Introduction

Microtubules are essential components of the eukaryotic cytoskeleton, supporting a wide range of cellular processes, including cell division, polarization, and migration (Alberts, 2015). However, their properties can vary considerably across different cellular contexts and structures (Janke and Magiera, 2020; Kapitein and Hoogenraad, 2015; Wittmann et al., 2001). This variability reflects the complex and tightly regulated mechanisms that govern the dynamics of cellular microtubules, among which the microtubule-associated proteins (MAPs) play crucial roles.

Kinesin superfamily members not only facilitate the transport of cargo along microtubules but also serve as regulators of microtubule dynamics, as exemplified by the kinesin-13 family. Initially identified through studies of spindle functions and neurons (Aizawa et al., 1992; Noda et al., 1995; Walczak et al., 1996; Wordeman and Mitchison, 1995), kinesin-13 has since been implicated in a broad range of cellular processes, including spindle assembly (Walczak et al., 2002), chromosome segregation (Kline-Smith et al., 2004), directional migration (Zong et al., 2021), ciliary length control (Piao et al., 2009; Vasudevan et al., 2015), and neuronal polarization (Ghosh-Roy et al., 2012; Puri et al., 2021).The loss of kinesin-13 often results in altered structural and dynamic properties of cellular microtubules. Moreover, the subcellular localizations of kinesin-13, such as at growing microtubule ends, centrosomes, and centromeres, are key regulatory sites for microtubule dynamics. These observations suggest that kinesin-13 family members are key regulators for the length, mass, and polarity of cellular microtubules.

As a representative member of the kinesin-13 family, MCAK has been characterized as both a microtubule depolymerase and a catastrophe factor (Desai et al., 1999; Gardner et al., 2011; Hunter et al., 2003; Kline-Smith and Walczak, 2002; Walczak et al., 1996). It has been proposed that MCAK targets growing microtubule ends by interacting with end-binding proteins (EBs) or motors, such as Kif18b (Gouveia et al., 2010; Honnappa et al., 2009; McHugh and Welburn, 2023; Tanenbaum et al., 2011). Upon binding, MCAK destabilizes the protective GTP cap at the growing microtubule ends by bending and disassembling the underlying substrate, including terminal tubulin dimers or protofilaments (Benoit et al., 2018; Moores and Milligan, 2006; Wang et al., 2017), thereby increasing the frequency of catastrophe events. MCAK can also function independently (Desai et al., 1999; Gardner et al., 2011; Hunter et al., 2003), potentially targeting stabilized microtubule ends through direct binding or rapid diffusion along the microtubule lattice (Desai et al., 1999; Helenius et al., 2006; Wang et al., 2012). Previous structural studies suggest that MCAK recognizes curved protofilaments at microtubule ends (Asenjo et al., 2013; Benoit et al., 2018; Tan et al., 2008; Trofimova et al., 2018; Wang et al., 2017). However, these studies primarily focus on stabilized microtubules, short protofilaments, or tubulin dimers, leaving the mechanism by which MCAK binds to growing microtubule ends unresolved.

In this study, we use single-molecule imaging to investigate the direct binding of MCAK to growing microtubule ends. A key finding is that MCAK binds throughout the entire GTP cap, encompassing both the EB cap, where EB1 preferentially binds, and the distalmost cap, where XMAP215 binds to curved protofilaments. Notably, this is the first discovery that MCAK exhibits a binding preference to the EB cap. Further analysis reveals that MCAK strongly binds to GTPγS microtubules, which are thought to mimic GDP·Pi-tubulin conformations enriched in the EB cap. Having confirmed that MCAK binds to the entire GTP cap structure, we next examine how MCAK co-regulates microtubule dynamics in collaboration with XMAP215, and reveal their additive, rather than mutually exclusive, regulatory effects. Overall, this study provides novel insights into how MCAK binds to and acts at growing microtubule ends.

Results

MCAK shows a binding preference for growing microtubule ends

We began by measuring the single-molecule binding kinetics of MCAK at growing microtubule ends using the in vitro microtubule dynamics assay. This experiment was performed in BRB80 supplemented with 50 mM KCl and 1 mM ATP, providing a near-physiological ion strength of approximately 0.2 M, similar to what was previously reported (Scopes, 2002; Storey, 2004). The size-exclusion chromatography analysis showed that the purified GFP-MCAK exists as dimers under this condition (Fig. s1).

We observed MCAK binding events both at growing microtubule ends and along the lattice (Fig. 1A). To characterize these events, we measured the fluorescence intensity of each binding event (see Methods) and plot a probability distribution of all binding events (Fig. s1). We used the range of fluorescence intensity from the probability distribution (μ±2σ: 300 ± 145 A.U.) to identify single MCAK dimers. Using a custom software (Maurer et al., 2014; Song et al., 2020), we determined the end-binding position of each individual MCAK molecule in each frame by fitting a Gaussian function to the intensity profile along the longitudinal axis of the microtubule, with the plus-end serving as the positional reference (Fig. 1B). The average position over the dwell time of each MCAK molecule was taken as the position of this binding event (Fig. s2). From this, we generated a probability distribution of all binding positions, derived from 152 binding events across three assays (Fig. 1C). This analysis revealed that the MCAK binding region was 162 nm in length (FWHM, Full Width at Half Maximum), corresponding to approximately 20 layers of tubulin dimers. We categorized the binding events of MCAK within this region as “end binding” and those located more proximally as “lattice binding”.

Figure 1.

To investigate the binding kinetics, we first compared the apparent association rates of MCAK on the plus-end (k_on-P) and the lattice (k_on-L) in the presence of ATP. The ratio (R_E/L) of k_on-P ((22. 6 ± 1.4) ×10⁻⁶ s⁻¹ nM⁻¹, per tubulin dimer, mean±SEM, hereinafter unless otherwise specified) to k_on-L ((2.8 ± 0.3) ×10⁻⁶ s⁻¹ nM⁻¹) was 13 ± 1 (n=66 microtubules from 3 assays) (Fig. 1 D-E). We also measured the dwell time, which reflects the dissociation rate (k_off) (Fig. s1). MCAK exhibited a longer dwell time at the plus-end than on the lattice (plus end: 0.75 ± 0.02 s, n=966 events from 3 assays; lattice: 0.62 ± 0.02 s, n=702 events from 3 assays, p<0.001) (Fig. 1F). Using these kinetic parameters, we calculated the dissociation constant (K_d) of MCAK for individual tubulin dimers at the plus-end as 69 μM. This affinity may appear relatively low, but when considering the microtubule end as a multivalent receptor with multiple MCAK-binding sites (end length=160 nm) - where only a few bound MCAK molecules are required to regulate microtubule dynamics parameters - the effective dissociation constant reaches the nanomolar range (260 nM). The MCAK’s lattice binding affinity was even lower (K_d-L=1057 μM, per tubulin dimer), demonstrating that MCAK has a clear end-binding preference. We also observed single-molecule binding events of MCAK at the minus-ends (Fig. 1A), with a similar R_E/L (13 ± 2, n=28 microtubules from 3 assays) and K_d (40 μM, per tubulin dimer). We focused on the plus-end binding events (referred to as “end-binding” hereinafter), due to their greater physiological relevance.

