Malaria is a mosquito-borne infectious disease caused by a protozoan parasite of the genus Plasmodium. Prior to transmission, the parasite proceeds through both asymptomatic and symptomatic developmental stages in the host (Miller et al., 2002; Cowman et al., 2012; White et al., 2014). After an asymptomatic development stage within the liver, merozoites are released into the bloodstream. They have an egg-like shape with a typical size of approximately 1.5µm (Bannister et al., 1986b; Dasgupta et al., 2017; Dasgupta et al., 2014b). During the blood stage of infection, which is a clinically symptomatic stage, parasites invade healthy red blood cells (RBCs) and multiply inside them. This process aids parasites to evade the immune response. The total life-cycle within each infected RBC lasts for about 48 hr, after which the cell membrane is ruptured and new merozoites are released into the bloodstream.

Invasion of RBCs by parasites is a complex process that involves the following steps: (i) initial random attachment, (ii) reorientation (or alignment) of the apex toward cell membrane, and (iii) formation of a tight junction followed by the final invasion (Koch and Baum, 2016; Cowman and Crabb, 2006). The parasite’s apex contains the required machinery for the invasion process, and thus, apex alignment toward the cell membrane is a necessary step for a successful invasion to follow. Merozoite adhesion to a RBC is facilitated by proteins at the parasite surface which can bind to the cell membrane (Bannister et al., 1986b; Gilson et al., 2006; Beeson et al., 2016). Recent optical tweezers experiments provide an estimation for the force required to detach a parasite adhered to RBC membrane to be in the range of 10–40 pN (Crick et al., 2014). Other experiments (Dvorak et al., 1975; Gilson and Crabb, 2009; Glushakova et al., 2005; Crick et al., 2013) demonstrate that the parasite is dynamic at the RBC membrane, and induces considerable membrane deformation during alignment. Furthermore, there is a positive correlation between such deformations and parasite alignment. The time required for the parasite to align is found to be on the order of 16 s (Weiss et al., 2015). Our recent investigation of the parasite alignment with adhesion modeled by a homogeneous interaction potential has confirmed the importance of membrane deformations for proper alignment, but the alignment times were found to be significantly less than 1 s (Hillringhaus et al., 2019). The main shortcoming of this model is that it produces only static membrane deformations and the parasite exhibits very little dynamics at the RBC surface. This model has been extended by including realistic bond-based adhesion interactions between the parasite and RBC membrane (Hillringhaus et al., 2020), which results in alignment times consistent with the experimental measurements.

A typical merozoite has an egg-like shape with the apical complex sitting on the pointed edge. Our previous work (Hillringhaus et al., 2020) suggests that parasite alignment occurs due to RBC deformability and stochastic fluctuations in bond dynamics. Stochastic fluctuations and consequent rolling-like (or rotational) motion of the parasite at the membrane surface are especially important at low adhesion strengths, as they facilitate alignment toward pointed apex. The egg-like shape naturally adheres to RBC membrane with its less curved side, as this adhesion state corresponds to the largest contact area. Then, a rotational motion of the parasite toward the apex is required to establish an apex-membrane contact. If parasite adhesion interactions with a membrane are strong, merozoite mobility is significantly suppressed, and the alignment is mainly facilitated through wrapping of the parasite by cell membrane, emphasizing the importance of RBC deformability. These are two major mechanisms for the alignment of an egg-like merozoite.

Even though most types of Plasmodium merozoites have an egg-like shape, the merozoite of Plasmodium yoelii changes its shape from an oval to a spherical shape right before its attachment followed by alignment and invasion (Yahata et al., 2012). The alignment time for Plasmodium yoelii is also reported to be longer than for Plasmodium falciparum (Yahata et al., 2012). It is not clear why the Plasmodium yoelii parasite adapts its shape before the alignment process at the RBC membrane. This raises a question whether the parasite shape has important advantages/disadvantages in the alignment process or simply results from the structural organization of its internal elements. Therefore, it is important to understand the effect of parasite shape in the alignment process.

In this article, the role of parasite shape in the alignment process at the RBC membrane is studied by mesoscopic computer simulations. In particular, we show that basic dynamical properties, such as parasite mobility, RBC membrane deformation, and the number of adhesion bonds, are significantly affected by different parasite shapes. In turn, these are tightly coupled to parasite alignment characteristics that determine its alignment success. In general, parasite shapes with large aspect ratios (e.g. oblate and long prolate ellipsoid) are disadvantageous for alignment, as these shapes result in a significant reduction of parasite mobility at the membrane. A spherical parasite is more mobile than an egg-like merozoite, which is advantageous in cases with low adhesion interactions or increased membrane stiffness. However, the spherical shape is disadvantageous for strong adhesion interactions, when parasite mobility is suppressed, as parasite alignment by membrane wrapping is often unsuccessful because the apex may not be within the adhesion area. As a result, the egg-like shape exhibits an alignment performance that is generally not worse than of other studied shapes, but more robust for disparate conditions in parasite adhesion strength and RBC membrane deformability.

