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Interactions of Multimodal Ligands with Proteins: Insights into Selectivity using Molecular Dynamics Simulations Siddharth Parimal, Shekhar Garde, and Steven M. Cramer Langmuir, Just Accepted Manuscript • DOI: 10.1021/acs.langmuir.5b00236 • Publication Date (Web): 01 Jun 2015 Downloaded from http://pubs.acs.org on June 14, 2015

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Interactions of Multimodal Ligands with Proteins: Insights into Selectivity using Molecular Dynamics Simulations

Siddharth Parimal, Shekhar Garde, Steven M. Cramer* Howard P. Isermann Department of Chemical and Biological Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180 *Corresponding author. E-mail: [email protected]; Phone: (518) 276-6198.

Abstract Fundamental understanding of protein-ligand interactions is important for the development of efficient bioseparations in multimodal chromatography. Here we employ molecular dynamics (MD) simulations to investigate the interactions of three different proteins – ubiquitin, cytochrome C and α-chymotrypsinogen A, sampling a range of charge from +1e to +9e – with two multimodal chromatographic ligands containing similar chemical moieties – aromatic, carboxyl, and amide – in

different structural arrangements. We use a spherical harmonic

expansion to analyze ligand and individual moiety density profiles around the proteins. We find that the Capto MMC ligand, which contains an additional aliphatic group, displays stronger interactions than Nuvia CPrime ligand with all three proteins. Studying the ligand densities at the moiety level suggests that hydrophobic interactions play a major role in determining the locations of high ligand densities. Finally, the greater structural flexibility of the Capto MMC ligand than that of the Nuvia cPrime ligand, allows for stronger structural complementarity, and enables stronger hydrophobic interactions. These subtle and not-so-subtle differences in binding affinities and modalities for multimodal ligands can result in significantly different binding behavior towards proteins with important implications for bioprocessing. Keywords: multimodal, protein-ligand interactions, Molecular Dynamics, spherical harmonics

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1. Introduction Chromatography is an important downstream processing technology in the biopharmaceutical industry [1, 2] in which purification of biomolecules is achieved using the differences in interactions of various mobile phase components (e.g. proteins, impurities) with ligands immobilized on a stationary phase (resin). Multimodal chromatography has emerged as a promising form of chromatography over the recent years [3-12]. Unlike that in ion-exchange or hydrophobic interaction chromatography, where only one physicochemical property (e.g. charge or hydrophobicity) of a biomolecule plays the dominant role in purification, multimodal chromatography harnesses different modes of interactions simultaneously by using ligands with multiple interacting moieties. An appropriate choice of ligand chemistry and architecture [12-19] coupled with mobile phase conditions [20-22] allows one to modulate the different modes of interactions present in multimodal systems to achieve enhanced selectivities relative to traditional single-mode chromatography. Despite this success, fundamental understanding of the mechanisms involved in these systems remains limited, and presents a bottleneck to optimizing desired separations and to developing new multimodal chromatographic materials. Multimodal interactions in chromatographic systems exist between four key components: proteins, multimodal ligands immobilized on a resin, fluid phase modifiers, and water. The chemical and topographical heterogeneity of different proteins can lead to significantly different interactions with small molecules (e.g., multimodal ligands, fluid phase modifiers). The complex interplay of these water-mediated interactions can be harnessed to yield unique selectivities. Recent studies have focused on understanding the molecular details of protein interactions with multimodal molecules in chromatographic systems. For example, a combination of nuclear magnetic

resonance

(NMR)

spectroscopy,

coarse-grained

docking

calculations

and

chromatographic experiments [23] revealed that the protein ubiquitin has a “preferred binding face” for interactions with the multimodal ligand Capto MMC. Molecular dynamics (MD) simulations of aqueous ubiquitin-ligand systems confirmed these results [24, 25]. Other studies have focused on the role of arginine – another multimodal molecule – in eluting proteins from chromatographic columns. Specifically, Trout and coworkers used a combination of MD simulations and vapor pressure osmometry [26-28] to quantify protein-arginine interactions – e.g., preferential interaction parameters of arginine – and explain affinity chromatography

