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Two methods for studying the rotation/torsion coupling in H5(+) are described. The first involves a fixed-node treatment in which the nodal surfaces are obtained from a reduced dimensional calculation in which only the rotations of the outer H2 groups are considered. In the second, the torsion and rotation dependence of the wave function is described in state space, and the other internal coordinates are described in configuration space. Such treatments are necessary for molecules, like H5(+), where there is a very low-energy barrier to internal rotation. The results of the two approaches are found to be in good agreement with previously reported energies for J = 0. The diffusion Monte Carlo treatment allows us to extend the calculations to low J, and results are reported for the three lowest energy torsion excited states with J ≤ 3. For the level of rotational and vibrational excitation investigated, only modest changes in the vibrational wave functions are found. The effects of deuteration are also investigated, focusing on D5(+) and the symmetric variants of H4D(+) and HD4(+).

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There's a huge gap between the sequence and structural knowledge of proteins. Determining proteins structures experimentally is expensive and not always amenable. Computational tools such as homology modeling have been successful at bridging this gap. However, they are still subject to database information and they don't have a principled way of selecting structures. This and other issues are treated in the blind CASP competition (Critical Assessment of Structure Prediction). We took part in the recent CASP11 competition by using a new physics based approach called MELD[1,2] and combining with either proposed data during CASP or with general insights coming from our knowledge of globular protein structures (e.g. they have hydrophobic cores). Physics based methods are usually too computationally demanding for the tight deadlines in CASP, which has mostly excluded them from participation. In this talk I will show how MELD was useful during CASP. In some cases MELD provided value beyond what database methods could do-including the best blind prediction for one of the proteins in CASP. As such, physics based methods are starting to be useful for structure prediction to complement existing methods. 1.MacCallum, J. L., Perez, A. & Dill, K. Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference.