Protein conformational transitions explored by a morphing approach based on normal mode analysis in internal coordinates

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.

Geometry Optimization Speedup Through a Geodesic Approach to Internal Coordinates

We present a new geodesic-based method for geometry optimization in a basis of redundant internal coordinates. Our method updates the molecular geometry by following the geodesic generated by a displacement vector on the internal coordinate manifold, which dramatically reduces the number of steps required to reach convergence. Our method can be implemented in any existing optimization code, requiring only implementation of derivatives of the Wilson B-matrix and the ability to solve an ordinary differential equation.

Geometry Optimization Speedup Through a Geodesic Approach to Internal Coordinate Optimization

We present a new geodesic-based method for geometry optimization in a basis of redundant internal coordinates.<br>This method realizes displacements along internal coordinates by following the geodesic generated by the displacement vector on the internal coordinate manifold.<br>Compared to the traditional Newton method approach to taking displacements in internal coordinates, this geodesic approach substantially reduces the number of steps required to reach convergence on a molecular structure minimization benchmark.<br>This new geodesic method can in principle be implemented in any existing optimization code, and only requires the implementation of derivatives of the Wilson B-matrix and the ability to solve a relatively inexpensive ordinary differential equation.

Geometry Optimization Speedup Through a Geodesic Approach to Internal Coordinate Optimization

We present a new geodesic-based method for geometry optimization in a basis of redundant internal coordinates.<br>This method realizes displacements along internal coordinates by following the geodesic generated by the displacement vector on the internal coordinate manifold.<br>Compared to the traditional Newton method approach to taking displacements in internal coordinates, this geodesic approach substantially reduces the number of steps required to reach convergence on a molecular structure minimization benchmark.<br>This new geodesic method can in principle be implemented in any existing optimization code, and only requires the implementation of derivatives of the Wilson B-matrix and the ability to solve a relatively inexpensive ordinary differential equation.