Structure-Based Models of Biomolecules: Stretching of Proteins, Dynamics of Knots, Hydrodynamic Effects, and Indentation of Virus Capsids

  • Marek Cieplak
  • Joanna I. Sułkowska


Coarse-grained models of biomolecules are developed to provide ways of simulating situations which involve system sizes and time scales that are hard to study by all-atom approaches. These situations usually are associated with occurrence of large conformational transformations. This review discusses a subclass of the coarse-grained descriptions: the structure-based models. These models are defined in a phenomenological way and make use of the knowledge of the native structure that is determined experimentally. We discuss the cases of the DNA molecule and dendrimers but the focus of the review is on proteins. For proteins, the reduction of the number of the degrees of freedom is achieved by representing amino acids by single beads located at the Cα atoms. The beads are tethered together into chains and effective attractive contact interactions are introduced so that the lowest energy state corresponds to the native conformation. There are many variants of such models and each variant comes with its own set of properties. Optimal variants can be selected by making comparisons to experimental data on single-molecule stretching. We discuss the best-performing variants of such models. Among them, there is a model with the Lennard–Jones potential in the native contacts and with the uniform (sequence independent) energy parameter. We apply this model to several problems: theoretical survey of mechanical resistance to stretching of 17,134 proteins comprising not more than 250 amino acids, dynamics of proteins with knots, pulling proteins out of membranes, the role of hydrodynamic interactions, and nanoindentation of virus capsids.


Hydrodynamic Interaction Brownian Dynamic Native Contact Cowpea Chlorotic Mottle Virus Elastic Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work summarizes a decade of research performed with a group of collaborators.

In particular with J.R. Banavar, M. Carrion-Vazquez, S. Filipek, T.X. Hoang, H. Janovjak, A. Maritan, P. Marszałek, S. Niewieczerzał, A. Pastore, M.O. Robbins, M. Sikora, K. Staroń, P. Sułkowski, O. Szklarczyk, P. Szymczak, D. Thompson, and M. Wojciechowski. We appreciate S. Niewieczerzał’s help in preparation of the first three figures displayed here. This work has been supported by the grant N N202 0852 33 from the Ministry of Science and Higher Education in Poland, by the EC FUNMOL project under FP7-NMP-2007-SMALL-1, and by the European Union within European Regional Development Fund, through grant Innovative Economy (POIG.01.01.02-00-008/08).


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© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Institute of PhysicsPolish Academy of SciencesWarsawPoland
  2. 2.CTBP, University of CaliforniaSan DiegoUSA

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