Structural features that predict real‐value fluctuations of globular proteins

Abstract

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics trajectories of non-homologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real-value of residue fluctuations using the support vector regression. It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in molecular dynamics trajectories. Moreover, support vector regression that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson’s correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed for the prediction by the Gaussian network model. An advantage of the developed method over the Gaussian network models is that the former predicts the real-value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins.

CABS-flex: server for fast simulation of protein structure fluctuations

“…The average Spearman’s correlation coefficient for the residue fluctuation profiles between CABS and MD, from the 2-fold cross-validation test on the microMoDEL set was shown to be 0.7 [this consistency measure as well as other dynamics metrics seemed to be close to those found when different MD force fields are compared (2)]. This level of prediction is better than analogical predictions recently achieved by other methods: support vector regression and Gaussian network model [respectively, 0.67 and 0.64, as presented previously in Jamroz et al (20)]. In addition, we validated the method on a parameterization-independent set of proteins having 10-ns MD trajectories deposited in the MoDEL database (19).…”

Characterizing of functional human coding RNA editing from evolutionary, structural, and dynamic perspectives

“…Structural‐derived information, which can hint on functionality of particular site in the protein structure context, is the protein dynamics. Important sites, crucial for stability and catalysis are usually located at positions being relatively fixed and rigid, which can be distinguished as minima on dynamic profiles. This tendency can be examined, for example, by looking on X‐ray derived temperature factors (b‐factors).…”

Peer Review #3 of "Simple approach for ranking structure determining residues (v0.2)"

“…We compared the distribution of the non-normalized SDRIs obtained in our study with scores from the structure-based flexibility as well as the sequence-based intrinsic disorder predisposition of the query proteins. The structural flexibility of these proteins was obtained by utilizing the FlexPred tool that predicts the absolute fluctuations per-residue from a three-dimensional structure using the B-factors of a query protein (Jamroz et al, 2012). The intrinsic disorder propensities per-residue of these proteins was obtained by using PONDR ® VSL2B predictor, which is one of the more accurate stand-alone disorder predictors (Fan & Kurgan 2014;Peng et al, 2005;Peng & Kurgan 2012).…”