Having measured the single-molecular binding events, we next investigated whether the binding of MCAK to growing microtubule ends occurs through direct end-binding (3D diffusion) or 1D lattice-diffusion in our experimental conditions (Gouveia et al., 2010; Helenius et al., 2006). These two mechanisms are not mutually exclusive. Our data revealed that the lattice-diffusion coefficient of MCAK (D) on the lattice of dynamic microtubules was 0.023 ± 0.001 μm²‧s⁻¹ (Fig. 1G), comparable to the values reported under high-salt conditions (Cooper et al., 2010; Gouveia et al., 2010), but lower than that in low-salt conditions (Helenius et al., 2006). Because the dwell time of the lattice-binding events was approximately 0.6 s, the average length scanned by a MCAK molecule via 1D lattice-diffusion was ∼160 nm, roughly the length the GTP cap. Using an established method (Helenius et al., 2006), we calculated the flux of MCAK arriving at growing ends via 1D diffusion was 0.004 s⁻¹. This was significantly lower than the observed MCAK arrival rate at the plus-end observed (N_p= k_on‧C =0.067 s⁻¹. C: MCAK concentration). Therefore, in our experimental conditions, 1D lattice-diffusion contributes minimally to the growing end-binding of MCAK (∼ 6%), suggesting that the end-binding primarily results from the direct binding pathway.

MCAK binds to the entire GTP cap

We sought to understand how MCAK recognizes growing microtubule ends. Previous studies, mostly based on tubulin dimers, short protofilaments, or stabilized microtubules, suggesting that MCAK binds microtubule ends by recognizing curved protofilaments (Asenjo et al., 2013; Benoit et al., 2018; Trofimova et al., 2018). This model predicts that MCAK binds to the distalmost end of growing microtubules, overlapping with the binding region of XMAP215 family members (i.e. the distalmost cap) (Ayaz et al., 2012; Brouhard et al., 2008). To test this hypothesis, we quantified the end-binding regions of MCAK, EB1, and XMAP215 (Fig. 2A-B) by measuring the FWHM of their spatial distribution. To our surprise, the probability distribution of MCAK’s positions exhibited a significant difference from that of XMAP215 (p < 10⁻⁵, Kolmogorov-Smirnov test). The end-binding region of MCAK (FWHM=185 nm) was longer than that of XMAP215 (FWHM=123 nm) (Fig. 2B). It extended proximally towards the lattice, covering the binding region of EB1 (i.e. the EB cap, FWHM=118 nm). To further understand whether MCAK binds to the distalmost cap and the EB cap with different kinetics, we compared the dwell time of MCAK at the distal and proximal locations (Fig. 2B). As shown in Fig.2B, MCAK molecules located to the left of the XMAP215 binding region were classified as proximally distributed, while those located to the right of the EB1 binding region were considered distally distributed. No significant difference was found between the two groups (τ_distal=0.8 ± 0.1 s, n=28 binding events; τ_proximal=0.7±0.1 s, n=45 binding events; p>0.3, the two-tailed Mann-Whitney U test with Bonferroni correction). Additionally, no significant differences were observed in the intensity profiles of the microtubule ends in the presence of MCAK (1 nM), EB1 (10 nM) and XMAP215 (1 nM), respectively (End_EB1 vs End_MCAK, p=0.68; End_MCAK vs End_XMAP215, p=0.68; End_EB1 vs End_XMAP215, p=1. Kolmogorov-Smirnov test) (Fig. 2C).

Figure 2.

The results suggest that MCAK’s end-binding region partially overlaps with that of XMAP215, with more MCAK positioned behind XMAP215, closer to the lattice. To confirm this, we assessed the colocalization of MCAK and XMAP215. Given the low probability of both molecules simultaneously appearing at microtubule ends at low concentrations, we increased the concentration of XMAP215 to ensure its consistent tracking of growing microtubule ends while simultaneously recording single-molecule binding events of MCAK. In each frame, we measured the distance between the intensity peaks of XMAP215 and MCAK along the microtubule’s longitudinal axis (Fig. 2D). The average distance over MCAK’s binding duration was then calculated to determine its relative position to XMAP215. Subsequently, we plotted a probability distribution of all relative distances (Fig. 2E). This distribution peaked at −65 nm, indicating that MCAK predominantly binds behind XMAP215, with 14.5% of binding events occurring within XMAP215’s binding region (distance > 0). These findings support the notion that MCAK binds to both the distalmost cap and the EB cap.

The end-binding preference of MCAK is nucleotide state-dependent

It has been reported that MCAK tightly binds to microtubules in both the AMPPNP (a non-hydrolysable ATP analogue, mimicking the ATP-bound state) and nucleotide-free states, but exhibits weaker binding in the ADP-bound state (Friel and Howard, 2011; Helenius et al., 2006; Wang et al., 2012). Moreover, kinesin-13s in the AMPPNP state preferentially form protofilament curls or rings with tubulins (Asenjo et al., 2013; Benoit et al., 2018; Tan et al., 2008). These findings suggest that the interaction between kinesin-13s and microtubules is regulated by their nucleotide-bound state. Given this, we sought to determine whether the end-binding preference of MCAK is also nucleotide state-dependent. To explore this, we measured the end-binding kinetics of MCAK in complex with AMPPNP, ADP, or in the nucleotide-free (APO) state (Fig. 3A), under conditions that maintain similar microtubule growth rates (Fig. s1).

Figure 3.

Comparing the binding kinetics of MCAK in different nucleotide states, we found that k_on-P and k_on-L of MCAK‧AMPPNP were both lower than those of MCAK‧ATP (Fig. 3 B-C), yet resulting in a similar end-binding preference (R_E/L=11 ± 2, n=29 microtubules from 2 assays, p>1). However, the binding of MCAK‧AMPPNP at the end and lattice both showed a longer dwell time compared to that of MCAK‧ATP (3.32 ± 0.24 s at the end, n=231 events from 2 assays, p<0.001; 1.46 ± 0.21 s at lattice, n=142 events from 2 assays, p<0.001), indicating lower k_off (Fig. 3D and Fig. s1). As a result of the proportionally reduced k_on-P and k_off, the end-binding affinity of MCAK‧AMPPNP (K_d-P=61 μM, per tubulin dimer, hereinafter) was comparable to that of MCAK‧ATP. In contrast, MCAK‧ADP showed nearly unchanged k_on-P but increased k_on-L, leading to a lower R_E/L (2 ± 0.3, n=21 microtubules from 2 assays, p<0.001) and suggesting the reduction, but not a complete loss, of the end-binding preference (Fig. 3 B-C). Additionally, MCAK‧ADP showed a significantly shorter end-binding and lattice-binding dwell time (0.46 ± 0.02 s at the end, n=327 events from 2 assays, p<0.001) compared to MCAK‧ATP (Fig. 3 D). Consequently, MCAK‧ADP has a lower end-binding affinity (K_d-P=163 μM). MCAK‧APO, the nucleotide-free state, showed a large increase in k_on-P and k_on-L, thereby having a lower R_E/L (2 ± 0.2, n=36 microtubules from 3 assays, p<0.001) (Fig. 3 B-C). It also showed a significantly longer end-binding dwell time (1.89 ± 0.23 s, n=71 events from 3 assays, p<0.001) (Fig. 3D), indicating a lower k_off (Fig. s1). As a result, MCAK‧APO had a higher end-binding affinity (K_d-P=1.8 μM), but it partially loses the end-binding preference. Overall, these findings indicate that MCAK’s end-binding preference and affinity are dependent on its nucleotide state, with the ATP-bound state exhibiting the highest end-binding preference.

MCAK strongly binds to GTPγS microtubules

The binding region of MCAK at growing microtubule ends encompasses the EB cap. This region consists mostly of GDP‧Pi-tubulin dimers, which are thought to form at least some lateral contacts and adopt a straighter conformation (Guesdon et al., 2016; Maurer et al., 2012). This raises the question of how MCAK recognizes the EB cap. To understand this, we examined that to what extent, MCAK may adopt the end-binding mechanism utilized by EB1.

We first investigated the contribution of the nucleotide state-dependent feature of the EB cap, which underlies the end-binding preference of EBs (Maurer et al., 2011; Maurer et al., 2012). Specifically, we compared the binding affinities of MCAK‧ATP on GTPγS and GDP microtubules, mimicking the tubulin dimers in the EB cap and the lattice of growing microtubules, respectively (Fig. 4A). Because MCAK‧ATP at the concentration required for full lattice decoration would rapidly disassemble microtubules, we cannot directly compare the total binding of MCAK on microtubules as what was previously done for the EBs (Maurer et al., 2011; Maurer et al., 2012). Instead, we adopted an alternative approach by recording the binding of MCAK‧ATP at a low concentration (10 nM) on microtubules and measuring fluorescence intensity summation over a period of time (1000 frames, 0.3s per frame) (Fig. s3). Strikingly, quantitative analysis revealed that MCAK‧ATP showed a significantly stronger binding to GTPγS microtubules than to GDP microtubules (Fig. 4 B-C). Because the tubulin dimers in GTPγS microtubules were considered to be the analogue of GDP‧Pi-tubulins in the EB cap (Manka and Moores, 2018; Maurer et al., 2011; Maurer et al., 2014; Zhang et al., 2015), this result provides a biochemical basis for the MCAK‧ATP’s binding preference for the EB cap. Here, we used EB1 as a control (Fig. s4).

Figure 4.

Given that the growing end-binding affinity and preference of MCAK depend on the nucleotide state, we wondered whether its preference for GTPγS microtubules follows a similar trend. To address this, we performed the experiments for MCAK‧AMPPNP, MCAK‧ADP and MCAK‧APO. The MCAK variants bound to the lattice of microtubules with different affinities (MCAK‧APO>MCAK‧AMPPNP>MCAK‧ADP > MCAK‧ATP) (Fig. 4B), consistent with the results observed on the lattice of dynamic microtubules. Additionally, MCAK maintained a binding preference for GTPγS microtubules over GDP microtubules across all nucleotide states, but the degree of binding preference varied with the nucleotide state (MCAK‧AMPPNP > MCAK‧ATP ≈ MCAK‧APO >MCAK‧ADP) (Fig. 4C). This finding indicates that MCAK’s binding preference for GTPγS microtubules (i.e., GDP‧Pi-tubulin) is strongest at the ATP state, mirroring its preference for growing microtubule ends.

MCAK binds to the end region of stabilized GMPCPP microtubules in a nucleotide state-dependent manner

Our findings also support the notion that MCAK binds to the distalmost cap, consistent with the previous reports that MCAK binds to the curved protofilaments of microtubule ends (Asenjo et al., 2013; Benoit et al., 2018; Desai et al., 1999; Tan et al., 2008; Wang et al., 2017). Given the nucleotide state-dependence of the growing end-binding preference, we questioned whether this binding preference for curved protofilaments also depends on the nucleotide state of MCAK. To investigate this issue, we analyzed the binding of MCAK at the ends of stabilized GMPCPP microtubules (Fig. 5A), where curved protofilaments are thought to resemble those at the ends of dynamic microtubules, but without the presence of GDP‧Pi-tubulins (Atherton et al., 2018; Manka and Moores, 2018; McIntosh et al., 2018). Taxol stabilized GDP-microtubules were here used as a control (Fig. s5). We found that MCAK, in both the ATP and AMPPNP state, exhibited a binding preference for the ends of GMPCPP-stabilized microtubules (Fig. 5A-F). Similarly, MCAK‧ATP showed a binding preference for the ends of taxol-stabilized GDP microtubules, albeit to a lesser extent (Fig. s5). This observation suggests that the ends of stable microtubules mimic the structure of growing microtubule ends to varying degrees, with the GMPCPP-stabilized ends providing the closest resemblance. In contrast, MCAK‧APO and MCAK‧ADP, did not show the end-binding preference (Fig. 5A-F), consistent with the observation that MCAK‧ADP and MCAK‧APO also had a lower binding preference for growing microtubule ends.

Figure 5.

The α4 helix and the L2 loop of MCAK contribute to the binding preference to the EB cap

Intuitively, the binding preference for the EB cap, in addition to the previously established preference for the curved protofilaments, provides more binding sites for MCAK at growing microtubule ends. We sought to identify the key domains responsible for MCAK’s preference for the EB cap. Previous studies have shown that the α4 helix and the L2 loop are critical for mediating MCAK’s interaction with curved protofilaments (Asenjo et al., 2013; Benoit et al., 2018; Patel et al., 2016; Tan et al., 2008; Wang et al., 2017). Specifically, the α4 helix of MCAK interacts with the intradimer interface of the tubulin heterodimer, while the L2 loop contacts the interdimer interface (Fig. 6A). Moreover, the electrostatic interactions between the α4 helix and the L2 loop of MCAK with tubulin heterodimer are important for MCAK’s interactions with microtubule ends (Patel et al., 2016; Tan et al., 2008), as phosphorylation at T537 disrupts MCAK’s microtubule-end binding and depolymerization activity (Sanhaji et al., 2010). We then wondered whether these domains also contribute to the binding preference for the EB cap. To test this hypothesis, we introduced two mutations: the K524A mutation in the α4 helix, which is expected to disrupt the electrostatic interaction between MCAK and the tubulin heterodimer (Patel et al., 2016; Wang et al., 2017), and the V298S mutation in the L2 loop, which is predicted to impair the hydrophobic interaction between the L2 loop and α-tubulin (Wang et al., 2015). Consistent with previously reports (Wang et al., 2017; Wang et al., 2015), both mutants exhibited deficiencies in depolymerization and reduced ATPase activity (Fig. s6).

Figure 6.

Compared to the wild-type MCAK, both MCAK^K524A and MCAK^V298S were capable of binding to growing microtubule ends (Fig. 6B), but their k_on-P and dwell time at growing microtubule ends were significantly reduced (Fig. 6C-D). This indicates that the binding affinity of the mutants to growing microtubule ends is substantially decreased. Here, due to the minimal to negligible number of observed binding events of the mutants at growing microtubule ends and the lattice, reliable calculations of R_E/L and k_off were not feasible for the mutants. Therefore, we directly assayed the binding preference for GTPγS microtubules mimicking the EB cap. Both mutants showed significantly reduced binding intensity and a decreased, but not the loss of, preference for GTPγS microtubules compared to wild-type MCAK (Fig. 6E-G vs Fig. 4B-C). These results suggest that both the α4 helix and the L2 loop contributes the binding preference for the EB cap.

Functional specification of MCAK and XMAP215

MCAK is a microtubule catastrophe factor, whereas XMAP215 functions as a microtubule polymerase (Brouhard et al., 2008; Desai et al., 1999; Gardner et al., 2011). Both are capable of binding to dynamic microtubule ends and regulating microtubule dynamics. In the previous reports, they were shown to antagonistically regulate microtubule assembly (Barr and Gergely, 2008; Kinoshita et al., 2001; Moriwaki and Goshima, 2016; Tournebize et al., 2000). Additionally, structural models have indicated that both MCAK and XMAP215 bind to curved tubulin dimers (Ayaz et al., 2012; Trofimova et al., 2018), raising the possibility of competition for binding sites in the distalmost cap. However, while XMAP215 was reported to promote microtubule catastrophe (Farmer et al., 2021), our findings showed that MCAK binds more proximally relative to XMAP215 at growing microtubule ends. The extent to which competition from XMAP215 would antagonize the function of MCAK remains to be elucidated. Therefore, the collective effect of MCAK and XMAP215 on microtubule dynamics remains an intriguing question.

To investigate this issue, we studied how MCAK and XMAP215 together regulate microtubule grow rate and catastrophe frequency. Based on our pilot experiments, we chose 20 nM for MCAK and 50 nM for XMAP215 as representative concentrations. In the presence of 20 nM MCAK, dynamic microtubules could grow, but their catastrophe frequency was significantly increased, and their lifetime was shortened (Fig. 7 A-B). In contrast, 50 nM XMAP215 increased the growth rate more than fourfold (control: 0.8 ± 0.1 μm min⁻¹, n=97 microtubules from 6 assays; 50 nM XMAP215: 3.7 ± 0.3 μm min^{− 1}, n=50 microtubules from 3 assays). Notably, XMAP215 alone also caused a mild shortening of microtubule lifetime (Fig. 7A-B) (50 nM XMAP215: 264.6 ± 12.9 seconds, n=237 growing events from 3 assays), as compared to that of the control group (323.4 ± 12.6 seconds of control, n=443 growing events from 6 assays; p>1, the two-tailed Mann-Whitney U test with Bonferroni correction). This is consistent with previous reports suggesting that XMAP215 enhances fluctuations in microtubule end structure (Farmer et al., 2021). When both 20 nM MCAK and 50 nM XMAP215 were added, the microtubule growth rate was comparable to that observed with XMAP215 alone (3.9 ± 0.3 μm min⁻¹, n=52 microtubules from 3 assays; p=0.5, the two-tailed Mann-Whitney U test with Bonferroni correction), suggesting that MCAK does not significantly affect XMAP215’s function as a polymerase. However, the lifetime of dynamic microtubules (170.3 ± 7.5 seconds, n=283 growing events from 3 assays) was shorter than that observed with XMAP215 alone (p=2×10⁻⁶, the two-tailed Mann-Whitney U test with Bonferroni correction), while it was similar to that observed with MCAK alone (196.7 ± 6.6 seconds, n=621 growing events from 6 assays; p>1, the two-tailed Mann-Whitney U test with Bonferroni correction) (Fig. 7B). To determine whether this reflects the simple summation of the individual contributions of MCAK and XMAP215 or a synergistic effect, we calculated the probability-based catastrophe frequency for all conditions (Fig. 7C) (Gardner et al., 2011). We found that the effects of MCAK and XMAP215 on the probability of microtubule catastrophe were additive, indicating that there was no synergistic effect on catastrophe frequency (Fig. 7C). This suggests that, at least at this concentration combination, MCAK and XMAP215 independently regulate different parameters of microtubule dynamics, namely catastrophe frequency and growth rate, while simultaneously interacting with microtubule ends.

Figure 7.

Discussion

This study provides mechanistic insights into the end-binding mechanism of MCAK. The previous established view on the end-binding mechanism of MCAK is based on its specific recognition of curved protofilaments at the distalmost end of growing microtubules (Asenjo et al., 2013; Benoit et al., 2018; Tan et al., 2008). The findings presented here expand on this model by showing that MCAK also binds to the EB cap, a binding preference that likely facilitates both end-binding and the subsequent depolymerizing activity of MCAK. Below, we discuss our key findings and their implications.

