Dataset Information

TMAO-Protein Preferential Interaction Profile Determines TMAO's Conditional In Vivo Compatibility.

ABSTRACT: Trimethylamine N-oxide (TMAO) exemplifies how Nature uses the solute effect as a simple chemical strategy to cope with hydrodynamic pressure or urea stress to maintain proteostasis. It is a gut-microbe-generated metabolite that strongly promotes the development of atherosclerosis. It remains unclear how TMAO exerts its effects. In this study, we experimentally characterized the profile of the preferential interaction potential of TMAO with proteins, a thermodynamic key to understanding the effects of TMAO on protein processes and the distinction of TMAO among osmolytes. TMAO is thus found to be highly preferentially excluded from most types of protein surface, which explains why TMAO is a strong globular protein stabilizer and identifies the dominant stabilizing factor as the unfavorable interaction of TMAO with the hydrophobic surface exposed upon unfolding. We dissected the mechanism of the counteracting effects of TMAO and urea: the contrary feature of the interaction profiles of the two solutes maximizes the possibility for them to offset each other's perturbing effect on protein processes. The interaction profile also predicts that TMAO promotes aggregation of amyloidogenic intrinsically disordered peptide, as demonstrated here in A?42, and that TMAO has a strong potential to impact protein processes in the absence of stressors. Our data suggest that although TMAO is an evolutionally selected chemical chaperone for some organisms or organs, its compatibility in vivo is conditional and determined by its interaction profile with biopolymers and the nature of the essential biopolymer processes. Our thermodynamic framework plus the TMAO-protein interaction profile provides a basis for exploring the broad biological significance of TMAO, including its pathological impact in the absence of stressors. We argue for the general importance of controlling in vivo background solutes and the pathological significance of a control failure.

SUBMITTER: Hong J

PROVIDER: S-EPMC5103029 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

TMAO-Protein Preferential Interaction Profile Determines TMAO's Conditional In Vivo Compatibility.

Hong Jiang J Xiong Shangqin S

Biophysical journal 20161101 9

Trimethylamine N-oxide (TMAO) exemplifies how Nature uses the solute effect as a simple chemical strategy to cope with hydrodynamic pressure or urea stress to maintain proteostasis. It is a gut-microbe-generated metabolite that strongly promotes the development of atherosclerosis. It remains unclear how TMAO exerts its effects. In this study, we experimentally characterized the profile of the preferential interaction potential of TMAO with proteins, a thermodynamic key to understanding the effec ...[more]

PMID: 27806268

Similar Datasets

Project description:BACKGROUND: The increasing number of protein sequences and 3D structure obtained from genomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimental and computational efforts on the creation and analysis of information derived from 3D structure. In particular, the high-throughput generation of protein-protein interaction data from a few organisms makes such an approach very important towards understanding the molecular recognition that make-up the entire protein-protein interaction network. Since the generation of sequences, and experimental protein-protein interactions increases faster than the 3D structure determination of protein complexes, there is tremendous interest in developing in silico methods that generate such structure for prediction and classification purposes. In this study we focused on classifying protein family members based on their protein-protein interaction distinctiveness. Structure-based classification of protein-protein interfaces has been described initially by Ponstingl et al. 1 and more recently by Valdar et al. 2 and Mintseris et al. 3, from complex structures that have been solved experimentally. However, little has been done on protein classification based on the prediction of protein-protein complexes obtained from homology modeling and docking simulation. RESULTS: We have developed an in silico classification system entitled HODOCO (Homology modeling, Docking and Classification Oracle), in which protein Residue Potential Interaction Profiles (RPIPS) are used to summarize protein-protein interaction characteristics. This system applied to a dataset of 64 proteins of the death domain superfamily was used to classify each member into its proper subfamily. Two classification methods were attempted, heuristic and support vector machine learning. Both methods were tested with a 5-fold cross-validation. The heuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an 89% average accuracy. CONCLUSION: We have confirmed the reliability and potential value of classifying proteins via their predicted interactions. Our results are in the same range of accuracy as other studies that classify protein-protein interactions from 3D complex structure obtained experimentally. While our classification scheme does not take directly into account sequence information our results are in agreement with functional and sequence based classification of death domain family members.

Dataset Information

TMAO-Protein Preferential Interaction Profile Determines TMAO's Conditional In Vivo Compatibility.

Publications

TMAO-Protein Preferential Interaction Profile Determines TMAO's Conditional In Vivo Compatibility.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets