Search for the pathogenesis of the differing phenotype in two compound heterozygote Hungarian brothers with the same genotypic triosephosphate isomerase deficiency

In a Hungarian family with triosephosphate isomerase (TPI) deficiency, two compound heterozygote brothers were found with the same severe decrease in TPI activity, but only one of them had the classical symptoms. In search for the pathogenesis of the differing phenotype of the same genotypic TPI deficiency, an increase in red cell membrane fluidity was found. There were roughly 100% and 30% more 16:0/20:4 and 18:0/20:4 diacyl-phosphatidylcholine species in erythrocytes from the two TPI-deficient brothers than in the probes from healthy controls. The activities of acethylcholinesterase and calmodulin induced Ca2+ ATPase were significantly enhanced in erythrocytes from the propositus as compared with those of the neurologically symptom-free brother and other members of the TPI-deficient family as well as to those from healthy controls. Both enzymes are crucially involved in the function of nerve cells. The observed differences in membrane fluidity and enzyme activities between the erythrocytes from the phenotypically differing TPI-deficient brothers underline the importance of investigations into the effect of biophysical changes in the lipid environment of the membrane proteins on the development of disseminated focal neurological disorders of unknown pathogenic origin.

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.