MCAK binds to the EB cap at growing microtubule ends

Our primary discovery is that MCAK preferentially binds to the EB cap, where the GDP‧Pi-tubulins accumulate. This conclusion is based on four main lines of evidence. First, localization analysis demonstrates that MCAK binds to the entire GTP cap, including both the EB cap and the distalmost cap. Second, MCAK‧ADP and MCAK‧APO show the significantly reduced, but not abolished, binding preference for growing microtubule ends, while their binding preference for curved protofilaments is absent. These different binding behaviors suggest that additional structural features, beyond curved protofilaments, contribute to MCAK’s growing end-binding preference. Third, MCAK preferentially binds to GTPγS microtubules over GDP microtubules. Since the tubulins in GTPγS microtubules are considered to mimic, at least, some of the key features of the GDP‧Pi-tubulins in the EB cap (Maurer et al., 2011; Maurer et al., 2012), this suggests that MCAK can recognize the molecular features of tubulin dimers within the EB cap. Fourth, previous cryo-EM studies have shown that GTP hydrolysis leads to longitudinal compression of tubulin dimers, increasing the contact area at the interdimer interface (LaFrance et al., 2022; Manka and Moores, 2018; Zhang et al., 2015). Therefore, the interface in GTPγS microtubules is characteristic, being larger than that of a straight GMPCPP-tubulin lattice but smaller than that of a straight GDP-tubulin lattice. We find that the L2 loop contributes to the binding-preference to the EB cap, and it was previously reported that the L2 loop of MCAK directly contacts the interdimer interface (Fig. 6A) (Trofimova et al., 2018; Wang et al., 2017), potentially providing a structural basis for MCAK to distinguish the nucleotide-dependent features of tubulin dimers.

The end-binding affinity of MCAK

In our experiments, we observed that MCAK exhibits a relatively low end-binding affinity (69 µM), raising the question of its physiological relevance. Several factors may contribute to this observation. First, the in vitro system used may not fully recapitulate physiological conditions. For instance, lowering the salt concentration significantly increases binding affinity (Helenius et al., 2006; Maurer et al., 2011; McHugh et al., 2019). Additionally, while numerous binding events with short durations were detected, we excluded transient interactions from our analysis to facilitate quantification. This likely led to an underestimation of the on-rate and, consequently, the binding affinity. Furthermore, previous studies have shown that retaining a His-tag markedly enhances microtubule binding (Maurer et al., 2011; Zhu et al., 2009). To minimize interference from purification tags, we ensured complete removal of the His-tag during protein preparation (Fig. s1), which may explain the lower apparent binding affinity in our study compared to others. Finally, a low affinity is not necessarily unexpected. Considering the microtubule end as a receptor with multiple binding sites for MCAK, the overall binding affinity is in the nanomolar range (260 nM). Indeed, only a few MCAK molecules may be sufficient to induce microtubule catastrophe.

A natural follow-up question is the physiological relevance of MCAK’s direct binding to microtubule ends compared to the well-established EB1-mediated pathway (Gouveia et al., 2010; Honnappa et al., 2009; Lee et al., 2008; McHugh and Welburn, 2023). The relative contributions of these two pathways depend on MCAK’s end-binding affinity. To estimate their proportional contributions, we employed a simple model (Supplementary Appendix-2 and Fig. s7). The modeling results suggest that MCAK’s direct end-binding affinity contribute through two distinct mechanisms (Supplementary Appendix-2). First, MCAK establishes an EB1-independent regulatory pathway. Simultaneously, it enhances the EB1-dependent pathway by increasing the microtubule-end affinity of the MCAK-EB1 complex. Notably, these two mechanisms are not mutually exclusive. Particularly intriguing is that the direct regulatory pathway may account for previously observed experimental phenomena. For example, prior studies have shown that both MCAK and EB1 accumulate at the centromere and centrosome during cell division (Bahmanyar et al., 2009; Fodde et al., 2001; Kline-Smith et al., 2004; Maney et al., 1998; Wordeman and Mitchison, 1995). However, EB1 neither influences MCAK’s localization at these sites nor significantly modulates its local functions (Domnitz et al., 2012).

Implications for the working mechanism of MCAK

We think that our findings add to the current working model of MCAK in several ways. First, the end-binding preference of MCAK for growing microtubule ends is nucleotide state-dependent. The present study demonstrates that MCAK exhibits the strongest end-binding preference in the ATP-bound state, suggesting that MCAK‧ATP could more effectively distinguish between the end and the lattice. This supports the view that MCAK acts at the growing microtubule ends in the ATP-bound state (Asenjo et al., 2013; Benoit et al., 2018; Desai et al., 1999; Tan et al., 2008; Wang et al., 2015).

Second, the binding preference of MCAK to the GDP‧Pi-tubulins provides a mechanism for MCAK remaining bound to the GTP cap after ATP hydrolysis. In a speculative scenario (Fig. 8A), after the terminal tubulin dimers are removed, the MCAK motor domain transitions to the ADP state and loses its affinity for curved protofilaments. However, it still preferentially binds to GDP‧Pi-tubulins, allowing MCAK to stay within the GTP cap. As ADP is replaced by a new ATP, the motor domain could locally diffuse to explore a new working site for the next round of catalysis, which may facilitate MCAK’s processivity (Cooper et al., 2010; Friel and Howard, 2011; Helenius et al., 2006; Hunter et al., 2003).

Figure 8.

Third, we found that XMAP215 and MCAK independently regulate the growth rate and catastrophe frequency of microtubules, respectively (Fig. 7 and 8B). Previous studies have suggested that XMAP215 and MCAK exert antagonistic effects on microtubule assembly (Barr and Gergely, 2008; Kinoshita et al., 2001; Moriwaki and Goshima, 2016; Tournebize et al., 2000), possibly through competition for binding sites. Structural studies have indicated overlap in the binding sites of MCAK and XMAP215 on tubulin dimers (Ayaz et al., 2012; Trofimova et al., 2018), lending support to this hypothesis. However, in the present study, we observed that the binding regions of XMAP215 and MCAK at growing microtubule ends are not entirely overlapping, with MCAK predominantly binding proximally to the binding region of XMAP215. These findings suggest an alternative mechanism for the combined effects of XMAP215 and MCAK. In this model, MCAK and XMAP215 independently regulate catastrophe frequency and growth rate, respectively, modulating microtubule dynamics through additive effects, rather than through direct competition for binding sites.

