Genetically encoded discovery of perfluoroaryl macrocycles that bind to albumin and exhibit extended circulation in vivo.

Peptide-based therapeutics have gained attention as promising therapeutic modalities, however, their prevalent drawback is poor circulation half-life in vivo. In this paper, we report the selection of albumin-binding macrocyclic peptides from genetically encoded libraries of peptides modified by perfluoroaryl-cysteine SNAr chemistry, with decafluoro-diphenylsulfone (DFS). Testing of the binding of the selected peptides to albumin identified SICRFFC as the lead sequence. We replaced DFS with isosteric pentafluorophenyl sulfide (PFS) and the PFS-SICRFFCGG exhibited KD = 4-6 µM towards human serum albumin. When injected in mice, the concentration of the PFS-SICRFFCGG in plasma was indistinguishable from the reference peptide, SA-21. More importantly, a conjugate of PFS-SICRFFCGG and peptide apelin-17 analogue (N3-PEG6-NMe17A2) showed retention in circulation similar to SA-21; in contrast, apelin-17 analogue was cleared from the circulation after 2 min. The PFS-SICRFFC is the smallest known peptide macrocycle with a significant affinity for human albumin and substantial in vivo circulation half-life. It is a productive starting point for future development of compact macrocycles with extended half-life in vivo.

Possible Co-Evolution of Polyglutamine and Polyproline in Huntingtin Protein: Proline-Rich Domain as Transient Folding Chaperone.

Huntington's disease is an inherited neurodegenerative disorder caused by the overduplication of CAG repeats in the Huntingtin gene. Recent findings revealed that among the orthologs, the expansion of CAG repeats (polyQ) in the Huntingtin gene occurs in tandem with the duplication of CCG repeats (polyP). However, the molecular mechanism of this possible co-evolution remains unknown. We examined the structures of Huntingtin exon 1 (HttEx1) from six species along with five designed mutants. We found that the polyP segments "chaperone" the rest of the HttEx1 by forming ad hoc polyP binding grooves. Such a process elongates the otherwise poorly solvated polyQ domain, while modulating its secondary structure propensity from ß-strands to α-helices. This chaperoning effect is achieved mostly through transient hydrogen bond interactions between polyP and the rest of HttEx1, resulting in a striking golden ratio of ∼2:1 between the chain lengths of polyQ and polyP.

Exploration of HIV-1 fusion peptide-antibody VRC34.01 binding reveals fundamental neutralization sites.

Antibody binding to a vulnerable site of HIV envelope glycoprotein (Env), the eight N-terminal residues of the gp41 fusion peptide, renders robust HIV neutralization. Here, we theoretically investigate HIV-1 fusion peptide binding to the neutralizing antibody N123-VRC34.01. We explore numerous fusion peptide mutations using all-atom molecular dynamics simulation with explicit-solvent models. Simulation results show that the hydrophobic interaction between Ile515 in the HIV-1 fusion peptide and the antibody VRC34.01 Fab plays an important role in antibody binding. Furthermore, we verify by free energy perturbation (FEP) calculations that two point mutations of Ile515Thr or Ile515Ala can dramatically weaken the binding affinity. Our findings provide new insights into fusion peptide-VRC34.01 binding, which can ultimately be utilized to design effective HIV vaccines.

Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties.

We establish a comprehensive quantitative structure-activity relationship (QSAR) model termed AlphaQ through the machine learning algorithm to associate the fully quantum mechanical molecular descriptors with various biochemical and pharmacological properties. Preliminarily, a novel method for molecular structural alignments was developed in such a way to maximize the quantum mechanical cross correlations among the molecules. Besides the improvement of structural alignments, three-dimensional (3D) distribution of the molecular electrostatic potential was introduced as the unique numerical descriptor for individual molecules. These dual modifications lead to a substantial accuracy enhancement in multifarious 3D-QSAR prediction models of AlphaQ. Most remarkably, AlphaQ has been proven to be applicable to structurally diverse molecules to the extent that it outperforms the conventional QSAR methods in estimating the inhibitory activity against thrombin, the water-cyclohexane distribution coefficient, the permeability across the membrane of the Caco-2 cell, and the metabolic stability in human liver microsomes. Due to the simplicity in model building and the high predictive capability for varying biochemical and pharmacological properties, AlphaQ is anticipated to serve as a valuable screening tool at both early and late stages of drug discovery.

Glassy dynamics in mutant huntingtin proteins.

Causative to the neurodegenerative Huntington's disease (HD), a mutational huntingtin (HTT) protein consists of an unusual expansion on the poly-glutamine (polyQ) region in the first exon (exon-1) domain. Despite its significance on HD progression, the structural role of the exon-1 with the polyQ region is still elusive. As HTT is an intrinsically disordered protein (IDP), a large ensemble of various conformations (instead of a mostly single native conformation) is required to characterize its structural properties and to infer biological functions, which is challenging even for the most state-of-the-art experimental techniques. For this reason, molecular dynamics (MD) simulations with enhanced sampling techniques are ideal to compliment experiment on collecting such a large ensemble of thermodynamically accessible structures. Here, we performed large-scale temperature replica-exchange MD (T-REMD) simulations on the exon-1 with an illustration on the necessity of using T-REMD instead of unbiased regular MD. By comparing T-REMD data and unbiased MD data, we discovered that (1) the dynamics of polyQ regions are extremely sluggish and glassy at the room temperature and the relaxation of the system cannot be achieved within a reasonable amount of time without utilizing an enhanced sampling method and (2) an ensemble of protein structures containing the surprising cis-peptide bonds in the proline-rich domain can be obtained at much elevated temperatures. Our results may provide valuable insights for future studies on the HTT as well as other IDPs using the T-REMD method.

Lanosterol Disrupts Aggregation of Human γD-Crystallin by Binding to the Hydrophobic Dimerization Interface.

Cataracts are a leading cause of vision impairment, which stem from the misfolding and aggregation of crystallins in the eye lens. Despite its prevalence and severity, the detailed mechanism by which misfolded crystallins aggregate into cataracts remains elusive. Recently, in vitro and in vivo experiments demonstrated that lanosterol, a steroid-type compound found in human and animal eyes, can not only prevent cataract formation but also reverse the formation. Inspired by these experimental observations, we investigate the preventive activity of lanosterol in the aggregate formation of human γD-crystallins (HγD-Crys) using all atom molecular dynamics (MD) simulation and free energy perturbation (FEP) techniques. Our results reveal that lanosterol preferentially binds to the HγD-Crys hydrophobic dimerization interface, in particular, to the structured C-terminus (near residues 135-165) with a stronger binding affinity than the unfolded N-terminus. Furthermore, we observe that the C-terminal binding is more favorable than lanosterol self-aggregation, further attesting to lanosterol's efficacy. Finally, we compare the binding free energy of lanosterol with cholesterol using alchemical transformation and discuss the possible correlation of the molecular geometry of steroids with binding affinity.

Emerging ß-Sheet Rich Conformations in Supercompact Huntingtin Exon-1 Mutant Structures.

There exists strong correlation between the extended polyglutamines (polyQ) within exon-1 of Huntingtin protein (Htt) and age onset of Huntington's disease (HD); however, the underlying molecular mechanism is still poorly understood. Here we apply extensive molecular dynamics simulations to study the folding of Htt-exon-1 across five different polyQ-lengths. We find an increase in secondary structure motifs at longer Q-lengths, including ß-sheet content that seems to contribute to the formation of increasingly compact structures. More strikingly, these longer Q-lengths adopt supercompact structures as evidenced by a surprisingly small power-law scaling exponent (0.22) between the radius-of-gyration and Q-length that is substantially below expected values for compact globule structures (∼0.33) and unstructured proteins (∼0.50). Hydrogen bond analyses further revealed that the supercompact behavior of polyQ is mainly due to the "glue-like" behavior of glutamine's side chains with significantly more side chain-side chain H-bonds than regular proteins in the Protein Data Bank (PDB). The orientation of the glutamine side chains also tend to be "buried" inside, explaining why polyQ domains are insoluble on their own.

Molecular Mechanism of Stabilizing the Helical Structure of Huntingtin N17 in a Micellar Environment.

