DNA actively regulates the “safety-belt” dynamics of condensin during loop extrusion

Domain: physics
Published: 2026-01-07
Editorial outcome: minor_revision

Abstract
Condensin plays an essential role in genome folding through its active DNA loop extrusion activity. Condensin contains a binding interface between its Ycg1 HEAT-repeat subunit and the Brn1 kleisin, together forming a “safety-belt” DNA-binding groove. This safety-belt architecture traps DNA inside the structural maintenance of chromosomes complex and prevents its dissociation during loop extrusion. The entrapment of DNA within the binding pocket of the complex is crucial for ATPase activity and loop extrusion. However, the molecular mechanism underlying DNA entrapment remains unclear. Here, we employ a multiscale computational approach to understand how DNA modulates yeast condensin’s safety-belt dynamics. Using all-atom simulations combined with AlphaFold3 predictions, we demonstrate that DNA binding stabilizes the Ycg1-Brn1 safety belt. Coarse-grained simulations capture the entire DNA-entrapment process and reveal an active regulatory role for DNA: outside the safety belt, DNA triggers opening, whereas once inside, it promotes closure and stabilizes the complex. Kinetic analyses show that the rate-limiting step in DNA entrapment depends on the tightness of the safety belt. A loose safety belt makes the stable closure of its “latch” and “buckle” components rate-limiting, whereas a tighter safety belt shifts the barrier to initial DNA entry.

Introduction
Mitosis and meiosis are fundamental processes of eukaryotic cell division. During these processes, the stable and orderly transmission of genetic information is crucial for maintaining species continuity and the proper development of organisms1–3. The spatial organization of chromosomes is of paramount importance in mitosis, and achieving this organization relies on structural maintenance of chromosomes (SMC) complexes, such as condensin.

Condensin, a five-subunit complex of the SMC family, plays a vital role in condensation and segregation during this process4–6. It is primarily composed of two key parts: the “SMC dimers” subcomplex and the “non-SMC” subcomplex7. The SMC dimers subcomplex consists of a heterodimer formed by SMC2 and SMC4 subunits, and the non-SMC subcomplex is a subcomplex composed of the kleisin subunit and two HEAT-repeat subunits8. Together, these two subcomplexes enable condensin to compact chromatin into highly ordered structures necessary for faithful chromosome segregation. Condensin has long been recognized as a pivotal player in mitotic chromosome assembly. However, the precise mechanism by which it compacts and organizes DNA remains unclear. Several models have been proposed over the years, and a simple and elegant mechanism that could explain most of the cellular tasks of SMC complexes called “loop extrusion” is one of the most widely accepted4,9–14.

Condensin complexes are characterized by conformational changes coupled with the ATP hydrolysis cycle and facilitate the lengthwise compaction of DNA through ATPase-driven loop extrusion12,15–20. Current research indicates that condensin contains multiple DNA-binding interfaces21. A safety-belt mechanism, as revealed by DNA-condensin co-crystal structures, indicates that DNA is confined within a conserved, positively charged groove formed by Saccharomyces cerevisiae (Sc) condensin Ycg1 HEAT-repeat and Brn1 kleisin subunits and is restricted by a ring-shaped Brn1 loop of the non-SMC subcomplex (Fig. 1A, B)22. In ATP-engaged states, DNA is also clamped at the SMC2/SMC4 head module in conjunction with Ycs4 HEAT-repeat subunit, indicating that both the non-SMC subunits and the SMC heads contribute to DNA binding21. These interfaces have been observed across the SMC cycle and are consistent with models in which condensin captures and transports DNA during loop extrusion14.

The observation that DNA binding by the non-SMC subcomplex of condensin decisively enhances the activity of the SMC ATPase indicates that a multistep mechanism is involved in the loading of condensin onto chromosomes. This implies that the DNA binding site in the non-SMC subcomplex might be able to act as a sensor that triggers activation of the SMC2-SMC4 ATPase23. The initial step in the condensin loading mechanism involves the binding of DNA to the non-SMC subcomplex, a process that is thought to occur independently of ATP hydrolysis24. A disruption in the interaction between DNA and the non-SMC subcomplex markedly affects condensin loading efficiency, underscoring the critical role of this interface in DNA recognition23. DNA entrapment by the Brn1 loop and its subsequent closure around the DNA double helix via the safety-belt mechanism are critical for stable condensin-DNA engagement22,23. Although DNA can access the basic Ycg1-Brn1 groove without being encircled by the Brn1 loop, it is only through the latch-mediated entrapment that the affinity is sufficiently increased to enable stable interaction with chromatin22. Since the “latch” and “buckle” segments can engage with each other even without DNA, the latch must temporarily disengage from the buckle to allow DNA to enter the Brn1 kleisin loop22. However, if the safety belt opens too rarely, DNA would seldom be captured, making loop extrusion inefficient. Conversely, if it opens too easily or frequently, the condensin complex might become unstable (losing DNA or failing to maintain ATPase engagement). Therefore, understanding how the safety-belt mechanism achieves a balance between effective DNA entrapment and structural stability is essential for elucidating its functional mechanism.

Previous studies have suggested, based on biochemical and structural evidence, that cohesin and condensin may topologically encircle DNA25–31. However, experiments also showed that simultaneous closure of all three SMC-kleisin ring interfaces does not inhibit DNA loop extrusion by cohesin and condensin. This finding challenges the hypothesis that DNA passes through the SMC-kleisin ring in a strictly topological manner11,15. In the “hold-and-feed” model, condensin is proposed to load DNA into the SMC-kleisin ring in a “pseudotopological” manner. In this scenario, the DNA is topologically entrapped within two distinct kleisin compartments, referred to as the “motor” and “anchor” chambers15. The topologically bound DNA segments within the safety belt during the SMC reaction cycle are thought to determine the directionality of DNA loop extrusion. A recent study suggested that both cohesin and condensin are believed to extrude DNA loops in an asymmetric manner, and the frequent switching of extrusion direction has been proposed as a possible explanation for the experimentally observed symmetric loop extrusion9. Notably, if the barrier between the “motor” and “anchor” chambers is removed (allowing them to connect), condensin could theoretically extrude DNA in both directions instead of one, essentially converting it from a one-sided to a two-sided loop extruder. Therefore, a detailed understanding of the safety-belt mechanism may provide important insights into the mechanistic differences in loop extrusion between condensin and cohesin.

Single-molecule and structural studies have established that condensin and cohesin compact DNA by ATP-dependent loop extrusion, placing SMCs at the center of 3D genome architecture10,11,32. Within this framework, capture and entrapment of DNA are key control points. In Sc condensin, the kleisin-HEAT “safety-belt” forms a positively charged groove and a ring-like Brn1 loop that can encircle DNA and prevent dissociation, providing a structural basis for anchoring and the one-sided loop growth observed for condensin10,22. Thus, local protein-DNA mechanics at the safety belt connect directly to long-range chromosome organization. The established principle that condensin’s safety belt couples local DNA mechanics to chromosome organization motivates exploration of whether analogous DNA-clamping mechanisms operate in other SMC complexes. Structural work on budding-yeast cohesin has revealed a “safety-belt-like” Scc3-Scc1 DNA-binding interface, supporting the idea that secure DNA latching by non-SMC subunits may represent a conserved biophysical strategy, even if the underlying architectures differ33. By contrast, the Smc5/6 complex lacks canonical HEAT-repeat subunits and instead employs KITE-family non-SMC elements, implying a distinct mode of DNA interaction34. A detailed investigation of condensin’s DNA capture and latch stability is therefore well placed to inform mechanistic models across the SMC family, while also clarifying which features are specific to condensin. Finally, SMC dysfunction has biomedical impact: cohesin alterations are frequent in human cancers and associate with transcriptional dysregulation and chromatin changes, underscoring the importance of clarifying SMC-mediated DNA capture and control35,36.

Recent advances in AI-based protein structure prediction methods, such as AlphaFold37,38, have enabled computational exploration of protein conformational diversity. Further studies have demonstrated that these predictive models can not only accurately reproduce experimentally determined static structures but also sample multiple conformational states by adjusting input parameters39,40. Two confidence/readout scores are especially useful for interpreting such ensembles: the predicted local distance difference test (pLDDT), a per-residue measure of local reliability, and the predicted aligned error (PAE), a residue-pair measure of confidence in the relative placement of regions. Both scores correlate with intrinsic flexibility, helping to flag mobile segments and inter-domain motions41,42. Moreover, structural changes predicted by AlphaFold have been successfully applied to assess the impact of single-point mutations on protein conformation, with results showing remarkable agreement with experiments43,44. These findings highlight the potential of predicted scores in AlphaFold to capture aspects of protein dynamics, providing insights into conformational transitions and functional regulation.