Materials and Methods

Protein expression and purification

MCAK purification

GFP-MCAK, its mutants and MCAK-RFP (construct schematics shown in Fig. s1) were expressed using a modified BAC-to-BAC protocol. Purification was performed with adaptations based on previously described methods (Helenius et al., 2006). Briefly, the Sf9 cells were lysed by Dounce homogenization with ice-cold lysis buffer. The lysate was centrifuged at 40,000 rpm for 60 min at 4°C (XPN-100 ultracentrifuge, Type 45 Ti rotor, Beckman, USA). Cleared supernatant was filtered through a 0.45 μm filter (Pall, 66229) and then purified using a cation-exchange column (HiTrap SP-HP, Cat.17115101, GE, USA). The protein was eluted using the elution buffer supplemented with a continuous salt gradient (0.15∼1.0 M NaCl). Peak fractions were pooled together and then subjected to a Ni-sepharose column (QIAGEN, 30210). The column was then washed with the washing buffer I. After the washing step, 3C Protease (final concentration 10 µg/ml) (Thermo Fisher Scientific, 88946) was employed to perform on-column his-tag cleavage overnight at 4°C. The successful removal of His-tag was subsequently confirmed using Western blot analysis (Fig. s1). Cleaved protein was further purified through size-exclusion chromatography (Superdex 200 increase 10/300 GL column, Cat. 28990944, Cytiva, USA) and eluted with the washing buffer II. Purified MCAK aliquots were frozen in liquid N₂ and stored at −80°C.

Lysis buffer: 50 mM HEPES pH 7.5, 150 mM NaCl, 5% glycerol, 0.1% Tween 20, 1.5 mM MgCl₂, 3 mM EGTA, 1 mM DTT, 0.5 mM Mg-ATP, 10 units/ml Benzonase. Elution buffer: 6.7 mM HEPES pH 7.5, 6.7 mM MES, 6.7 mM sodium acetate, 1.5 mM MgCl₂, 10 µM Mg-ATP. Washing buffer I: 50 mM NaPO₄ buffer pH 7.5, 300 mM NaCl, 10 mM imidazole, 10% glycerol, 1 mM MgCl₂, 10 µM Mg-ATP. Washing buffer II: BRB80, 300 mM KCl, 1 mM DTT and 10 µM Mg-ATP. BRB80: 80 mM PIPES/KOH pH 6.9, 1 mM MgCl₂, 1 mM EGTA.

EB1 purification

EB1-GFP (construct schematic shown in Fig. s1) was expressed and purified as previously described (Song et al., 2020). Briefly, EB1-GFP was expressed in the BL21 E. coli strain and lysed by sonication with ice-cold lysis buffer. The lysate was centrifuged at 40,000 rpm for 60 min at 4°C. Cleared supernatant was filtered through a 0.45 μm filter and loaded onto a Ni-sepharose column. The column was then washed using the washing buffer. After the washing step, 3C Protease (final concentration 10 µg/ml) was employed to perform on-column his-tag cleavage overnight at 4°C. Eluates were desalted into storage buffer using the PD-10 desalting column (GE, 17085101). The final purified protein aliquots were frozen in liquid N₂ and stored at −80 °C.

Lysis buffer: 50 mM NaPO₄ buffer pH 7.5, 300 mM NaCl, 10% glycerol, 10 mM imidazole, 1 mM DTT, 0.1% Tween 20. Washing buffer: 50 mM NaPO₄ buffer pH 7.5, 300 mM NaCl, 10% glycerol, 30 mM imidazole, 1 mM DTT. Storage buffer: BRB80, 100 mM KCl, 10% glycerol and 1 mM DTT.

XMAP215 purification

XMAP215-GFP-his₆ (construct schematic shown in Fig. s1) was expressed using a modified BAC-to-BAC protocol. The purification was performed as previously described (Brouhard et al., 2008). Briefly, the cells were lysed by Dounce homogenization with ice-cold lysis buffer. The lysate was centrifuged at 40,000 rpm for 60 min at 4°C. Supernatant was filtered through a 0.45 μm filter and then purified using a cation-exchange column. The protein was eluted using the elution buffer supplemented with a continuous salt gradient (0.15∼1.0 M NaCl). Peak fractions were pooled together and then loaded onto a Ni-sepharose column. The column was then washed with the washing buffer I. The protein was eluted using the washing buffer I supplemented with a continuous imidazole gradient (20-300 mM). Eluted fractions with best purity were pooled together and further purified was further purified through size-exclusion chromatography (Superdex 200 increase 10/300 GL column, Cat. 28990944, Cytiva, USA) and eluted using the washing buffer II. The final purified XMAP215 was frozen in liquid N₂ and stored at −80°C.

Lysis buffer: 50 mM HEPES pH 7.5, 50 mM NaCl, 5% glycerol, 0.1% Tween 20, 1 mM DTT, 10 units/ml Benzonase. Elution buffer: 6.7 mM HEPES pH 7.5, 6.7 mM MES, 6.7 mM sodium acetate. Washing buffer I: 50 mM NaPO4 buffer pH 7.5, 300 mM NaCl, 10 mM imidazole, 10% glycerol, 1 mM MgCl₂, 10 µM Mg-ATP. Washing buffer II: BRB80, 150 mM KCl, 1 mM DTT.

Tubulin preparation

Tubulin was purified from porcine brain tissues through two cycles of temperature-dependent polymerization and depolymerization followed by affinity purification using the TOG-based affinity column, as previously described (Gell et al., 2010; Widlund et al., 2012). Purified tubulin was labeled with biotin (Thermo Fisher Scientific, 20217), TAMRA (Thermo Fisher Scientific, C1171) and Alexa Fluor 647 (Thermo Fisher Scientific, A20106) using the NHS esters according to the standard protocols (Gell et al., 2010).

Size-exclusion chromatography

To confirm the oligomeric state of the purified proteins, size-exclusion chromatography analysis (Superose™ 6 Increase 5/150 GL column, Cat. 29091597, cytiva, USA) was performed using BRB80 supplemented with 50 mM KCl and 1 mM ATP.

Microtubule depolymerization assay

Microtubules (5% Alexa Fluor 647 labeled and 20% biotin labeled) were polymerized in the presence of GMPCPP (JenaBioscience, NU-405L) as previously described (Song et al., 2020). The GMPCPP-stabilized microtubules were immobilized on the surface of a cover glass using the biotin-NeutrAvidin protein links (Thermo Fisher Scientific, 31000). GFP-MCAK was then added into the flow cell in the imaging buffer. The sample was kept at 35°C using a temperature controller (Tokai Hit, Japan).

