Thiourea enhances mapping of the proteome from murine white adipose tissue.

Adipose tissue imposes problems in two-dimensional (2-D) analysis due to its extremely high content of fat. To improve protein separation detergents and chaotropes were varied in the IEF step. The most important factor for obtaining distinct spots in the 2-D gel was whether thiourea was included or not. Many high molecular weight spots became resolved by using thiourea, while no spots disappeared or showed inferior characteristics, thus approximately twice as many spots were possible to quantify. Hydrophobic indices were compared for a set of proteins that gave rise to sharper spots with proteins that were not improved on the use of thiourea. The comparison did not give any statistically significant difference between the two groups of proteins. One of the effects obtained by inclusion of thiourea was that the dominating protein, serum albumin, appeared as more condensed spots allowing other minor proteins to be detected. This work resulted in a protocol which greatly enhances the resolution of proteins in adipose tissue. A 2-D map of mouse white adipose tissue from epididymal fat pads was constructed in which 140 spots were identified by mass spectrometry. This work lays the ground for our further studies on white adipose tissue in metabolic diseases such as obesity and dyslipidemia.

Design of sequences with good folding properties in coarse-grained protein models.

BACKGROUND: Designing amino acid sequences that are stable in a given target structure amounts to maximizing a conditional probability. A straightforward approach to accomplishing this is a nested Monte Carlo where the conformation space is explored over and over again for different fixed sequences; this requires excessive computational demand. Several approximate attempts to remedy this situation, based on energy minimization for fixed structure or high-T expansions, have been proposed. These methods are fast but often not accurate, as folding occurs at low T. RESULTS: We have developed a multisequence Monte Carlo procedure where both sequence and conformational space are simultaneously probed with efficient prescriptions for pruning sequence space. The method is explored on hydrophobic/polar models. First we discuss short lattice chains in order to compare with exact data and with other methods. The method is then successfully applied to lattice chains with up to 50 monomers and to off-lattice 20mers. CONCLUSIONS: The multisequence Monte Carlo method offers a new approach to sequence design in coarse-grained models. It is much more efficient than previous Monte Carlo methods, and is, as it stands, applicable to a fairly wide range of two-letter models.

Binary assignments of amino acids from pattern conservation.

We have developed a simple optimization procedure for assigning binary values to amino acids. The binary values are determined by a maximization of the degree of pattern conservation in groups of closely related protein sequences. The maximization is carried out at fixed composition. For compositions approximately corresponding to an equipartition of the residues, the optimal encoding is found to be strongly correlated with hydrophobicity. The stability of the procedure is demonstrated. Our calculations are based upon sequences in the SWISS-PROT database.

Evidence for nonrandom hydrophobicity structures in protein chains.

The question of whether proteins originate from random sequences of amino acids is addressed. A statistical analysis is performed in terms of blocked and random walk values formed by binary hydrophobic assignments of the amino acids along the protein chains. Theoretical expectations of these variables from random distributions of hydrophobicities are compared with those obtained from functional proteins. The results, which are based upon proteins in the SWISS-PROT data base, convincingly show that the amino acid sequences in proteins differ from what is expected from random sequences in a statistically significant way. By performing Fourier transforms on the random walks, one obtains additional evidence for nonrandomness of the distributions. We have also analyzed results from a synthetic model containing only two amino acid types, hydrophobic and hydrophilic. With reasonable criteria on good folding properties in terms of thermodynamical and kinetic behavior, sequences that fold well are isolated. Performing the same statistical analysis on the sequences that fold well indicates similar deviations from randomness as for the functional proteins. The deviations from randomness can be interpreted as originating from anticorrelations in terms of an Ising spin model for the hydrophobicities. Our results, which differ from some previous investigations using other methods, might have impact on how permissive with respect to sequence specificity protein folding process is-only sequences with nonrandom hydrophobicity distributions fold well. Other distributions give rise to energy landscapes with poor folding properties and hence did not survive the evolution.