Energetics and stability of transmembrane helix packing: A replica-exchange simulation with a knowledge-based membrane potential

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

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.

Bias-Exchange Metadynamics Simulation of Membrane Permeation of 20 Amino Acids

Thermodynamics of the permeation of amino acids from water to lipid bilayers is an important first step for understanding the mechanism of cell-permeating peptides and the thermodynamics of membrane protein structure and stability. In this work, we employed bias-exchange metadynamics simulations to simulate the membrane permeation of all 20 amino acids from water to the center of a dipalmitoylphosphatidylcholine (DPPC) membrane (consists of 256 lipids) by using both directional and torsion angles for conformational sampling. The overall accuracy for the free energy profiles obtained is supported by significant correlation coefficients (correlation coefficient at 0.5-0.6) between our results and previous experimental or computational studies. The free energy profiles indicated that (1) polar amino acids have larger free energy barriers than nonpolar amino acids; (2) negatively charged amino acids are the most difficult to enter into the membrane; and (3) conformational transitions for many amino acids during membrane crossing is the key for reduced free energy barriers. These results represent the first set of simulated free energy profiles of membrane crossing for all 20 amino acids.

Multidimensional umbrella sampling and replica-exchange molecular dynamics simulations for structure prediction of transmembrane helix dimers

Structural information of a transmembrane (TM) helix dimer is useful in understanding molecular mechanisms of important biological phenomena such as signal transduction across the cell membrane. Here, we describe an umbrella sampling (US) scheme for predicting the structure of a TM helix dimer in implicit membrane using the interhelical crossing angle and the TM-TM relative rotation angles as the reaction coordinates. This scheme conducts an efficient conformational search on TM-TM contact interfaces, and its robustness is tested by predicting the structures of glycophorin A (GpA) and receptor tyrosine kinase EphA1 (EphA1) TM dimers. The nuclear magnetic resonance (NMR) structures of both proteins correspond to the global free-energy minimum states in their free-energy landscapes. In addition, using the landscape of GpA as a reference, we also examine the protocols of temperature replica-exchange molecular dynamics (REMD) simulations for structure prediction of TM helix dimers in implicit membrane. A wide temperature range in REMD simulations, for example, 250-1000 K, is required to efficiently obtain a free-energy landscape consistent with the US simulations. The interhelical crossing angle and the TM-TM relative rotation angles can be used as reaction coordinates in multidimensional US and be good measures for conformational sampling of REMD simulations.

STRUCTURE PREDICTION OF HELICAL TRANSMEMBRANE PROTEINS AT TWO LENGTH SCALES

As the first step toward a multi-scale, hierarchical computational approach for membrane protein structure prediction, the packing of transmembrane helices was modeled at the residue and atom levels, respectively. For predictions at the residue level, the helix-helix and helix-membrane interactions were described by a set of knowledge-based energy functions. For predictions at the atom level, CHARMM19 force field was used. To facilitate the system to overcome energy barriers, the Wang–Landau method was employed, where a random walk is performed in the energy space with a uniform probability. Native-like structures were predicted at both levels for two model systems, each of which consists of two transmembrane helices. Interestingly, consistent results were obtained from simulations at the residue and atom levels for the same system, strongly suggesting the feasibility of a hierarchical approach for membrane protein structure predictions.

Application of the Wang-Landau algorithm to the dimerization of glycophorin A

A two-step Monte Carlo procedure is developed to investigate the dimerization process of the homodimer glycophorin A. In the first step, the energy density of states of the system is estimated by the Wang-Landau algorithm. In the second step, a production run is performed during which various energetical and structural observables are sampled to provide insight into the thermodynamics of the system. All seven residues LIxxGVxxGVxxT constituting the contact interface play a dominating role in the dimerization, however at different stages of the process. The leucine motif and to some extent the GxxxG motif are involved at the very beginning of the dimerization when the two helices come into contact, ensuring an interface already similar to the native one. At a lower temperature, the threonine motif stabilizes by hydrogen bonding the dimer, which finally converges toward its native state at around 300 K. The power and flexibility of the procedure employed here makes it an interesting alternative to other Monte Carlo methods for the study of similar protein systems.

Computational approaches for the design of peptides with anti-breast cancer properties

Background: Breast cancer is the most common cancer among women. Tamoxifen is the preferred drug for estrogen receptor-positive breast cancer treatment, yet many of these cancers are intrinsically resistant to tamoxifen or acquire resistance during treatment. Therefore, scientists are searching for breast cancer drugs that have different molecular targets. Methodology: Recently, a computational approach was used to successfully design peptides that are new lead compounds against breast cancer. We used replica exchange molecular dynamics to predict the structure and dynamics of active peptides, leading to the discovery of smaller bioactive peptides. Conclusions: These analogs inhibit estrogen-dependent cell growth in a mouse uterine growth assay, a test showing reliable correlation with human breast cancer inhibition. We outline the computational methods that were tried and used along with the experimental information that led to the successful completion of this research.