Images were recorded using a total internal reflection fluorescence microscope (TIRFM) (Olympus, Japan) equipped with a 100×1.49 N.A. oil TIRF objective (Olympus, Japan) and an Andor 897 Ultra EMCCD camera (Andor, Belfast, UK). Images were recorded every 5 s with a 100 ms exposure. Imaging buffer: BRB80 supplemented with 1 mM ATP, 50 mM KCl, 80 mM D-glucose, 0.4 mg/ml glucose oxidase, 0.2 mg/ml catalase, 0.8 mg/ml casein, 1% β-mercaptoethanol, 0.001% Tween 20.

Microtubule dynamics assay

Microtubule dynamics assay was performed as previously described (Song et al., 2020). Briefly, the immobilized GMPCPP-stabilized microtubules were used as the template for microtubule growth. Tubulin dimers (13% TAMRA labeled) and the protein of interest were then added into the flow cell in the imaging buffer. The sample was kept at 35°C using a temperature controller (Tokai Hit, Japan). Images were recorded using a TIRFM (Olympus, Japan). To record microtubule dynamics, images were recorded every 5 s with a 100 ms exposure. To record single molecule binding events, images were recorded every 100 ms with a 50 ms exposure. Imaging buffer: BRB80 supplemented with 1 mM ATP, 2 mM GTP, 50 mM KCl, 0.15% sodium carboxymethylcellulose, 80 mM D-glucose, 0.4 mg/ml glucose oxidase, 0.2 mg/ml catalase, 0.8 mg/ml casein, 1% β-mercaptoethanol, 0.001% Tween 20.

Screen for single molecules

The average fluorescence intensity over the dwell time of each binding event was used to represent the fluorescence signal of this binding event. Subsequently, a probability distribution of the fluorescence intensities of all binding events was constructed and fitted by Gaussian fitting. A binding event was deemed to represent a single molecule if its intensity fell within the range (μ±2σ) of the probability distribution. In addition, only binding events with a dwell time exceeding the duration corresponding to 2 pixels (∼200 ms) were included in the statistical analysis. As a control for the dimeric GFP-MCAK, we used the fluorescence intensity probability distribution of GFP-MCAK^sN+M, a monomeric variant of MCAK (Wang et al., 2012) (Fig. s1).

The diffusion coefficient of MCAK at the lattice of dynamic microtubules

The diffusion coefficient of MCAK on dynamic microtubule lattices was calculated as previously described (Helenius et al., 2006). First, we tracked MCAK’s position along its trajectory on dynamic microtubule lattices using the TrackMate plugin in ImageJ (Fiji) (Tinevez et al., 2017), maintaining a minimum track length of 400 ms (100 ms/frame). Next, the mean-squared displacement (MSD) was computed from 203 trajectories across three assays using MATLAB. Finally, MSD values were plotted against the time interval, and the diffusion coefficient (D) was determined by linear regression analysis (<x²> = 2Dt) using Origin 8.0 (OriginLab Corporation, USA).

The flux of MCAK reaching the growing microtubule ends

The of the flux of MCAK reaching growing microtubule ends was calculated by a one-dimensional lattice-diffusion model, based on Fick’s first equation of diffusion (Helenius et al., 2006):

where J represents the flux of MCAK reaching growing microtubule ends, c(x, t) is the concentration of MCAK on microtubule lattice at position x from the growing microtubule end and at time t.

where

where C is the concentration of MCAK. Therefore, the flux was calculated by taking the measured parameters (D, k_on, C and k_off) into the equations.

Localization analysis at growing microtubule ends

The single-molecule localization at growing microtubule ends was conducted as previously described (Maurer et al., 2014; Song et al., 2020). Briefly, Fluorescence calibration of the captured movie was performed using fluorescent beads (T7279, Thermo Fisher Scientific). During the dwell time of a single-molecule binding event, the microtubule lattice was considered as a Gaussian wall and its end as a half-Gaussian in every frame. The peak position of the half-Gaussian was used as a reference to measure the intensity profile of single-molecule binding events frame by frame. A Gaussian function was employed to fit the intensity profile of individual molecules. The position at the peak of the intensity profile was taken as the single molecule’s location in every frame (Fig. s2). Subsequently, the average of these positions was taken to represent the single-molecule binding event’s position during its dwell time (Fig. s2). This procedure was iterated for all single-molecule binding events to determine their positions during their respective dwell times. Then, the spatial positional distributions of GFP-MCAK and XMAP215-GFP were fitted by Gaussian fitting and the spatial positional distribution of EB1-GFP was fitted to an exponentially modified Gaussian function, from which we calculated the FWHM as an estimation for their length of the binding regions (Fig. 2C).

Location precision analysis

The observed image results from the convolution of the fluorescent object with the system’s point spread function (PSF). Consequently, the standard deviation of the observed image reflects a combination of measurement error (standard deviation) in determining the object’s position and the width of the PSF (Eq. 5) (Bohner et al., 2016; Demchouk et al., 2011). To characterize the PSF of the imaging system, we used multicolor fluorescent beads (T7279, ThermoFisher Scientific) with a 100 nm diameter (Hirvonen et al., 2009). When measuring the position of a microtubule end in a single image, we modeled the microtubule lattice as a Gaussian wall and its end as a half-Gaussian. Using this fitting approach, we estimated the measurement error (standard deviation) of the convolved microtubule end positions and deconvolved the system’s PSF contribution using Eq. 5. The mean standard deviation of end positions over the dwell time of a binding event was then used as the measurement error for the microtubule end position. This procedure was iterated for all microtubule ends with single-molecule binding events to construct their measurement errors. Then, the probability distribution of measurement errors was fitted by Gaussian fitting. The standard deviation of the probability distribution was taken as the precision in estimating microtubule end positions (∼1.3 nm). The same calculation method was applied to determine the positional precision of other single molecules (∼18 nm).

