Intrinsic compressibility and volume compression in solvated proteins by molecular dynamics simulation at high pressure.

Constant pressure and temperature molecular dynamics techniques have been employed to investigate the changes in structure and volumes of two globular proteins, superoxide dismutase and lysozyme, under pressure. Compression (the relative changes in the proteins' volumes), computed with the Voronoi technique, is closely related with the so-called protein intrinsic compressibility, estimated by sound velocity measurements. In particular, compression computed with Voronoi volumes predicts, in agreement with experimental estimates, a negative bound water contribution to the apparent protein compression. While the use of van der Waals and molecular volumes underestimates the intrinsic compressibilities of proteins, Voronoi volumes produce results closer to experimental estimates. Remarkably, for two globular proteins of very different secondary structures, we compute identical (within statistical error) protein intrinsic compressions, as predicted by recent experimental studies. Changes in the protein interatomic distances under compression are also investigated. It is found that, on average, short distances compress less than longer ones. This nonuniform contraction underlines the peculiar nature of the structural changes due to pressure in contrast with temperature effects, which instead produce spatially uniform changes in proteins. The structural effects observed in the simulations at high pressure can explain protein compressibility measurements carried out by fluorimetric and hole burning techniques. Finally, the calculation of the proteins static structure factor shows significant shifts in the peaks at short wavenumber as pressure changes. These effects might provide an alternative way to obtain information concerning compressibilities of selected protein regions.

A unified statistical framework for sequence comparison and structure comparison

We present an approach for assessing the significance of sequence and structure comparisons by using nearly identical statistical formalisms for both sequence and structure. Doing so involves an all-vs.-all comparison of protein domains [taken here from the Structural Classification of Proteins (scop) database] and then fitting a simple distribution function to the observed scores. By using this distribution, we can attach a statistical significance to each comparison score in the form of a P value, the probability that a better score would occur by chance. As expected, we find that the scores for sequence matching follow an extreme-value distribution. The agreement, moreover, between the P values that we derive from this distribution and those reported by standard programs (e.g., blast and fasta validates our approach. Structure comparison scores also follow an extreme-value distribution when the statistics are expressed in terms of a structural alignment score (essentially the sum of reciprocated distances between aligned atoms minus gap penalties). We find that the traditional metric of structural similarity, the rms deviation in atom positions after fitting aligned atoms, follows a different distribution of scores and does not perform as well as the structural alignment score. Comparison of the sequence and structure statistics for pairs of proteins known to be related distantly shows that structural comparison is able to detect approximately twice as many distant relationships as sequence comparison at the same error rate. The comparison also indicates that there are very few pairs with significant similarity in terms of sequence but not structure whereas many pairs have significant similarity in terms of structure but not sequence.