Here, we integrate AlphaFold3-based predictions, all-atom and coarse-grained (CG) molecular dynamics (MD) simulations to study the conformational mechanisms of Sc condensin’s Ycg1-Brn1 safety belt. This multiscale approach reconstructs the full DNA-entrapment pathway and delineates its intermediate states. We find that DNA acts bidirectionally: when positioned outside the Brn1 loop it biases the latch toward opening, whereas once inside it promotes latch-buckle closure and stabilizes the complex during ATPase cycling. Kinetic analyses show that the rate-limiting step depends on belt “tightness”: weaker latch-buckle coupling makes closure limiting, whereas stronger couplings shifts the barrier to initial DNA entry. We further identify an optimal interaction regime that balances efficient capture with stable interface engagement, and we uncover a strong link between safety-belt stability and one-dimensional sliding along DNA. Together, these results provide a molecular framework for DNA-guided control of the safety belt and clarify how condensin achieves robust topological capture during loop extrusion.

Results
AlphaFold3 Predictions of Safety-Belt Flexibility in DNA-unbound state (US) and DNA-bound state (BS)
We first asked how the Sc Brn1 kleisin loop (residues 383-524) behaves in the DNA-bound state (BS) versus DNA-unbound state (US). To analyze its dynamics and identify functionally relevant conformational changes of Brn1 kleisin loop, we calculated the effective strain (ES) per residue, ESi, as a measure of local flexibility, with higher ESi indicating larger deformation (lower stability). This method is analogous to the local deformation concept from previous work43, with the key difference being that we compute the normalized PAE for each aligned residue i as 〈ESi〉 = 〈PAEij/rij〉 for all scored residues j within 13 Å averaged across all 10 predicted structures. Normalizing PAE by inter-residue distance removes the strong distance bias in raw PAE values, enabling fair comparisons across regions and interaction scales. Higher normalized PAE values indicate greater uncertainty in the placement of aligned residue i relative to scored residue j, whereas lower values indicate higher certainty. Consequently, a higher value of 〈ESi〉 corresponds to increased flexibility in the local structural region, while a lower value of 〈ESi〉 corresponds to decreased flexibility in the local structural region (see Methods for details). Before using PAE to derive effective strain measures, we verified that the AlphaFold3 models reproduce the known crystal structures of the Ycg1-Brn1 complex in the US and BS with backbone Cα root-mean-square deviations (RMSDs) of 1.466 ± 0.047 Å and 0.795 ± 0.022 Å, respectively (Table S2). This supports the global accuracy of the AlphaFold3 models used here.

To assess whether the PAE-derived effective strain 〈ESi〉 is a sensitive readout of local structural integrity within the safety belt, we modeled four latch mutations that were experimentally found to weaken latch-buckle engagement (M391D, F394D, W402D, W408D) and compared wild-type and mutant ensembles (Fig. S1)22. For each variant, we computed change of effective strain upon mutation (Δ〈ESi〉) and summarized the response by shell sums within 13, 15, and 18 Å of the mutated site. Three mutations (M391D, F394D, W402D) produced clear, localized increases in 〈ESi〉, consistent with destabilization of latch-buckle contacts; W408D yielded a weaker negative response, likely because this residue lies near the boundary of robust PAE coverage in our models. These findings support 〈ESi〉 as a practical indicator of mutation-induced strain at the safety-belt interface. More broadly, this provides internal validation for our PAE-based analysis and may offer a general, low-cost screen to prioritize residues for mechanistic tests that tune safety-belt dynamics and, by extension, loop-extrusion competence.

We then projected 〈ESi〉 directly onto the structure of the Brn1 kleisin loop to clearly visualize the structural stability of the Brn1 kleisin loop (Fig. 1A). The latch region of Brn1 exhibits markedly higher 〈ESi〉 values, which is closely associated with the fact that the latch region serves as the primary functional area responsible for the opening and closing mechanism of the safety belt. To assess how the presence or absence of DNA influences safety-belt stability, we then projected 〈ESi(BS)〉 − 〈ESi(US)〉 directly onto the structure of the Brn1 kleisin loop, thereby providing a direct visualization of DNA-induced changes in the structural stability of this region. Notably, 〈ESi(BS)〉 values are markedly reduced compared with the 〈ESi(US)〉 in the latch segment (~residues 383-410), indicating enhanced local stability (Fig. 1B).

To understand why 〈ESi〉 decreases upon DNA binding, we examined the pairwise strain matrix 〈ESij〉, which reports how confidently residue i is positioned relative to nearby residues j (derived from normalized PAE; see Methods). Across the latch region, 〈ESij〉 is substantially lower in the BS than US (Fig. 1C), indicating a broad DNA-induced stabilization. A per-residue view of 〈ESi〉 refines this picture (Fig. 1D): the largest stabilization occurs in the flexible latch segment ~383-410, whereas intrinsically more rigid segments (lower normalized PAE) change less but follow the same overall trend. These patterns are robust to reasonable choices of the neighborhood cutoff and other analysis parameters (Fig. S2). For completeness, 〈ESi〉 and the matrices of 〈ESij〉 over the full Brn1 loop are provided in Fig. S3.

Together, these findings suggest that DNA binding notably stabilizes a particular flexible segment of the Brn1 loop (residues 383-410), as indicated by a drop in the effective strain in the BS relative to the US. This segment corresponds to the latch region of the Brn1 safety-belt that closes around DNA. Regions that AlphaFold3 predicted to be highly flexible (those with larger prediction error) showed the greatest DNA-induced stabilization, whereas inherently rigid regions changed little. Our results suggest that DNA binding preferentially stabilizes the parts of the Brn1 loop that need to flex during latch opening and closing.

Insights into DNA-Stabilized Safety-Belt Mechanism from atomistic MD Simulations
The prediction results of local deformations suggest that the DNA entrapment by the safety belt can facilitate the closure of the Brn1 loop. As AlphaFold3 primarily provides static structural predictions, additional validation through MD simulations or experimental approaches is required to assess the conformational dynamics. To further investigate how the safety belt maintains structural stability following DNA entrapment, we constructed two simulation systems, one with DNA and one without, and then performed unbiased all-atom MD simulations on each. The BS was retrieved from the Protein Data Bank (PDB: 5OQO)22 and the US was generated by deleting DNA from the PDB. The missing residues in the crystal structure are reconstructed (see Methods for details). As shown in Fig. 2A, we illustrate the key structural concepts within the complex, particularly the latch region, buckle region, and the primary active region (PAR, Brn1 residues 383-456, latch + linker). The pale-green segment marks the intrinsically disordered linker within the Brn1 loop, and the four Brn1 α-helices (α1-α4) are labeled for reference.

To quantitatively assess the structural dynamics of the Brn1 loop at the residue level in the presence and absence of DNA, we first compared the residue-wise root-mean-square fluctuation (RMSF) within the US and BS. RMSF measures how much each residue moves during the simulation with higher (lower) RMSF corresponding to greater (weaker) flexibility. The RMSF profile of the US is generally larger than that of the BS, indicating that the presence of DNA within the Brn1 loop enhances the overall structural stability of the safety belt (Fig. 2B). Notably, residues near position D424 in the BS show a modest increase in RMSF compared to the US. This results from a higher concentration of negatively charged residues in that region, which, together with the negative charge of the DNA helix, weakens local interactions at the protein-DNA interface. Figure 2C illustrates that the primary contributor to RMSF stabilization is the direct interaction between DNA and the PAR of the Brn1 loop. Moreover, the slightly higher contact number between the PAR and the rest of the protein (excluding DNA) in the BS suggest that DNA binding may indirectly stabilize adjacent structural regions.

Motivated by this observation, we next asked whether the DNA-induced “latch flipping” reported by Kschonsak et al. is evident in our MD simulations22. In that mechanism, DNA promotes a reorientation near Brn1 K409/R411 such that V410 packs with W408 (Brn1) and F808 (Ycg1), consolidating a small hydrophobic cluster at the latch-buckle interface. We therefore quantified side-chain centroid distances and contact probabilities for V410-F808 (hydrophobic packing) and R411-F808 (Arg reorientation) in the US and BS (Fig. S4). In the BS, V410-F808 shifts toward shorter distances and higher contact probability, whereas R411-F808 shifts toward longer distances with a modest contact decrease. These reproducible BS-US differences are consistent with DNA-triggered latch flipping and local interface consolidation22.

To pinpoint which amino acids at the latch-buckle interface change their energetic roles upon DNA binding, we applied an end-point free-energy decomposition (MM/GBSA) method on MD trajectories and ranked per-residue contributions (Fig. S5)45,46. Overall, the leading latch residues remain important in both the US and BS. Notably, R400 and R405 provide more stabilizing contributions in BS, consistent with stronger electrostatic engagement with the DNA phosphate backbone in the DNA-bound architecture. Hydrophobic latch residues M391, F394, W402, and W408 also rank among the top stabilizers, aligning with experimental evidence that these sites help maintain latch-buckle integrity22. The Ycg1 buckle shows only modest rank shifts between states, whereas the presence of DNA broadens the set of Brn1 buckle residues that contact and stabilize the interface, indicating a more extensively engaged latch-buckle surface in the BS.

The RMSF and contact number analyses from all-atom MD simulations showed that DNA stabilized the Brn1 loop by forming additional interactions. To further assess how this interaction influences latch disengagement from the buckle in the safety belt, we performed steered MD simulations, unraveling the safety belt in the US and BS. To preserve the flexibility of the buckle region at its interface with the latch region while preventing large-scale translations or rotations of the buckle region, positional restraints were applied to atoms of the Ycg1 subunit that do not directly interact with the PAR (see Fig. S6 and Methods for details). We present the average force profiles as a function of the z-axis distance between the centers of mass (COMs) of the first Cα atom of the latch region of the Brn1 loop and Ycg1 subunits, derived from three independent steered MD simulations. Notably, the BS exhibits generally higher force values compared to the US (Fig. 2D). The results indicate that DNA enhances the interaction network at the interface, leading to an increased force required for latch release. The presence of DNA stabilizes the interaction between the latch and buckle, making the Brn1 loop less likely to open. This stability is important for the safety-belt function and subsequent ATPase hydrolysis.

In the steered MD process, the latch region is pulled by a force at constant velocity, leading to its gradual detachment from the buckle region. The changes in the contact number between the PAR and the remaining part during this process are shown in Fig. 2E. Even under steered MD, the comparison of contact numbers between the US and BS remain highly correlated with the presence or absence of DNA near the crystal structure in the unbiased simulation. The difference in contact number between the US and BS comes mainly from the contact between DNA and the PAR. Quantitative analysis of contact numbers reveals comparable interaction profiles between the US and BS when excluding contacts between DNA and the PAR of the Brn1 loop in the BS (Fig. 2E). In particular, the number of contacts between the US and BS reaches nearly the same value at almost the same time after the elimination of the interactions between the PAR and DNA (orangeshaded region), strongly suggesting that these specific contacts are critical for maintaining the closure of the Brn1 loop. Combined evidence from unbiased MD and steered MD simulations demonstrates that DNA entry stabilizes the closed conformation of the Brn1 loop. This mechanistic insight suggests that DNA-mediated loop stabilization can constitute a prerequisite for subsequent activation of SMC ATPase activity, ensuring the stability of the loop extrusion process.

Dynamics of DNA and Its Role in Modulating Safety-Belt Dynamics
AlphaFold predictions and all-atom MD simulations suggest that DNA entry into the Brn1 loop enhances the stability of the safety belt. However, these findings are confined to conformations near the closed crystal structure of the Brn1 loop, leaving the initial mechanism of DNA entry unresolved. Notably, our simulations indicate that the transition of the safety belt between the open and closed states is rarely observed within several microseconds in all-atom MD simulations (Fig. S7), highlighting a limitation in capturing large-scale conformational transitions at atomic resolution. To overcome this problem and investigate the mechanism of DNA entrapment at spatio-temporal scales inaccessible with all-atom models, we developed a CG model of the Ycg1-Brn1-DNA complex. This model incorporates several key interaction terms to facilitate efficient exploration of the DNA capture process (Fig. 3).

First, a sequence-independent interaction between DNA and Ycg1 is considered, which, in contrast to classical protein-DNA recognition, may enable condensin to access open chromatin regions, such as transcriptionally active genes or nucleosome-depleted sites47,48. This non-specific interaction facilitates one-dimensional sliding of the complex along DNA and enables subsequent capture by the safety belt49. By ensuring the diffusion coefficients D are close in order of magnitude to the measured movement of cohesins and condensins moving diffusively along naked DNA in vitro (Figs. 3A, S8 and S9)6,32,50–54, and the DNA bending angle θ observed in the crystal structure is reproduced with a longer DNA sequence (Fig. 3B)21, we evaluated a range of interaction strengths between the Ycg1 HEAT-repeat subunit and DNA. The simulation closely matches experimental data in both D and θ, supporting the choice of εHD = 0.6 as the optimal interaction strength between DNA and Ycg1 (εHD is the scaling factor of the interaction strength between DNA and Ycg1, see Methods for details).

Second, the loop regions of Brn1 are largely unstructured, and the structured regions make few contacts with DNA, resulting in essentially no native contacts in the available structures. In our all-atom MD simulations with loop reconstruction, DNA interactions were primarily mediated by charged residues. Based on these observations, we focused on modeling Brn1-DNA interactions through electrostatics (Fig. 3C).

Third, the strength of the interaction between the latch and the buckle (εLB, the scaling factor of the interaction strength between the latch and buckle) is challenging to determine, as there are no direct experimental data available. To avoid artifacts caused by an inappropriate choice of interaction strength, we systematically explored a range of εLB values, allowing the transition of the safety belt from a fully open to a fully closed state during the simulation. To quantify the degree of closure, we calculated the order parameter Q, defined as the average fraction of native contacts formed with respect to the fully closed reference state55. Thus, a higher Q value indicates a conformation that is more similar to the closed structure (Fig. 3D, E, see Methods for details).

Notably, this systematic tuning of εLB is not only a technical control to avoid simulation artifacts but also reflects potential biological modulation of the latch-buckle interface. We hypothesized that post-translational modifications, such as phosphorylation within the latch and buckle, could effectively alter the local interaction strength in vivo. Given that condensin is regulated by phosphorylation in multiple organisms, with effects on loading and activity that implicate kleisin and HEAT subunits56–58, we examined how phosphorylation could tune the opening and closure of the kleisin safety belt. Focusing on Brn1 sites in and around the latch-buckle region (including experimentally annotated positions, such as S423 and S469), all-atom simulations with MM/GBSA analysis revealed site-specific shifts in latch-buckle affinity relative to wild type (Fig. S10). Some phospho-variants weakened interfacial binding, whereas others strengthened it, and contact/electrostatic analyses rationalized the direction of change. In our CG framework these energetic shifts map naturally onto the latch-buckle interaction parameter εLB, providing a mechanistic route by which phosphorylation could bias safety-belt dynamics and, consequently, DNA capture58. This suggests clear experimental tests: phosphomimetic or phospho-null mutations at the identified sites should modulate the balance between opening (DNA outside the loop) and closure (DNA inside the loop).

Our simulations successfully recapitulated the complete process of DNA entrapment by the safety belt. To identify when DNA becomes truly encircled by the Brn1 loop, we calculated a topological linking number (see Methods for details). This allows us to distinguish the states when DNA is outside the loop (Snl, “non-looped”) versus the ones when DNA is entrapped inside (Sl, “looped”, see Fig. 4A). We then used Q between the latch and buckle to quantify the extent of safety-belt closure across trajectories. Compared to the DNA-free state (Sf), the Q value decreases in the Snl, but increases in the Sl (Fig. 4B). These results suggest that DNA can act bidirectionally: promoting opening when put outside the loop, and stabilizing closure upon entry. To further illustrate these findings, we provide a supplementary movie (Movie S1 and Fig. S11) that directly visualizes how DNA binding influences the flexibility and dynamic behavior of the kleisin safety belt. This DNA-dependent stabilization is consistent with AlphaFold3 predictions and all-atom MD simulations. The observed trends are robust across a broad range of εLB and εHD values (Figs. 4B, S12 and S13). Notably, when the latch-buckle interaction becomes excessively strong (e.g., εLB = 0.45), the modulatory effect of DNA is largely abolished, likely due to saturation of the closure interface.