Microtubule polymerization

GMPCPP, GDP and GTPγS microtubules were polymerized as previously described (Manka and Moores, 2018). Briefly, GMPCPP-microtubules were polymerized using a mixture of 10 μM tubulin, 1 mM GMPCPP (JenaBioscience, NU-405L) and 4 mM MgCl₂ in BRB80, which was incubated for 2 hours at 37°C. The polymerized microtubules were then pelleted using an Air-Driven Ultracentrifuge (Beckman, 340401), resuspended in BRB80 and stored at 37°C. GDP-microtubules were polymerized using a mixture of 40 μM tubulin, 1 mM GTP (Roche, 10106399001), 4 mM MgCl₂ and 4% DMSO (Sigma, 276855). The mixture was incubated for 30 min at 37°C. The polymerized microtubules were collected using an Air-Driven Ultracentrifuge, resuspended in BRB80 with 20 μM taxol (Cell Signaling Technology, 9807) and stored at 37°C. GTPγS microtubules were prepared using two rounds of polymerizations. In the first round, a mixture of 40 μM tubulin, 2 mM GTPγS (Roche, 10220647001) and 4 mM MgCl₂ in BRB80 was incubated for 5 hours at 37°C. In the second round, 5 μl of the product from the first polymerization was mixed with additional 40 μM tubulin, 2 mM GTPγS and 4 mM MgCl₂ to create a final 25 μl reaction mixture. This mixture was incubated overnight at 37°C to obtain long GTPγS microtubules. The polymerized microtubules were collected using an Air-Driven Ultracentrifuge, resuspended in BRB80 with 20 μM taxol and stored at 37°C.

Microtubule-Stimulated ATPase Assay

The microtubule stimulated ATPase activities of MCAK and its mutants were performed by using the Kinesin ATPase End-Point Biochem Kit (Cytoskeleton, Inc., BK053). The measurements were based on the malachite green phosphate assay to probe inorganic phosphate generated during the reaction. Wild-type MCAK served as the control, and each type of variant was subjected to multiple replicate measurements.

Binding of MCAK to different microtubules

The microtubules in different nucleotide states were first immobilized in flow cells. GFP-MCAK or its mutants were then added into the flow cell in the imaging buffer. Images were recorded every 300 ms with a 100 ms exposure. Imaging buffer: BRB80 supplemented with 1 mM ATP, ADP or AMPPNP, 20 μM taxol, 50 mM KCl, 80 mM D-glucose, 0.4 mg/ml glucose oxidase, 0.2 mg/ml catalase, 0.8 mg/ml casein, 1% β-mercaptoethanol, 0.001% Tween 20.

The binding intensity of GFP-MCAK at GMPCPP microtubule ends

To measure the binding intensity of GFP-MCAK at GMPCPP microtubule ends, an intensity profile of GFP-MCAK or the GMPCPP microtubule was plotted along the microtubule. The average binding intensity of GFP-MCAK of three pixels represents the end-binding intensity of GFP-MCAK at the corresponding position of the end, to the lattice from the midpoint of the microtubule intensity attenuation at the microtubule end.

Gamma fitting of microtubule lifetimes and catastrophe frequency analysis

The lifetime data of dynamic microtubules were fitted to the gamma density function using the dfittool toolbox in MATLAB (Mathworks, USA). The probability-based catastrophe frequency was calculated as the ratio of the number of catastrophe events observed at a certain lifetime to the total number of microtubules that reached this lifetime (Gardner et al., 2011).

Supplementary appendix-1

Figure s1.

Figure s2.

Figure s3.

Figure s4.

Figure s5.

Figure s6.

Supplementary appendix-2

Here, we presented a simple model to calculate the relative contribution of the direct and EB-dependent end-binding of MCAK (Fig. s7).

Figure s7.

Based on the model, we had the dissociation constants:

Then, the relative contribution of the direct and EB-dependent end-binding of MCAK can be expressed as α:

Here, we considered two scenarios. In the cytoplasm, both EB1 and MCAK undergo free diffusion and can associate with each other without restrictions. The relative concentrations of MCAK and EB1 are critical parameters, but they may vary across different cell types and remain unknown. We also considered the second scenario in which MCAK is locally enriched at specific cellular localizations through an EB-independent mechanism. For example, EB1 does not affect the localization of MCAK at centromere and centrosome, nor does EB1 significantly affect the function of MCAK there (Domnitz et al., 2012). Here, we assumed that that the local concentration of anchored-state MCAK is relatively high; and EB1 remains diffusive and its concentration is nearly constant, as it is continuously replenished in the local space from the vast cytoplasmic pool. In both cases, the ratio of K₃ to K₁ emerges as a key determinant.

K₃ is the end-binding affinity of MCAK·EB1 complex. Intuitively, it depends on the respective microtubule-binding affinities of MCAK and EB1, as well as the cooperativity, if any, of their microtubule-binding behaviors. K₃ can be expressed as:

where a and b represent the weighting factors of binding sites or cooperativity factors of the binding behaviors. Therefore, K₃ shows a positively correlated, monotonically increasing dependence on K₁, indicating that the increase in the end-binding affinity of MCAK contributes to that of the MCAK·EB1complex. Therefore, we think that MCAK’s functional impact at microtubule ends derives not only from its intrinsic end-binding capacity, but also its ability to strengthen the EB1-mediated end association pathway.

In the simplest case, the formation of the MCAK·EB1-MTE complex arises from the binding of either MCAK or EB1 to microtubule ends; and the binding behaviors for MCAK and EB1 are independent (a=1; b=1). Consequently, K₃ can be expressed as:

if K₁ ≪ K₂, then

if K₁ ≫ K₂, then

If K₁ ≈ K₂, then

In our experiments, we measured the dissociation constants of MCAK to growing microtubule ends is 69 µM (K₁). We also performed similar experiments with EB1 and found that EB1 showed the dissociation constant of 722 µM (K₂) for growing microtubule ends (Fig. s8), similar to the value reported in our previous report (Song et al., 2020). Therefore, substituting Eq. 14 into Eq. 10, we obtained

Here, if we assume that the cytoplasmic concentration of EB1 is twice the value of K₀, then α=0.5, indicating that 50% of MCAK binds to microtubule ends via the direct binding pathway; even if the EB1 concentration reaches ten times the value of K₀, 10% of MCAK still utilizes the direct binding pathway. Overall, as the EB1 concentration increases relative to K₀, α decreases, reflecting a decline in the proportion of MCAK that associates with microtubule ends through the direct binding mechanism.

Figure s8.

Acknowledgements

The authors thank Pengpeng Yu (School of Life Sciences and technology, TongJi University), Chunguang Wang (School of Life Sciences and technology, TongJi University), Peng Shi (School of Basic Medical Sciences, Peking University) and Congying Wu (School of Basic Medical Sciences, Peking University) for technical assistance. Special thanks to the light microscopy and protein facility in Tsinghua University. We also acknowledge our funding from National Natural Sciences Foundation of China (32070704, 32370730), IDG/McGovern Institute for Brain Research (Tsinghua University) and the State Key Laboratory of Complex, Severe, and Rare Diseases.