PVT behaviour of fluids with potential models optimised for phase equilibria

NPT and NVT Monte Carlo simulations are applied to models for methane and water to predict the PVT behaviour of these fluids over a wide range of temperatures and pressures. The potential models examined in this paper have previously been presented in the literature with their specific parameters optimised to fit phase coexistence data. The exponential-6 potential for methane gives generally good prediction of PVT behaviour over the full range of temperature and pressures studied with the only significant deviation from experimental data seen at high temperatures and pressures. The NSPCE water model shows very poor prediction of PVT behaviour, particularly at dense conditions. To improve this. the charge separation in the NSPCE model is varied with density. Improvements for vapour and liquid phase PVT predictions are achieved with this variation. No improvement was found in the prediction of the oxygen-oxygen radial distribution by varying charge separation under dense phase conditions. (C) 2004 Elsevier B.V. All rights reserved.

A new method for identification of protein (sub)families in a set of proteins based on hydropathy distribution in proteins

Structural similarity among proteins is reflected in the distribution of hydropathicity along the amino acids in the protein sequence. Similarities in the hydropathy distributions are obvious for homologous proteins within a protein family. They also were observed for proteins with related structures, even when sequence similarities were undetectable. Here we present a novel method that employs the hydropathy distribution in proteins for identification of (sub)families in a set of (homologous) proteins. We represent proteins as points in a generalized hydropathy space, represented by vectors of specifically defined features. The features are derived from hydropathy of the individual amino acids. Projection of this space onto principal axes reveals groups of proteins with related hydropathy distributions. The groups identified correspond well to families of structurally and functionally related proteins. We found that this method accurately identifies protein families in a set of proteins, or subfamilies in a set of homologous proteins. Our results show that protein families can be identified by the analysis of hydropathy distribution, without the need for sequence alignment. (C) 2005 Wiley-Liss, Inc.