibody's epitope is continuously exposed, explaining its high efficacy.

ACE2 glycans preferentially interact with SARS-CoV-2 over SARS-CoV.

We report a distinct difference in the interactions of the glycans of the host-cell receptor, ACE2, with SARS-CoV-2 and SARS-CoV S-protein receptor-binding domains (RBDs). Our analysis demonstrates that the ACE2 glycan at N322 enhances interactions with the SARS-CoV-2 RBD while the ACE2 glycan at N90 may offer protection against infections of both coronaviruses depending on its composition. The interactions of the ACE2 glycan at N322 with SARS-CoV RBD are blocked by the presence of the RBD glycan at N357 of the SARS-CoV RBD. The absence of this glycosylation site on SARS-CoV-2 RBD may enhance its binding with ACE2.

Machine Learning Reveals the Critical Interactions for SARS-CoV-2 Spike Protein Binding to ACE2.

SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Herein, we have used molecular dynamics (MD) simulations, machine learning (ML), and free-energy perturbation (FEP) calculations to elucidate the differences in binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.

Combinatorial phosphorylation modulates the structure and function of the G protein γ subunit in yeast.

Intrinsically disordered regions (IDRs) in proteins are often targets of combinatorial posttranslational modifications, which serve to regulate protein structure and function. Emerging evidence suggests that the N-terminal tails of G protein γ subunits, which are essential components of heterotrimeric G proteins, are intrinsically disordered, phosphorylation-dependent determinants of G protein signaling. Here, we found that the yeast Gγ subunit Ste18 underwent combinatorial, multisite phosphorylation events within its N-terminal IDR. G protein-coupled receptor (GPCR) activation and osmotic stress induced phosphorylation at Ser7, whereas glucose and acid stress induced phosphorylation at Ser3, which was a quantitative indicator of intracellular pH. Each site was phosphorylated by a distinct set of kinases, and phosphorylation of one site affected phosphorylation of the other, as determined through exposure to serial stimuli and through phosphosite mutagenesis. Last, we showed that phosphorylation resulted in changes in IDR structure and that different combinations of phosphorylation events modulated the activation rate and amplitude of the downstream mitogen-activated protein kinase Fus3. These data place Gγ subunits among intrinsically disordered proteins that undergo combinatorial posttranslational modifications that govern signaling pathway output.

Plasticity within the barrel domain of BamA mediates a hybrid-barrel mechanism by BAM.

In Gram-negative bacteria, the biogenesis of ß-barrel outer membrane proteins is mediated by the ß-barrel assembly machinery (BAM). The mechanism employed by BAM is complex and so far- incompletely understood. Here, we report the structures of BAM in nanodiscs, prepared using polar lipids and native membranes, where we observe an outward-open state. Mutations in the barrel domain of BamA reveal that plasticity in BAM is essential, particularly along the lateral seam of the barrel domain, which is further supported by molecular dynamics simulations that show conformational dynamics in BAM are modulated by the accessory proteins. We also report the structure of BAM in complex with EspP, which reveals an early folding intermediate where EspP threads from the underside of BAM and incorporates into the barrel domain of BamA, supporting a hybrid-barrel budding mechanism in which the substrate is folded into the membrane sequentially rather than as a single unit.

Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease.

The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics. Recently, many high-resolution apo and inhibitor-bound structures of Mpro, a cysteine protease, have been determined, facilitating structure-based drug design. Mpro plays a central role in the viral life cycle by catalyzing the cleavage of SARS-CoV-2 polyproteins. In addition to the catalytic dyad His41-Cys145, Mpro contains multiple histidines including His163, His164, and His172. The protonation states of these histidines and the catalytic nucleophile Cys145 have been debated in previous studies of SARS-CoV Mpro, but have yet to be investigated for SARS-CoV-2. In this work we have used molecular dynamics simulations to determine the structural stability of SARS-CoV-2 Mpro as a function of the protonation assignments for these residues. We simulated both the apo and inhibitor-bound enzyme and found that the conformational stability of the binding site, bound inhibitors, and the hydrogen bond networks of Mpro are highly sensitive to these assignments. Additionally, the two inhibitors studied, the peptidomimetic N3 and an α-ketoamide, display distinct His41/His164 protonation-state-dependent stabilities. While the apo and the N3-bound systems favored N δ (HD) and N ϵ (HE) protonation of His41 and His164, respectively, the α-ketoamide was not stably bound in this state. Our results illustrate the importance of using appropriate histidine protonation states to accurately model the structure and dynamics of SARS-CoV-2 Mpro in both the apo and inhibitor-bound states, a necessary prerequisite for drug-design efforts.

Pore formation induced by nanoparticles binding to a lipid membrane.

