scholarly journals Changes in Apparent Free Energy of Helix–Helix Dimerization in a Biological Membrane Due to Point Mutations

2007 ◽  
Vol 371 (2) ◽  
pp. 422-434 ◽  
Author(s):  
Mylinh T. Duong ◽  
Todd M. Jaszewski ◽  
Karen G. Fleming ◽  
Kevin R. MacKenzie
2019 ◽  
Vol 116 (7) ◽  
pp. 2571-2576 ◽  
Author(s):  
Yuliya G. Smirnova ◽  
Herre Jelger Risselada ◽  
Marcus Müller

Biological membrane fusion proceeds via an essential topological transition of the two membranes involved. Known players such as certain lipid species and fusion proteins are generally believed to alter the free energy and thus the rate of the fusion reaction. Quantifying these effects by theory poses a major challenge since the essential reaction intermediates are collective, diffusive and of a molecular length scale. We conducted molecular dynamics simulations in conjunction with a state-of-the-art string method to resolve the minimum free-energy path of the first fusion intermediate state, the so-called stalk. We demonstrate that the isolated transmembrane domains (TMDs) of fusion proteins such as SNARE molecules drastically lower the free energy of both the stalk barrier and metastable stalk, which is not trivially explained by molecular shape arguments. We relate this effect to the local thinning of the membrane (negative hydrophobic mismatch) imposed by the TMDs which favors the nearby presence of the highly bent stalk structure or prestalk dimple. The distance between the membranes is the most crucial determinant of the free energy of the stalk, whereas the free-energy barrier changes only slightly. Surprisingly, fusion enhancing lipids, i.e., lipids with a negative spontaneous curvature, such as PE lipids have little effect on the free energy of the stalk barrier, likely because of its single molecular nature. In contrast, the lipid shape plays a crucial role in overcoming the hydration repulsion between two membranes and thus rather lowers the total work required to form a stalk.


Biopolymers ◽  
2001 ◽  
Vol 58 (3) ◽  
pp. 347-353 ◽  
Author(s):  
Sean D. Mooney ◽  
Conrad C. Huang ◽  
Peter A. Kollman ◽  
Teri E. Klein
Keyword(s):  

2018 ◽  
Vol 9 (32) ◽  
pp. 6703-6710 ◽  
Author(s):  
Shreyas Supekar ◽  
Ville R. I. Kaila

Cytochrome c oxidase (CcO) drives aerobic respiratory chains in all organisms by transducing the free energy from oxygen reduction into an electrochemical proton gradient across a biological membrane.


2012 ◽  
Vol 28 (5) ◽  
pp. 664-671 ◽  
Author(s):  
Zhe Zhang ◽  
Lin Wang ◽  
Yang Gao ◽  
Jie Zhang ◽  
Maxim Zhenirovskyy ◽  
...  

Marine Drugs ◽  
2021 ◽  
Vol 19 (7) ◽  
pp. 367
Author(s):  
Dana Katz ◽  
Michael A. DiMattia ◽  
Dan Sindhikara ◽  
Hubert Li ◽  
Nikita Abraham ◽  
...  

Nicotinic acetylcholine receptor (nAChR) subtypes are key drug targets, but it is challenging to pharmacologically differentiate between them because of their highly similar sequence identities. Furthermore, α-conotoxins (α-CTXs) are naturally selective and competitive antagonists for nAChRs and hold great potential for treating nAChR disorders. Identifying selectivity-enhancing mutations is the chief aim of most α-CTX mutagenesis studies, although doing so with traditional docking methods is difficult due to the lack of α-CTX/nAChR crystal structures. Here, we use homology modeling to predict the structures of α-CTXs bound to two nearly identical nAChR subtypes, α3β2 and α3β4, and use free-energy perturbation (FEP) to re-predict the relative potency and selectivity of α-CTX mutants at these subtypes. First, we use three available crystal structures of the nAChR homologue, acetylcholine-binding protein (AChBP), and re-predict the relative affinities of twenty point mutations made to the α-CTXs LvIA, LsIA, and GIC, with an overall root mean square error (RMSE) of 1.08 ± 0.15 kcal/mol and an R2 of 0.62, equivalent to experimental uncertainty. We then use AChBP as a template for α3β2 and α3β4 nAChR homology models bound to the α-CTX LvIA and re-predict the potencies of eleven point mutations at both subtypes, with an overall RMSE of 0.85 ± 0.08 kcal/mol and an R2 of 0.49. This is significantly better than the widely used molecular mechanics—generalized born/surface area (MM-GB/SA) method, which gives an RMSE of 1.96 ± 0.24 kcal/mol and an R2 of 0.06 on the same test set. Next, we demonstrate that FEP accurately classifies α3β2 nAChR selective LvIA mutants while MM-GB/SA does not. Finally, we use FEP to perform an exhaustive amino acid mutational scan of LvIA and predict fifty-two mutations of LvIA to have greater than 100X selectivity for the α3β2 nAChR. Our results demonstrate the FEP is well-suited to accurately predict potency- and selectivity-enhancing mutations of α-CTXs for nAChRs and to identify alternative strategies for developing selective α-CTXs.


2020 ◽  
Vol 117 (44) ◽  
pp. 27116-27123 ◽  
Author(s):  
Rohit Satija ◽  
Alexander M. Berezhkovskii ◽  
Dmitrii E. Makarov

Recent single-molecule experiments have observed transition paths, i.e., brief events where molecules (particularly biomolecules) are caught in the act of surmounting activation barriers. Such measurements offer unprecedented mechanistic insights into the dynamics of biomolecular folding and binding, molecular machines, and biological membrane channels. A key challenge to these studies is to infer the complex details of the multidimensional energy landscape traversed by the transition paths from inherently low-dimensional experimental signals. A common minimalist model attempting to do so is that of one-dimensional diffusion along a reaction coordinate, yet its validity has been called into question. Here, we show that the distribution of the transition path time, which is a common experimental observable, can be used to differentiate between the dynamics described by models of one-dimensional diffusion from the dynamics in which multidimensionality is essential. Specifically, we prove that the coefficient of variation obtained from this distribution cannot possibly exceed 1 for any one-dimensional diffusive model, no matter how rugged its underlying free energy landscape is: In other words, this distribution cannot be broader than the single-exponential one. Thus, a coefficient of variation exceeding 1 is a fingerprint of multidimensional dynamics. Analysis of transition paths in atomistic simulations of proteins shows that this coefficient often exceeds 1, signifying essential multidimensionality of those systems.


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