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Efficient encapsulation of proteins with random copolymers
Contributed by Monica Olvera de la Cruz, May 16, 2018 (sent for review April 12, 2018; reviewed by Andrey V. Dobrynin and Alexander Y. Grosberg)

Significance
Inside cells of living organisms, aggregates rich in disordered proteins organize the local environment to promote cellular functions. These membraneless organelles are able to concentrate enzymes and biomolecules to regulate interactions via the multiple conformations and compositions of disordered proteins. The interior of these organelles seems to behave akin to organic solvents. This opens the possibility of assembling synthetic organelles using random copolymers that mimic disordered proteins to disperse and stabilize enzymatic proteins in different environments, including organic solvents. Here, we demonstrate that random copolymers with solvophobic and solvophilic groups can encapsulate numerous proteins, including Candida antarctica lipase B, subtilisin, cutinase, and pseudolysin, in basically any solvent. These aggregates are promising constituents of synthetic membraneless organelles.
Abstract
Membraneless organelles are aggregates of disordered proteins that form spontaneously to promote specific cellular functions in vivo. The possibility of synthesizing membraneless organelles out of cells will therefore enable fabrication of protein-based materials with functions inherent to biological matter. Since random copolymers contain various compositions and sequences of solvophobic and solvophilic groups, they are expected to function in nonbiological media similarly to a set of disordered proteins in membraneless organelles. Interestingly, the internal environment of these organelles has been noted to behave more like an organic solvent than like water. Therefore, an adsorbed layer of random copolymers that mimics the function of disordered proteins could, in principle, protect and enhance the proteins’ enzymatic activity even in organic solvents, which are ideal when the products and/or the reactants have limited solubility in aqueous media. Here, we demonstrate via multiscale simulations that random copolymers efficiently incorporate proteins into different solvents with the potential to optimize their enzymatic activity. We investigate the key factors that govern the ability of random copolymers to encapsulate proteins, including the adsorption energy, copolymer average composition, and solvent selectivity. The adsorbed polymer chains have remarkably similar sequences, indicating that the proteins are able to select certain sequences that best reduce their exposure to the solvent. We also find that the protein surface coverage decreases when the fluctuation in the average distance between the protein adsorption sites increases. The results herein set the stage for computational design of random copolymers for stabilizing and delivering proteins across multiple media.
The ability to maintain the enzymatic activity of proteins out of their native aqueous medium is crucial for the pharmaceutical and chemical-processing industries. Among the most important reasons to use organic solvents in place of water is to reduce aggregation of hydrophobic reactants and products, to enable reactions that are not favored in water, and to allow for energy-efficient downstream processing with volatile solvents to suppress side reactions caused by water, as well as to prevent microbial growth, to name a few (1). However, in foreign media such as organic and highly polar solvents, proteins are prone to a substantial reduction in their functionalities, as their structure, solubility, and conformational mobility are heavily affected (2⇓–4). Common approaches for stabilizing proteins in nonnative solvents are devoted to mediate protein–solvent interactions such as via protein sequence design, solvent modification, polymer conjugation, reverse micelles, and nanoconfinement (5). However, these methods are often protein- and solvent-specific and yet not cost-effective in retaining protein functions. The challenge here is to encapsulate the proteins with a coating shell that minimizes nonfavorable protein–solvent contacts while preserving the structure and conformational mobility of the proteins.
Random copolymers have long been recognized as an interesting choice for applications involving pattern recognition of molecules and multifunctional disordered surfaces (6⇓⇓⇓–10). Due to the randomness in their monomer sequence reminiscent of intrinsically disordered proteins (11), this special family of copolymers is expected to allow for unique versatility that helps the surfaces to select the most favorable sequences for adsorption. The phase behavior of random copolymers in solution and in melt (12⇓⇓⇓⇓⇓⇓⇓⇓⇓–22), as well as their adsorption onto numerous surfaces and interfaces (7⇓⇓–10, 23, 24), have been under extensive investigation. Bratko et al. showed that random copolymers recognize disordered surfaces when the statistics characterizing the disordered surfaces and the polymer sequences satisfy certain conditions, so-called statistical pattern matching (7). Srebnik et al. (8) demonstrated that random copolymers in selective solvent undergo a sharp (but continuous) transition from weak to strong adsorption to a surface with disordered adsorption sites when the density of the adsorption sites, or, equivalently, the net adsorption strength, exceeds a certain threshold. The adsorption strength threshold is dependent on the attraction strength between polymer segments, which effectively controls the entropy difference between the nonadsorbed state and adsorbed state. Ge and Rubinstein (23) developed a scaling model for adsorption of a solution of polymers with regularly distributed sticky monomers adsorbed on a flat surface with sticky sites. Their model predicted that at a given bulk density the morphologies of the adsorbed layers, which vary from mushrooms to stretched loops to self-similar carpets, result from the competition between two length scales: the average nearest distance between the sticky sites, l, on the polymer chain, and that between neighboring sticky sites on the substrate, d. Hershkovits et al. proposed a scaling model for adsorption of homopolymers on spherical clusters in a good solvent (25). They showed that the structural properties of the adsorbed polymer layers are influenced by surface curvature when the cluster size is similar to, or smaller than, the polymer radius of gyration. Meenakshisundaram et al. analyzed the effects of copolymer sequences as compatibilizers that adsorb at the liquid–liquid interface of two immiscible homopolymers (24). They found that the monomer sequence specificity of the binary copolymers plays an important role in minimizing the interfacial energy. Given the inherent multicomponent nature in composition and in sequences of random copolymer solutions, it is not clear which sequences adsorb more favorably onto a finite-size patterned substrate with high curvature, like a protein, and the degree of adsorption as the quality of the solvent varies. The above-mentioned studies as well as previous scaling and mean field adsorption models (26⇓⇓⇓⇓⇓–32) cannot readily be extended to describe this random copolymer adsorption process, given that the copolymers should not be too long so that the attraction between the polymers and the protein permits the protein to maintain its structure and functionalities. This constraint generates large fluctuations in the correlations between the disorder quenched in the copolymer sequences and in the protein surface disordered pattern.
It is evident that by varying the volume fraction and composition of the constituent monomers, one can tune the effective attraction between the copolymers and the substrate. Interestingly, recent studies in membraneless organelles, which are spatiotemporal aggregates with high concentrations of disordered proteins (33), demonstrate that disordered proteins hold aggregates of biomolecules including enzymes together in an environment that behaves more like an organic solvent than water (34). Therefore, random copolymer solutions, which contain chains of various compositions and sequences, can be designed to mimic disordered proteins and used to disperse enzymatically active proteins in organic solvents. Recently, Xu and coworkers demonstrated that a special case of random heteropolymers with four different monomers allows for highly effective encapsulation of numerous enzymes (35). The enzymes coated by this type of random heteropolymers are soluble in toluene and retain their enzymatic activity after days. The key challenge for this strategy is to predict the optimal average composition of the monomers that yields maximum surface coverage for a specified protein in a given solvent condition. It is therefore crucial to find the connection between the characteristics of the target protein and the desirable composition of the random copolymers. In the present study, using multiscale modeling and molecular simulation, we investigate the key factors that influence the encapsulation of random binary copolymers for several model proteins. By examining the microscopic details of the adsorbed polymer layers, we find the relationship between the protein surface coverage and adsorption strength, solvent selectivity, and the average composition of the adsorbing monomers. Our study predicts the optimal average composition
Simulation Model
We briefly present our simulation model of the proteins and random copolymers with the key parameters and relevant terms used throughout the work. Additional details on the simulation procedure and data analysis are given in Materials and Methods and SI Appendix, section I.
Protein.