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results. Recently, Arakawa and coworkers have shed light onto the effects of arginine in multimodal cation-exchange chromatography using MD simulations [29]. To harness the full potential of multimodal chromatography, it is crucial to understand the binding of multimodal ligands to proteins. How does a multimodal ligand identify binding sites around a protein? What are the driving forces for this binding process? How do multiple interactions between proteins and ligands act simultaneously? The molecular nature of these questions as well as the lack of a priori knowledge of multimodal ligand binding sites on a protein surface makes these investigations quite challenging. To this end, all-atom, explicit solvent MD simulations of proteins dissolved in aqueous solutions of ligands can help elucidate the molecular details of multimodal ligand-protein interactions [24, 25, 30]. For direct correlations with chromatographic studies, it may be ideal to perform large-scale molecular simulations where the interactions of all possible orientations of a protein with ligands immobilized on a surface could be studied. However, the current computational resources do not allow for such extensive studies and limit the size of the proteins as well as the number of protein orientations whose binding could be sampled conclusively with the ligand surface. Instead, in the current work we examine multimodal ligand-protein interactions using solution based studies to gain molecular level understanding of these systems while acknowledging some of the limitations of directly connecting these results to protein multimodal chromatography. Analysis of detailed MD simulations can present its own challenges. We recently employed a spherical harmonic expansion approach – a way to analyze and visualize atomic densities – to obtain spatially-resolved 3D distributions of multimodal ligands around proteins from MD simulations [31]. We demonstrated the method by calculating water density profiles around model solutes as well as high density locations of ligands Capto MMC, benzene, and guanidinium ion around ubiquitin. Here we apply this approach [31] to investigate the interactions of three different proteins – ubiquitin, cytochrome C and α-chymotrypsinogen A – with two multimodal chromatographic ligands, Capto MMC and Nuvia cPrime. The three proteins sample a range of total charge from +1e, +6e to +9e. The corresponding distributions of charge and hydrophobicity on their surfaces are also sufficiently different and cover a range of charged and hydrophobic patches relevant to multimodal interactions. The two ligands include similar chemical moieties – an aromatic group,

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a negatively charged carboxyl group, and a hydrogen-bonding amide group – in different structural arrangements. The Capto MMC ligand includes an additional aliphatic tail instead of the amine group which is present in the Nuvia cPrime ligand. The two ligands also have slightly different conformational flexibility. Electrostatic interactions between the positively charged proteins and the negatively charged ligands are expected to provide the background driving force for a favorable protein-ligand interaction. However, comparing the high density regions of the two ligands on the same protein allows us to understand the role of ligand architecture and flexibility in binding to a chemically and topographically heterogeneous protein surface. Further, comparing the binding of the same ligand to the three different proteins allows us to understand the role of distributions of charge and hydrophobicity on protein surfaces in determining the binding of a given multimodal ligand. While the landscape of multimodal ligands-proteins is far larger and more complex to be covered by a combination of two ligands and three proteins, our study contains sufficient complexity and constitutes a first step towards developing more predictive approaches for complex chromatographic separations.

2. Methods 2.1 MD simulations of proteins in aqueous solutions of ligands The simulation protocol was similar to the one used in our previous work [31]. All-atom MD simulations were carried out for systems containing the three proteins solvated in dilute aqueous solutions of the two ligands. The initial structure for the proteins was taken from the RCSB Protein Data Bank (PDB IDs – ubiquitin: 1D3Z, cytochrome C: 1HRC, α-chymotrypsinogen A: 2CGA). The protonation states of ionizable residues were assigned at a pH of 5, making the carboxyl (-1e), ammonium (+1e), and histidine (+1e) residues charged. However, for cytochrome C, lysine 73 and histidine 27 were deprotonated (neutral) based on Protonate3D of MOE [32] calculations. Thus, ubiquitin has a charge of +1e, cytochrome C +9e, and α-chymotrypsinogen A +6e. The heme group was removed from the structure of cytochrome C. Each simulation included a cubic periodic box containing one protein molecule, multiple copies of a ligand, explicit water molecules, and neutralizing counter-ions (see Table 1). Ligands were

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randomly placed around the proteins in each simulation box. Each system was initially energyminimized using a combination of steepest descent and conjugate gradient algorithms. MD simulations were then performed in the isothermal-isobaric (N,P,T) ensemble using the GROMACS 4.5.3 molecular dynamics package [33-35]. Temperature (300K) and pressure (1 bar) were maintained using the Nose-Hoover thermostat [36, 37] and Parrinello-Rahman barostat [38], respectively. The electrostatic interactions were handled using the Particle Mesh Ewald (PME) method [39] with a real space cut-off of 1 nm. The cut-off for Lennard-Jones potential was also 1 nm. The bonds in the water molecules were constrained using the LINCS algorithm. A time step of 2 fs was used in the integrator and configurations were stored every 1 ps. Periodic boundary conditions were applied in all three directions. To obtain sufficient sampling of the space by ligands around the protein, one 200 ns long simulation was performed for each proteinligand system. Out of this, 50 ns were used for equilibration and the remaining 150 ns were used to calculate ligand density distributions. Force fields We used the AMBER 94 [40] to describe proteins. Parameters for constituents of both ligands are available in the AMBER 94 force field, which were used to generate topology files for these ligands using the LEAP program [41] (note: the parameters for the Capto MMC ligand were the same as used in our previous study [31]). Water molecules were modeled using the extended simple point charge (SPC/E) model [42], which represents water with three partial charge sites (one located on oxygen and two on the hydrogen atoms) and a Lennard-Jones interactions site located at the oxygen center. Table 1 Details of protein-ligand systems in MD simulations System

Protein

Ligand

# Ligands

Box size (nm)

