Hydrophobic sequence minimization of the α-lactalbumin molten globule

The molten globule, a widespread protein-folding intermediate, can attain a native-like backbone topology, even in the apparent absence of rigid side-chain packing. Nonetheless, mutagenesis studies suggest that molten globules are stabilized by some degree of side-chain packing among specific hydrophobic residues. Here we investigate the importance of hydrophobic side-chain diversity in determining the overall fold of the α-lactalbumin molten globule. We have replaced all of the hydrophobic amino acids in the sequence of the helical domain with a representative amino acid, leucine. Remarkably, the minimized molecule forms a molten globule that retains many structural features characteristic of a native α-lactalbumin fold. Thus, nonspecific hydrophobic interactions may be sufficient to determine the global fold of a protein.

Deliberate self harm: systematic review of efficacy of psychosocial and pharmacological treatments in preventing repetition

Objective: To identify and synthesise the findings from all randomised controlled trials that have examined the effectiveness of treatments of patients who have deliberately harmed themselves.

Top-down free-energy minimization on protein potential energy landscapes

The hierarchical properties of potential energy landscapes have been used to gain insight into thermodynamic and kinetic properties of protein ensembles. It also may be possible to use them to direct computational searches for thermodynamically stable macroscopic states, i.e., computational protein folding. To this end, we have developed a top-down search procedure in which conformation space is recursively dissected according to the intrinsic hierarchical structure of a landscape's effective-energy barriers. This procedure generates an inverted tree similar to the disconnectivity graphs generated by local minima-clustering methods, but it fundamentally differs in the manner in which the portion of the tree that is to be computationally explored is selected. A key ingredient is a branch-selection algorithm that takes advantage of statistically predictive properties of the landscape to guide searches down the tree branches that are most likely to lead to the physically relevant macroscopic states. Using the computational folding of a β-hairpin-forming peptide as an example, we show that such predictive properties indeed exist and can be used for structure prediction by free-energy global minimization.