Interestingly, analysis of the DNA-Brn1 loop contacts reveals a compensatory mechanism: as εLB increases, a more stable loop conformation exposes additional DNA-binding sites, resulting in stronger interactions with DNA (Fig. 4C). This is evident in the continuous increase in contact numbers in the Sl, while the Snl shows only marginal changes. To dissect which interactions drive this behavior, we calculated the contact frequency between charged Brn1 residues and DNA phosphates (Fig. S14). Our analysis shows that both Snl and Sl are dominated by electrostatic interactions localized in basic Brn1 patches (residues 409-417 and 445-455), with R430 serving as a recurrent anchor; a further contact hotspot is seen near residue R480. These patches lie immediately adjacent to the latch-buckle closure path, so when DNA occupies them in the Snl, phosphate-Lys/Arg engagement introduces both electrostatic attraction and steric interference that act to destabilize the latch-buckle interface. Consistent with this view, the difference in electrostatic contact frequency between Snl and Sl increases with higher εLB, paralleling trends in Fig. 4C. Together, these data support a mechanism in which DNA located outside the loop stabilizes Snl by engaging basic Brn1 patches, thereby biasing the interface toward the open latch state. Intriguingly, this suggests that DNA, acting as a passive “passenger”, may guide its own entrapment by promoting tighter interactions when the safety belt approaches closure with electrostatic interactions appearing to provide the driving forces.

To further assess template dependence of our CG model, we compared protein-DNA contact maps from our CG simulation trajectories with those derived from the Ycg1-Brn1 crystal structure (PDB: 5OQO22) and from cryo-EM structures that contain longer DNA duplexes (PDB: 7QFW15, PDB: 7Q2Z21). The simulations reproduce the core, high-occupancy contacts present in 5OQO and additionally form interactions that overlap with those in 7QFW and 7Q2Z, particularly at the Ycg1 N- and C-terminal regions (Fig. S15). Although 7QFW and 7Q2Z reveal additional interfaces enabled by their longer DNA substrates, the anchoring contacts that define the interface are already captured by 5OQO, and our simulations recover the extended contacts as the DNA explores additional registers. These results indicate that our conclusions are not sensitive to the choice of initial template.

Effect of Latch-buckle Interaction Strength on the Kinetics of DNA Entrapment and Diffusion along DNA
Guided by the observation that DNA outside the Brn1 loop promotes opening while DNA inside the loop stabilizes closure, we next asked how the interaction strength between the latch and buckle (εLB) tunes the kinetics of DNA entrapment. We analyzed 50 independent CG MD simulation trajectories for each εLB value. Throughout the simulations, we identified four different states: (i) DNA unbound to the Ycg1-Brn1 complex (US), (ii) DNA interacts with the protein complex but located outside the Brn1 kleisin loop (ISout), (iii) DNA surrounded by the Brn1 kleisin loop but with an unclosed latch-buckle interface (ISin), and (iv) DNA fully entrapped by a closed loop (BS) (Fig. 5A). The state classification was based on a combination of the Q, the number of contacts between DNA and the Ycg1-Brn1 complex, and the topological linking number (Fig. S16). Note that ISout is defined by the DNA path relative to the loop, not by latch geometry, so the latch can be either open or closed in ISout.

We subsequently measured the mean first passage times for the transitions US→ISout, ISout→ISin, ISin→BS, and the complete transition US→BS (Figs. 5B and S17). Our results reveal that increasing εLB prolongs the transition from ISout to ISin, suggesting that stronger latch-buckle interactions hinder loop opening and thus delay DNA entry. Conversely, the transition from ISin to BS becomes markedly faster with increasing εLB, reflecting facilitated loop closure once DNA is in place. The opposing influences of these two steps result in a non-monotonic transition time dependence of the process from US to BS under different εLB, which initially decreases and subsequently increases. The D has been shown to influence condensin in locating appropriate anchoring sites and promoting linear compaction of chromatin54. Motivated by this, we hypothesized that the stability of the safety-belt structure might similarly regulate the diffusion of DNA. To test this idea, we measured the one-dimensional diffusion rate of the safety belt (essentially, the diffusivity of the condensin complex along DNA) as a function of latch-buckle interaction strength. Our results revealed a clear inverse correlation: stronger interactions between the latch and buckle significantly slowed the diffusion of the safety belt on DNA (Figs. 5C and S18).

A schematic free energy landscape illustrating these kinetic trends is shown in Fig. 5D. Mechanistically, enhanced latch-buckle interactions impede DNA surrounded by the Brn1 loop (ISout → ISin), but promote rapid closure of the Brn1 loop once DNA has entered (ISin → BS). From a functional standpoint, strengthening εLB increases the stability of the fully engaged BS state, which in turn can facilitate the hydrolysis of ATP by the SMC2-SMC4 ATPase domains22. However, if εLB becomes too strong, the energetic barrier of DNA entrapment by Brn1 loop becomes prohibitively high, compromising the efficiency of DNA capture. Together, these findings suggest that an optimal latch-buckle interaction strength must balance structural stability with accessibility. Moreover, the strength of the safety belt can also regulate DNA diffusion during the process of loop extrusion, thereby assisting condensin in locating appropriate anchoring sites and facilitating linear compaction of the chromatin9,54. The observed phenomena remain robust over a wider range of CG modeling parameters, underlining the generality of the mechanism (Fig. S19).

Methods
PAE-Predicted Effective Strain
AlphaFold provides confidence-derived outputs that are informative about conformational variability, in addition to static structural models39,40,42,43. We quantify local deformation with the residue-wise metric ESi, designed to sensitively report changes in local structure. Operationally, ESi summarizes the mean relative change in inter-residue PAE values around residue i (formal definitions below); the pairwise quantity ESij analogously tracks changes for a specific residue-partner pair. The PAE is provided by AlphaFold for every residue pair and reports the expected positional error (in Å) at residue i if the predicted and true structures were aligned on residue j. Low PAE indicates high confidence in the relative placement of segments (e.g., domains), whereas high PAE indicates uncertainty consistent with flexible or variably oriented regions. Previous work shows that patterns in PAE correlate with MD markers of motion (e.g., distance-variation matrices), supporting the use of PAE as a reasonable proxy for conformational heterogeneity41. Community tools routinely use PAE to identify mobile domains and hinges and to parse domain groupings in predicted models. On this basis, we use ESi and ESij to summarize changes in local PAE within and across states; we emphasize that PAE is a confidence-based surrogate rather than a direct kinetic observable and therefore corroborate PAE-based trends with MD simulations.

For each residue i, we define a neighborhood set ∥Ni∥ comprising n number of residues j ∈ ∥Ni∥ with Cα distances rij within 13 Å. Here, the calculation only involved the residues with AlphaFold3 predicted pLDDT > 70, which is a commonly used threshold indicating at least correct backbone geometry43,44. For each state (US and BS), we generated 10 independent AlphaFold3 predictions. Predicted models were compared to the corresponding experimental structures (US: PDB 5OQQ; BS: PDB 5OQO) by computing backbone Cα RMSD after superposition on protein Cα atoms; DNA atoms were excluded from the alignment. Summary statistics are reported in Table S2. The ESi is the average of ESij over the Ni neighbors: 

ESi = 1/ni Σni j=1 ESij = 1/ni Σni j=1 PAEij/rij (1)

The ESij is defined for a single predicted structure, representing the average of different ESij, whereas 〈ESi〉 denotes the average of ESi values across 10 independent structures, providing a measure of the residue’s consistency and reliability over multiple predictions. If ni = 0 (i.e., no neighbors of residue i), 〈ESi〉 was deemed undefined, thus not plotted. In addition to Fig. 1C, D, which highlights the Brn1 residues 383-423 window most relevant to safety-belt opening, a belt-wide 〈ESi〉 profile and the full 〈ESij〉 matrix (Brn1 residues 383-524) are reported in the Supplementary Information for reference (Fig. S3).

To assess whether PAE-derived effective strain captures local sensitivity to perturbations in the latch region, we modeled four mutations with reported latch-buckle weakening effects (M391D, F394D, W402D, W408D) and compared wild-type and mutant ensembles22. For each variant we calculated

Δ〈ESi〉 = 〈ESiMut〉 - 〈ESiWT〉 (2)

and summarized the local response by aggregating Δ〈ESi〉 within radial shells of 13, 15, and 18 Å centered on the mutated residue (Fig. S1). This shell-based analysis follows recent work relating AlphaFold-predicted local deformation to experimental mutational effects43,44. Full calculation details are provided in the Supplementary information.