To investigate the role of parasite’s shape in the alignment process, five different shapes with varying aspect ratios are chosen: $(i)$ an egg-like (EG) shape that is the typical shape of Plasmodium falciparum merozoite, $(ii)$ a sphere (SP), $(iii)$ a short ellipsoid (SE) whose dimensions are similar to the egg-like shape, $(iv)$ a long ellipsoid (LE), and $(v)$ an oblate (OB) shape, see Figure 1(a). The corresponding maximum and minimum dimensions for these shapes are ${\displaystyle r_{\mathrm{m}\mathrm{a}\mathrm{x}}=1.5\mu \text{m}}$ & ${\displaystyle r_{\mathrm{m}\mathrm{i}\mathrm{n}}=1.08\mu \text{m}}$ for EG, ${\displaystyle r_{\mathrm{m}\mathrm{a}\mathrm{x}}=r_{\mathrm{m}\mathrm{i}\mathrm{n}}=1.2\mu \text{m}}$ for SP, ${\displaystyle r_{\mathrm{m}\mathrm{a}\mathrm{x}}=1.6\mu \text{m}}$ & ${\displaystyle r_{\mathrm{m}\mathrm{i}\mathrm{n}}=1.02\mu \text{m}}$ for SE, ${\displaystyle r_{\mathrm{m}\mathrm{a}\mathrm{x}}=2.4\mu \text{m}}$ & ${\displaystyle r_{\mathrm{m}\mathrm{i}\mathrm{n}}=0.76\mu \text{m}}$ for LE, and ${\displaystyle r_{\mathrm{m}\mathrm{a}\mathrm{x}}=1.5\mu \text{m}}$ & ${\displaystyle r_{\mathrm{m}\mathrm{i}\mathrm{n}}=0.64\mu \text{m}}$ for OB. All shapes are selected such that they have approximately the same surface area and the same number of vertices or equivalently the same density of adhesion receptors. The fraction of receptors that can form long bonds is kept at 0.4 similarly to our previous study (Hillringhaus et al., 2020), while the fraction of receptors for short bonds is equal to 0.6. Other parasite-RBC interaction parameters are calibrated through the displacement of an egg-shaped parasite at RBC membrane (see Figure 1(b) and Video 1) against available experimental data (Weiss et al., 2015). In this calibration procedure, kinetic rates and strength of the bonds are adjusted, so that simulated and experimental translational displacements of the parasite match well, see Table 1 and the Materials and methods section for details. Note that the translational displacement of the parasite results from its stochastic rolling-like motion. The set of calibrated parameters in Table 1 will be referred to as the reference parameter set. All other parameters including bond rates are kept same unless stated otherwise.

Figure 1

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Video 1

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Dynamical properties of different shapes

Figure 1(c-e) presents basic dynamical measures of merozoites with different shapes. These include [Figure 1(c)] fixed-time displacements $\mathrm{\Delta }d$ traveled by the parasite over fixed time intervals of ${\displaystyle \mathrm{\Delta }t=1\mathrm{s}}$ and normalized by an effective parasite diameter $D_{\text{p}}=\sqrt{A_{\text{p}}/\pi }$ (${\displaystyle A_{\text{p}}\simeq 4.6\mu {\text{m}}^2}$ is the parasite membrane area for all shapes), [Figure 1(d)] change in membrane total energy $\mathrm{\Delta }E/k_{\mathrm{B}}T$ due to deformation induced by parasite adhesion, and [Figure 1(e)] the number of bonds $n_{\mathrm{b}}/N_{\mathrm{para}}$. The spherical shape is most mobile (i.e. has the largest $\mathrm{\Delta }d/D_{\text{p}}$, see Video 2), while the oblate shape is slowest with the lowest $\mathrm{\Delta }d$. Intuitively, a shape with a lower local curvature (e.g. OB shape) should form a larger adhesion area, and thus be less dynamic or mobile. This is in agreement with our results in Figure 1(e), where the SP (OB) shape has the smallest (largest) number of bonds, which is directly proportional to the adhesion area. Note that the egg-like and short-ellipsoid shapes show very similar dynamic characteristics, as these shapes are very close to each other. Fixed-time displacement of the long ellipsoid (see Videos 2 and 3) has values between those for the SE and OB shapes, which is consistent with the number of bonds (or equivalently the adhesion area) in Figure 1(e).

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The RBC deformation energy $E/k_{\mathrm{B}}T$ can generally be expected to be proportional to the adhesion area or the number of formed bonds. This is true for the spherical shape that induces the lowest deformation energy in Figure 1(d). Furthermore, both oblate and long ellipsoid shapes result in a large deformation energy. However, the LE shape has a slightly larger value of $\mathrm{\Delta }E$ than the OB shape, even though the oblate shape has a larger adhesion area. This can be rationalized by the fact that the adhesion of the oblate shape to RBC membrane induces a lower deformation than the LE shape, as the OB shape has a lower curvature at its flat side (see Video 3). Furthermore, the exact position where parasite adheres to RBC membrane is important, as local curvature at the membrane (e.g. negative curvature in the dimple areas) can oppositely match the curvature at the parasite surface. This is the main reason why a successful alignment occurs more frequently in the concave areas of RBC dimples than at the convex rim of the membrane (Hillringhaus et al., 2020). Note that the RBC deformation energy in Figure 1(d) displays opposite trends in its shape dependence than the parasite mobility or fixed-time displacement in Figure 1(c). As a result, parasite shapes with a large asphericity are less dynamic at the RBC surface, while the EG, SP, and SE shapes show comparable dynamical characteristics.