Packing of transmembrane helices in bacteriorhodopsin folding: structure and thermodynamics

We propose a coarse-grained (CG) model to study the native structure and physical properties of helical membrane proteins (HMPs) using off-lattice computer simulations. Instead of considering sequence heterogeneity explicitly, we model its effect on the packing of helices by employing a mean packing parameter r(0), which is calculated from an all-atom (AA) model. Specifically, this CG model is applied to investigate the packing of helices in bacteriorhodopsin (BR), and predicts the seven helix bundle structure of BR with a root mean square deviation (RMSD) in coordinates of helix backbone atoms (N, C, C(alpha)) of 3.99 A from its crystal structure. This predicted structure is further refined in an AA model by Amber and the refined structure has a RMSD (in coordinates of helix backbone atoms) of 2.64 A. The predicted packing position, tilting angle, and orientation angle of each helix in the refined structure are consistent with experimental data and their physical origins can be well understood in our model. Our results show that a reasonably good structure of BR can be predicted by using such a dual-scale approach, provided that its secondary structure is known. Starting from a random initial configuration, the folded structure can be obtained in days using a regular desktop computer. Various thermodynamic properties of helix packing of BR are also investigated in this CG model.

In Silico Screening of Mutational Effects on Transmembrane Helix Dimerization: Insights from Rigid-Body Docking and Molecular Dynamics Simulations

In this study, a docking-based protocol has been probed for its ability to predict the effects of 32 single and double mutations on glycophorin A (GpA) homodimerization. Rigid-body docking simulations have been paralleled by molecular dynamics (MD) simulations in implicit membrane. The rigid-body docking-based approach proved effective in reconstituting the native architecture of the GpA dimer for the wild type and the wild-type-like mutants. The good correlative models between the intermolecular interaction descriptors derived both by rigid-body docking simulations and by MD simulations and experimental relative free energies support the assumption that the mutation-induced changes in the association free energy of GpA helices are essentially ascribed to differences in the packing interactions, whereas almost all the variations in the entropic contributions to the association free energy are constant and/or negligible. The MD-based models achieved provide insights into the structural determinants for disruptive and restoring mutational effects. The computational approaches presented in this study are fast and effective, and constitute simple and promising tools for in silico screening of mutational effects on the association properties of integral membrane proteins.

Computational design and experimental discovery of an antiestrogenic peptide derived from alpha-fetoprotein

Breast cancer is the most common cancer among women, and tamoxifen is the preferred drug for estrogen receptor-positive breast cancer treatment. Many of these cancers are intrinsically resistant to tamoxifen or acquire resistance during treatment. Consequently, there is an ongoing need for breast cancer drugs that have different molecular targets. Previous work has shown that 8-mer and cyclic 9-mer peptides inhibit breast cancer in mouse and rat models, interacting with an unsolved receptor, while peptides smaller than eight amino acids did not. We show that the use of replica exchange molecular dynamics predicts the structure and dynamics of active peptides, leading to the discovery of smaller peptides with full biological activity. Simulations identified smaller peptide analogues with the same conserved reverse turn demonstrated in the larger peptides. These analogues were synthesized and shown to inhibit estrogen-dependent cell growth in a mouse uterine growth assay, a test showing reliable correlation with human breast cancer inhibition.

Mutagenesis data in the automated prediction of transmembrane helix dimers

We present a molecular modeling protocol that selects modeled protein structures based on experimental mutagenesis results. The computed effect of a point mutation should be consistent with its experimental effect for correct models; mutations that do not affect protein stability and function should not affect the computed energy of a correct model while destabilizing mutations should have unfavorable computed energies. On the other hand, an incorrect model will likely display computed energies that are inconsistent with experimental results. We added terms to our energy function which penalize models that are inconsistent with experimental results. This creates a selective advantage for models that are consistent with experimental results in the Monte Carlo simulated annealing protocol we use to search conformational space. We calibrated our protocol to predict the structure of transmembrane helix dimers using glycophorin A as a model system. Inclusion of mutational data in this protocol compensates for the limitations of our force field and the limitations of our conformational search. We demonstrate an application of this structure prediction protocol by modeling the transmembrane region of the BNIP3 apoptosis factor.

Accurate estimation of the density of states from Monte Carlo transition probability data

This study develops an efficient approach for calculating the density of states from energy transition probability matrices generated from extended sampling Monte Carlo simulations. Direct and iterative variants of the method are shown to achieve high accuracy when applied to the two-dimensional Ising model for which the density of states function can be determined exactly. They are also used to calculate the density of states of lattice protein and Lennard-Jones models which generate more complex nonzero matrix structures. Whereas the protein simulations test the method on a system exhibiting a rugged free energy landscape, the Lennard-Jones calculations highlight implementation details that arise in applications to continuous energy systems. Density of states results for these two systems agree with estimates from multiple histogram reweighting, demonstrating that the new method provides an alternative approach for computing the thermodynamic properties of complex systems.

Energetics of membrane protein folding and stability

The critical role of membrane proteins in a myriad of biological and physiological functions has spawned numerous investigations over the past several decades with the long-term goal of identifying the molecular origins and energetic forces that stabilize these proteins within the membrane. Parallel structural and thermodynamics studies on several systems have provided significant insight regarding the driving forces governing folding, assembly, insertion, and translocation of membrane proteins. The present review surveys families of membrane-associated proteins including alpha-helical and beta-barrel structures, viral surface receptors, and pore-forming toxins, citing representative proteins within each of these classes for further scrutiny in terms of structure-function relationships and global conformational stability. This overview presents seminal findings from pioneering studies on the energetics of membrane protein folding and stability to modern techniques that are exploiting the use of molecular genetics and single molecule studies. An overall consensus regarding the molecular origins of membrane protein stability is that a number of intrinsic properties resemble features of soluble proteins, yet there are distinct energetic differences arising from specific intra- and intermolecular interactions within the membrane. The combined efforts from structural, energetics, and dynamics approaches offer unique insights and improve our fundamental understanding of the driving forces dictating membrane protein folding and stability.