Allosteric transitions of supramolecular systems explored by network models: application to chaperonin GroEL.
Bottom Line:
Coarse-grained models that lend themselves to analytical solutions appear to be the only possible means of approaching such cases.Application to bacterial chaperonin GroEL and comparisons with experimental data, results from action minimization algorithm, and previous simulations support the utility of aANM as a computationally efficient, yet physically plausible, tool for unraveling potential transition pathways sampled by large complexes/assemblies.An important outcome is the assessment of the critical inter-residue interactions formed/broken near the transition state(s), most of which involve conserved residues.
View Article:
PubMed Central - PubMed
Affiliation: Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
ABSTRACT
Show MeSH
Identification of pathways involved in the structural transitions of biomolecular systems is often complicated by the transient nature of the conformations visited across energy barriers and the multiplicity of paths accessible in the multidimensional energy landscape. This task becomes even more challenging in exploring molecular systems on the order of megadaltons. Coarse-grained models that lend themselves to analytical solutions appear to be the only possible means of approaching such cases. Motivated by the utility of elastic network models for describing the collective dynamics of biomolecular systems and by the growing theoretical and experimental evidence in support of the intrinsic accessibility of functional substates, we introduce a new method, adaptive anisotropic network model (aANM), for exploring functional transitions. Application to bacterial chaperonin GroEL and comparisons with experimental data, results from action minimization algorithm, and previous simulations support the utility of aANM as a computationally efficient, yet physically plausible, tool for unraveling potential transition pathways sampled by large complexes/assemblies. An important outcome is the assessment of the critical inter-residue interactions formed/broken near the transition state(s), most of which involve conserved residues. |
Related In:
Results -
Collection
getmorefigures.php?uid=PMC2664929&req=5
Mentions: Figure 3 illustrates the procedure for selecting mA(k) for the transition R″→T (step E in Figure 1). Results are shown for the aANM iterations k = 1 (top), 7 (middle) and 13 (bottom). The bars displays the correlation cosine as a function of mode number 1≤i≤25 (left ordinate), and the blue curve is the cumulative squared cosine [C(mA(k))]2 (right). For k = 1, the lowest frequency mode (i = 1) alone yields a correlation cosine of 0.82: it suffices, therefore, to take mA(1) = 1 mode at this step to meet the criterion [C(mA(k))]2≥Fmin , if the threshold Fmin = 0.5. For k = 7, on the other hand, the same criterion is met by mA(7) = 3 modes (see the red line), and for k = 13, we need mA(13) = 23 modes. |
View Article: PubMed Central - PubMed
Affiliation: Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.