2015
DOI: 10.1021/bi501152d
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Mapping Allostery through Computational Glycine Scanning and Correlation Analysis of Residue–Residue Contacts

Abstract: Understanding allosteric mechanisms is essential for the physical control of molecular switches and downstream cellular responses. However, it is difficult to decode essential allosteric motions in a high-throughput scheme. A general two-pronged approach to performing automatic data reduction of simulation trajectories is presented here. The first step involves coarse-graining and identifying the most dynamic residue–residue contacts. The second step is performing principal component analysis of these contacts… Show more

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Cited by 39 publications
(68 citation statements)
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References 49 publications
(90 reference statements)
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“…After all, the nonbonded interactions are intimately coupled to the presence of contacts and their rearrangements. As a matter of fact, in the context of allosteric coupling it has been suggested that the residues that are in spatial proximity forming contacts are more likely to be coupled compared with the distal residues (37), and models have been proposed to construct a biophysical framework based on contacts acting as structural support for the propagation of information (41)(42)(43). We took a similar approach of identifying the possible differences in the side-chain contacts between the two end states by constructing the differential contact map (Materials and Methods) in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…After all, the nonbonded interactions are intimately coupled to the presence of contacts and their rearrangements. As a matter of fact, in the context of allosteric coupling it has been suggested that the residues that are in spatial proximity forming contacts are more likely to be coupled compared with the distal residues (37), and models have been proposed to construct a biophysical framework based on contacts acting as structural support for the propagation of information (41)(42)(43). We took a similar approach of identifying the possible differences in the side-chain contacts between the two end states by constructing the differential contact map (Materials and Methods) in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Mining the time evolution data of 3N atomic coordinates (n = 2,505 atoms of CypA) for key features presents a "big data" problem. To simplify the complexity of the high-dimensional data, we established a data reduction scheme similar to the Computation of Allosteric Mechanism by Evaluating Residue-Residue Associations approach (35). The 3D Cartesian coordinates of the trajectory is condensed to a binary trajectory (consisting of 0s and 1s) of N res *(N res -1)/2 residue-residue contacts.…”
Section: Resultsmentioning
confidence: 99%
“…The values themselves are sensitive to m , and they have little direct meaning and should be mainly used for comparing the relative values. To connect the normalized values to the range of fluctuation of these contacts, one can look at eigenvalues and PC projections, such as Figure 4 of ref 36. We estimate the amplitude of (anti)cooperative ligand-binding motion expressed in Figure 4b for RXR:TR and RXR:CAR is a fluctuation of a few contact units.…”
Section: Resultsmentioning
confidence: 99%
“…These apo-approaching mutants used here are identical to those of RXR:TR complexes that we have studied previously. 36 As listed in Table S1 of the supporting information in ref 36, these 18 residues (V265, C269, A271, A272, Q275, L309, I310, F313, R316, A327, I345, F346, V349, C432, L433, H435, L436, F439) that constitute the binding pocket were selected by examining the crystal structure and representative snapshots of the wild-type RXR:TR. Through the simulations of these glycine mutants which have a weakened protein–ligand interaction, we can obtain a large ensemble of structures that are closer to the apo form ensemble.…”
Section: Systems and Methodsmentioning
confidence: 99%