Fig. 1A shows the all-atom model of the enzyme Candida antarctica lipase B (1TCA) equilibrated in water. Using the shaped-based coarse-graining algorithm mentioned in SI Appendix, we find the coarse-grained (CG) beads for the atoms in the hydrophobic groups and for those in the hydrophilic groups (Fig. 1B and SI Appendix, Fig. S2). The number of the CG beads is chosen so that the distance between the nearest beads of the same type is ∼5.0 Å, as measured by their radial distribution functions. Additionally, we keep the ratio between the numbers of the CG beads in the hydrophilic and hydrophobic groups approximately equal to that between the number of atoms in the respective groups in the all-atom simulation. For instance, our model protein 1TCA is composed of 97 hydrophilic CG beads and 77 hydrophobic CG beads. Without loss in generality, the hydrophilic beads are chosen as adsorption sites (green) for the polymer beads (blue). This coarse-graining procedure is in good agreement with all-atom simulations with regard to the spatial distribution of the hydrophobic and hydrophilic domains (SI Appendix, Fig. S1).
Models and snapshots. (A) Representative configuration of the protein 1TCA equilibrated in water using all-atom simulations, with hydrophobic (red) and hydrophilic (green) residues. (B) Shaped-based CG model obtained from the all-atom configuration in A and an instance of the random copolymers composed of
Random Copolymers.
We consider a classical model of binary random copolymers composed of two monomer types, A and B (12, 13). For simplicity, we assume that the polymer chains have the same total number of beads,
Results and Discussion
Fig. 1 D and E shows two representative snapshots for partial and nearly complete coverage of the protein by
We first analyze the similarity in the monomer sequences of the adsorbed polymer chains. Subsequently, we characterize the correlation between the spatial distribution of protein adsorption sites and the protein surface coverage of the random copolymers. We then report the dependence of the protein surface coverage of the random copolymers on the adsorption energy (
Sequence Similarity Between the Adsorbed Polymer Chains.
To examine whether the protein would favor certain sequences out of the available random sequences, we characterize the correlation in the monomer sequence of the adsorbed polymer chains for the given model protein. We define the order parameters to characterize the similarity between the sequences within the same batch and between those from two batches (Materials and Methods). For
Our simulation results reveal that the sequence similarity between the adsorbed chains within the individual batches is well beyond that between all of the chains and the threshold value of 0.6 for close matches. We performed simulation of 20 different batches, each with
Sequence similarity analysis. (A) Sequence similarity order parameter between the batches
Versatility.
As shown in the work by Panganiban et al. (35), random heteropolymers of the same average composition are able to retain the activity of numerous enzymes in toluene equally efficiently. This is of practical importance because it suggests that the approach is not restricted to specific proteins or solvents, which is advantageous compared with other approaches such as polymer conjugation. Consistent with experimental findings in ref. 35, our simulation results indicate that random copolymers with the same average composition covers the proteins under investigation (Fig. 3A) fairly effectively. For instance, for
Encapsulation of numerous proteins. (A) CG model of the proteins under investigation: ubiquitin [Protein Data Bank (PDB) ID code 1UBQ], cutinase (PDB ID code 1CEX), pseudolysin (PDB ID code 1EZM), and subtilisin (PDB ID code 1A2Q). (B) Surface coverage as a function of
To better correlate the ability of the random copolymers to encapsulate the proteins with the features of the protein surface, we compute the distance distribution of the protein adsorption sites
Surface Coverage as a Function of Average Polymer Adsorbing Fraction and Solvent Selectivity.
Large values of
Surface coverage Γ as a function of the average adsorbing fraction
Multilayer Adsorption Model.
