Replica exchange Monte-Carlo simulations of helix bundle membrane proteins: rotational parameters of helices

Wu, Hongjie; Chen, C.-C.

doi:10.1007/s10822-012-9562-1

Cited by 8 publications

(5 citation statements)

References 48 publications

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“…It has been shown that the structures of transmembrane helices are well reproduced in an implicit lipid membrane model by Monte Carlo (MC) simulation [26] and MD simulation [27,28]. Packing of modeled transmembrane helices by the Metropolis MC algorithm accurately predicts the structures of bacteriorhodopsin [29] and halorhodopsin [30]. An important implication of these studies is that the two-stage folding scheme [31] can be used for structure prediction by computational assembly of helical membrane proteins.…”

Section: C1↔o1↔d0↔d1mentioning

confidence: 99%

Exploring conformational states and helical packings in the P2X receptor transmembrane domain by molecular dynamics simulation

2018

J Biol Phys

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The P2X receptor is a trimeric transmembrane protein that acts as an ATP-gated ion channel. Its transmembrane domain (TMD) contains only six helices and three of them, the M2 helices, line the ion conduction pathway. Here, using molecular dynamics simulation, I identify four conformational states of the TMD that are associated with four types of packing between M2 helices. Packing in the extracellular half of the M2 helix produces closed conformations, while packing in the intracellular half produces both open and closed conformations. State transition is observed and supports a mechanism where iris-like twisting of the M2 helices switches the location of helical packing between the extracellular and the intracellular halves of the helices. In addition, this twisting motion alters the position and orientation of residue side-chains relative to the pore and therefore influences the pore geometry and possibly ion permeation. Helical packing, on the other hand, may restrict the twisting motion and generate discrete conformational states.

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Section: C1↔o1↔d0↔d1mentioning

confidence: 99%

Exploring conformational states and helical packings in the P2X receptor transmembrane domain by molecular dynamics simulation

2018

J Biol Phys

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“…Their clinical importance is demonstrated by the fact that [mt]50% of known drugs in use today target MPs, which are also responsible for the uptake, metabolism, and clearance of these pharmacologically active substances. Despite their biological and pharmaceutical importance, due to difficulties in crystallizing MPs, only about 500 unique structures have been derived so far . As the attempts of using experimental methods to study MPs have encountered difficulties, there exist great incentives for computational and statistical studies of MPs.…”

Section: Introductionmentioning

confidence: 99%

“…To investigate the sequence–structure relationship of MPs, we have previously shown that the structure of several MPs, including bacteriorhodopsin, halorhodopsin, and sensory rhodopsin, can be predicted from their sequence using thermodynamic principles . Furthermore, we have combined molecular dynamics simulations and fold identification procedures to predict the structure of 696 kinked and 120 unkinked transmembrane (TM) helices from their sequence in the protein data bank of transmembrane proteins (PDBTM) .…”

Section: Introductionmentioning

confidence: 99%

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Clustering and visualizing similarity networks of membrane proteins

Mai

Chen

2015

Proteins

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We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence-structure-function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence-structure-function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information.

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“…Previously, we have constructed several physical models of TM proteins to predict their three-dimensional structures based on the sequence information [11,12,26]. In these studies, we simplified the folding of TM proteins by the packing of standard helices and predicted their structure by computer simulations.…”

Section: Introductionmentioning

confidence: 99%

Statistical analyses and computational prediction of helical kinks in membrane proteins

Huang

Chen

2012

J Comput Aided Mol Des

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We have carried out statistical analyses and computer simulations of helical kinks for TM helices in the PDBTM database. About 59 % of 1562 TM helices showed a significant kink, and 38 % of these kinks are associated with prolines in a range of ±4 residues. Our analyses show that helical kinks are more populated in the central region of helices, particularly in the range of 1-3 residues away from the helix center. Among 1,053 helical kinks analyzed, 88 % of kinks are bends (change in helix axis without loss of helical character) and 12 % are disruptions (change in helix axis and loss of helical character). It is found that proline residues tend to cause larger kink angles in helical bends, while this effect is not observed in helical disruptions. A further analysis of these kinked helices suggests that a kinked helix usually has 1-2 broken backbone hydrogen bonds with the corresponding N-O distance in the range of 4.2-8.7 Å, whose distribution is sharply peaked at 4.9 Å followed by an exponential decay with increasing distance. Our main aims of this study are to understand the formation of helical kinks and to predict their structural features. Therefore we further performed molecular dynamics (MD) simulations under four simulation scenarios to investigate kink formation in 37 kinked TM helices and 5 unkinked TM helices. The representative models of these kinked helices are predicted by a clustering algorithm, SPICKER, from numerous decoy structures possessing the above generic features of kinked helices. Our results show an accuracy of 95 % in predicting the kink position of kinked TM helices and an error less than 10° in the angle prediction of 71.4 % kinked helices. For unkinked helices, based on various structure similarity tests, our predicted models are highly consistent with their crystal structure. These results provide strong supports for the validity of our method in predicting the structure of TM helices.

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Replica exchange Monte-Carlo simulations of helix bundle membrane proteins: rotational parameters of helices

Cited by 8 publications

References 48 publications

Exploring conformational states and helical packings in the P2X receptor transmembrane domain by molecular dynamics simulation

Exploring conformational states and helical packings in the P2X receptor transmembrane domain by molecular dynamics simulation

Clustering and visualizing similarity networks of membrane proteins

Statistical analyses and computational prediction of helical kinks in membrane proteins

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