Parasite alignment characteristics

To characterize parasite alignment, we introduce two quantities: $(i)$ apex distance d_apex and $(ii)$ alignment angle θ (Hillringhaus et al., 2020) given by

(1) $d_{\mathrm{apex}}=\underset{i}{\min }(|{𝐫}_{\mathrm{apex}}-{𝐫}_i|),\theta =\arccos (𝐧\cdot {𝐧}^{\mathrm{face}}),$

where ${\mathbf{𝐫}}_{\mathrm{apex}}$ is the apex position, ${\mathbf{𝐫}}_i$ is the position of vertex $i$ at the membrane, $\mathbf{𝐧}$ is the parasite’s directional vector pointing from its back to the apex, and ${\mathbf{𝐧}}^{\mathrm{face}}$ is the normal vector of a RBC membrane triangular face whose center of mass is closest to the parasite’s apex. Both the apex distance $d_{\text{apex}}$ and the alignment angle θ are schematically depicted in Figure 2(a). The directional vector is defined for all shapes by selecting two opposite vertices along the shape axis, which represent the apex and the back. A perfect alignment is achieved when the apex distance is equal to the distance at which the repulsive interaction vanishes (i.e. at $\simeq 2^{1/6}\sigma$) and the alignment angle θ is equal to π. Due to limitations in the discretization of both the RBC and the parasite (Hillringhaus et al., 2020), a successful parasite alignment can be characterized by the criteria

(2) $d_{\mathrm{apex}}\le 2^{1/6}\sigma +r_{\mathrm{junc}}\mathit{\hspace{1em}}\&\mathit{\hspace{1em}}\theta \ge 0.8\pi ,$

 where ${\displaystyle r_{\mathrm{j}\mathrm{u}\mathrm{n}\mathrm{c}}=10}$ nm defines the junctional interaction range of the parasite’s apex (Bannister et al., 1986b).

Figure 2

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Figure 2 presents apex-distance and alignment-angle distributions for different parasite shapes, where the alignment criteria from Eq (2) are indicated by the dashed lines. Even though alignment of the SE shape is similar to the EG shape, it is slightly worse for the SE shape as the d_apex distribution in Figure 2(a) is shifted further away from the alignment criterion for d_apex than that for the EG shape. This is due to the fore-aft asymmetry of the EG shape, whose largest-adhesion configuration corresponds to a slightly tilted orientation of the parasite with its apex closer to the membrane than its back. Figure 2(b) and (d) compares the alignment characteristics of the egg-like shape with oblate and long-ellipsoid shapes. The key advantage of the egg-like shape in comparison to LE and OB shapes is that the EG shape has a reduced adhesion area due to a larger local curvature, which allows the EG parasite to fluctuate more around its directional vector, leading to wider distributions of its alignment characteristics. Since the OB shape forms the largest adhesion area in comparison to the EG and LE shapes, it has the narrowest distributions (i.e. lowest fluctuations) for both the apex distance and alignment angle. A further advantage of the EG shape is the aforementioned fore-aft asymmetry, which results in a shift of the apex-distance and alignment-angle distributions toward a better alignment in comparison to those for the OB and LE shapes.

Clearly, parasite alignment characteristics for the spherical shape are qualitatively different from all other (ellipsoid-like) shapes. At first glance, d_apex and θ distributions in Figure 2(d) and (f) seem to suggest that the SP shape might be best for parasite alignment. However, the membrane deformability breaks the ’up-down’ symmetry of these distributions, leading to two distinct cases: $(i)$ the apex is not within the parasite-membrane adhesion area [Figure 2(b)] for which the alignment characteristics are very poor and (ii) the apex is within the adhesion area [Figure 2(c)] resulting in good alignment. Hence, alignment performance of the spherical parasite is more subtle than indicated by the distributions in Figure 2 and will be discussed further below.

Alignment of a spherical parasite

Figure 3 shows apex-distance and alignment-angle distributions of the spherical parasite for different values of the off-rate k_off. Both d_apex and θ distributions generally display a sharp peak near the alignment criteria and a long tail with a wider distribution for non-aligned parasite orientations. Thus, the sharp peak represents parasite orientations when its apex is within the membrane-parasite contact area $A_{\mathrm{m}}$, as schematically illustrated in Figure 2(c). Correspondingly, the long tail characterizes orientations when the parasite’s apex is not within the adhesion area $A_{\mathrm{m}}$, as sketched in Figure 2(b).

