Dataset Information

Evaluation of protein adsorption and preferred binding regions in multimodal chromatography using NMR.

ABSTRACT: NMR titration experiments with labeled human ubiquitin were employed in concert with chromatographic data obtained with a library of ubiquitin mutants to study the nature of protein adsorption in multimodal (MM) chromatography. The elution order of the mutants on the MM resin was significantly different from that obtained by ion-exchange chromatography. Further, the chromatographic results with the protein library indicated that mutations in a defined region induced greater changes in protein affinity to the solid support. Chemical shift mapping and determination of dissociation constants from NMR titration experiments with the MM ligand and isotopically enriched ubiquitin were used to determine and rank the relative binding affinities of interaction sites on the protein surface. The results with NMR confirmed that the protein possessed a distinct preferred binding region for the MM ligand in agreement with the chromatographic results. Finally, coarse-grained ligand docking simulations were employed to study the modes of interaction between the MM ligand and ubiquitin. The use of NMR titration experiments in concert with chromatographic data obtained with protein libraries represents a previously undescribed approach for elucidating the structural basis of protein binding affinity in MM chromatographic systems.

SUBMITTER: Chung WK

PROVIDER: S-EPMC2947875 | biostudies-other | 2010 Sep

REPOSITORIES: biostudies-other

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Evaluation of protein adsorption and preferred binding regions in multimodal chromatography using NMR.

Chung Wai Keen WK Freed Alexander S AS Holstein Melissa A MA McCallum Scott A SA Cramer Steven M SM

Proceedings of the National Academy of Sciences of the United States of America 20100913 39

NMR titration experiments with labeled human ubiquitin were employed in concert with chromatographic data obtained with a library of ubiquitin mutants to study the nature of protein adsorption in multimodal (MM) chromatography. The elution order of the mutants on the MM resin was significantly different from that obtained by ion-exchange chromatography. Further, the chromatographic results with the protein library indicated that mutations in a defined region induced greater changes in protein af ...[more]

PMID: 20837551

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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

Evaluation of protein adsorption and preferred binding regions in multimodal chromatography using NMR.

Publications

Evaluation of protein adsorption and preferred binding regions in multimodal chromatography using NMR.

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