Ubq-C

Ubiquitin

Capto MMC

24

7x7x7

Ubq-N

Ubiquitin

Nuvia cPrime

24

7x7x7

Cyt-C

Cytochrome C

Capto MMC

29

7.5 x 7.5 x 7.5

Cyt-N

Cytochrome C

Nuvia cPrime

29

7.5 x 7.5 x 7.5

Ach-C

α-chymotrypsinogen A

Capto MMC

42

8.5 x 8.5 x 8.5

Ach-N

α-chymotrypsinogen A

Nuvia cPrime

42

8.5 x 8.5 x 8.5

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2.2 Calculation and visualization of density distributions of ligands around proteins The distributions of the center-of-geometry of a ligand and its different constituent groups were calculated using the spherical harmonics expansion approach demonstrated in our previous work [31]. A brief overview of the approach is presented here for completeness. The three dimensional density of atoms, or a group of atoms (e.g., center-of-geometry of ligands, etc.) in space around the protein is expanded in terms of spherical harmonic functions as follows: 𝜌(𝒓) 𝜌0

𝑛 𝑒 𝑜 = ∑𝑁 𝑛=0 ∑𝑚=0[𝐶𝑛𝑚 (𝑟)𝑌𝑛𝑚 (𝜃, 𝜑) + 𝑆𝑛𝑚 (𝑟)𝑌𝑛𝑚 (𝜃, 𝜑)]

(1a)

where 𝑒 (𝜃, 𝑌𝑛𝑚 𝜑) = 𝑃𝑛𝑚 (𝑐𝑜𝑠𝜃)𝑐𝑜𝑠(𝑚𝜑)

(1b)

𝑜 (𝜃, 𝑌𝑛𝑚 𝜑) = 𝑃𝑛𝑚 (𝑐𝑜𝑠𝜃)𝑠𝑖𝑛(𝑚𝜑)

(1c).

and Cnm(r) and Snm(r) are the spherical harmonic coefficients, and Pnm(cos(θ)), are the associated Legendre polynomials. The coefficients Cnm(r) and Snm(r) can be obtained for atom(s) of interest using a simulation trajectory, and the definition 𝑁

𝑎𝑡𝑜𝑚𝑠 𝜌(𝒓) = ∑𝑖=1 𝛿(𝒓 − 𝒓𝑖 )

(1d)

and the orthonormality relation 2𝜋

𝜋

𝑒 𝑜𝑟 𝑜 (𝜃, 𝜑)]2 𝑠𝑖𝑛𝜃 𝑑𝜃 𝑑𝜑 = ∫0 ∫0 [𝑌𝑛𝑚

4𝜋

(𝑛+𝑚)!

2(2𝑛+1) (𝑛−𝑚)!

(1 ± 𝛿𝑚0 ).

(1e)

Once the coefficients of the expansion are obtained as a function of the distance from the protein center (using overlapping radial bins of 1 Å width, centered every 0.1 Å), the ligand density profile is represented on a surface envelope around the protein using the Eq. 1a. A version of Willard and Chandler [43] “instantaneous interface” was used to define this surface envelope. Specifically, all protein heavy atoms contribute towards a coarse-grained protein density in the space such that this density is highest at the center of the protein and decreases as we move towards the bulk. Using the density at the core of the protein as a reference, a surface envelope can be defined as locus of all points which have a small fixed value of this reference coarse-

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grained density (ρʹ = ρprotein/ρprotein,core). Similar to our previous work, we used ρʹ = 0.2 to define a surface envelope near the protein and visualize ligand density profiles on this envelope. 2.3 Contributions of individual moieties to protein-ligand interactions After calculating the spherical harmonic coefficients of each ligand moiety using the above approach, we went back to the MD simulation trajectories to identify important ligand binding events. For every trajectory, we calculated the density of each ligand moiety using the corresponding spherical harmonic coefficients obtained earlier (i.e. each ligand instance during the trajectory contained four moieties and we calculated density (ρ/ρ0)moiety for each of these moieties at every ligand instance). We then chose all the ligand instances during this trajectory where one or more of these moieties exhibited (ρ/ρ0)moiety greater than a threshold density (ρ/ρ0)threshold. This subset of ligands represented all events where the interaction strength of one or more moieties was greater than the threshold density (ρ/ρ0)threshold. We then calculated the number of times a specific moiety contributed to these events, and performed this analysis for different values of threshold density.

2.4 Ligand conformations To compare the structural flexibility of the ligands, we performed separate simulations for each ligand in an aqueous solution. Clustering of structures was performed to identify the dominant conformations for both the ligands in bulk water. Further, these structures in bulk were compared with bound conformations of the ligand in the protein-ligand simulations to understand the role of flexibility of the ligand in binding to the protein.

3. Results and Discussion We wish to obtain insights into the origins of selectivity in multimodal chromatographic systems. To this end, we studied the interactions of two multimodal ligands with three model proteins to quantify the different binding patterns in these systems. We hypothesized that because the protein surface properties are quite different, multimodal ligands would preferentially interact

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with different regions on the proteins in qualitatively different ways. First, the densities of the center-of-geometry of the multimodal ligands around the proteins were calculated to compare the relative strengths of interactions and to identify different preferred binding regions for these ligands on the protein surfaces. Further, we examined the densities of the individual moieties in these multimodal ligands to determine what types of interactions with the underlying protein residues were important for different scenarios. While the identified ligand-binding motifs are specific to the six protein-ligand systems studied here, we expect that the general trends that emerge from the analysis will be applicable to a much broader range of multimodal ligandprotein systems. Proteins The three proteins studied here have different distributions of surface charge and hydrophobicity. Surface representation of the three proteins is presented in Fig. 1. The positively charged residues are colored in blue, negatively charged residues in red and hydrophobic residues in green. As seen in the figure, a cluster of several hydrophobic residues (L8, I44, V70 and L73), surrounded by positively charged residues (K6, R42, K48, H68, R72 and R74), are present on one face of ubiquitin. This hydrophobic patch has been shown to be responsible for ubiquitin’s interactions with surfactants [44] and ubiquitin-binding domains in vivo [45]. We have previously reported MD simulations of ubiquitin interacting with the ligand Capto MMC which highlight the importance of the front face of this protein for binding to a multimodal ligand [23, 31]. Cytochrome C is a highly charged protein containing 105 amino acids. One face of this protein is primarily positively charged (Fig.1b, front) and contains a few dispersed hydrophobic residues (F47, I82 and F83). The other face of this protein has positive and negative patches. αchymotrypsinogen A is a larger protein (245 amino acids) and displays significant surface hydrophobicity. It also contains a cluster of negatively charged residues (E20, E21, D72 and D153) next to a hydrophobic patch (V23, V9, L13, I16 and V17).

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Figure 1. Charged (blue=positive, red=negative) and hydrophobic (green) residues on the front and back face of (a) ubiquitin, (b) cytochrome C, and (c) α-chymotrypsinogen A. A84 for cytochrome C, and A244, A131, A132, T134, T135, S186 and S159 for α-chymotrypsinogen A, have also been labelled for facilitating discussion in the text. Figure 1a is reprinted (adapted) with permission from [31]. Copyright © 2014 American Chemical Society.

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Ligands Both the multimodal ligands studied here have multiple chemical moieties, each with a different dominant mode of interaction. Figure S1 shows the structure of these ligands. While Capto MMC has benzene (hydrophobic), amide (hydrogen-bond), carboxylate (electrostatic) and aliphatic (hydrophobic) groups, Nuvia cPrime has carboxylate (electrostatic), amide (hydrogenbond), benzene (hydrophobic) and amine (hydrogen-bond) groups. In a chromatographic resin, Capto MMC is attached to the base matrix at the end of its aliphatic group while Nuvia cPrime is attached at the end of its amine group.

3.1 Analysis of ligand density distributions: center-of-geometry Ubiquitin Fig. 2 shows density profiles obtained from the spherical harmonic analysis of the MD simulations for the center-of-geometry of Capto MMC and Nuvia cPrime ligands near the front and back face of ubiquitin. As mentioned earlier, the results for ubiquitin interactions with Capto MMC have been presented in a recent work [31] by the authors and are reproduced here to facilitate comparison with the other ligand and proteins. It turns out that for all simulations in this study, the Capto MMC ligand exhibits stronger binding than the Nuvia cPrime ligand. Accordingly, to facilitate an analysis of their protein surface binding behavior, the density profiles of the two ligands have been normalized on different scales. Red indicates high density (Capto MMC: ρ/ρ0~200, Nuvia cPrime: ρ/ρ0~130) and blue indicates low density (ρ/ρ0~1) locations (note: this color scheme has also been employed for the other two proteins described below). The orientations of ubiquitin in Fig. 2 are the same as shown in Fig.1a. For Capto MMC, the primary region of interaction (C1) is on the front face of ubiquitin, with two weaker interaction sites on the back face. In contrast, Nuvia cPrime exhibits interactions on a broader region on the front face of ubiquitin. The highest density region of Nuvia cPrime ligands is located near the bottom of the front face (N1), which is not observed for the Capto MMC ligand. An additional lower affinity diffuse binding region is observed for Nuvia cPrime (N2), located near site C1 for Capto MMC. On the back face of ubiquitin, there are similar interaction sites for the two ligands along with an additional site (N3) for Nuvia. Comparing the relative binding

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strengths of the multimodal ligands to the front and back faces of the protein, it becomes clear that Capto MMC preferentially interacts with the front face while Nuvia cPrime has comparable affinities to both faces of this protein. These differences in binding patterns for these two similar multimodal ligands is quite striking and will be evaluated further in the analysis of the individual moieties presented in the next section.

Figure 2. ρ/ρ0 profile for the (a) Capto MMC, and (b) Nuvia cPrime ligands in the local domain of ubiquitin: front (top), and back (bottom). Red indicates regions of high ligand density (Capto: ρ/ρ0~200, Nuvia: ρ/ρ0~130) while blue indicates regions of low ligand density (ρ/ρ0~1). Values are plotted at the protein density instantaneous interface with ρʹ = 0.2. Note that the high density locations have been labelled C1, C2, N1, N2, N3, and N4 for facilitating discussion in the text. Figure 2a is reprinted (adapted) with permission from [31]. Copyright © 2014 American Chemical Society.