All-atom MD Simulations
The initial structure of all-atom MD simulation was constructed based on the crystal structure (PDB code: 5OQO)22. Missing residues in the crystal structure were reconstructed using Modeler 10.479. The DNA-free structure was obtained by removing the DNA from the reconstructed structure. All systems were built using the web-based CHARMM-GUI Membrane Builder80. CHARMM36m force field was used to describe the interactions81. The steered MD was conducted to compare the impact of the Brn1 loop encircling DNA on the safety belt. A single reaction coordinate was defined between two groups, the first N-terminal Cα atom of the latch region in the Brn1 loop (the pulling group) and the Ycg1 (the reference group). An external harmonic potential was applied along the z-axis using the “direction” geometry, with pulling dimensions set to only affect the z-coordinate (Fig. S6). The pulling was performed at a constant rate of 0.005 nm ps−1, with a force constant of 600 kJ mol−1 nm−2. The initial distance between the COMs of the two groups was used as a starting point, and the periodic boundary conditions were accounted for by updating the reference coordinates at each step. The protein placed in the center of the box and is rotated in a way that the DNA binding groove is located in the direction of the z-axis. The atom positions of the protein with z < 14 nm in our box are fixed to ensure that the position of the Ycg1 HEAT-repeat does not change, and the remaining parts are not fixed to keep the binding position as flexible as possible (Fig. S6). The steered MD runs of 2500 ps fully extract the latch region from the buckle region.

Unbiased MD and steered MD simulations were conducted using the GROMACS 2022.5 package with an integration timestep of 2 fs82. Coulomb interactions were shifted to zero between 1.0 and 1.2 nm, and a cutoff radius of 1.2 nm was applied for Lennard-Jones interactions. The system temperature was maintained at 310 K using a velocity-rescaling thermostat, and the pressure was kept at 1 bar in all directions with a Parrinello-Rahman barostat. Simulation boxes were set to 17 × 17 × 17 nm3, which was filled with TIP3P water molecules, and 0.15 M NaCl was added to mimic the physiological conditions. We performed three independent unbiased MD simulations for each system, each running for 1 μs.

CG MD simulations of Ycg1-Brn1 in Complex with DNA
To investigate the large-scale dynamics of DNA entering the safety belt, we employed CG MD simulations. For the Ycg1-Brn1 complex, a residue-level CG model (AICG2+) was used83–85, with each bead positioned at the Cα atom location and interactions only including those appearing in the native crystal structure (PDB: 5OQO)22. Considering the lack of structural information for the unstructured region (residues 413-456) in Brn1, we removed the native contact potentials and retained only the generic local potentials derived from a “loop-segment library” based on Protein Data Bank statistics to better capture its dynamic behavior. These generic local potentials effectively describe the conformational distribution of unfolded states or intrinsically disordered regions by accounting for secondary structure propensities86. Complete CG model parameters and Ycg1-Brn1 simulation settings are provided in the Supplementary information. For DNA, we employed the 3SPN.2C model87, which captures base-pairing, hybridization, groove widths, and local curvature. In this model, each nucleotide is represented by three beads corresponding to the phosphate, sugar, and base groups. To prevent the false capture of DNA, where one end of short linear DNA fragments may pass through the safety belt, we extended the original 18-bp DNA double helix by 40 bp at each end, resulting in a 98-bp DNA double helix structure. A spherical flat-bottom potential, with a radius of 10 nm, was applied between the COMs of the original 18-bp DNA residues and the protein center. No periodic boundary conditions were used in any direction in CG MD simulations.

The Ycg1-Brn1 complex and DNA interact through long-range electrostatics, excluded volume, and a nonspecific interaction of the protein-DNA sequence, which expands the Gō-like potential for the Ycg1-DNA interaction observed in the crystal structure. Similar simulation setups have proven broadly applicable and effective in modeling protein-DNA systems61–65,88. We first extract the native contact list between Ycg1 and DNA. These interactions were formed between a set of protein residues and related phosphate or sugar beads of DNA in the crystal structure. We then expanded this list by modifying the index of phosphate and sugar in DNA to cover the entire 98-bp range of DNA.

To determine the interaction strength between the DNA duplex and Ycg1 (the scaling factor εHD from the default values in the original CG model), we first compared the mean experimentally accessible value of the D for cohesins and condensins diffusing along naked DNA. The experimentally measured range of D value in vitro is approximately from 0.01 to 35 kb2/s54. When εHD ≥ 0.5, the simulated diffusion coefficient D reaches values ranging from several tens to over a hundred kb2/s, approaching the upper bound of in vitro experimental measurements. Given that CG simulations generally overestimate diffusion due to reduced frictional damping89, this level of agreement is remarkably close to experimental observations. Notably, when εLB ≤ 0.6, the DNA bending angle (θ) observed in the simulations aligns well with that seen in crystal structures with a longer DNA fragment (PDB: 7Q2Z)21. By comparing the D and θ at different interaction strengths with the angle in the crystal structure, we determined that the interaction strength scaling factor (εHD) is 0.6. Separately, to validate our choice of the 5OQO structure as a starting model, we performed a comparative analysis of protein-DNA contacts. This analysis confirms that our simulation successfully recapitulates the primary DNA contact map observed in more physiologically relevant cryo-EM structures with longer DNA substrates (Fig. S15). Additionally, we focused on the impact of the interaction strength between the latch and the buckle (the scaling factor εLB from the default values in the original CG model) in the safety-belt model on the overall dynamics of DNA entry. To explore this, we tested a range of εLB with varying interaction strengths, specifically between 0.3 and 0.5, performing ten independent simulations for each parameter. Each simulation system was run for a total of 100 μs. The probability distribution of Q under these parameters is shown in Fig. 3D. Our results demonstrate that the interaction strength between latch and buckle produces a shift in the mean value of Q from approximately 0 to 1 (Fig. 3E), indicating that the safety belt can transition from almost fully open to fully closed under the tested conditions.

Our all-atom MD simulations reveal that the interaction between DNA and Brn1 is primarily electrostatic near the crystal structure (Fig. 3C). Considering that there are only a few amino acid residues on Brn1 kleisin that have native contact with DNA in the crystal structure, and these residues are predominantly charged, we focus on electrostatic interactions and volume repulsion between DNA and Brn1 without adding additional Gō-like potential. Following the suggested parameters87,90, we assigned a charge of -0.6e to each phosphate bead for DNA-DNA electrostatic interactions, ensuring the appropriate DNA persistence length and accounting for the Oosawa-Manning condensation of counterions surrounding DNA. For Ycg1-Brn1-DNA electrostatics, we set the phosphate charges to -1e, as described previously91,92. Electrostatics is modeled according to the Debye-Huckel theory. Notably, similar CG models have been used for studying diverse protein-DNA dynamics, including parental H3/H4 recycling at replication forks61, Nap1-mediated H2A/H2B eviction from partially unwrapped nucleosomes62, MutS sliding along DNA during mismatch recognition63, and nucleosome repositioning via lane-switching driven by DNA translocases64. These precedents support the generality of CG force-field components we used here.

All CG MD simulations were performed using GENESIS (version 2.1.2) with an integration time step of 0.01 ps93. Langevin dynamics with default parameters were employed at a temperature of 310 K. To investigate the kinetics of DNA entry under different conditions, 50 independent 10 μs simulations were conducted for each condition. A summary of the simulation systems and durations is provided in Table S3. The total simulation time for the Ycg1-Brn1-DNA complex system was 6700 μs, while for the Ycg1-Brn1 complex system it was 600 μs. The ionic strength was set to 0.15 M.

Data Analysis
Visualization of the structures was performed with Visual Molecular Dynamics (VMD, version 1.9.4)94 and PyMOL (version 0.99)95 software. The GROMACS built-in tools were used to analyze MD trajectories. To examine how the presence of DNA affects the stability of the safety belt in all-atom MD simulations, “gmx_mindist” was adopted with a cutoff distance of 0.6 nm to calculate the contact number. The interactions between the DNA and protein residues were described by the normalized number of contacts, which is defined by the ratio of the average number of contacts per frame of each residue to the maximum calculated above. RMSF was calculated with “gmx_rmsf”. The fraction of native contacts Q formed by the latch and buckle in the crystal structure is determined by the switching function:

Q = 1/Nnc Σ 1/(1+exp(β(rij - λr0ij))) (3)

where Nnc is the number of native contacts, r0ij is the distance between Cα atoms of residues i and j forming native contacts in the crystal structure, and parameters λ=1.5, β=50 nm−1 control the slope of the switching function. The calculation of probability distribution of Q was carried out with the aid of the PLUMED plugin (version 2.9.0)96,97.

