Conformational Preferences of an Intrinsically Disordered Protein Domain: A Case Study for Modern Force Fields.

Molecular simulations of intrinsically disordered proteins (IDPs) are challenging because they require sampling a very large number of relevant conformations, corresponding to a multitude of shallow minima in a flat free energy landscape. However, in the presence of a binding partner, the free energy landscape of an IDP can be dominated by few deep minima. This characteristic imposes high demands on the accuracy of the force field used to describe the molecular interactions. Here, as a model system for an IDP that is unstructured in solution but folds upon binding to a structured interaction partner, the transactivation domain of c-Myb was studied both in the unbound (free) form and when bound to the KIX domain. Six modern biomolecular force fields were systematically tested and compared in terms of their ability to describe the structural ensemble of the IDP. The protein force field/water model combinations included in this study are AMBER ff99SB-disp with its corresponding water model that was derived from TIP4P-D, CHARMM36m with TIP3P, ff15ipq with SPC/Eb, ff99SB*-ILDNP with TIP3P and TIP4P-D, and FB15 with TIP3P-FB water. Comparing the results from REST2-enhanced sampling simulations with experimental CD spectra and secondary chemical shifts reveals that the ff99SB-disp force field can realistically capture the broad and mildly helical structural ensemble of free c-Myb. The structural ensembles yielded by CHARMM36m, ff99SB*-ILDNP together with TIP4P-D water, and FB15 are also mildly helical; however, each of these force fields can be assigned a specific subset of c-Myb residues for which the simulations could not reproduce the experimental secondary chemical shifts. In addition, microsecond-timescale MD simulations of the KIX/c-Myb complex show that most force fields used preserve a stable helix fold of c-Myb in the complex. Still, all force fields predict a KIX/c-Myb complex interface that differs slightly from the structures provided by NMR because several NOE-derived distances between KIX and c-Myb were exceeded in the simulations. Taken together, the ff99SB-disp force field in the first place but also CHARMM36m, ff99SB*-ILDNP together with TIP4P-D water, and FB15 can be suitable choices for future simulation studies of the coupled folding and binding mechanism of the KIX/c-Myb complex and potentially also other IDPs.

Lipid-dependent conformational landscape of the ErbB2 growth factor receptor dimers.

Altered lipid metabolism has been linked to cancer development and progression. Several roles have been attributed to the increased saturation and length of lipid acyl tails observed in tumors, but its effect on signaling receptors is still emerging. In this work, we have analyzed the lipid dependence of the ErbB2 growth factor receptor dimerization that plays an important role in the pathogenesis of breast cancer. We have performed coarse-grain ensemble molecular dynamics simulations to comprehensively sample the ErbB2 monomer-dimer association. Our results indicate a dynamic dimer state with a complex conformational landscape that is modulated with increasing lipid tail length. We resolve the native N-terminal "active" and C-terminal "inactive" conformations in all membrane compositions. However, the relative population of the N-terminal and C-terminal conformers is dependent on length of the saturated lipid tails. In short-tail membranes, additional non-specific dimers are observed which are reduced or absent in long-tailed bilayers. Our results indicate that the relative population as well as the structure of the dimer state is modulated by membrane composition. We have correlated these differences to local perturbations of the membrane around the receptor. Our work is an important step in characterizing ErbB dimers in healthy and diseased states and emphasize the importance of sampling lipid dynamics in understanding receptor association.

Solvent effects on ligand binding to a serine protease.

Solvation plays an important role in virtually all biomolecular recognition and binding processes. However, the consequences of changes in solvation conditions often remain elusive. In this work, we combined isothermal titration calorimetry (ITC) and molecular dynamics (MD) simulations to investigate the effect of solvent composition on the thermodynamics of protein-ligand binding. We studied the binding of p-aminobenzamidine (PAB) to trypsin in various water/methanol mixtures as a model system for a biomolecular complex. Our ITC experiments show that the free energy of binding changes only very modestly with methanol concentration, and that this small change is due to strong enthalpy-entropy compensation. The MD and free energy simulations not only reproduce the experimental binding free energies, but also provide atomic-level insights into the mechanisms behind the thermodynamic observations. The more favorable binding enthalpy at increased methanol concentrations (when compared to pure water) is attributed to stronger protein-ligand and intramolecular protein-protein interactions. The stronger protein-ligand interaction is linked to a small-scale conformational rearrangement of the L2 binding pocket loop, which senses the solvent environment. Remarkably, the stronger interactions do not substantially reduce the configurational entropy of the protein. Instead, the more disfavorable entropy contribution to the binding free energy at increased methanol concentrations is due to the desolvation of the ligand from the bulk, which is more favorable in pure aqueous solution than in the presence of methanol. Our work thus underpins the importance of including conformational flexibility, even for an overall rather rigid complex, since even small-amplitude motions can significantly alter the binding energetics. Furthermore, the ability of our combined ITC/MD approach to assign different thermodynamic contributions to distinct conformational states might contribute to an enhanced understanding of biomolecular binding processes in general.

