School of Mechanical Engineering, Sungkyunkwan University, Suwon, South Korea.
Department of Physics and Institute of Basic Science, Sungkyunkwan University, Suwon, South Korea.
PLoS One. 2021 Nov 4;16(11):e0258818. doi: 10.1371/journal.pone.0258818. eCollection 2021.
Large-scale conformational changes are essential for proteins to function properly. Given that these transition events rarely occur, however, it is challenging to comprehend their underlying mechanisms through experimental and theoretical approaches. In this study, we propose a new computational methodology called internal coordinate normal mode-guided elastic network interpolation (ICONGENI) to predict conformational transition pathways in proteins. Its basic approach is to sample intermediate conformations by interpolating the interatomic distance between two end-point conformations with the degrees of freedom constrained by the low-frequency dynamics afforded by normal mode analysis in internal coordinates. For validation of ICONGENI, it is applied to proteins that undergo open-closed transitions, and the simulation results (i.e., simulated transition pathways) are compared with those of another technique, to demonstrate that ICONGENI can explore highly reliable pathways in terms of thermal and chemical stability. Furthermore, we generate an ensemble of transition pathways through ICONGENI and investigate the possibility of using this method to reveal the transition mechanisms even when there are unknown metastable states on rough energy landscapes.
大规模构象变化对于蛋白质正常发挥功能至关重要。然而，由于这些转变事件很少发生，因此通过实验和理论方法来理解其潜在机制具有挑战性。在这项研究中，我们提出了一种名为“内部坐标正则模态引导弹性网络插值（ICONGENI）”的新计算方法，用于预测蛋白质中的构象转变途径。其基本方法是通过在两个端点构象之间的原子间距离进行插值，并用正则模态分析在内部坐标中提供的低频动力学自由度来约束自由度，从而采样中间构象。为了验证 ICONGENI，我们将其应用于经历开-闭转变的蛋白质，并且将模拟结果（即模拟的转变途径）与另一种技术的结果进行比较，以证明 ICONGENI 可以在热和化学稳定性方面探索高度可靠的途径。此外，我们通过 ICONGENI 生成了一组转变途径，并研究了即使在粗糙能量景观上存在未知的亚稳态时，该方法也有可能揭示转变机制的可能性。