To quantify the one-dimensional diffusion of the condensin along DNA, we calculated the mean squared displacement (MSD) based on the index of the DNA base pair nearest to the binding site at each frame. Specifically, the MSD was computed as:

MSD(n, N) = 1/(N-n) ΣN-n k=1 (bpk+n - bpk)2 (4)

where bpk denotes the base pair index closest to the protein binding site at frame k, N is the total number of frames in the trajectory, n is the frame lag, and Δt is the time interval between two consecutive position measurements. Although this coordinate system is discrete, it captures the stepwise transitions of the protein binding site along the DNA sequence and is suitable for analyzing sliding or hopping behavior98.

To estimate the diffusion coefficient D, we focused on the initial linear regime of the MSD curve, where MSD increases approximately linearly with lag time:

MSD(n, N) = 2DnΔt (5)

To ensure reliable fitting and minimize statistical uncertainty, we imposed a cutoff lag n, chosen so that the relative error in MSD remained below 20%98,99. The MSD variance was estimated as:

σ2(n, N) = (2DΔt)2 (2n(N-n+1) / (N-n)2) (6)

so that the cutoff was determined by satisfying:

σ(n, N) / 2DnΔt < 0.2 (7)

To minimize potential artifacts in the MSD analysis introduced by the flat-bottom potential (10 nm radius) between the COMs of the original 18-bp DNA residues and the protein complex, we excluded trajectory segments where the protein approached the ends of the DNA duplex. The DNA molecule spans 98 base pairs, with an average spacing of 0.34 nm per base pair. Thus, the central region of approximately 58 base pairs corresponds to a 20 nm potential-free zone (diameter, calculated as 10 nm radius × 2). To avoid edge effects, we only analyzed trajectory segments where the DNA base pair closest to the protein binding site continuously resided within the central 50 base pairs (well within the ~58 bp potential-free zone) for at least 1000 frames (100 ns). Based on this criterion, the number of valid trajectory segments were: 114 (εLB = 0.3), 116 (εLB = 0.35), 108 (εLB = 0.37), 121 (εLB = 0.4), and 109 (εLB = 0.45).

Under our simulation conditions, we selected nc ≈ 60 frames (6 ns) for segments of 1000 frames (100 ns) and nc ≈ 300 frames (30 ns) for segments of 5000 frames (500 ns). This choice balances statistical accuracy and time resolution and ensures that the estimated D reflects genuine sequence-position dynamics.

We calculated the average DNA bending angle (θ) from the CG MD trajectories using MDAnalysis100. Only frames that met both Q ≥ 0.8 and linking number ≥ 0.8 (i.e., the BS state) were used for bending analysis. For each such frame, we constructed the DNA central axis as follows: first we located a dynamic pivot-the DNA base atom (named “DB”) closest to the COMs of key residues on Ycg1 (residue indices 240, 241, 242, 243, 245, 246, 247), which define its primary DNA binding site. Then, using symmetric windows of approximately 31 base pairs upstream and 13 base pairs downstream from that pivot, we computed geometric centroids of complementary DB atom pairs to approximate the double-helix axis. To quantify bending in each frame, we used a sliding-window approach along this axis: for each point, one vector was drawn to the centroid of the three preceding points, and another to the centroid of the three succeeding points. The angle between these two vectors was taken as the bending angle θ for that frame; smaller angles correspond to sharper bends. We repeated this for all valid frames across 10 independent simulation replicas. The mean bending angle for each system was then obtained by averaging across trajectory angle time series, and the standard error of the mean (SEM) was estimated from the replicate averages.

The number of contacts between two specified groups in the CG MD simulations was calculated using the “COORDINATION” collective variable implemented in PLUMED plugin96. This collective variable computes a smooth coordination number, defined as a differentiable sum over all pairs of CG beads i ∈ A and j ∈ B:

C = Σi∈A Σj∈B s(rij) (8)

where rij is the distance between beads i and j, and

s(rij) = 1 / (1 + (rij/r0)n)m (9)

is a switching (or rational) function that smoothly interpolates between 1 for rij ≪ r0 and 0 for rij ≫ r0, avoiding discontinuities in derivative. According to PLUMED documentation, by default n=6 and m=12, producing a sharp but continuous transition around r096. In our implementation, we set r0 = 0.8 nm, while n=6 and m=12 remained at the default values. This configuration gives a smooth counting function: bead pairs much closer than r0 contribute values close to 1 to the sum, while bead-pairs significantly farther contribute values near 0. This ensures that the coordination number reflects nearby contacts without abrupt cutoffs.

The topological state between the Brn1 loop and DNA was quantified using the concept of “linking number”, a fundamental invariant in knot theory that measures the entanglement of two closed curves in three-dimensional space101. For two non-intersecting closed curves γ1 and γ2, the linking number Φ(γ1, γ2), defined via the Gauss linking integral,

Φ(γ1, γ2) = 1/4π ∮γ1 ∮γ2 (r1 - r2) / |r1 - r2|3 · (dr1 × dr2) (10)