Nanoparticles (NPs) enter a cell primarily via endocytosis, during which they are engulfed by the cell and reside in lipid vesicles named endosomes. Apart from when an endosome is pinched off the plasma membrane, structural integrity of its lipid membrane is usually well maintained. Under certain circumstances, however, such structural integrity can be considerably perturbed by a nanoparticle. For instance, recent experiments [Chu et al., Sci. Rep., 2014, 4, 4495] indicate that nanodiamonds with sharp corners can escape from an endosome by piercing its lipid membrane. Nonetheless, the energetics of this behavior and how it may be controlled by membrane adhesion and NP morphology remain unclear. In this work, we employ continuum modeling to investigate membrane pore formation induced by the spontaneous binding of a sharp nanoparticle. Based on two axial-symmetric NP models, we characterize the indispensable role played by curvature heterogeneity, membrane adhesion, and the sharpness as well as the size of a nanoparticle in 'breaking' a lipid membrane. Apart from revealing a general mechanism of NP binding-induced membrane pore formation, our results provide the reference for improving the endosomal escape of nanoparticles through manipulating their morphology, a direction that can be explored either independently or combined with existing strategies targeting NP surface chemistry.

BamA is required for autotransporter secretion.

BACKGROUND: In Gram-negative bacteria, type Va and Vc autotransporters are proteins that contain both a secreted virulence factor (the "passenger" domain) and a ß-barrel that aids its export. While it is known that the folding and insertion of the ß-barrel domain utilize the ß-barrel assembly machinery (BAM) complex, how the passenger domain is secreted and folded across the membrane remains to be determined. The hairpin model states that passenger domain secretion occurs independently through the fully-formed and membrane-inserted ß-barrel domain via a hairpin folding intermediate. In contrast, the BamA-assisted model states that the passenger domain is secreted through a hybrid of BamA, the essential subunit of the BAM complex, and the ß-barrel domain of the autotransporter. METHODS: To ascertain the models' plausibility, we have used molecular dynamics to simulate passenger domain secretion for two autotransporters, EspP and YadA. RESULTS: We observed that each protein's ß-barrel is unable to accommodate the secreting passenger domain in a hairpin configuration without major structural distortions. Additionally, the force required for secretion through EspP's ß-barrel is more than that through the BamA ß-barrel. CONCLUSIONS: Secretion of autotransporters most likely occurs through an incompletely formed ß-barrel domain of the autotransporter in conjunction with BamA. GENERAL SIGNIFICANCE: Secretion of virulence factors is a process used by practically all pathogenic Gram-negative bacteria. Understanding this process is a necessary step towards limiting their infectious capacity.

Inhibitor binding influences the protonation states of histidines in SARS-CoV-2 main protease.

The main protease (M pro ) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an attractive target for antiviral therapeutics. Recently, many high-resolution apo and inhibitor-bound structures of M pro , a cysteine protease, have been determined, facilitating structure-based drug design. M pro plays a central role in the viral life cycle by catalyzing the cleavage of SARS-CoV-2 polyproteins. In addition to the catalytic dyad His41-Cys145, M pro contains multiple histidines including His163, His164, and His172. The protonation states of these histidines and the catalytic nu-cleophile Cys145 have been debated in previous studies of SARS-CoV M pro , but have yet to be investigated for SARS-CoV-2. In this work we have used molecular dynamics simulations to determine the structural stability of SARS-CoV-2 M pro as a function of the protonation assignments for these residues. We simulated both the apo and inhibitor-bound enzyme and found that the conformational stability of the binding site, bound inhibitors, and the hydrogen bond networks of M pro are highly sensitive to these assignments. Additionally, the two inhibitors studied, the peptidomimetic N3 and an α -ketoamide, display distinct His41/His164 protonation-state-dependent stabilities. While the apo and the N3-bound systems favored N δ (HD) and N ϵ (HE) protonation of His41 and His164, respectively, the α -ketoamide was not stably bound in this state. Our results illustrate the importance of using appropriate histidine protonation states to accurately model the structure and dynamics of SARS-CoV-2 M pro in both the apo and inhibitor-bound states, a necessary prerequisite for drug-design efforts.

Gaussian Accelerated Molecular Dynamics in NAMD.

Gaussian accelerated molecular dynamics (GaMD) is a recently developed enhanced sampling technique that provides efficient free energy calculations of biomolecules. Like the previous accelerated molecular dynamics (aMD), GaMD allows for "unconstrained" enhanced sampling without the need to set predefined collective variables and so is useful for studying complex biomolecular conformational changes such as protein folding and ligand binding. Furthermore, because the boost potential is constructed using a harmonic function that follows Gaussian distribution in GaMD, cumulant expansion to the second order can be applied to recover the original free energy profiles of proteins and other large biomolecules, which solves a long-standing energetic reweighting problem of the previous aMD method. Taken together, GaMD offers major advantages for both unconstrained enhanced sampling and free energy calculations of large biomolecules. Here, we have implemented GaMD in the NAMD package on top of the existing aMD feature and validated it on three model systems: alanine dipeptide, the chignolin fast-folding protein, and the M3 muscarinic G protein-coupled receptor (GPCR). For alanine dipeptide, while conventional molecular dynamics (cMD) simulations performed for 30 ns are poorly converged, GaMD simulations of the same length yield free energy profiles that agree quantitatively with those of 1000 ns cMD simulation. Further GaMD simulations have captured folding of the chignolin and binding of the acetylcholine (ACh) endogenous agonist to the M3 muscarinic receptor. The reweighted free energy profiles are used to characterize the protein folding and ligand binding pathways quantitatively. GaMD implemented in the scalable NAMD is widely applicable to enhanced sampling and free energy calculations of large biomolecules.