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Cytochrome C Fig. 3 shows the density distributions of the Capto MMC and Nuvia cPrime ligands around cytochrome C. Similar to ubiquitin, the Capto MMC interactions with cytochrome C are stronger than those of Nuvia cPrime. For Capto MMC, high density is observed over a large diffused region on the front face of the protein with C1 and C2 indicating the presence of two higher density subregions. In addition, there are two more interaction sites of comparable densities (C3 and C4) on the side and the back face of this protein, respectively. In contrast to the results with Capto MMC, for Nuvia cPrime, there is minimal binding to the front face of the protein with two relatively low density sites (N1 and N2) observed. An additional low density site is observed on the side of the protein (N3). The strongest interaction site for the Nuvia cPrime ligand is located on the back face of the protein (N4) which is close to the site C4 observed for Capto MMC. Thus, for cytochrome C, while the interaction sites for both ligands are in broadly similar regions, the relative importance of these sites is quite different for the two ligands.

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Figure 3. ρ/ρ0 profile for (a) Capto MMC and (b) Nuvia cPrime ligands in the local domain of cytochrome C: front (top), and back (bottom). Red indicates regions of high ligand density (Capto: ρ/ρ0~200, Nuvia: ρ/ρ0~130) while blue indicates regions of low ligand density (ρ/ρ0~1). Values are plotted at the protein density interface with ρʹ = 0.2. Note that the high density locations have been labelled C1, C2, C3, and C4 (Capto MMC), and N1, N2, N3, and N4 (Nuvia cPrime) for facilitating discussion in the text.

α-chymotrypsinogen A Fig. 4 shows the density profiles for the center-of-geometry of the Capto MMC and Nuvia cPrime ligands around the protein α-chymotrypsinogen A. While the density of the Capto MMC ligand is again higher than that of the Nuvia cPrime ligand, the differences are not as significant as those observed for the other two proteins. For Capto MMC, there are two distinct regions of high ligand density (C1 and C4). In addition, there is weaker binding present in three more locations (C2, C3 and C5). While the density hotspots for Nuvia cPrime are located in the same general regions around the protein, the relative strength of interactions are markedly different for

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these sites. For the Nuvia cPrime ligand there is a clear preference for binding site N2, which is not the most important region for Capto MMC (C2).

Figure 4. ρ/ρ0 profile for the (a) Capto MMC, and (b) Nuvia cPrime ligands in the local domain of α-chymotrypsinogen A: front (top), and back (bottom). Red indicates regions of high ligand density (Capto: ρ/ρ0~200, Nuvia: ρ/ρ0~130) while blue indicates regions of low ligand density (ρ/ρ0~1). Values are plotted at the protein density interface with ρʹ = 0.2. Note that the high density locations have been labelled for facilitating discussion in the text.

The results in Figures 2-4 demonstrate that multimodal ligands, such as Capto MMC and Nuvia C prime, can have multiple interaction sites of varying strengths on a protein surface. This was earlier reported for the ubiquitin-Capto MMC system [31] and appears to be a feature of

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multimodal ligand-protein interactions. Interestingly, the results show that the specificity of two multimodal ligands which contain similar moieties in different structural arrangements can be quite different. For ubiquitin, the high density sites for the Capto MMC ligand are different than those observed with Nuvia cPrime, both in terms of their locations and strengths. For cytochrome C and α-chymotrypsinogen A, the binding locations for the two ligands are similar but their relative affinities are very different.

This can have important implications for protein

interactions with these ligands immobilized on a surface, such as in a chromatographic resin. For example, while interactions of ubiquitin with a surface of Capto MMC ligands will be dominated by the front face of the protein (as has been observed in [23]), its interactions with a Nuvia cPrime presenting surface will be a result of multiple binding orientations of this protein, each contributing significantly to the overall binding strength. These subtle and not-so-subtle differences in binding affinities and modalities can result in dramatic differences in selectivity when developing multimodal chromatographic separations. 3.2 Analysis of ligand density distributions: individual moieties While the spatial preferences of center-of-geometries of the multimodal ligands around these proteins highlight the broad differences between the various ligand-protein systems, it is also of interest to examine the contributions of various types of interactions (electrostatic, hydrophobic and hydrogen-bonding). We believe that the high density of ligands near certain regions of a protein is primarily due to the interactions between one or more moieties of the ligand molecules with the vicinal protein residues. To investigate this in more detail, we calculated the density profiles for different subgroups present within the ligand molecules. The locations of hotspots in these density profiles were then correlated with the underlying protein residues to elucidate the possible modes of interactions (note: the reader is encouraged to refer throughout this section to Figure 1 for the location of specific residues on the front and back faces of the proteins). Figures 5-7 show the density profiles for the individual moieties of the Capto MMC ligand around the three proteins examined in this study. As mentioned previously, the results for ubiquitin-Capto MMC system have been reported earlier [31] and are presented here for the sake of completeness and to facilitate the comparison with ubiquitin-Nuvia system and with the other protein-ligand systems.