On Using Atomistic Solvent Layers in Hybrid All-Atom/Coarse-Grained Molecular Dynamics Simulations.

Hybrid all-atom/coarse-grained (AA-CG) simulations in which AA solutes are embedded in a CG environment can provide a significant computational speed-up over conventional fully atomistic simulations and thus alleviate the current length and time scale limitations of molecular dynamics (MD) simulations of large biomolecular systems. On one hand, coarse graining the solvent is particularly appealing, since it typically constitutes the largest part of the simulation system and thus dominates computational cost. On the other hand, retaining atomic-level solvent layers around the solute is desirable for a realistic description of hydrogen bonds and other local solvation effects. Here, we devise and systematically validate fixed resolution AA-CG schemes, both with and without atomistic water layers. To quantify the accuracy and diagnose possible pitfalls, Gibbs free energies of solvation of amino acid side chain analogues were calculated, and the influence of the nature of the CG solvent surrounding (polarizable vs nonpolarizable CG water) and the size of the AA solvent region was investigated. We show that distance restraints to keep the AA solvent around the solute lead to too high of a density in the inner shell. Together with a long-ranged effect due to orientational ordering of water molecules at the AA-CG boundary, this affects solvation free energies. Shifting the onset of the distance restraints slightly away from the central solute significantly improves solvation free energies, down to mean unsigned errors with respect to experiment of 2.3 and 2.6 kJ/mol for the polarizable and nonpolarizable CG water surrounding, respectively. The speed-up of the nonpolarizable model renders it computationally more attractive. The present work thus highlights challenges, and outlines possible solutions, involved with modeling the boundary between different levels of resolution in hybrid AA-CG simulations.

Systematic evaluation of bundled SPC water for biomolecular simulations.

In bundled SPC water models, the relative motion of groups of four water molecules is restrained by distance-dependent potentials. Bundled SPC models have been used in hybrid all-atom/coarse-grained (AA/CG) multiscale simulations, since they enable to couple atomistic SPC water with supra-molecular CG water models that effectively represent more than a single water molecule. In the present work, we systematically validated and critically tested bundled SPC water models as solvent for biomolecular simulations. To that aim, we investigated both thermodynamic and structural properties of various biomolecular systems through molecular dynamics (MD) simulations. Potentials of mean force of dimerization of pairs of amino acid side chains as well as hydration free energies of single side chains obtained with bundled SPC and standard (unrestrained) SPC water agree closely with each other and with experimental data. Decomposition of the hydration free energies into enthalpic and entropic contributions reveals that in bundled SPC, this favorable agreement of the free energies is due to a larger degree of error compensation between hydration enthalpy and entropy. The Ramachandran maps of Ala3, Ala5, and Ala7 peptides are similar in bundled and unrestrained SPC, whereas for the (GS)2 peptide, bundled water leads to a slight overpopulation of extended conformations. Analysis of the end-to-end distance autocorrelation times of the Ala5 and (GS)2 peptides shows that sampling in more viscous bundled SPC water is about two times slower. Pronounced differences between the water models were found for the structure of a coiled-coil dimer, which is instable in bundled SPC but not in standard SPC. In addition, the hydration of the active site of the serine protease α-chymotrypsin depends on the water model. Bundled SPC leads to an increased hydration of the active site region, more hydrogen bonds between water and catalytic triad residues, and a significantly slower exchange of water molecules between the active site and the bulk. Our results form a basis for assessing the accuracy that can be expected from bundled SPC water models. At the same time, this study also highlights the importance of evaluating beforehand the effects of water bundling on the biomolecular system of interest for a particular multiscale simulation application.

Thermodynamic and kinetic characterization of transmembrane helix association.

The transient dimerization of transmembrane proteins is an important event in several cellular processes and computational methods are being increasingly used to quantify their underlying energetics. Here, we probe the thermodynamics and kinetics of a simple transmembrane dimer to understand membrane protein association. A multi-step framework has been developed in which the dimerization profiles are calculated from coarse-grain molecular dynamics simulations, followed by meso-scale simulations using parameters calculated from the coarse-grain model. The calculated value of ΔGassoc is approx. -20 kJ mol(-1) and is consistent between three methods. Interestingly, the meso-scale stochastic model reveals low dimer percentages at physiologically-relevant concentrations, despite a favorable ΔGassoc. We identify generic driving forces arising from the protein backbone and lipid bilayer and complementary factors, such as protein density, that govern self-interactions in membranes. Our results provide an important contribution in understanding membrane protein organization and linking molecular, nano-scale computational studies to meso-scale experimental data.

