Dataset Information

A binding mechanism in protein-nucleotide interactions: implication for U1A RNA binding.

ABSTRACT: We present a close electronic view of the protein-base interface for the N-terminal domain of the human protein U1A. Combining accurate mixed quantum mechanics/molecular mechanics techniques and protein structure prediction methods, we provide a detailed electronic structure description of the protein-RNA stacking interactions. Our analysis indicates the evolution of the protein structure optimizing the interaction between Asp-92 and the RNA bases. The results show a direct coupling of the C-terminal tail and Asp-92, providing a direct rationalization of the experimentally determined role of the C-terminal domain in RNA binding. Here, we propose a mechanism where a protein side chain, with a delocalized electronic pi system, assists in the nucleotide binding. The binding mechanism involves a short-range interaction of the side chain with the nucleotide base and an electronic long-range interaction through a sandwich-stacking motif. The structural motif of the binding mechanism is observed in similar protein-RNA interactions and in various protein-ATP-binding sites.

SUBMITTER: Guallar V

PROVIDER: S-EPMC554833 | biostudies-literature | 2005 Mar

REPOSITORIES: biostudies-literature

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A binding mechanism in protein-nucleotide interactions: implication for U1A RNA binding.

Guallar Victor V Borrelli Kenneth W KW

Proceedings of the National Academy of Sciences of the United States of America 20050307 11

We present a close electronic view of the protein-base interface for the N-terminal domain of the human protein U1A. Combining accurate mixed quantum mechanics/molecular mechanics techniques and protein structure prediction methods, we provide a detailed electronic structure description of the protein-RNA stacking interactions. Our analysis indicates the evolution of the protein structure optimizing the interaction between Asp-92 and the RNA bases. The results show a direct coupling of the C-ter ...[more]

PMID: 15753311

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Project description:Human histidine triad nucleotide binding protein 1 (hHint1) is a member of a ubiquitous and ancient branch of the histidine triad protein superfamily. hHint1 is a homodimeric protein that catalyzes the hydrolysis of model substrates, phosphoramidate and acyl adenylate, with a high efficiency. Recently, catalytically inactive hHint1 has been identified as the cause of inherited peripheral neuropathy [Zimon, M., et al. (2012) Nat. Genet. 44, 1080-1083]. We have conducted the first detailed kinetic mechanistic studies of hHint1 and have found that the reaction mechanism is consistent with a double-displacement mechanism, in which the active site nucleophile His112 is first adenylylated by the substrate, followed by hydrolysis of the AMP-enzyme intermediate. A transient burst phase followed by a linear phase from the stopped-flow fluorescence assay indicated that enzyme adenylylation was faster than the subsequent intermediate hydrolysis and product release. Solvent viscosity experiments suggested that both chemical transformation and diffusion-sensitive events (product release or protein conformational change) limit the overall turnover. The catalytic trapping experiments and data simulation indicated that the true koff rate of the final product AMP is unlikely to control the overall kcat. Therefore, a protein conformational change associated with product release is likely rate-limiting. In addition, the rate of Hint1 adenylylation was found to be dependent on two residues with pKa values of 6.5 and 8, with the former pKa agreeing well with the nuclear magnetic resonance titration results for the pKa of the active site nucleophile His112. In comparison to the uncatalyzed rates, hHint1 was shown to enhance acyl-AMP and AMP phosphoramidate hydrolysis by 10(6)-10(8)-fold. Taken together, our analysis indicates that hHint1 catalyzes the hydrolysis of phosphoramidate and acyl adenylate with high efficiency, through a mechanism that relies on rapid adenylylation of the active residue, His112, while being partially rate-limited by intermediate hydrolysis and product release associated with a conformational change. Given the high degree of sequence homology of Hint proteins across all kingdoms of life, it is likely that their kinetic and catalytic mechanisms will be similar to those elucidated for hHint1.

Project description:BackgroundMotivated by the increased amount of data on protein-RNA interactions and the availability of complete genome sequences of several organisms, many computational methods have been proposed to predict binding sites in protein-RNA interactions. However, most computational methods are limited to finding RNA-binding sites in proteins instead of protein-binding sites in RNAs. Predicting protein-binding sites in RNA is more challenging than predicting RNA-binding sites in proteins. Recent computational methods for finding protein-binding sites in RNAs have several drawbacks for practical use.ResultsWe developed a new support vector machine (SVM) model for predicting protein-binding regions in mRNA sequences. The model uses sequence profiles constructed from log-odds scores of mono- and di-nucleotides and nucleotide compositions. The model was evaluated by standard 10-fold cross validation, leave-one-protein-out (LOPO) cross validation and independent testing. Since actual mRNA sequences have more non-binding regions than protein-binding regions, we tested the model on several datasets with different ratios of protein-binding regions to non-binding regions. The best performance of the model was obtained in a balanced dataset of positive and negative instances. 10-fold cross validation with a balanced dataset achieved a sensitivity of 91.6%, a specificity of 92.4%, an accuracy of 92.0%, a positive predictive value (PPV) of 91.7%, a negative predictive value (NPV) of 92.3% and a Matthews correlation coefficient (MCC) of 0.840. LOPO cross validation showed a lower performance than the 10-fold cross validation, but the performance remains high (87.6% accuracy and 0.752 MCC). In testing the model on independent datasets, it achieved an accuracy of 82.2% and an MCC of 0.656. Testing of our model and other state-of-the-art methods on a same dataset showed that our model is better than the others.ConclusionsSequence profiles of log-odds scores of mono- and di-nucleotides were much more powerful features than nucleotide compositions in finding protein-binding regions in RNA sequences. But, a slight performance gain was obtained when using the sequence profiles along with nucleotide compositions. These are preliminary results of ongoing research, but demonstrate the potential of our approach as a powerful predictor of protein-binding regions in RNA. The program and supporting data are available at http://bclab.inha.ac.kr/RBPbinding .

Dataset Information

A binding mechanism in protein-nucleotide interactions: implication for U1A RNA binding.

Publications

A binding mechanism in protein-nucleotide interactions: implication for U1A RNA binding.

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