Analysis of domain motions by approximate normal mode calculations

Hinsen, Konrad

doi:10.1002/(sici)1097-0134(19981115)33:3<417::aid-prot10>3.0.co;2-8

Cited by 692 publications

(782 citation statements)

References 20 publications

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“…24 Alternatively, hinge prediction can be based on sequence statistics 25 or assessed by normal mode analysis. 26,12 StoneHinge StoneHinge predicts hinges based on the consensus between StoneHingeP and StoneHingeD results. StoneHingeP is unique in using constraint counting from rigidity theory, as implemented in ProFlex, as the basis for predictions.…”

Section: Identifying Hingesmentioning

confidence: 99%

StoneHinge: Hinge prediction by network analysis of individual protein structures

et al. 2009

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Hinge motions are important for molecular recognition, and knowledge of their location can guide the sampling of protein conformations for docking. Predicting domains and intervening hinges is also important for identifying structurally self-determinate units and anticipating the influence of mutations on protein flexibility and stability. Here we present StoneHinge, a novel approach for predicting hinges between domains using input from two complementary analyses of noncovalent bond networks: StoneHingeP, which identifies domain-hinge-domain signatures in ProFlex constraint counting results, and StoneHingeD, which does the same for DomDecomp Gaussian network analyses. Predictions for the two methods are compared to hinges defined in the literature and by visual inspection of interpolated motions between conformations in a series of proteins. For StoneHingeP, all the predicted hinges agree with hinge sites reported in the literature or observed visually, although some predictions include extra residues. Furthermore, no hinges are predicted in six hinge-free proteins. On the other hand, StoneHingeD tends to overpredict the number of hinges, while accurately pinpointing hinge locations. By determining the consensus of their results, StoneHinge improves the specificity, predicting 11 of 13 hinges found both visually and in the literature for nine different open protein structures, and making no false-positive predictions. By comparison, a popular hinge detection method that requires knowledge of both the open and closed conformations finds 10 of the 13 known hinges, while predicting four additional, false hinges.

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Section: Identifying Hingesmentioning

confidence: 99%

StoneHinge: Hinge prediction by network analysis of individual protein structures

et al. 2009

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“…Our default value of τ is set to 20 as the interesting domains are usually more than this. Setting τ = 20, we ran our program on five arbitrarily chosen pairs of conformations from DynDom [12,13] on a range of values of . The results are shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…These include the pioneering work by Gerstein et al [8], MolMovDB [7,14,5] by Gerstein et al, HingeFind [22] by Wrigger et al, DomainFinder [15,16] by Hinsen, DynDom [10,11,12,13] by Hayward et al,and [20] by Nichols et al Most of these papers consider more general problems than identifying rigid domains; for example, they including the one by Nichols et al More specifically, the descriptions of rigid domains in these papers include the following two characteristics: (1) the residues in a rigid domain move relatively rigidly; in other words, the residues in a domain move with similar rigid motions. (2) The distances of residues in a rigid domain are approximately conserved, i.e.…”

Section: Introductionmentioning

confidence: 99%

An Algorithmic Approach to the Identification of Rigid Domains in Proteins

Choi

Goyal

2007

Algorithmica

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Many proteins undergo conformational changes to perform their functions. A simple mechanism of conformational change in proteins is a rigid domain motion, in which two parts of a structure move rigidly with respect to each other. The identification of rigid domains is therefore useful in understanding the structure-function relationship of proteins. Many algorithms, including [14,22,15,10,20], have been developed to identify rigid domains.In this paper, we complement these works by proposing a mathematical definition of a rigid domain. We argue that our definition more accurately captures the intuitive notion of rigid domain in the previous work, than the quantitative definition of a rigid domain introduced by Nichols et al. [20]. Furthermore, our definition admits a practical approximation algorithm. We can prove theoretical guarantee on the quality of the output of our algorithm. We implement a randomized version of our algorithm, and demonstrate its effectiveness on several known protein complexes.

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“…Note that some of the above matrix expressions have also been used in other flexibility analysis approaches. 19,[32][33][34][35][36] However, the present construction of these functional forms was based on the driven and response relation of coupled dynamical systems. 27 Expressions (1)-(3) map a molecular geometry into topological relations or connectivities.…”

Section: A Geometry To Topology Mappingmentioning

confidence: 99%

“…Crystal structures have been also taken into considerations. [45][46][47][48] Due to the simplified potential and reduced representation, these coarse-grained based ENM and GNM approaches 19,20,[32][33][34][35][36] gain popularity and have been applied to the study of macroproteins or protein complexes, such as, hemoglobin, 49 F1 ATPase, 50,51 chaperonin GroEL, 52,53 viral capsids, 54,55 and ribosome. 56,57 More applications can be found in a few good review papers.…”

Section: Ildm Based B-factor Predictionmentioning

confidence: 99%

Molecular nonlinear dynamics and protein thermal uncertainty quantification

Xia

Guo

2014

Chaos

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This work introduces molecular nonlinear dynamics (MND) as a new approach for describing protein folding and aggregation. By using a mode system, we show that the MND of disordered proteins is chaotic while that of folded proteins exhibits intrinsically low dimensional manifolds (ILDMs). The stability of ILDMs is found to strongly correlate with protein energies. We propose a novel method for protein thermal uncertainty quantification based on persistently invariant ILDMs. Extensive comparison with experimental data and the state-of-the-art methods in the field validate the proposed new method for protein B-factor prediction. V C 2014 AIP Publishing LLC.[http://dx.doi.org/10.1063/1.4861202]Protein folding produces characteristic and functional three-dimensional structures from unfolded polypeptides or disordered coils. [1][2][3][4][5] The emergence of extraordinary complexity in the protein folding process poses astonishing challenges to theoretical modeling and computer simulations. 6,7 The present work introduces molecular nonlinear dynamics (MND), or molecular chaotic dynamics, as a theoretical framework for describing and analyzing protein folding. We represent the dynamics of macromolecular particles (i.e., atoms or coarse-grained superatoms) by a set of intrinsically chaotic oscillators. A geometry to topology mapping is employed to create driving and response relations among chaotic oscillators. We unveil the existence of intrinsically low dimensional manifolds (ILDMs) in the chaotic dynamics of folded proteins. Additionally, we reveal that the transition from disordered to ordered conformations in protein folding increases the transverse stability of the ILDM. Stated differently, protein folding reduces the chaoticity of the nonlinear dynamical system, and a folded protein has the best ability to tame chaos. Furthermore, we bring to light the connection between the ILDM stability and the thermodynamic stability, which enables us to quantify the disorderliness and relative energies of folded, misfolded, and unfolded protein states. Finally, we exploit chaos for protein uncertainty quantification and develop a robust chaotic algorithm for the prediction of Debye-Waller factors, or temperature factors, of protein structures.

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Analysis of domain motions by approximate normal mode calculations

Cited by 692 publications

References 20 publications

StoneHinge: Hinge prediction by network analysis of individual protein structures

StoneHinge: Hinge prediction by network analysis of individual protein structures

An Algorithmic Approach to the Identification of Rigid Domains in Proteins

Molecular nonlinear dynamics and protein thermal uncertainty quantification

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