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The results for ubiquitin and cytochrome C are very similar. For ubiquitin (Fig. 5), the density hotspots for all the moieties of the Capto MMC ligand are in similar locations and of comparable strength. Comparison of the location of these high density regions with the underlying protein residues revealed the surface properties important for interactions with the Capto MMC ligand. As expected, the density hotspots in the primary region of interaction (C1, Fig. 5) are present in a region which has a cluster of aliphatic groups surrounded by positively charged residues. Similarly, for cytochrome C (Fig. 6), all the ligand moieties are present with similar densities and the underlying protein surface again has positive and hydrophobic groups. For αchymotrypsinogen A (Fig. 7), one of the two important interaction sites (C1) follows a similar pattern with all the moieties involved at comparable strengths and the underlying protein surface possessing a cluster of aliphatic residues (L13, I16 and V17) and a positively charged residue (R15). In contrast, while the other important interaction site (C4) has density hotspots for all the individual moieties, the amine group possesses a distinctly higher density. Comparisons with Fig. 1c revealed that while this region is flanked by positively charged residues (K202, K203 and R15), it also contains multiple polar residues (T134, T135, S186 and S159). This suggests that hydrogen-bonding can play an important role in the interactions of multimodal ligands with certain proteins. These results with Capto MMC suggest that this ligand interacts with proteins surfaces primarily through multiple moieties, rather than a single dominant moiety.

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Figure 5. ρ/ρ0 values plotted for different subgroups of the Capto MMC ligand in the local domain of ubiquitin (top: front face, and bottom: back face): (a) benzene group, (b) amide group, (c) carboxylate group, and (d) aliphatic group. Red indicates regions of high ligand density (ρ/ρ0~200) while blue indicates regions of low ligand density (ρ/ρ0~1). Values are plotted at the protein density interface with ρʹ = 0.2. Figure reprinted (adapted) with permission from [31]. Copyright © 2014 American Chemical Society.

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Figure 6. ρ/ρ0 values plotted for different subgroups of the Capto MMC ligand in the local domain of cytochrome C (top: front face, and bottom: back face): (a) benzene group, (b) amide group, (c) carboxylate group, and (d) aliphatic group. Red indicates regions of high ligand density (ρ/ρ0~200) while blue indicates regions of low ligand density (ρ/ρ0~1). Values are plotted at the protein density interface with ρʹ = 0.2.

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Figure 7. ρ/ρ0 values plotted for different subgroups of the Capto MMC ligand in the local domain of α-chymotrypsinogen A (top: front face, and bottom: back face): (a) benzene group, (b) amide group, (c) carboxylate group, and (d) aliphatic group. Red indicates regions of high ligand density (ρ/ρ0~200) while blue indicates regions of low ligand density (ρ/ρ0~1). Values are plotted at the protein density interface with ρʹ = 0.2.

A similar analysis was performed for the individual moieties present within the Nuvia cPrime ligand and the results are presented in Fig. 8 (ubiquitin) and Figs. S2-S3 (cytochrome C and αchymotrypsinogen A, respectively). Nuvia cPrime interactions with ubiquitin and cytochrome C are markedly different than those observed with Capto MMC. For ubiquitin, near its primary binding site N1 (Fig. 8), the density values for the benzene moiety are significantly higher than the other moieties. This suggests a dominant mode of interaction where the benzene moiety interacts primarily with residue F45 (Fig. 1a) on the ubiquitin surface. For this site (N1), the carboxylate group has two high density regions on either side of the benzene hotspot, which corresponds to H68 and K48 on the underlying ubiquitin surface, indicating two possible orientations of the Nuvia cPrime ligand in this region. For site N4, while all the modes of

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interaction are present, the carboxylate group has minimal contribution to the binding affinity. Similarly, for cytochrome C, the interactions of the individual modes of Nuvia cPrime are dominated by the benzene interactions. This is clearly evident for site N4 (Fig. S2) which has a density hotspot for the benzene moiety corresponding to residue R39 on the underlying cytochrome C surface (Fig. 1b). For the protein α-chymotrypsinogen A, Nuvia cPrime interactions are primarily observed in one interaction site (N2, Fig. S3) which possesses high densities of all the moieties and corresponds to residues R145, H57, W215 and V213 on this protein’s surface (Fig. 1c).

Figure 8. ρ/ρ0 values plotted for different subgroups of the Nuvia cPrime ligand in the local domain of ubiquitin (top: front face, and bottom: back face): (a) carboxylate group, (b) amide group, (c) benzene group, and (d) amine group. Red indicates regions of high ligand density (ρ/ρ0~130) while blue indicates regions of low ligand density (ρ/ρ0~1). Values are plotted at the protein density interface with ρʹ = 0.2. Results for cytochrome C and α-chymotrypsinogen A are presented in Figs. S2 and S3, respectively.

