@article {259, title = {CABS-flex: server for fast simulation of protein structure fluctuations}, journal = {Nucleic Acids Research}, volume = {41}, year = {2013}, month = {2013 May 8}, pages = {W427-W431}, abstract = {The CABS-flex server (http://biocomp.chem.uw.edu.pl/CABSflex) implements CABS-model-based protocol for the fast simulations of near-native dynamics of globular proteins. In this application, the CABS model was shown to be a computationally efficient alternative to all-atom molecular dynamics-a classical simulation approach. The simulation method has been validated on a large set of molecular dynamics simulation data. Using a single input (user-provided file in PDB format), the CABS-flex server outputs an ensemble of protein models (in all-atom PDB format) reflecting the flexibility of the input structure, together with the accompanying analysis (residue mean-square-fluctuation profile and others). The ensemble of predicted models can be used in structure-based studies of protein functions and interactions.}, keywords = {molecular dynamics, near-native dynamics, protein dynamics, protein flexibility, simulation}, issn = {1362-4962}, doi = {10.1093/nar/gkt332}, url = {http://nar.oxfordjournals.org/cgi/content/full/gkt332}, author = {Michal Jamroz and Andrzej Koli{\'n}ski and Sebastian Kmiecik} } @article {260, title = {Consistent View of Protein Fluctuations from All-Atom Molecular Dynamics and Coarse-Grained Dynamics with Knowledge-Based Force-Field}, journal = {Journal of Chemical Theory and Computation}, volume = {9}, year = {2013}, month = {12/2012}, pages = {119 - 125}, abstract = {It is widely recognized that atomistic Molecular Dynamics (MD), a classical simulation method, captures the essential physics of protein dynamics. That idea is supported by a theoretical study showing that various MD force-fields provide a consensus picture of protein fluctuations in aqueous solution [Rueda, M. et al. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 796-801]. However, atomistic MD cannot be applied to most biologically relevant processes due to its limitation to relatively short time scales. Much longer time scales can be accessed by properly designed coarse-grained models. We demonstrate that the aforementioned consensus view of protein dynamics from short (nanosecond) time scale MD simulations is fairly consistent with the dynamics of the coarse-grained protein model - the CABS model. The CABS model employs stochastic dynamics (a Monte Carlo method) and a knowledge-based force-field, which is not biased toward the native structure of a simulated protein. Since CABS-based dynamics allows for the simulation of entire folding (or multiple folding events) in a single run, integration of the CABS approach with all-atom MD promises a convenient (and computationally feasible) means for the long-time multiscale molecular modeling of protein systems with atomistic resolution.}, keywords = {molecular dynamics, near-native dynamics, protein dynamics, protein flexibility, simulation}, isbn = {1549-9618}, doi = {10.1021/ct300854w}, url = {http://dx.doi.org/10.1021/ct300854w}, author = {Michal Jamroz and Modesto Orozco and Andrzej Koli{\'n}ski and Sebastian Kmiecik} } @article {Jamroz2012, title = {Structural features that predict real-value fluctuations of globular proteins}, journal = {Proteins}, volume = {80}, number = {5}, year = {2012}, month = {may}, pages = {1425{\textendash}35}, abstract = {It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous 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 (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson{\textquoteright}s correlation coefficient of 0.669 and a root mean square error of 1.04 \AA. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs 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.}, keywords = {fluctuation prediction, molecular dynamics, protein dynamics, protein flexibility, structure-dynamics relationship, support vector regression}, issn = {1097-0134}, doi = {10.1002/prot.24040}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22328193}, author = {Michal Jamroz and Andrzej Koli{\'n}ski and Daisuke Kihara} }