Fig. 5 shows that the surface coverage Γ varies with the adsorption strength,
(A–C) Surface coverage Γ as a function of the attraction strength between sticky protein beads and polymer beads,
The variation of surface coverage with respect to the adsorption energy (Fig. 5 A–C) is reminiscent of the S-shaped adsorption isotherms (i.e., type 5 in the International Union of Pure and Applied Chemistry classification), where the polymer beads of type A of the adsorbed chains act as secondary adsorption sites, leading to the formation of multilayers of adsorbed type A beads. Qi et al. (37) proposed an analytical model for the adsorption of water vapor adsorbing onto activated carbon, where the adsorption capacity is governed by the ratio between the actual partial and saturated partial water vapor pressures. We propose a similar model where the protein surface coverage (Γ) and adsorption energy (
Structural Features of the Adsorbed Polymer Layers.
To elucidate the relationship between the protein surface coverage, Γ, and the average polymer composition,
Structural features of the adsorbed polymer layers. (A) Number of adsorbed polymer chains,
For larger values of
The pair correlation function of the attractive beads on the polymer chains and those on the protein and the radial density profile of the polymer beads (SI Appendix, Fig. S9) further reveal that the adsorbed polymer chains form a multilayer structure and that the polymer layers would behave like a liquid with number density well below
The effective interaction between the protein and the implicit solvent in the presence of the encapsulating random copolymers is characterized by the outer layers of the polymer shell and the fraction of the protein surface that is uncovered. To characterize the effective interaction between the protein and the solvent mediated by the adsorbed polymer layers, we analyze the structural property of the polymer outer layer. Using the same method to estimate the surface coverage, we compute the area fraction,
Conclusions
Random copolymers with two types of monomers appear to possess interesting capabilities of reducing the unfavorable exposure of the enzymes to the foreign media. Using CG simulations, we show here (i) that the sequence randomness of the random copolymers with only two components, solvophobic and solvophilic, enables the proteins to selectively pick the most favorable copolymer sequences; (ii) that the random copolymers encapsulate the proteins with a narrow distribution of the adsorption sites equally well regardless of their average composition; and (iii) that the average adsorption fraction, adsorption strength, and solvent selectivity are the key factors determining the encapsulation of a given protein by random copolymers. By examining the structural features of the adsorbed polymer layers, we have demonstrated that the effective protein–solvent interaction can be mediated by tailoring the average polymer composition. Our findings strongly suggest the possibility of designing random copolymers to stabilize proteins in foreign environments, leading to the fabrication of synthetic aggregates structurally similar to cellular membraneless organelles (34). We envision that polymer architecture, monomer chemistry, and charge polydispersity would introduce additional flexibility toward mimicking disordered proteins. Other potential applications of such rationally designed random copolymers can be neutralizing toxic agents (35) or viruses (39) and stabilizing proteins in healthy fats (40).
Materials and Methods
Simulation Method.
For the CG models, we simulate a single protein located at the center of the simulation box surrounded by
Polymer Sequence Similarity Analysis.
We use the Python module difflib (41) for convenience reasons. The similarity order parameter s for two sequences varies from zero for two completely different sequences to one for identical sequences. According to ref. 41, a value of
Surface Coverage Estimate.
We estimate the protein surface coverage Γ from particle-based molecular simulation data as follows. First, the protein beads that interact with the polymer beads (i.e., within the LJ cutoff
Acknowledgments
T.D.N. thanks M. Tasinkevych and G. Vernizzi for helpful comments and discussion. The research was supported by Department of Energy Award DE-FG02-08ER46539 and the Sherman Fairchild Foundation. T.D.N. was supported by the Midwest Integrated Center for Computational Materials (MICCoM).
Footnotes
- ↵1To whom correspondence should be addressed. Email: m-olvera{at}northwestern.edu.
Author contributions: T.D.N. and M.O.d.l.C. designed research; T.D.N. and B.Q. performed research; T.D.N., B.Q., and M.O.d.l.C. analyzed data; and T.D.N., B.Q., and M.O.d.l.C. wrote the paper.
Reviewers: A.V.D., University of Akron; and A.Y.G., Center for Soft Matter Research, New York University.
The authors declare no conflict of interest.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1806207115/-/DCSupplemental.
Published under the PNAS license.
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