Abstract
We analyze the effect of point mutations on the energy landscape of a coiled-coil peptide, GCN4-pLI, where the native state is a parallel tetrameric configuration formed from two identical dimers. Experimentally, a single mutation, E20S, supports both antiparallel and parallel structures. Here, we analyze the potential energy landscapes of the dimeric units for the parent sequence and four mutants, namely E20S, E20A, E20P, and E20G. Despite sharing characteristic funnels containing the parallel and antiparallel structures, the point mutations change some parts of the landscape quite dramatically, and we predict new intermediate structures and characterize the associated heat capacities. For the mutants we predict that kinked intermediate structures facilitate the transition between parallel and antiparallel morphologies, in contrast to the parent sequence. Furthermore, we predict a change from a multifunnel energy landscape in the E20S mutant to a landscape dominated by an underlying single funnel in the parent sequence, with accompanying heat capacity signatures. Our results imply that changes in the landscape due to mutations might provide useful tools for functional protein design.
Highlights
In principle, the energy landscape of a protein contains all the information required to understand its structure, thermodynamics, and kinetics.[1−11] An exploration of the energy landscape provides insight into experimentally observable properties.[12−14] The computational potential energy landscape framework employs discrete path sampling[15,16] to construct kinetic transition networks,[17,18] exploiting efficient geometry optimization procedures
The use of geometry optimization facilitates sampling that is largely independent of kinetic traps caused by high energy barriers, and permits us to explore a wide range of time scales
We explored the potential energy landscapes using discrete path sampling[15,16] to construct kinetic transition networks[17,18] consisting of local minima and the transition states that connect them
Summary
The energy landscape of a protein contains all the information required to understand its structure, thermodynamics, and kinetics.[1−11] An exploration of the energy landscape provides insight into experimentally observable properties.[12−14] The computational potential energy landscape framework employs discrete path sampling[15,16] to construct kinetic transition networks,[17,18] exploiting efficient geometry optimization procedures. A point mutation in such a system may not change the overall topography, but is likely to change the depth (enthalpy) and width (entropy) of funnels, as well as the relative energies, which in turn will perturb the observed behavior. Understanding such changes is important, as many human diseases are affected or caused by mutations.[29−32]
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