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Predictive Energy Landscapes for Folding Α-Helical Transmembrane Proteins

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Predictive Energy Landscapes for Folding Α-Helical Transmembrane Proteins Predictive energy landscapes for folding α-helical transmembrane proteins Bobby L. Kim, Nicholas P. Schafer, and Peter G. Wolynes1 Departments of Chemistry and Physics and Astronomy and the Center for Theoretical Biological Physics, Rice University, Houston, TX 77005 Contributed by Peter G. Wolynes, June 11, 2014 (sent for review May 19, 2014; reviewed by Zaida A. Luthey-Schulten, Shoji Takada, and Margaret Cheung) We explore the hypothesis that the folding landscapes of mem- folding. Starting with Khorana’swork(7),numerousα-helical brane proteins are funneled once the proteins’ topology within the transmembrane proteins have been refolded from a chemically membrane is established. We extend a protein folding model, denatured state in vitro (8). This indicates that at least some the associative memory, water-mediated, structure, and energy transmembrane domains may not require the translocon to fold model (AWSEM) by adding an implicit membrane potential and properly. In addition, recent experiments on a few α-helical reoptimizing the force field to account for the differing nature of transmembrane proteins have succeeded in characterizing the the interactions that stabilize proteins within lipid membranes, structure of transition state ensembles, in a manner like that used yielding a model that we call AWSEM-membrane. Once the pro- for globular proteins. These studies suggest that native contacts tein topology is set in the membrane, hydrophobic attractions are important in the folding nucleus but may not represent the play a lesser role in finding the native structure, whereas po- whole story (9, 10). Whether membrane proteins possess energy lar–polar attractions are more important than for globular pro- landscapes as funneled as globular proteins remains an open teins. We examine both the quality of predictions made with question. Experimentally, resolving this question is complicated by AWSEM-membrane when accurate knowledge of the topology the fact that the many ways membrane proteins are unfolded also and secondary structure is available and the quality of predictions disrupts their membrane environment. One way to circumvent this made without such knowledge, instead using bioinformatically problem is to use detergent to solubilize the denatured state, but inferred topology and secondary structure based on sequence as of yet, it is unknown how closely the resulting folding mecha- alone. When no major errors are made by the bioinformatic meth- nisms resemble the folding mechanisms in more realistic ods used to assign the topology of the transmembrane helices, membrane environments. these two types of structure predictions yield roughly equiva- We address the role of thermodynamic control and funneled lent quality structures. Although the predictive energy landscape landscapes in α-helical transmembrane protein folding once the is transferable and not structure based, within the correct topo- protein’s overall topology is set by using coarse-grained molec- logical sector we find the landscape is indeed very funneled: ular dynamics simulations to examine the consequences of the Thermodynamic landscape analysis indicates that both the total principle of minimal frustration for the second stage of mem- potential energy and the contact energy decrease as native con- brane protein folding in which the helices arrange into a specific tacts are formed. Nevertheless the near symmetry of different α helical packings with respect to native contact formation can result structure. If the landscapes of -helical transmembrane mem- brane proteins indeed are funneled, by using a sufficiently large in multiple packings with nearly equal thermodynamic occupancy, α especially at temperatures just below collapse. database of -helical transmembrane protein structures (11), the principle of minimal frustration provides a strategy to learn an α energy landscape theory | molecular dynamics energy function potentially capable of folding -helical trans- membrane proteins via molecular dynamics. We explore this strategy in this paper by extending to membrane proteins an he folding of globular proteins has come to be well under- Tstood starting from Anfinsen’s thermodynamic hypothesis (1), by means of statistical energy landscape theory (2–5) and its Significance principle of minimal frustration. Evolution selects the sequences of most globular proteins so that folding is, by and large, ther- The understanding of how membrane proteins fold pales in modynamically controlled and the landscape is dominated by the comparison with the understanding of globular protein folding. interactions between residues that are close together