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These results at the moiety level demonstrate that Capto MMC interacts in a qualitatively different way than Nuvia cPrime with these proteins. It is instructive to compare the ligandbinding motifs for the different protein-ligand systems. Two proteins, ubiquitin and αchymotrypsinogen A (Fig 1a and c), possess a cluster of aliphatic residues, next to positive residues, on their surfaces. Capto MMC, containing two hydrophobic groups – benzene and aliphatic – and a negatively charged group, interacts strongly with both these regions (C1 for ubiquitin and C1 for α-chymotrypsinogen A). While cytochrome C doesn’t have any clusters of aliphatic residues, the binding region C1 for Capto MMC on the front face of cytochrome C contains an alanine residue and a deprotonated lysine residue, which provides an extended hydrophobic surface in a positively charged region. Further, hydrogen-bonding is also observed to be important for protein-ligand interactions in one case (for α-chymotrypsinogen A). In sharp contrast, Nuvia cPrime interactions with these proteins are not preferred in regions with aliphatic clusters. Instead, these interactions are dominated by the benzene moiety of the Nuvia cPrime ligand and all high density locations occur over protein residues, both aromatic and positively charged, which can interact strongly primarily with the benzene group. The results indicate that the presence of the additional aliphatic group on Capto MMC provides two important contributions to multimodal interactions. First, it results in more “anchored” ligand orientations near the protein surface, allowing for simultaneous interactions of multiple ligand moieties likely contributing to the comparatively higher densities observed for Capto MMC. Secondly, this hydrophobic group, which possesses a qualitatively different type of hydrophobicity (aliphatic versus aromatic) enables additional interactions with the underlying alkyl residues on the protein surface. In contrast, for Nuvia cPrime, the single hydrophobic interaction with the benzene moiety results in less “anchored” ligand orientations where ligand binding to the underlying protein residues is driven primarily by the aromatic interactions. While these results allow us to understand the differences between various protein-ligand interactions, there are additional factors which can also influence the specific ligand binding sites on a protein in a chromatographic system. The results presented above are based on the interactions of free ligands with proteins, where all the ligand chemical moieties are available for interactions with the protein. If these ligands are immobilized using a particular moiety, the

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accessibility of that moiety to the protein surface would be reduced, thus hindering its possible interactions and likely reducing the strength of binding. To further assess the relative importance of the different modes of interactions in the overall protein-ligand binding, percentage involvement of each ligand moiety at different high density locations (as determined by a threshold ρ/ρ0) was calculated for each protein-ligand system. The details of this calculation are presented in the methods section and the results are presented in Fig. 9 for all the six systems examined in this study. The percentage involvement of a particular mode is an indication of the contribution of that mode to all binding events which had ρ/ρ0 values greater than the specified threshold ρ/ρ0. It should be noted that the higher the threshold ρ/ρ0, the stronger the binding affinity.

Figure 9. Percentage involvement of each mode of a multimodal ligand in a specific proteinligand system. Values are plotted for different threshold ρ/ρ0 values.

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The results for the Capto MMC ligand systems (Figs. 9a, 9c and 9e) are very similar. The benzene and aliphatic groups have a significantly greater contribution than the carboxylate group at higher density cutoffs. In contrast, the contributions of the different modes are comparable at lower cutoffs. Similar trends are observed for the benzene group of Nuvia cPrime, which is the most dominant contributor at higher density cutoffs. This suggests that hydrophobic interactions are extremely important for driving protein-ligand interactions for the six systems studied here. All the proteins studied here have multiple positively charged residues, resulting in multiple regions of positive electrostatic potential on their surface (Figs. S4-S6). Thus, the electrostatic interactions between the negatively charged carboxylate groups of the ligands and the positively charged protein residues would be expected to occur over a broad region of the protein surface. Further, from the above analysis, we conclude that hydrophobic interactions are important at high density locations. This indicates that once the multimodal ligands are in the vicinity of the protein surface (due to electrostatic interactions), their localization is likely determined by the presence of nearby hydrophobic residues. Thus, while the electrostatic interactions play a role in determining the broad binding face of a protein (as reflected by the fact that all the carboxylate moiety density hotspots for the six protein-ligand systems studied here contain regions of positive electrostatic potential), it is the simultaneous interactions of the hydrophobic moieties of the ligands that determine their orientations near a protein’s surface. Additionally, differences in these hydrophobic moieties would result in differences in the specific orientations for various ligands. This binding process can have significant implications. For example, while Nuvia cPrime, which only has a benzene ring as its hydrophobic group, prefers sites providing possible aromatic interactions, the Capto MMC ligand has two hydrophobic groups and can thus interact with a broader range of hydrophobic residues on the protein’s surface, including the clusters of aliphatic residues. 3.3 Ligand structure As described in the methods section, ligand structures in bulk water and in high density locations near ubiquitin were compared. Bulk

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The conformational flexibility of Capto MMC results from the carbon atoms of the aliphatic group and the central carbon atom connecting the carboxylate group, the amide group and the aliphatic group. This allows different conformations where the aliphatic group is positioned in different orientations with respect to the benzene group. Therefore, clustering was done by first overlapping the benzene moiety and then clustering the location of the aliphatic end of the molecule. This resulted in two dominant structural clusters, shown in Fig. S7 as Capto1 and Capto2. In both structures, the aliphatic group is folded towards the benzene group. For Nuvia cPrime, most of the ligand structure is expected to be fairly rigid, due to the planar nature of the amide group and the benzene group. This was indeed found to be the case from the simulations and a single dominant cluster was found (Fig. S7c). The other structures observed with Nuvia cPrime consist of rotations of the carboxylate group, which have minimal effect on the overall conformation. Capto MMC and Nuvia cPrime ligand conformations near Ubiquitin Similar clustering was performed for all structures of Capto MMC and Nuvia cPrime in their high density locations around ubiquitin. As expected, Nuvia cPrime, with almost no conformational flexibility, maintains the same conformation as in the bulk while interacting with ubiquitin at its various density hotspots. Similarly, clustering of Capto MMC structures obtained at binding regions C1 and C2 (Fig. 2) revealed the same two dominant conformations as identified in bulk. These results indicate that Capto MMC has greater structural flexibility which in concert with its two hydrophobic groups may further increase its ability to form hydrophobic associations through structural complementarity with the protein surface.

4. Conclusions We have studied the interactions of two multimodal chromatographic ligands with three different proteins using MD simulations. The two ligands had similar chemical moieties in different structural arrangements, with one ligand containing an additional (aliphatic) hydrophobic group.

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The three proteins offered diverse chemical and topographical interfaces for interactions with the ligands. These six different protein-ligand pairs allowed us to examine a broad range of binding scenarios in multimodal systems and identify various ligand-binding motifs on protein surfaces for multimodal interactions. We employed an approach based on spherical harmonic expansion of atomic densities to identify the high density locations of the ligands and their constituent groups around these proteins. As expected, these multimodal ligands, containing both negatively charged and hydrophobic groups, interacted with positively charged and hydrophobic protein residues. We found that, the Capto MMC ligand consistently exhibited stronger interactions with the proteins compared to the Nuvia cPrime ligand. Further, for each protein, the density hotspots for the two multimodal ligands were different in terms of their binding locations and relative strengths. The results at the moiety level showed that the type of protein residues involved in hydrophobic interactions for the two ligands were quite different. The Nuvia cPrime ligand, containing only a benzene moiety as its hydrophobic group, interacted primarily with aromatic and positively charged residues. In contrast, the Capto MMC ligand had two hydrophobic groups – a benzene ring and an aliphatic tail. This enhanced hydrophobic component was reflected in its interactions, which involved aliphatic clusters as well as aromatic residues on the protein surfaces. Further analysis of ligand conformations revealed that the Capto MMC ligand had significant structural flexibility, which likely allows for greater structural complementarity for the interactions between its hydrophobic groups and the protein residues. This was less likely for the Nuvia cPrime ligand, which had a predominantly planar structure. These results showed that while the electrostatic interactions determine the broad binding face of a protein, a combination of electrostatic and hydrophobic interactions determines the specific ligand orientations near the protein surfaces. Differences in the chemical and structural features of the hydrophobic moieties of ligands result in differences in the specific interactions sites for various ligands. For chromatography, this approach can facilitate both the design of improved multimodal ligands and the development of predictive tools in the future. The results presented in this study suggest that the differences in the kinds of hydrophobicity (aliphatic versus aromatic) could provide opportunities for discriminating protein variants containing differences in a specific region on the protein surface. One can envision a scenario where relatively simple hydrophobic moieties can

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be used in concert with charged moieties to build a library of ligands with different hydrophobic characteristics. While the number and type of hydrophobic moieties are expected to be of primary importance, the hydrogen-bonding groups can be added as linkers to provide an additional handle on the strength and specificity of these interactions. It is important to note that the results presented in this work from free ligand-protein simulations cannot be directly compared to chromatographic results. While all the chemical moieties are available to interact with proteins in the case of free ligands, immobilized ligands will present steric hindrance and limit the accessibility of certain moieties. Ligand density and distribution on the resin surface are also expected to be important. For future studies, incorporation of these effects can provide a better picture of possible protein-ligand orientations in chromatographic systems. The insights from this work can also be employed to develop protein surface property based predictive tools such as QSPR models for prediction of protein retention behavior in multimodal systems.

Acknowledgements We gratefully acknowledge financial support from National Science Foundation grant CBET1160039.

Supporting Information Available Structures of the two multimodal ligands – Capto MMC and Nuvia cPrime – are presented in Figure S1. Results for the interactions of Nuvia cPrime ligand subgroups with cytochrome C and α-chymotrypsinogen A are presented in Figures S2 and S3, respectively. Electrostatic potential maps for ubiquitin, cytochrome C and α-chymotrypsinogen A, calculated using Adaptive Poisson Boltzmann Solver [46], are presented in Figures S4, S5 and S6, respectively. Figure S7 shows the dominant ligand conformations identified for the two multimodal ligands. This material is available free of charge via the Internet at http://pubs.acs.org.

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ACS Paragon Plus Environment

Langmuir

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

248x186mm (150 x 150 DPI)

ACS Paragon Plus Environment

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Langmuir

247x193mm (150 x 150 DPI)

ACS Paragon Plus Environment

Langmuir

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

250x193mm (150 x 150 DPI)

ACS Paragon Plus Environment

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Langmuir

247x193mm (150 x 150 DPI)

ACS Paragon Plus Environment

Langmuir

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

311x220mm (150 x 150 DPI)

ACS Paragon Plus Environment

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