Model-free analysis of protein dynamics: assessment of accuracy and model selection protocols based on molecular dynamics simulation

• Published in: Journal of biomolecular NMR 2004-07, Vol.29 (3), p.243-257
• Main Authors: Chen, Jianhan, Brooks, 3rd, Charles L, Wright, Peter E
• Format: Article
• Language: English
• Subjects: Anisotropy; Bayes Theorem; Dihydrofolate reductase; E coli; Escherichia coli; Escherichia coli - enzymology; Hypothesis testing; Magnetic Resonance Spectroscopy - methods; Models, Statistical; Models, Theoretical; Nitrogen - chemistry; NMR; Nuclear magnetic resonance; Protein Folding; Proteins - chemistry; Software; Studies; Tetrahydrofolate Dehydrogenase - chemistry; Time Factors
• ISSN: 0925-2738; 1573-5001
• DOI: 10.1023/b:jnmr.0000032504.70912.58

The popular model-free approach to analyze NMR relaxation measurements has been examined using artificial amide (15)N relaxation data sets generated from a 10 nanosecond molecular dynamics trajectory of a dihydrofolate reductase ternary complex in explicit water. With access to a detailed picture of the underlying internal motions, the efficacy of model-free analysis and impact of model selection protocols on the interpretation of NMR data can be studied. In the limit of uncorrelated global tumbling and internal motions, fitting the relaxation data to the model-free models can recover a significant amount of quantitative information on the internal dynamics. Despite a slight overestimation, the generalized order parameter is quite accurately determined. However, the model-free analysis appears to be insensitive to the presence of nanosecond time scale motions with relatively small magnitude. For such cases, the effective correlation time can be significantly underestimated. As a result, proteins appear to be more rigid than they really are. The model selection protocols have a major impact on the information one can reliably obtain. The commonly employed protocol based on step-up hypothesis testing has severe drawbacks of oversimplification and underfitting. The consequences are that the order parameter is more severely overestimated and the correlation time more severely underestimated. Instead, model selection based on Bayesian Information Criteria (BIC), recently introduced to the model-free analysis by d'Auvergne and Gooley (2003), provides a better balance between bias and variance. More appropriate models can be selected, leading to improved estimate of both the order parameter and correlation time. In addition, the computational cost is significantly reduced and subjective parameters such as the significance level are unnecessary.