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At large values of the off-rate (e.g. $k_{\mathrm{off}}/k_{\mathrm{on}}^{\mathrm{long}}=4.0$), the spherical parasite is very mobile at the membrane surface (i.e. has a large effective rotational diffusion) and induces nearly no membrane deformations. In such cases, there is no significant wrapping of the parasite by the membrane, resulting in wide d_apex and θ distributions in Figure 3. As k_off is decreased, the merozoite becomes partially wrapped by the membrane, leading to the development of the sharp peak in both distributions. At the smallest off-rate of $k_{\mathrm{off}}/k_{\mathrm{on}}^{\mathrm{long}}=0.5$, the alignment properties in Figure 3 seem to be qualitatively different from those for larger off-rates. Note that for small off-rates, the parasite forms a large number of bonds with the membrane, resulting essentially in its arrest with nearly zero rotational diffusion. Therefore, these simulations are too short to fully capture d_apex and θ distributions, which are also expected to have the sharp peak characterized by the wrapped area $A_{\mathrm{m}}$.

To rationalize apex-distance and alignment-angle distributions for a spherical parasite, we use a simple model of a sphere with radius $R$ partially wrapped by the membrane, as illustrated in Figure 2(b) and (c). Since the adhered parasite is mobile, the parasite’s directional vector can point toward any possible direction when sampled over times longer than a characteristic time of parasite rotational motion. Therefore, the probability of alignment can be approximated as $A_{\mathrm{m}}/A_{\mathrm{p}}$, where $A_{\mathrm{m}}$ and $A_{\mathrm{p}}$ are the adhesion area and the total surface area of a sphere, respectively. As a result, the sharp peak in d_apex and θ distributions near the alignment criteria must increase with an increase in adhesion strength or a decrease in k_off. A theoretical model for sphere wrapping based on energy minimization results in Hillringhaus et al., 2019,

(3) $\frac{A_{\mathrm{m}}}{A_{\mathrm{p}}}=2\sqrt{\frac{2}{Y}\left(\frac{\mathrm{\Delta }{U}_{\mathrm{b}}}{A_{\mathrm{c}}}-\frac{2\kappa }{R^2}\right)},$

where $Y$ is the Young’s modulus of the membrane, κ is the bending rigidity, and $\mathrm{\Delta }{U}_{\mathrm{b}}\sim k_{\mathrm{B}}T\ln (k_{\mathrm{on}}/k_{\mathrm{off}})$ is the energy gained through single-bond association per area $A_{\mathrm{c}}$, which can be considered as the effective area of a single bond. Therefore, $A_{\mathrm{m}}$ increases with a decrease in k_off. Furthermore, for small deformations, $A_{\mathrm{m}}$ is essentially governed by the competition of bending and adhesion energies, while for strong adhesion, stretching elasticity of the membrane also becomes important.

When the parasite is not aligned, the apex distance can be approximated by a height of the parasite’s apex with respect to the flat part of the membrane, see Figure 2(b). Note that this assumption becomes strictly valid for a flat membrane without wrapping or a weak adhesion of the merozoite. Then, the probability distribution for the apex distance d_apex is characterized by the area of a spherical segment (or frustum) with height $\mathrm{\Delta }h$ as

(4) $P(d_{\mathrm{apex}})\mathrm{\Delta }h=\frac{A_{\mathrm{\Delta }h}}{A_{\mathrm{p}}}=\frac{2\pi R\mathrm{\Delta }h}{A_{\mathrm{p}}}=\frac{1}{2R}\mathrm{\Delta }h,$

where $\mathrm{\Delta }h$ is an infinitesimally small interval around d_apex. Therefore, $P(d_{\mathrm{apex}})=1/(2R)$ is independent of the apex distance, which is consistent with nearly flat d_apex distributions for $k_{\mathrm{off}}/k_{\mathrm{on}}^{\mathrm{long}}\ge 2.0$ in Figure 3(a) and Figure 2(d). Similarly, the probability distribution $P(\theta )$ can be approximated using segment areas $A_{\mathrm{\Delta }\theta }=2\pi R^2\left(\cos (\theta )-\cos (\theta +\mathrm{\Delta }\theta )\right)\approx 2\pi R^2\sin (\theta )\mathrm{\Delta }\theta$, resulting in

(5) $P(\theta )\approx \frac{\sin (\theta )}{2}$

for not aligned parasite orientations. This approximation is consistent with the data in Figure 3(b) and Figure 2(f). In summary, such unique distributions of alignment properties for the SP shape are possible due to the spherical symmetry. For non-spherical parasite shapes, a sharp peak disappears because parasite adhesion to the membrane favors a specific parasite orientation.

Effect of adhesion strength on parasite alignment time

Figure 4(a) and (b) show apex-distance and alignment-angle properties for different parasite shapes and various off-rates which is used to control the strength of merozoite adhesion to the membrane. The apex distance decreases when the off-rate is decreased or the strength of adhesion is increased. Similarly, the alignment angle increases toward the alignment criterion in Equation (2), as the adhesion strength is increased. For all non-spherical shape cases, successful alignment is generally achieved at low enough k_off values, which imply strong membrane deformations and a significant wrapping of the parasite by the membrane. This is consistent with deformation energies shown in Figure 4(c), which significantly increase with decreasing k_off. The main difference for the spherical parasite is that the best alignment is achieved for intermediate values of off-rates (e.g. $k_{\mathrm{off}}/k_{\mathrm{on}}^{\mathrm{long}}\approx 1.0$). As mentioned before, small values of k_off significantly suppress parasite mobility, which is required for successful alignment of the spherical parasite because its apex may not be immediately within the parasite-membrane contact area after initial adhesion. Interestingly, the OB shape results in a significantly lower deformation energy than other merozoite shapes for $k_{\mathrm{off}}/k_{\mathrm{on}}^{\mathrm{long}}<2.0$. Here, the magnitude of local curvature has a pronounced effect, such that the OB shape forms a large adhesion area over its nearly flat part with very low curvature, while close to the rim, where the curvature is large, adhesion interactions are too weak to induce membrane wrapping and deformation. For the other shapes, the adhesion strength is still sufficient to induce partial wrapping of the parasite by the membrane over moderate curvatures.

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To compute alignment times, we employ Monte Carlo simulations based on $(d_{\mathrm{apex}},\theta )$ probability maps constructed from approximately 10 independent direct simulation trajectories for each parameter set (Hillringhaus et al., 2020). Figure 4(d) shows alignment times ${\tau }_{\mathrm{n}}$ for different parasite shapes and off-rates, where all times are normalized by the alignment time of an egg-like shape for the reference parameter set (Hillringhaus et al., 2020). In some cases, the bars are missing in the plot, indicating that the alignment has not occurred in direct simulations whose maximum time length is about 26 s. Alignment times of the spherical parasite are very long at small off-rates and become comparable with those of the egg-like shape at intermediate and high values of k_off. The SE, LE, and OB shapes generally align very fast at small off-rates, but often do not align at all when adhesion becomes weak. This means that these spheroidal shapes require substantial membrane deformation for a successful alignment.

Alignment at a rigid RBC

To understand the importance of RBC deformability in the alignment process for different parasite shapes, we have simulated parasite alignment at a rigid RBC. Figure 5(a) and (b) presents fixed-time displacement and alignment time for different parasite shapes and two off-rates. Generally, a small fixed-time displacement (or low mobility at the membrane) results in a long alignment time and vise versa. Both long-ellipsoid and oblate shapes do not align or have a very long alignment time at a rigid membrane, as they require considerable amount of membrane deformation for the alignment. Both egg-like shape and short ellipsoid exhibit similar fixed-time displacements and alignment times. However, for the egg-like shape, an increase in off-rate (i.e. more mobility) improves the alignment whereas for the short ellipsoid an opposite trend is observed. The spherical parasite shows the fastest alignment in comparison to the egg-like and short ellipsoid shapes due to its increased mobility. Thus, at a rigid RBC, the spherical shape shows best alignment properties, at least for intermediate off-rate values.

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We have investigated the importance of merozoite shape for its alignment at the RBC membrane which is a prerequisite for the invasion process. This study is a continuation of our previous work (Hillringhaus et al., 2020) where the alignment of an egg-like parasite, a natural shape of merozoite, was investigated. Motivated by experimental observations by Bannister et al., 1986a, adhesion between the RBC membrane and the parasite is implemented by discrete bonds of two different types, with long and short interaction ranges. The density of both long and short bonds, their kinetic rates and extensional rigidities are calibrated through fixed-time displacement of an egg-like shaped parasite against available experimental data (Weiss et al., 2015). Alignment times from two independent experiments are found to be 16 s (Weiss et al., 2015) and 7-44 s (Yahata et al., 2012), respectively. For the egg-like shape, an average alignment time of ${\displaystyle 10s}$ was obtained in our simulations (Hillringhaus et al., 2020).

To study the effect of parasite shape on alignment, five different parasite shapes, including the original egg-like (EG) shape, short ellipsoid (SE), sphere (SP), long ellipsoid (LE), and oblate (OB) shapes, were considered. The question ‘Which parasite shape performs best for apex alignment and potential invasion?’ does not have a unique answer, as parasite performance also depends on the membrane properties and the characteristics of the adhesion bonds, including their dynamics. In general, some parasite shapes are advantageous when the binding kinetics are slow or large RBC membrane deformations take place, while other shapes are advantageous in case of fast binding kinetics or small membrane deformations.

One of our key results is that the spherical parasite exhibits apex-distance and alignment-angle distributions different from those for non-spherical shapes. Distributions of alignment characteristics for the SP shape generally have a sharp peak near the alignment criteria representing parasite orientations with its apex within the adhesion area. At small off-rates or for strong adhesion interactions, there is a considerable parasite wrapping by the membrane and parasite mobility is suppressed. In this case, a successful alignment of the spherical parasite occurs only if the apex ends up directly within the wrapped part of the membrane. This means that the SP shape exhibits ‘all or nothing’ alignment behavior at small off-rates. In contrast, the egg-like shape adheres with its side to the membrane, and is able to establish a direct membrane-apex contact due to significant parasite wrapping. On the other hand, the SP shape performs better than the EG shape at large values of off-rates when membrane deformation is almost negligible. The spherical symmetry of the SP shape results in its faster mobility in comparison with the egg-like shape. Furthermore, a fluctuation of the EG parasite toward successful alignment due to adhesive dynamics is associated with a larger energetic barrier in comparison to the SP shape for which all directions of motion are statistically equivalent. This is also the main reason why the spherical shape leads to the fastest alignment at a rigid membrane in comparison to all other shapes. Interestingly, even though most types of Plasmodium merozoites have an egg-like shape, Plasmodium yoelii transforms into a spherical shape from an egg-like shape after the egress from an infected RBC (Yahata et al., 2012). This shape transition seems to be essential for the successful invasion. A plausible hypothesis is that Plasmodium yoelii exhibits a rather weak adhesion to RBCs in comparison to Plasmodium falciparum, so that the spherical shape becomes advantageous for the alignment process. However, we cannot exclude the possibility that this shape transition is just a result of some internal processes such as cytoskeleton rearrangement, in preparation of the merozoite for a subsequent invasion.

Short ellipsoid geometrically resembles the EG shape except that the egg-like shape has asymmetric ends along the cylindrical axis. Therefore, the SE shape shows alignment characteristics that are closest to the EG shape. However, the alignment of the SE shape is slightly worse than that of the egg-like parasite, as the alignment-angle distribution for short ellipsoid is shifted further away from the alignment criterion in comparison to the egg-like shape. This is due to the asymmetry of EG shape along the cylindrical axis, which favors an adhesion orientation tilted toward the apex. At low enough bond off-rates or strong adhesion interactions, alignment of the SE shape is faster than the egg-like shape, as the wrapping of more curved apex region of the EG parasite is slightly less energetically favorable than that of a symmetric SE shape. Furthermore, alignment of oblate and long-ellipsoid shapes proceeds only through significant wrapping of the parasite by the membrane, which occurs only at low off-rates or for strong adhesion interactions. This is also evident from simulations at a rigid RBC, where alignment times of the LE and OB shapes are either very long or no alignment potentially occurs. Despite the fact that the egg-like shape has some advantages over the investigated spheroidal shapes, it is not clear whether this asymmetry exists simply due to the internal parasite structure (e.g. placement of essential organelles of different sizes) or has some functional importance.

Finally, apical alignment at the RBC membrane is followed by parasite invasion, which requires the formation of a tight junction. During invasion, the tight junction is formed at the apical end and moves toward the back of the parasite with the aid of the actomyosin machinery (Keeley and Soldati, 2004; Robert-Paganin et al., 2019; Cowman and Crabb, 2006). Even though the invasion includes mainy mechanochemical processes, parasite shape must play an important role, as it significantly affects the energy required to deform RBC membrane. For instance, particles with a larger aspect ratio such as oblate and long ellipsoids require a larger energy for complete wrapping (or uptake) (Bahrami et al., 2014; Dasgupta et al., 2014a; Dasgupta et al., 2017). From a dynamical perspective, fluctuations that are important for particle uptake also depend on the geometry of a particle (Frey et al., 2019). Note that particle uptake studies are performed majorly for vesicles, while RBCs possess shear elasticity in addition to membrane bending rigidity. For instance, Hillringhaus et al., 2019 show that for small interaction strengths, bending energy has a dominant contribution to membrane deformation energy, while for strong interactions shear elastic energy exhibits a more dominant contribution. Different aspects related to the performance of various parasite shapes for RBC invasion clearly require further investigations.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data for all figures are provided.

1. 1. Bahrami AH
   2. Raatz M
   3. Agudo-Canalejo J
   4. Michel R
   5. Curtis EM
   6. Hall CK
   7. Gradzielski M
   8. Lipowsky R
   9. Weikl TR

   (2014) Wrapping of nanoparticles by membranes

   Advances in Colloid and Interface Science 208:214–224.

   https://doi.org/10.1016/j.cis.2014.02.012

   • PubMed
   • Google Scholar
2. 1. Bannister LH
   2. Mitchell GH
   3. Butcher GA
   4. Dennis ED

   (1986a) Lamellar membranes associated with rhoptries in Erythrocytic merozoites of Plasmodium knowlesi: a clue to the mechanism of invasion

   Parasitology 92 ( Pt 2:291–303.

   https://doi.org/10.1017/S0031182000064064

   • PubMed
   • Google Scholar
3. 1. Bannister LH
   2. Mitchell GH
   3. Butcher GA
   4. Dennis ED
   5. Cohen S

   (1986b) Structure and development of the surface coat of erythrocytic merozoites of Plasmodium knowlesi

   Cell and Tissue Research 245:281–290.

   https://doi.org/10.1007/BF00213933

   • PubMed
   • Google Scholar
4. 1. Beeson JG
   2. Drew DR
   3. Boyle MJ
   4. Feng G
   5. Fowkes FJ
   6. Richards JS

   (2016) Merozoite surface proteins in red blood cell invasion, immunity and vaccines against malaria

   FEMS Microbiology Reviews 40:343–372.

   https://doi.org/10.1093/femsre/fuw001

   • PubMed
   • Google Scholar
5. 1. Cowman AF
   2. Berry D
   3. Baum J

   (2012) The cellular and molecular basis for malaria parasite invasion of the human red blood cell

   Journal of Cell Biology 198:961–971.