is an integer that remains invariant under continuous deformations, representing the number of times one curve winds around the other. Here r1 and r2 are the position vectors of points in the curve γ1 and γ2, respectively. In our MD system, we modeled the Brn1 loop as a closed curve by connecting its termini with a straight line. This approximation is physically reasonable, as it captures the effective linking when the DNA is entangled by the Brn1 loop and the auxiliary segment. To treat the finite-length DNA duplex as a topologically closed curve, we extended the nearly linear DNA infinitely from its termini along the axial direction. Because contributions to the linking integral from distant segments are negligible, we truncated the extension beyond 4 nm from each terminus. The resulting DNA segment is sufficiently long relative to the Brn1 loop to effectively approximate an infinite-length DNA. The linking number between the Brn1 loop and the extended DNA segment was computed numerically. Convergence tests confirmed that extending the DNA beyond 106 nm did not alter the integral value, indicating a stable topological classification. Instances where the extended DNA overlapped with the Brn1 loop (resulting in non-integer values due to numerical singularities) were rare and excluded from analysis. The computed linking number was strictly quantized: Φ(γ1, γ2) = 0 indicated that the DNA duplex was located outside the loop without threading, whereas |Φ(γ1, γ2)| = 1 indicated that the DNA duplex passed through the Brn1 loop. This approach provided a robust metric to statistically resolve DNA-loop threading events in our simulations.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Chen et al. investigate the molecular mechanism of DNA entrapment within the SMC complex binding pocket—a process crucial for asymmetric loop extrusion. Using AlphaFold3-based PAE analysis and molecular dynamics simulations, the study focuses on the \"safety belt\" structure in the Ycg1–Brn1 system and its DNA-mediated regulation. Key questions addressed include: 1) whether DNA binding stabilizes the safety belt? 2) how DNA influences the conformation of the belt during the binding process? And 3) how the rigidity/flexibility of the “latch-buckle” region affects DNA association. The addressed questions and major findings are compelling. The manuscript is well written. I have the following comments.\n1. The specific residues comprising the latch and buckle were not clearly defined, and the force field and parameter settings used for the coarse-grained simulations were also unspecified. Since the study focuses on conformational dynamics in flexible regions, these regions need to be explicitly defined.\n2. Since all-atom MD trajectories were generated in this work, performing residue-level energetic analysis would provide valuable insights into key residues—particularly at the latch–buckle interface—mediating DNA-regulated Ycg1–Brn1 interactions. This analysis appears highly feasible.\n3. A previous work (Marc Kschonsak et al., Cell 2017) demonstrated that DNA binding triggers “latch flipping” (via R411), which tends to stabilize the latch-buckle interactions by forming hydrophobic contacts (W408, V410, F808). Can the \"latch flipping\" conformational change and hydrophobic core formation be detected in the all-atom MD simulations in this study?\n4. The current study relies on the PAE scores given by AF3 prediction in the calculation of the effective strain. Therefore, it is important to valid first the AF3 prediction, for example by comparing the predicted 3D structure and available experimental structure. The PAE scores are meaningful only the overall architecture of the 3D structure can be reasonably reproduced.\n5. How would the effective strain profile change if mutations or deletions were introduced to the latch region? Such analysis would test whether PAE-derived strain values can truly reflect the effective strain, which is expected to be sensitive to the structural integrity of the latch region.\n6. In Fig.3A, it's better to plot the diffusion coefficients on a log scale, which can clarify the comparison between the simulation and experimental results.", "reviewer_2": "Chen and colleagues investigate the thermodynamic and kinetic protperties of DNA entrapment by the so-called “safety-belt loop” of the DNA-anchoring domain of the budding yeast condensin complex. The authors use a multiscale computational approach based on Alphafold3 structure predictions, molecular dynamics simulations, and coarse grain simulations to evolve a model for how DNA enters the DNA binding domain and how the safety belt closes around the bound DNA. The authors conclude that a) DNA binding reduces the mobility of the flexible part of the safety belt loop and stabilizes the “latch and buckle” interfaces that keep the safety belt closed, and b) DNA outside the safety belt loop promotes loop opening, while DNA bound inside the safety belt loop suppresses loop opening.\nI very much appreciate the authors’ efforts to gain insights into the dynamics of DNA entrapment within the kleisin safety belt using computational approaches that have not previously been applied to study SMC complex function. However, the insights gained are only incremental and are barely surprising. It is obvious from the available structural information that DNA binding reduces the conformational flexibility of the kleisin safety belt loop (and consequently the latch-buckle interface). While the conclusion that DNA promotes its own entry into the safety belt is intriguing, the limited resolution of the coarse grain simulation falls short into revealing a molecular mechanism that could explain the preferred entrapment. The significance of the study could be considerably increased if, for example, the simulations could be expanded to include the SMC ATPase subunits to explain how DNA binding promotes condensin’s ATPase activity. Alternatively, might simulations be able to explain how opening of the kleisin safety belt be controlled by protein modifications such as phosphorylation, which has been suggested to play an important role in regulating condensin function?\nWhile the findings of this study may be of interest to specialists in the SMC field, the scope is quite narrow. As such, the work is unlikely to resonate with the broader readership of Nature Communications and may be better suited for publication in a more specialized journal.\nSuggestions for improvements for publication of a revised version elsewhere:\n1. The results section of the manuscript reads very technical, particularly for a general reader who lacks knowledge in bioinformatics. Moving technical details to the methods section and improving the clarity of the figures might make the manuscript more accessible.\n2. Why did the authors base their study on the Brn1–Ycg1 crystal structure 5OQO, which contains only a relatively short (18-bp) DNA-duplex, if there exist more recent cryoEM structures (7QFW, 7Q2Z) with 32 and 23-bp DNA-duplexes, respectively? These structures reveal more protein-DNA contacts and likely represent a more physiological DNA substrate.\n3. This following issues in Figure 1 should be clarified:\nThe effective strain ESi or ESij depicted in Fig. 1 are measures of local flexibility (variability) of the respective Brn1 residue relative to the position of the Ycs4 residues (Fig. 1A–C). Why are the linker residues of the safety belt (aa 412–457) showing relatively low values for ESi in Fig. 1A and 1B (shown as a thin green tubes)? The available experimental data (Kschonsak et. al. 2017, Shaltiel et. al. 2022, Lee et al. 2022) instead clearly suggests that the safety belt linker region in 5OQO (or 5OQP, 7QFW, 72QZ, respectively) should be among the regions with the highest flexibility in the safety belt, since it is absent in these structures due to conformational disorder.\nA color code for the Brn1 ESi magnitude in Fig. 1A–B should be indicated. Also, residue numbers of the amino and carboxy terminus of the shown Brn1 safety belt should be indicated for clarity.\nThe plot in Fig. 1C is difficult to interprete. To start, a plot comprising the entire safety belt (not only aa 383–423) would help to put the shown ESij values into context. This could serve as a reference to help evaluate what kind of values to expect for rather rigid or very flexible regions in the structure. Such a plot could be shown, for example, as an additional supplementary figure.\nWhereas the plot in Fig. 1C indicates relatively higher ESij values for Brn1 residues 403–423 for both US and BS, the lowest panel (US-BS) indicates a drop in flexibility for residues close to aa 423 (shown in light blue). However, what is the meaning of the increase for the remaining residues in the plot (shown in light red)? How should this be interpreted? Does this mean that these residues are more flexible in BS compared to US? If yes, then this is not reflected in Fig. 1D.\nFinally, in Fig. 1D, why are the ESi values for residues around 383 and 423 zero or close to zero?\n4. In Fig. 3D and E, the order parameter Q is shown for various magnitudes of latch-buckle interaction strengths. However, the concept of Q (average fraction of native contacts) is not introduced in the results section that refers to Fig. 3 but only in the next paragraph that refers to Fig. 4.\n5. In Fig. 5A, ISout seems to indicate DNA binding outside of the safety belt with the latch closed. This deviates from the depiction of ISout in Fig. 5D, which shows an open latch.\n6. Movies of the MD or course grain simulations might help general readers to gain an impression of the structural dynamics of the kleisin safety belt in the presence and absence of DNA.\n7. The following inconsistencies in the text should be revised:\nAbstract: “its HEAT-repeat protein and kleisin subunit”. Since condensin contains two HEAT-repeat subunits, the abstract should clarify that the authors refer to the Ycg1 subunit.\nAbstract: “we demonstrate that DNA binding (…) facilitating loop extrusion progression” implies that the study explains how DNA binding promotes loop extrusion, which is not the case.\nPage 1: “the non-SMC subcomplex is responsible for DNA recognition and anchoring to chromatin, while the SMC2-SMC4 subcomplex on the other side functions as the molecular motor to perform motor action”. This simplified view is incorrect, since several recent structures demonstrated that the SMC dimer “head” domains contribute substantially to the formation of a DNA binding site. None of the cited references resolved DNA binding sites in condensin.\nPage 3: “This segment corresponds to the primary “safety belt” region that closes around DNA.” Residues 383–423 actually represent the latch region of the “safety belt” and not the “primary active region”. The sentence should be adjusted accordingly.\nPage 4: “the missing portion”. The word “missing” should be replaced by “unstructured”.\nPage 6: “This nonspecific interaction facilitates DNA sliding along the protein complex and enables nonspecific captured by the safety belt.” The sentence is incomplete, and it is not clear what a non-specific interaction refers to. Sequence-unspecific binding?", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The manuscript by Chen et al. presents a comprehensive multiscale computational study on the dynamic regulation of the condensin safety-belt mechanism by DNA. By integrating AlphaFold3 predictions, all-atom molecular dynamics (MD), and coarse-grained (CG) simulations, the authors elucidate how DNA binding actively modulates the open/closed conformations of the kleisin loop in condensin, thereby influencing loop extrusion dynamics. The work is technically sound, methodologically thorough, and provides novel mechanistic insights into the dynamic behavior of SMC complexes. The manuscript is well-organized and clearly written, and the computational results are well-validated and appropriately interpreted. I believe the study makes a valuable contribution to the field of chromosome organization and is suitable for publication in Nature Communications after minor revisions.