Huntington's disease is a deadly neurodegenerative disease caused by the fibrilization of huntingtin (HTT) exon-1 protein mutants. Despite extensive efforts over the past decade, much remains unknown about the structures of (mutant) HTT exon-1 and their enigmatic roles in aggregation. Particularly, whether the first 17 residues in the N-terminal (HTT-N17) adopt a helical or a coiled structure remains unclear. Here, with the rigorous study of molecular dynamics simulations, we explored the most possible structures of HTT-N17 in both dodecylphosphocholine (DPC) micelles and aqueous solution, using three commonly applied force fields (OPLS-AA/L, CHARMM36, and AMBER99sb*-ILDNP) to examine the underlying molecular mechanisms and rule out potential artifacts. We show that local environments are essential for determining the secondary structure of HTT-N17. This is evidenced by the insertion of five hydrophobic residues of HTT-N17 into the DPC micelle, which promotes the formation of an amphipathic helix, whereas such amphipathic helices unfold quickly in aqueous solution. A relatively low free-energy barrier (∼3 kcal/mol) for the secondary structure transformation was also observed for all three force fields from their respective folding-free-energy landscapes, which accounts for possible HTT-N17 conformational changes upon environmental shifts such as membrane binding and protein complex aggregation.

Potential disruption of protein-protein interactions by graphene oxide.

Graphene oxide (GO) is a promising novel nanomaterial with a wide range of potential biomedical applications due to its many intriguing properties. However, very little research has been conducted to study its possible adverse effects on protein-protein interactions (and thus subsequent toxicity to human). Here, the potential cytotoxicity of GO is investigated at molecular level using large-scale, all-atom molecular dynamics simulations to explore the interaction mechanism between a protein dimer and a GO nanosheet oxidized at different levels. Our theoretical results reveal that GO nanosheet could intercalate between the two monomers of HIV-1 integrase dimer, disrupting the protein-protein interactions and eventually lead to dimer disassociation as graphene does [B. Luan et al., ACS Nano 9(1), 663 (2015)], albeit its insertion process is slower when compared with graphene due to the additional steric and attractive interactions. This study helps to better understand the toxicity of GO to cell functions which could shed light on how to improve its biocompatibility and biosafety for its wide potential biomedical applications.

EGCG in Green Tea Induces Aggregation of HMGB1 Protein through Large Conformational Changes with Polarized Charge Redistribution.

As a major effective component in green tea, (-)-epigallocatechin-3-gallate (EGCG)'s potential benefits to human health have been widely investigated. Recent experimental evidences indicate that EGCG can induce the aggregation of HMGB1 protein, a late mediator of inflammation, which subsequently stimulates the autophagic degradation and thus provides protection from lethal endotoxemia and sepsis. In this study, we use molecular dynamics (MD) simulations to explore the underlying molecular mechanism of this aggregation of HMGB1 facilitated by EGCG. Our simulation results reveal that EGCG firmly binds to HMGB1 near Cys106, which supports previous preliminary experimental evidence. A large HMGB1 conformational change is observed, where Box A and Box B, two homogenous domains of HMGB1, are repositioned and packed together by EGCG. This new HMGB1 conformation has large molecular polarity and distinctive electrostatic potential surface. We suggest that the highly polarized charge distribution leads to the aggregation of HMGB1, which differs from the previous hypothesis that two HMGB1 monomers are linked by the dimer of EGCG. Possible aggregating modes have also been investigated with potential of mean force (PMF) calculations. Finally, we conclude that the conformation induced by EGCG is more aggregation-prone with higher binding free energies as compared to those without EGCG.

Computational prediction of molecular hydration entropy with hybrid scaled particle theory and free-energy perturbation method.

Despite the importance of the knowledge of molecular hydration entropy (ΔShyd) in chemical and biological processes, the exact calculation of ΔShyd is very difficult, because of the complexity in solute-water interactions. Although free-energy perturbation (FEP) methods have been employed quite widely in the literature, the poor convergent behavior of the van der Waals interaction term in the potential function limited the accuracy and robustness. In this study, we propose a new method for estimating ΔShyd by means of combining the FEP approach and the scaled particle theory (or information theory) to separately calculate the electrostatic solute-water interaction term (ΔSelec) and the hydrophobic contribution approximated by the cavity formation entropy (ΔScav), respectively. Decomposition of ΔShyd into ΔScav and ΔSelec terms is found to be very effective with a substantial accuracy enhancement in ΔShyd estimation, when compared to the conventional full FEP calculations. ΔScav appears to dominate over ΔSelec in magnitude, even in the case of polar solutes, implying that the major contribution to the entropic cost for hydration comes from the formation of a solvent-excluded volume. Our hybrid scaled particle theory and FEP method is thus found to enhance the accuracy of ΔShyd prediction by effectively complementing the conventional full FEP method.

Confinement-Induced Glassy Dynamics in a Model for Chromosome Organization.

