Evolutionary Switches Structural Transitions via Coarse-Grained Models.

Transitions between different conformational states are ubiquitous in proteins. A vast class of conformation-changing proteins includes evolutionary switches, which vary their conformation as an effect of few mutations or weak environmental variations. However, modeling those processes is extremely difficult due to the need of efficiently exploring a vast conformational space to look for the actual transition path. In this study, we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to the protein, based on an empirical force field with a partial structural bias toward one or both the reference structures. We then explore the transition paths by means of stochastic molecular dynamics and select representative structures by means of a principal path-based clustering algorithm. We finally compare this trajectory with that produced by independent methods adopting a morphing-oriented approach. Our analysis indicates that the minimalist model returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multistable proteins transition pathways.

Structural Transition States Explored With Minimalist Coarse Grained Models: Applications to Calmodulin.

Transitions between different conformational states are ubiquitous in proteins, being involved in signaling, catalysis, and other fundamental activities in cells. However, modeling those processes is extremely difficult, due to the need of efficiently exploring a vast conformational space in order to seek for the actual transition path for systems whose complexity is already high in the stable states. Here we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to Calmodulin, based on an empirical force field with a partial structural bias, to explore the transition paths between the apo-closed state and the Ca-bound open state of the protein. We then select representative structures along the trajectory based on a structural clustering algorithm and build a cleaned-up trajectory with them. We finally compare this trajectory with that produced by the online tool MinActionPath, by minimizing the action integral using a harmonic network model, and with that obtained by the PROMPT morphing method, based on an optimal mass transportation-type approach including physical constraints. The comparison is performed both on the structural and energetic level, using the coarse-grained and the atomistic force fields upon reconstruction. Our analysis indicates that this method returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multi-stable proteins transition pathways.

Modeling conformational redox-switch modulation of human succinic semialdehyde dehydrogenase.

Succinic semialdehyde dehydrogenase (SSADH) converts succinic semialdehyde (SSA) to succinic acid in the mitochondrial matrix and is involved in the metabolism of the inhibitory neurotransmitter γ-aminobutyric acid (GABA). The molecular structure of human SSADH revealed the intrinsic regulatory mechanism--redox-switch modulation--by which large conformational changes are brought about in the catalytic loop through disulfide bonding. The crystal structures revealed two SSADH conformations, and computational modeling of transformation between them can provide substantial insights into detailed dynamic redox modulation. On the basis of these two clear crystal structures, we modeled the conformational motion between these structures in silico. For that purpose, we proposed and used a geometry-based coarse-grained mathematical model of long-range protein motion and the related modeling algorithm. The algorithm is based on solving the special optimization problem, which is similar to the classical Monge-Kantorovich mass transportation problem. The modeled transformation was supported by another morphing method based on a completely different framework. The result of the modeling facilitates better interpretation and understanding of the SSADH biological role.