   https://doi.org/10.1083/jcb.201206112

   • Google Scholar
6. 1. Cowman AF
   2. Crabb BS

   (2006) Invasion of red blood cells by malaria parasites

   Cell 124:755–766.

   https://doi.org/10.1016/j.cell.2006.02.006

   • Google Scholar
7. 1. Crick AJ
   2. Tiffert T
   3. Shah SM
   4. Kotar J
   5. Lew VL
   6. Cicuta P

   (2013) An automated live imaging platform for studying merozoite egress-invasion in malaria cultures

   Biophysical Journal 104:997–1005.

   https://doi.org/10.1016/j.bpj.2013.01.018

   • PubMed
   • Google Scholar
8. 1. Crick AJ
   2. Theron M
   3. Tiffert T
   4. Lew VL
   5. Cicuta P
   6. Rayner JC

   (2014) Quantitation of malaria parasite-erythrocyte cell-cell interactions using optical tweezers

   Biophysical Journal 107:846–853.

   https://doi.org/10.1016/j.bpj.2014.07.010

   • PubMed
   • Google Scholar
9. 1. Dasgupta S
   2. Auth T
   3. Gov NS
   4. Satchwell TJ
   5. Hanssen E
   6. Zuccala ES
   7. Riglar DT
   8. Toye AM
   9. Betz T
   10. Baum J
   11. Gompper G

   (2014a) Membrane-wrapping contributions to malaria parasite invasion of the human erythrocyte

   Biophysical Journal 107:43–54.

   https://doi.org/10.1016/j.bpj.2014.05.024

   • PubMed
   • Google Scholar
10. 1. Dasgupta S
    2. Auth T
    3. Gompper G

    (2014b) Shape and orientation matter for the cellular uptake of nonspherical particles

    Nano Letters 14:687–693.

    https://doi.org/10.1021/nl403949h

    • PubMed
    • Google Scholar
11. 1. Dasgupta S
    2. Auth T
    3. Gompper G

    (2017) Nano- and microparticles at biological and fluid interfaces

    Journal of Physics. Condensed Matter : An Institute of Physics Journal 29:373003.

    https://doi.org/10.1088/1361-648X/aa7933

    • Google Scholar
12. 1. Dvorak JA
    2. Miller LH
    3. Whitehouse WC
    4. Shiroishi T

    (1975) Invasion of erythrocytes by malaria merozoites

    Science 187:748–750.

    https://doi.org/10.1126/science.803712

    • PubMed
    • Google Scholar
13. 1. Español P
    2. Warren P

    (1995) Statistical mechanics of dissipative particle dynamics

    Europhysics Letters 30:191–196.

    https://doi.org/10.1209/0295-5075/30/4/001

    • Google Scholar
14. 1. Fedosov DA
    2. Caswell B
    3. Karniadakis GE

    (2010a) Systematic coarse-graining of spectrin-level red blood cell models

    Computer Methods in Applied Mechanics and Engineering 199:1937–1948.

    https://doi.org/10.1016/j.cma.2010.02.001

    • Google Scholar
15. 1. Fedosov DA
    2. Caswell B
    3. Karniadakis GE

    (2010b) A multiscale red blood cell model with accurate mechanics, rheology, and dynamics

    Biophysical Journal 98:2215–2225.

    https://doi.org/10.1016/j.bpj.2010.02.002

    • PubMed
    • Google Scholar
16. 1. Frey F
    2. Ziebert F
    3. Schwarz US

    (2019) Stochastic Dynamics of Nanoparticle and Virus Uptake

    Physical Review Letters 122:088102.

    https://doi.org/10.1103/PhysRevLett.122.088102

    • PubMed
    • Google Scholar
17. 1. Gilson PR
    2. Nebl T
    3. Vukcevic D
    4. Moritz RL
    5. Sargeant T
    6. Speed TP
    7. Schofield L
    8. Crabb BS

    (2006) Identification and stoichiometry of glycosylphosphatidylinositol-anchored membrane proteins of the human malaria parasite Plasmodium falciparum

    Molecular & Cellular Proteomics 5:1286–1299.

    https://doi.org/10.1074/mcp.M600035-MCP200

    • PubMed
    • Google Scholar
18. 1. Gilson PR
    2. Crabb BS

    (2009) Morphology and kinetics of the three distinct phases of red blood cell invasion by Plasmodium falciparum merozoites

    International Journal for Parasitology 39:91–96.

    https://doi.org/10.1016/j.ijpara.2008.09.007

    • PubMed
    • Google Scholar
19. 1. Glushakova S
    2. Yin D
    3. Li T
    4. Zimmerberg J

    (2005) Membrane transformation during malaria parasite release from human red blood cells

    Current Biology 15:1645–1650.

    https://doi.org/10.1016/j.cub.2005.07.067

    • PubMed
    • Google Scholar
20. 1. Gompper G
    2. Kroll DM

    (1996) Random surface discretizations and the renormalization of the bending rigidity

    Journal De Physique I 6:1305–1320.

    https://doi.org/10.1051/jp1:1996246

    • Google Scholar
21. Book

    1. Gompper G
    2. Kroll DM

    (2004)

    Triangulated-surface models of fluctuating membranes

    In: Nelson D. R, Piran T, Weinberg S, editors. Statistical Mechanics of Membranes and Surfaces. Singapore: World Scientific. pp. 359–426.

