Search

The 60s gave birth to the practical implementation of classical mechanics to unravel the dynamics and energetics of biomolecules. In the 70s the use of generalized force fields and more advanced integrative solutions to the microscopic understanding of nature (like hybrid QM/MM) were introduced. During the 80s, algorithms to obtain free energy values were further developed and in the 90s practical integration schemes of molecular mechanics force fields with other levels of detail (QM on one extreme and advances in implicit solvation on the other) were implemented in widely spread software. In the first decade of the XXIst century a considerable effort has been put in two seemingly discordant models for the simulation of biomolecules. On the one hand, extraordinary advances in computing technologies (both in terms of processor power and of new efficient parallel and distributed computing schemas) have allowed researchers to deal with bigger systems and longer simulations, reaching molecular processes including millions of particles or lying in the milisecond scale. On the other hand, the realization that the relevant answers to many biomolecular problems are not homogeneously distributed through the molecular structure, something already envisioned by the QM/MM pioneers more than three decades ago, has led researchers to find smart ways of putting different emphases on different ranges of the spatial or system time scale. In this context, e.g., molecular aggregation represents a paradigm for multiscalability, as molecular recognition can be understood with simple (semi-)macroscopic electrostatic terms when the two fragments are far apart, while the atomic interactions need to be considered in full detail upon close distances. In this manuscript the current status of the techniques that use multiple scale representations of biomolecules are reviewed, and the findings are synthesized in a modular schema that can be extensively used when studying aggregation processes. It is shown that a smart alternative to brute force and massive computation of uninteresting regions in the all atom potential energy surface is the consideration of a simplified reference potential, explored thoroughly in the relevant regions, combined with a free energy perturbation approach that transforms this simple representation to a full atom representation.

Neuronal cells undergo rapid growth cone collapse, neurite retraction, and cell rounding in response to certain G protein-coupled receptor agonists such as lysophosphatidic acid (LPA). These shape changes are driven by Rho-mediated contraction of the actomyosin-based cytoskeleton. To date, however, detection of Rho activation has been hampered by the lack of a suitable assay. Furthermore, the nature of the G protein(s) mediating LPA-induced neurite retraction remains unknown. We have developed a Rho activation assay that is based on the specific binding of active RhoA to its downstream effector Rho-kinase (ROK). A fusion protein of GST and the Rho-binding domain of ROK pulls down activated but not inactive RhoA from cell lysates. Using GST-ROK, we show that in N1E-115 neuronal cells LPA activates endogenous RhoA within 30 s, concomitant with growth cone collapse. Maximal activation occurs after 3 min when neurite retraction is complete and the actin cytoskeleton is fully contracted. LPA-induced RhoA activation is completely inhibited by tyrosine kinase inhibitors (tyrphostin 47 and genistein). Activated Galpha12 and Galpha13 subunits mimic LPA both in activating RhoA and in inducing RhoA-mediated cytoskeletal contraction, thereby preventing neurite outgrowth. We conclude that in neuronal cells, LPA activates RhoA to induce growth cone collapse and neurite retraction through a G12/13-initiated pathway that involves protein-tyrosine kinase activity.