The power of coarse graining in biomolecular simulations.

Computational modeling of biological systems is challenging because of the multitude of spatial and temporal scales involved. Replacing atomistic detail with lower resolution, coarse grained (CG), beads has opened the way to simulate large-scale biomolecular processes on time scales inaccessible to all-atom models. We provide an overview of some of the more popular CG models used in biomolecular applications to date, focusing on models that retain chemical specificity. A few state-of-the-art examples of protein folding, membrane protein gating and self-assembly, DNA hybridization, and modeling of carbohydrate fibers are used to illustrate the power and diversity of current CG modeling.

A peptidic unconjugated GRP78/BiP ligand modulates the unfolded protein response and induces prostate cancer cell death.

The molecular chaperone GRP78/BiP is a key regulator of protein folding in the endoplasmic reticulum, and it plays a pivotal role in cancer cell survival and chemoresistance. Inhibition of its function has therefore been an important strategy for inhibiting tumor cell growth in cancer therapy. Previous efforts to achieve this goal have used peptides that bind to GRP78/BiP conjugated to pro-drugs or cell-death-inducing sequences. Here, we describe a peptide that induces prostate tumor cell death without the need of any conjugating sequences. This peptide is a sequence derived from the cochaperone Bag-1. We have shown that this sequence interacts with and inhibits the refolding activity of GRP78/BiP. Furthermore, we have demonstrated that it modulates the unfolded protein response in ER stress resulting in PARP and caspase-4 cleavage. Prostate cancer cells stably expressing this peptide showed reduced growth and increased apoptosis in in vivo xenograft tumor models. Amino acid substitutions that destroyed binding of the Bag-1 peptide to GRP78/BiP or downregulation of the expression of GRP78 compromised the inhibitory effect of this peptide. This sequence therefore represents a candidate lead peptide for anti-tumor therapy.

Conformational sampling of peptides in the presence of protein crowders from AA/CG-multiscale simulations.

Macromolecular crowding is recognized as an important factor influencing folding and conformational dynamics of proteins and nucleic acids. Previous views of crowding have focused on the mostly entropic volume exclusion effect of crowding, but recent studies are indicating the importance of enthalpic effects, in particular, changes in electrostatic interactions due to crowding. Here, temperature replica exchange molecular dynamics simulations of trp-cage and melittin in the presence of explicit protein crowders are presented to further examine the effect of protein crowders on peptide dynamics. The simulations involve a three-component multiscale modeling scheme where the peptides are represented at an atomistic level, the crowder proteins at a coarse-grained level, and the surrounding aqueous solvent as implicit solvent. This scheme optimally balances a physically realistic description for the peptide with computational efficiency. The multiscale simulations were compared with simulations of the same peptides in different dielectric environments with dielectric constants ranging from 5 to 80. It is found that the sampling in the presence of the crowders resembles sampling with reduced dielectric constants between 10 and 40. Furthermore, diverse conformational ensembles are generated in the presence of crowders including partially unfolded states for trp-cage. These findings emphasize the importance of enthalpic interactions over volume exclusion effects in describing the effects of cellular crowding.

PRIMO/PRIMONA: a coarse-grained model for proteins and nucleic acids that preserves near-atomistic accuracy.

The new coarse graining model PRIMO/PRIMONA for proteins and nucleic acids is proposed. This model combines one to several heavy atoms into coarse-grained sites that are chosen to allow an analytical, high-resolution reconstruction of all-atom models based on molecular bonding geometry constraints. The accuracy of proposed reconstruction method in terms of structure and energetics is tested and compared with other popular reconstruction methods for a variety of protein and nucleic acid test sets.

All-atom de novo protein folding with a scalable evolutionary algorithm.

The search for efficient and predictive methods to describe the protein folding process at the all-atom level remains an important grand-computational challenge. The development of multi-teraflop architectures, such as the IBM BlueGene used in this study, has been motivated in part by the large computational requirements of such studies. Here we report the predictive all-atom folding of the forty-amino acid HIV accessory protein using an evolutionary stochastic optimization technique. We implemented the optimization method as a master-client model on an IBM BlueGene, where the algorithm scales near perfectly from 64 to 4096 processors in virtual processor mode. Starting from a completely extended conformation, we optimize a population of 64 conformations of the protein in our all-atom free-energy model PFF01. Using 2048 processors the algorithm predictively folds the protein to a near-native conformation with an RMS deviation of 3.43 A in < 24 h.