Transcriptional regulation of insulin-degrading enzyme modulates mitochondrial amyloid ß (Aß) peptide catabolism and functionality.

Studies of post-mortem brains from Alzheimer disease patients suggest that oxidative damage induced by mitochondrial amyloid ß (mitAß) accumulation is associated with mitochondrial dysfunction. However, the regulation of mitAß metabolism is unknown. One of the proteases involved in mitAß catabolism is the long insulin-degrading enzyme (IDE) isoform (IDE-Met(1)). However, the mechanisms of its expression are unknown, and its presence in brain is uncertain. We detected IDE-Met(1) in brain and showed that its expression is regulated by the mitochondrial biogenesis pathway (PGC-1α/NRF-1). A strong positive correlation between PGC-1α or NRF-1 and long IDE isoform transcripts was found in non-demented brains. This correlation was weaker in Alzheimer disease. In vitro inhibition of IDE increased mitAß and impaired mitochondrial respiration. These changes were restored by inhibition of γ-secretase or promotion of mitochondrial biogenesis. Our results suggest that IDE-Met(1) links the mitochondrial biogenesis pathway with mitAß levels and organelle functionality.

Disentangling evolutionary signals: conservation, specificity determining positions and coevolution. Implication for catalytic residue prediction.

BACKGROUND: A large panel of methods exists that aim to identify residues with critical impact on protein function based on evolutionary signals, sequence and structure information. However, it is not clear to what extent these different methods overlap, and if any of the methods have higher predictive potential compared to others when it comes to, in particular, the identification of catalytic residues (CR) in proteins. Using a large set of enzymatic protein families and measures based on different evolutionary signals, we sought to break up the different components of the information content within a multiple sequence alignment to investigate their predictive potential and degree of overlap. RESULTS: Our results demonstrate that the different methods included in the benchmark in general can be divided into three groups with a limited mutual overlap. One group containing real-value Evolutionary Trace (rvET) methods and conservation, another containing mutual information (MI) methods, and the last containing methods designed explicitly for the identification of specificity determining positions (SDPs): integer-value Evolutionary Trace (ivET), SDPfox, and XDET. In terms of prediction of CR, we find using a proximity score integrating structural information (as the sum of the scores of residues located within a given distance of the residue in question) that only the methods from the first two groups displayed a reliable performance. Next, we investigated to what degree proximity scores for conservation, rvET and cumulative MI (cMI) provide complementary information capable of improving the performance for CR identification. We found that integrating conservation with proximity scores for rvET and cMI achieved the highest performance. The proximity conservation score contained no complementary information when integrated with proximity rvET. Moreover, the signal from rvET provided only a limited gain in predictive performance when integrated with mutual information and conservation proximity scores. Combined, these observations demonstrate that the rvET and cMI scores add complementary information to the prediction system. CONCLUSIONS: This work contributes to the understanding of the different signals of evolution and also shows that it is possible to improve the detection of catalytic residues by integrating structural and higher order sequence evolutionary information with sequence conservation.

Correction for phylogeny, small number of observations and data redundancy improves the identification of coevolving amino acid pairs using mutual information.

MOTIVATION: Mutual information (MI) theory is often applied to predict positional correlations in a multiple sequence alignment (MSA) to make possible the analysis of those positions structurally or functionally important in a given fold or protein family. Accurate identification of coevolving positions in protein sequences is difficult due to the high background signal imposed by phylogeny and noise. Several methods have been proposed using MI to identify coevolving amino acids in protein families. RESULTS: After evaluating two current methods, we demonstrate how the use of sequence-weighting techniques to reduce sequence redundancy and low-count corrections to account for small number of observations in limited size sequence families, can significantly improve the predictability of MI. The evaluation is made on large sets of both in silico-generated alignments as well as on biological sequence data. The methods included in the analysis are the APC (average product correction) and RCW (row-column weighting) methods. The best performing method was APC including sequence-weighting and low-count corrections. The use of sequence-permutations to calculate a MI rescaling is shown to significantly improve the prediction accuracy and allows for direct comparison of information values across protein families. Finally, we demonstrate how a lower bound of 400 sequences <62% identical is needed in an MSA in order to achieve meaningful predictive performances. With our contribution, we achieve a noteworthy improvement on the current procedures to determine coevolution and residue contacts, and we believe that this will have potential impacts on the understanding of protein structure, function and folding.

Structural selection of a native fold by peptide recognition. Insights into the thioredoxin folding mechanism.

Thioredoxins (TRXs) are monomeric alpha/beta proteins with a fold characterized by a central twisted beta-sheet surrounded by alpha-helical elements. The interaction of the C-terminal alpha-helix 5 of TRX against the remainder of the protein involves a close packing of hydrophobic surfaces, offering the opportunity of studying a fine-tuned molecular recognition phenomenon with long-range consequences on the acquisition of tertiary structure. In this work, we focus on the significance of interactions involving residues L94, L99, E101, F102, L103 and L107 on the formation of the noncovalent complex between reduced TRX1-93 and TRX94-108. The conformational status of the system was assessed experimentally by circular dichroism, intrinsic fluorescence emission and enzymic activity; and theoretically by molecular dynamics simulations (MDS). Alterations in tertiary structure of the complexes, resulting as a consequence of site specific mutation, were also examined. To distinguish the effect of alanine scanning mutagenesis on secondary structure stability, the intrinsic helix-forming ability of the mutant peptides was monitored experimentally by far-UV CD spectroscopy upon the addition of 2,2,2-trifluoroethanol, and also theoretically by Monte Carlo conformational search and MDS. This evidence suggests a key role of residues L99, F102 and L103 on the stabilization of the secondary structure of alpha-helix 5, and on the acquisition of tertiary structure upon complex formation. We hypothesize that the transition between a partially folded and a native-like conformation of reduced TRX1-93 would fundamentally depend on the consolidation of a cooperative tertiary unit based on the interaction between alpha-helix 3 and alpha-helix 5.