Recent experiments showing scaling of the intrachromosomal contact probability, P(s)∼s(-1) with the genomic distance s, are interpreted to mean a self-similar fractal-like chromosome organization. However, scaling of P(s) varies across organisms, requiring an explanation. We illustrate dynamical arrest in a highly confined space as a discriminating marker for genome organization, by modeling chromosomes inside a nucleus as a homopolymer confined to a sphere of varying sizes. Brownian dynamics simulations show that the chain dynamics slows down as the polymer volume fraction (ϕ) inside the confinement approaches a critical value ϕ(c). The universal value of ϕ(c)(∞)≈0.44 for a sufficiently long polymer (N≫1) allows us to discuss genome dynamics using ϕ as the sole parameter. Our study shows that the onset of glassy dynamics is the reason for the segregated chromosome organization in humans (N≈3×10(9), ϕ≳ϕ(c)(∞)), whereas chromosomes of budding yeast (N≈10(8), ϕ<ϕ(c)(∞)) are equilibrated with no clear signature of such organization.

Unexpected Swelling of Stiff DNA in a Polydisperse Crowded Environment.

We investigate the conformations of DNA-like stiff chains, characterized by contour length (L) and persistence length (lp), in a variety of crowded environments containing monodisperse soft spherical (SS) and spherocylindrical (SC) particles, a mixture of SS and SC, and a milieu mimicking the composition of proteins in the Escherichia coli cytoplasm. The stiff chain, whose size modestly increases in SS crowders up to ϕ ≈ 0.1, is considerably more compact at low volume fractions (ϕ ≤ 0.2) in monodisperse SC particles than in a medium containing SS particles. A 1:1 mixture of SS and SC crowders induces greater chain compaction than the pure SS or SC crowders at the same ϕ, with the effect being highly nonadditive. We also discover a counterintuitive result that the polydisperse crowding environment, mimicking the composition of a cell lysate, swells the DNA-like polymer, which is in stark contrast to the size reduction of flexible polymers in the same milieu. Trapping of the stiff chain in a fluctuating tube-like environment created by large-sized crowders explains the dramatic increase in size and persistence length of the stiff chain. In the polydisperse medium, mimicking the cellular environment, the size of the DNA (or related RNA) is determined by L/lp. At low L/lp, the size of the polymer is unaffected, whereas there is a dramatic swelling at an intermediate value of L/lp. We use these results to provide insights into recent experiments on crowding effects on RNA and also make testable predictions.

Effects of macromolecular crowding on the collapse of biopolymers.

Experiments show that macromolecular crowding modestly reduces the size of intrinsically disordered proteins even at a volume fraction (ϕ) similar to that in the cytosol, whereas DNA undergoes a coil-to-globule transition at very small ϕ. We show using a combination of scaling arguments and simulations that the polymer size R̅(g)(ϕ) depends on x=R̅(g)(0)/D, where D is the ϕ-dependent distance between the crowders. If x≲O(1), there is only a small decrease in R̅(g)(ϕ) as ϕ increases. When x≫O(1), a cooperative coil-to-globule transition is induced. Our theory quantitatively explains a number of experiments.

Manifestation of random first-order transition theory in Wigner glasses.

We use Brownian dynamics simulations of a binary mixture of highly charged spherical colloidal particles to test some of the predictions of the random first-order transition (RFOT) theory [Phys. Rev. Lett. 58, 2091 (1987); Phys. Rev. A 40, 1045 (1989)]. In accord with mode-coupling theory and RFOT, we find that as the volume fraction of the colloidal particles ϕ approaches the dynamical transition value ϕ(A), three measures of dynamics show an effective ergodic to nonergodic transition. First, there is a dramatic slowing down of diffusion, with the translational diffusion constant decaying as a power law as ϕ→ϕ(A)(-). Second, the energy metric, a measure of ergodicity breaking in classical many-body systems, shows that the system becomes effectively nonergodic as ϕ(A) is approached. Finally, the time t(*), at which the four-point dynamical susceptibility achieves a maximum, also increases as a power law near ϕ(A). Remarkably, the translational diffusion coefficients, ergodic diffusion coefficient, and (t(*))(-) all vanish as (ϕ(-1)-ϕ(A)(-1))(γ) with both ϕ(A)(≈0.1) and γ being the roughly the same for all three quantities. Above ϕ(A), transport involves crossing free energy barriers. In this regime, the density-density correlation function decays as a stretched exponential [exp-(t/τ(α))(ß)] with ß≈0.45. The ϕ dependence of the relaxation time τ(α) could be fit using the Vogel-Tamman-Fulcher law with the ideal glass transition at ϕ(K)≈0.47. By using a local entropy measure, we show that the law of large numbers is not obeyed above ϕ(A), and gives rise to subsample to subsample fluctuations in all physical observables. We propose that dynamical heterogeneity is a consequence of violation of law of large numbers.