    • Google Scholar
22. 1. Hillringhaus S
    2. Dasanna AK
    3. Gompper G
    4. Fedosov DA

    (2019) Importance of erythrocyte deformability for the alignment of malaria parasite upon invasion

    Biophysical Journal 117:1202–1214.

    https://doi.org/10.1016/j.bpj.2019.08.027

    • PubMed
    • Google Scholar
23. 1. Hillringhaus S
    2. Dasanna AK
    3. Gompper G
    4. Fedosov DA

    (2020) Stochastic bond dynamics facilitates alignment of malaria parasite at Erythrocyte membrane upon invasion

    eLife 9:e56500.

    https://doi.org/10.7554/eLife.56500

    • PubMed
    • Google Scholar
24. 1. Hoogerbrugge PJ
    2. Koelman JMVA

    (1992) Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics

    Europhysics Letters 19:155–160.

    https://doi.org/10.1209/0295-5075/19/3/001

    • Google Scholar
25. 1. Keeley A
    2. Soldati D

    (2004) The glideosome: a molecular machine powering motility and host-cell invasion by apicomplexa

    Trends in Cell Biology 14:528–532.

    https://doi.org/10.1016/j.tcb.2004.08.002

    • PubMed
    • Google Scholar
26. 1. Koch M
    2. Baum J

    (2016) The mechanics of malaria parasite invasion of the human erythrocyte - towards a reassessment of the host cell contribution

    Cellular Microbiology 18:319–329.

    https://doi.org/10.1111/cmi.12557

    • PubMed
    • Google Scholar
27. 1. Krause D
    2. Thörnig P

    (2018) JURECA: modular supercomputer at jülich supercomputing centre

    Journal of Large-Scale Research Facilities JLSRF 4:A132.

    https://doi.org/10.17815/jlsrf-4-121-1

    • Google Scholar
28. 1. Miller LH
    2. Baruch DI
    3. Marsh K
    4. Doumbo OK

    (2002) The pathogenic basis of malaria

    Nature 415:673–679.

    https://doi.org/10.1038/415673a

    • PubMed
    • Google Scholar
29. 1. Robert-Paganin J
    2. Robblee JP
    3. Auguin D
    4. Blake TCA
    5. Bookwalter CS
    6. Krementsova EB
    7. Moussaoui D
    8. Previs MJ
    9. Jousset G
    10. Baum J
    11. Trybus KM
    12. Houdusse A

    (2019) Plasmodium myosin A drives parasite invasion by an atypical force generating mechanism

    Nature Communications 10:3286.

    https://doi.org/10.1038/s41467-019-11120-0

    • PubMed
    • Google Scholar
30. 1. Weiss GE
    2. Gilson PR
    3. Taechalertpaisarn T
    4. Tham WH
    5. de Jong NW
    6. Harvey KL
    7. Fowkes FJ
    8. Barlow PN
    9. Rayner JC
    10. Wright GJ
    11. Cowman AF
    12. Crabb BS

    (2015) Revealing the sequence and resulting cellular morphology of receptor-ligand interactions during Plasmodium falciparum invasion of erythrocytes

    PLOS Pathogens 11:e1004670.

    https://doi.org/10.1371/journal.ppat.1004670

    • PubMed
    • Google Scholar
31. 1. White NJ
    2. Pukrittayakamee S
    3. Hien TT
    4. Faiz MA
    5. Mokuolu OA
    6. Dondorp AM

    (2014) Malaria

    The Lancet 383:723–735.

    https://doi.org/10.1016/S0140-6736(13)60024-0

    • Google Scholar
32. 1. Yahata K
    2. Treeck M
    3. Culleton R
    4. Gilberger TW
    5. Kaneko O

    (2012) Time-lapse imaging of red blood cell invasion by the rodent malaria parasite Plasmodium yoelii

    PLOS ONE 7:e50780.

    https://doi.org/10.1371/journal.pone.0050780

    • PubMed
    • Google Scholar

Author details

1. Anil K Dasanna

   Theoretical Physics of Living Matter, Institute of Biological Information Processing and Institute for Advanced Simulation, Forschungszentrum Jülich, Jülich, Germany

   Contribution

   Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5960-4579

2. Sebastian Hillringhaus

   Theoretical Physics of Living Matter, Institute of Biological Information Processing and Institute for Advanced Simulation, Forschungszentrum Jülich, Jülich, Germany

   Contribution

   Software, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0100-9368

3. Gerhard Gompper

   Theoretical Physics of Living Matter, Institute of Biological Information Processing and Institute for Advanced Simulation, Forschungszentrum Jülich, Jülich, Germany

   Contribution

   Conceptualization, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8904-0986

4. Dmitry A Fedosov

   Theoretical Physics of Living Matter, Institute of Biological Information Processing and Institute for Advanced Simulation, Forschungszentrum Jülich, Jülich, Germany

   Contribution

   Conceptualization, Supervision, Project administration, Writing - review and editing

   For correspondence

   d.fedosov@fz-juelich.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7469-9844

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