in the fol- This discrepancy is partly due to the fact that membrane proteins ded state, i.e., the native contacts. In vivo folding of α-helical are difficult to work with experimentally. In turn, the lack of transmembrane proteins differs from the usually autonomous high-quality experimental data has caused modeling of mem- folding of globular proteins in that, during translation, another brane proteins to lag behind. Also, the extent to which the actor, the translocon, generally assists the nascent chain in either translocon assists transmembrane domains in folding is unclear. translocating across or integrating peptides into the lipid mem- The number of experimentally determined membrane protein brane. Topology, by which we mean the “specification of the structures has recently increased, and we may now be at the number of transmembrane helices and their in and/or out ori- stage where it has become possible to derive transferable sim- entations across the membrane” (ref. 6, p. 909), in vivo is thus ulation models for studying transmembrane protein folding. We initially established cotranslationally with few exceptions. Large describe the optimization of one such model and its application barriers between alternate topologies once the protein is folded, to predicting helical packings within the native topology. along with the involvement of the translocon catalyst, suggest a role α Author contributions: B.L.K., N.P.S., and P.G.W. designed research; B.L.K. and N.P.S. per- for kinetic control in folding of -helical transmembrane proteins. formed research; B.L.K. and N.P.S. contributed new reagents/analytic tools; B.L.K., N.P.S., In light of these differences, what aspects of energy landscape and P.G.W. analyzed data; and B.L.K., N.P.S., and P.G.W. wrote the paper. theory, based as it is on near-equilibrium statistical physics, can Reviewers: Z.A.L.-S., University of Illinois at Urbana-Champaign; S.T., Kyoto University; be applied for understanding and predicting membrane protein and M.C., University of Houston. structures and folding mechanisms? The authors declare no conflict of interest. Despite the known differences between globular and mem- 1To whom correspondence should be addressed. Email: [email protected]. brane protein folding, there is evidence that some of the ideas This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. BIOPHYSICS AND from energy landscape theory also apply to membrane protein 1073/pnas.1410529111/-/DCSupplemental. COMPUTATIONAL BIOLOGY www.pnas.org/cgi/doi/10.1073/pnas.1410529111 PNAS | July 29, 2014 | vol. 111 | no. 30 | 11031–11036 Downloaded by guest on September 27, 2021 associative memory, water-mediated, structure, and energy model barriers for topological rearrangements. These effects are cap- (AWSEM) (12), a coarse-grained molecular dynamics Hamiltonian tured by Vmembrane, a three-layer implicit membrane potential. that has been shown to predict globular protein structures to low The intramembrane region is 30 Å thick, which corresponds to resolution. Through an energy landscape optimization we learn the region that is spanned by the hydrophobic tails of the mem- the parameters of an intramembrane transferable interaction brane lipids. Because in this study we focus on transmembrane potential and include an implicit membrane potential. In this domains, most of the protein that is outside of the intramembrane work we describe these additions to AWSEM, motivate and detail region (the loops) actually resides in the interfacial layer occupied the optimization and learning algorithm, and evaluate the pre- by the polar head groups of the lipid molecules, either on the dictive abilities of the newly optimized AWSEM-membrane force cytoplasmic or periplasmic side. Vmembrane takes an initial as- field. We then use thermodynamic landscape analysis to quantify signment of the residue position relative to the membrane as its how funnel-like the resulting energy landscapes for folding mem- input. Residues are deemed to be periplasmic, transmembrane, brane proteins are when conformations are restricted to have a or cytoplasmic. In most of our calculations the assignment is de- proper topology within the membrane. termined from a 3D experimentally determined structure using the TMDET web server (17), thus confining the landscape to the Ingredients of AWSEM-Membrane proper topological sector, but we also present results using assign- Model Overview and Extensions. We learn the parameters in the ments of location predicted solely from sequence on a purely bio- energy function by first assuming that membrane proteins do informatic basis using the Membrane Protein Structure and actually have largely funneled landscapes once their topology Topology using Support Vector Machines program, MEMSAT- is set. The quality of structure prediction by annealing the re- SVM (18). During
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