\nThe manuscript makes extensive use of AlphaFold's predicted aligned error (PAE) scores as the basis for defining local flexibility metrics such as ESi and ESi_j. However, the concept of PAE itself is not clearly introduced. For readers unfamiliar with AlphaFold, it may not be immediately evident what PAE represents—namely, the expected positional error in aligning one residue with another in the predicted structure. A brief, high-level explanation of what PAE measures (in biological and structural terms) would significantly help the accessibility of this part of the analysis.\nThe structural representation in Figure 1A is somewhat difficult to interpret, particularly regarding the spatial relationship between the latch and buckle regions, and how the DNA is topologically entrapped within the Brn1 loop. In the original study by Haering et al. (2017), these structural concepts were effectively illustrated using simplified schematic diagrams. Including a similar cartoon-style illustration or annotated diagram in this manuscript would greatly enhance clarity and help readers unfamiliar with the safety-belt mechanism quickly grasp the spatial configuration.\nFigure 4B summarizes Q values for looped (Sl) and non-looped (Snl) states across various εLB values. To better illustrate how DNA actively regulates latch-buckle interaction over time, I suggest including representative time trajectories of Q at a fixed εLB (e.g., 0.35), separately for the looped and non-looped states. Such plots would help clarify the dynamic stability or fluctuation of the latch-buckle interaction under the influence of DNA and would visually support the manuscript's main argument that DNA actively modulates the safety-belt mechanism.\nThe authors show that DNA located outside the kleisin loop promotes latch opening (Figure 4B), but the molecular mechanism by which DNA–kleisin interactions destabilize the latch-buckle interface remains unclear. It would significantly strengthen the study if the authors could analyze which specific interactions (e.g., electrostatic repulsion, steric hindrance) between DNA and Brn1 contribute to this destabilization. For example, performing structural clustering of the non-looped state (Snl) configurations, combined with contact maps or electrostatic surface analysis, might help reveal recurring structural features that interfere with latch-buckle engagement.\nGiven the central role of CG simulations in this study, the authors should consider citing prior work that has applied similar techniques to study condensin function. In particular, the two papers (doi.org/10.1016/j.jmb.2025.169185, doi.org/10.1371/journal.pcbi.1009265) would help place the present work in a broader context:\nWhile the manuscript effectively applied CG simulations to investigate condensin–DNA interactions, the authors might consider emphasizing that similar simulation setups have been broadly and successfully applied to protein–DNA systems. This would help reinforce the general validity and robustness of their modeling strategy. For instance, the following studies demonstrate the versatility of related computational frameworks in capturing protein–DNA dynamics:\ndoi.org/10.1038/s41467-024-53187-4\ndoi.org/10.1093/nar/gkad396\ndoi.org/10.2142/biophysico.bppb-v19.0015\ndoi.org/10.1093/nar/gkab664\nOn page 9, there is a typographical error in the sentence: “(Figure (Figure 4B)”, which should be corrected to avoid redundancy. On page 11 (Methods section), the word “potentia” appears to be a typographical error and should likely be “potential.” A careful proofreading pass is recommended to catch other minor editorial issues that may remain in the manuscript."}, "author_response": "We thank Reviewer #1 for the thoughtful summary and positive evaluation of our study. We have carefully followed Reviewer #1’s comments and finally obtained a much clearer and stronger manuscript. We thank Reviewer #1 for his/her help in improving our manuscript. 1. The specific residues comprising the latch and buckle were not clearly defined, and the force field and parameter settings used for the coarse-grained simulations were also unspecified. Since the study focuses on conformational dynamics in flexible regions, these regions need to be explicitly defined. Response: We thank Reviewer #1 for this helpful suggestion. In the revision, we have now (i) explicitly defined the latch, buckle, and linker segments with residue indices and show them in the revised Figure 1 (Figure R1) and in the Table S1, and (ii) presented the details of the coarse-grained (CG) force field and parameter settings used in our simulations in the SI. ... [Response continues for all points] ... We sincerely thank Reviewer #2 for the thoughtful evaluation of our work and for the constructive feedback regarding both its contributions and its possible extensions. We appreciate Reviewer #2’s recognition of our use of computational approaches to study SMC complex functions, especially in probing the thermodynamic and kinetic aspects of DNA entry into the safety-belt loop. We have carefully considered Reviewer #2’s comments and have revised the manuscript accordingly. ... [Response continues for all points] ... We appreciate the Reviewer #4’s positive and encouraging assessment of our work. We are glad that the study was found to be technically sound, well-organized, and valuable to the field of chromosome organization."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the authors for their thorough responses to my previous comments. The manuscript has been significantly improved in this revision. I have two minor suggestions for further consideration:\nFor the comparison of ES_i with and without DNA binding in Figure 1, the authors use the ratio <ES_i^BS>/<ES_i^US>. Given that the <ES_i> values are close to zero (as seen in Fig. 1C), this ratio may be susceptible to large fluctuations. Using a difference metric, such as <ES_i^BS> - <ES_i^US>, would be more mathematically robust. Also, a difference metric would make the quantification more consistent since the authors used the similar difference metric when characterizing mutation effect (Equation 2 and Figure S18).\n2. The authors used the MM/GBSA method to estimate the binding free energy based on all-atom MD simulations. The binding free energies in Fig S4 and Fig S19, estimated via MM/GBSA, would be strengthened by the inclusion of error bars. Since the analysis is based on three independent MD simulations, reporting the standard deviation or standard error would provide a valuable measure of the estimates' reliability and reproducibility.", "reviewer_2": "We very much appreciate that the authors have considerably revised their manuscript according to the reviewers’ suggestions. The authors have addressed our previous concerns regarding accessibility for a general, non-specialist audience by revising large parts of the text.\nIn addition, the authors have\n• implemented molecular dynamics simulations of the phosphorylated Brn1 safety belt in the DNA-bound state (new Figure S19).\n• confirmed the validity of their simulations on available cryo-EM structures (6QFW or 7Q2Z) in addition to the already tested crystal structure 5OQO (new Figure S13).\n• revised Figure 1 in line with our comments.\n• clarified the distribution of effective strain across the full safety belt (new Figure S2).\n• resolved the issue concerning the apparent “drop in flexibility” due to missing data (now indicated in Figure 1D).\n• addressed several minor points raised during the first round of reviews.\nThe revised version represents a marked improvement over the original submission. We recommend publication in Nature Communications after the authors have addressed the minor issues listed below.\nMinor points:\n1. The paragraph beginning with “SMC complexes organize chromosomes across bacteria, archaea, and eukaryotes…” is largely redundant with earlier parts of the introduction. Moreover, the statement that “DNA engagement and loop control, mechanistic insight into capture, latch stability, and loop flexibility is broadly relevant and can inform models for cohesin and Smc5/6” is misleading. The cohesin kleisin subunit likely does not contain a safety belt, and the Smc5/6 complex lacks HEAT-repeat subunits altogether.\n2. The first paragraph of the Results section does not present actual findings and would therefore be more appropriately placed in the Introduction, where it can provide background or context for the study.\n3. While the clarity of the manuscript has greatly improved, parts of the Results section still omit key contextual information that may not be obvious to non-specialist readers. For instance, it is not clearly stated that the simulations were based on S. cerevisiae protein sequences. Yeast is first mentioned only in the latter half of the Discussion section.\n4. The paragraph beginning with “To assess whether the PAE-derived effective strain…” (p. 10), which presents new analyses of the four latch mutations, should be moved to the Results section rather than remaining in the Discussion and Conclusions section. The same applies to the paragraph starting with “Condensin is regulated by phosphorylation…” (p. 11), which also reports new findings and would therefore be more appropriately placed within the Results section.\n5. The authors’ suggestion that “Kschonsak et al. observed a slight upward shift of DNA even when the Brn1 kleisin loop was covalently crosslinked (preventing its opening) [22]. We propose that this corresponds to the ISout, in which DNA does not penetrate the Brn1 loop but remains in close proximity to the safety-belt structure.” appears highly unlikely. The proteins used in those experiments were denatured (i.e., unfolded) in the presence of detergent and thus cannot represent the proposed ISout conformation. Moreover, electrostatic (non-topological) interactions between the Brn1 safety belt and DNA are similarly implausible in the presence of high concentrations of the negatively charged detergent.\n6. Certain sections of the manuscript remain repetitive. For instance, the statement that DNA binding at the safety belt is linked to ATP hydrolysis by the SMC subunits appears four times in the Introduction alone. Since this relationship is not directly addressed in the manuscript, mentioning it once would suffice. Reducing such redundancies would considerably improve readability and conciseness.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The authors have constructively addressed my previous comments. With these additions to the manuscript, I recommend the paper for publication without further changes."}, "author_response": "We sincerely thank Reviewer #1 for the positive evaluation of our revised manuscript and for acknowledging the improvements made. We are grateful for Reviewer #1’s careful reading and constructive feedback. We have carefully addressed the two suggestions in the revised version, as detailed below. ... [Response continues for all points] ... We sincerely thank Reviewer #2 for the thoughtful and encouraging assessment of our revised manuscript. We are pleased that the revision is viewed as a marked improvement over the original submission. We have carefully addressed all remaining minor comments point by point below, and have further refined the text and figures to improve clarity, readability and conciseness. ... [Response continues for all points] ... We sincerely thank Reviewer #4 for the positive assessment and for recommending our manuscript for publication. We are grateful for Reviewer #4’s previous comments, which have helped us to improve the clarity and quality of the work."}]

Decision letter