Extended solvent-contact model for protein solvation: test cases for dipeptides.

Solvation effects are critically important in the structural stabilization and functional optimization of proteins. Here, we propose a new solvation free energy function for proteins, and test its applicability in predicting the solvation free energies of dipeptides. The present solvation model involves the improvement of the previous solvent-contact model assuming that the molecular solvation free energy could be given by the sum over the individual atomic contributions. In addition to the existing solvent-contact term, the modified solvation free energy function includes the self-solvation term that reflects the effects of intramolecular interactions in the solute molecule on solute-solvent interactions. Four kinds of atomic parameters should be determined in this solvation model: atomic fragmental volume, maximum atomic occupancy, atomic solvation, and atomic self-solvation parameters. All of these parameters for 16 atom types are optimized with a standard genetic algorithm in such a way to minimize the difference between the solvation free energies of dipeptides obtained from high-level quantum chemical calculations and those predicted by the solvation free energy function. The solvation free energies of dipeptides estimated from the new solvation model are in good agreement with the quantum chemical results. Therefore, the optimized solvation free energy function is expected to be useful for examining the structural and energetic features of proteins in aqueous solution.

New solvation free energy function comprising intermolecular solvation and intramolecular self-solvation terms.

Solvation free energy is a fundamental thermodynamic quantity that should be determined to estimate various physicochemical properties of a molecule and the desolvation cost for its binding to macromolecular receptors. Here, we propose a new solvation free energy function through the improvement of the solvent-contact model, and test its applicability in estimating the solvation free energies of organic molecules with varying sizes and shapes. This new solvation free energy function is constructed by combining the existing solute-solvent interaction term with the self-solvation term that reflects the effects of intramolecular interactions on solvation. Four kinds of atomic parameters should be determined in this solvation model: atomic fragmental volume, maximum atomic occupancy, atomic solvation, and atomic self-solvation parameters. All of these parameters for total 37 atom types are optimized by the operation of a standard genetic algorithm in such a way to minimize the difference between the experimental solvation free energies and those calculated by the solvation free energy function for 362 organic molecules. The solvation free energies estimated from the new solvation model compare well with the experimental results with the associated squared correlation coefficients of 0.88 and 0.85 for training and test sets, respectively. The present solvation model is thus expected to be useful for estimating the solvation free energies of organic molecules.

Extended Morse function model for angle-dependent hydrogen bond in protein-protein interactions.

Backbone-backbone, backbone-asparagine, and serine-backbone hydrogen bonds (HBs) are the most abundant interactions at the interface of protein-protein complex. Here, we propose an angle-dependent potential energy function for these HBs constructed by the product of the radial and the angular Morse functions whose various parameters are optimized with high-level density functional theory (DFT) calculations. The new angular variables, the interatomic distance between the donor and the acceptor atoms (R(theta)) and that between the hydrogen and the base atom of the acceptor (R(phi)), are employed to define the angular Morse functions. The angular part in the new potential function is found to be comparable in the magnitude of energy values to the radial one, which is consistent with the significant angular dependence of HBs. The HB binding energies calculated with the new potential function compare well with those obtained by high-level DFT calculations with the associated squared correlation coefficients ranging from 0.82 to 0.85. This agreement indicates the suitability of the new energy functions as a potential function for HB in modeling the protein-protein interactions.

New angle-dependent potential energy function for backbone-backbone hydrogen bond in protein-protein interactions.

Backbone-backbone hydrogen bonds (BBHBs) are one of the most abundant interactions at the interface of protein-protein complex. Here, we propose an angle-dependent potential energy function for BBHB based on density functional theory (DFT) calculations and the operation of a genetic algorithm to find the optimal parameters in the potential energy function. The angular part of the energy function is assumed to be the product of the power series of sine and cosine functions with respect to the two angles associated with BBHB. Two radial functions are taken into account in this study: Morse and Leonard-Jones 12-10 potential functions. Of these two functions under consideration, the former is found to be more accurate than the latter in terms of predicting the binding energies obtained from DFT calculations. The new HB potential function also compares well with the knowledge-based potential derived by applying Boltzmann statistics for a variety of protein-protein complexes in protein data bank.