Myosins may know when to hold and when to fold.
Myosins may know when to hold and when to fold.
复制标题
肌球蛋白可能知道何时保持和何时折叠。
DOI：
10.1016/j.bpj.2024.01.031
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发表时间：
2024
影响因子：
3.4
通讯作者：
Campbell,KennethS
中科院分区：
文献类型：
--
作者：
Squarci,Caterina;Campbell,KennethS
Science often progresses in bursts, and those of us currently working on muscle are fortunate to have the chance to contribute to one of these periods of rapid change. The discovery that myosin can transition to and from a state with suppressed activity (variously called the OFF, super-relaxed, or interacting heads motif configuration) has transformed the interpretation of many experiments (1, 2). Even more importantly, a drug that likely modulates transitions involving the suppressed state has been approved in the US to help patients with a particular form of cardiac disease (3, 4). This is a wonderful example of biophysics being applied to help people! Big changes also create uncertainty. As a field, we are still trying to understand the relationship between the OFF, super-relaxed, and interacting heads states. Are they the same or different? Some also suspect that reductionist approaches may no longer be sufficient. Numerous groups have suggested that perturbing myosin function can influence thin filament activation and modulate contractile properties via cooperative system-level effects. Similar functional effects are often attributed to titin and myosin-binding protein C. Many of these ideas seem both plausible and important, but the mechanisms are often proposed in qualitative terms and have proved hard to test with rigorous, unambiguous, experiments. Faced with this challenge, how do we move forward? The remarkable paper presented by Liu et al. in this issue of Biophysical Journal illustrates one potential approach. The authors use a new computer model to integrate experimental results from multiple scales into a single theoretical framework that can be probed to test fundamental mechanisms. It is possible that this multilab, team-based style represents the future of muscle biophysics. Liu et al.’s paper presents data that span six orders of spatial magnitude from single-molecule optical trapping (10 À9 m) through in vitro motility to fiber-level mechanics (10 À3 m). All data were collected using samples derived from rabbit psoas muscles and, as far as possible, similar conditions, yielding a comprehensive, self-consistent, dataset. The authors then fitted a mathematical model of cycling cross-bridges based on partial differential equations to portions of the fiber-level data. Strikingly, the model then predicted the remaining fiber-level data with high fidelity. Even more impressively, and some might say astonishingly, the model also predicted the molecularlevel data derived from the laser trap and the in vitro motility assays. This is a state-of-the-art demonstration of the power of computer modeling and its ability to bridge across structural scales. While multiple groups have built fiberlevel models based on molecular-level data, traversing in the other direction, from fibers to molecules, is much less common and a fantastic achievement. One of the unique features of Liu et al.’s model is its approach to simulating the suppressed myosin state. Some prior models of force-dependent recruitment have assumed that the transition rate from the suppressed state accelerates with force (5). Liu et al. invoke a different mechanism in which myosin molecules revert to a folded state when the force in the filament drops below a defined threshold. Intriguingly, different molecules are assumed to have different thresholds, so the number of heads participating in contraction depends on the prevailing force. The biophysical mechanisms underpinning these thresholds are not described, but it seems to us that computer models could be developed in future work that assign different functional properties to myosin crowns in the D, C, and P zones of the thick filament. It might then be …
