Conformational Transitions and Convergence of Absolute Binding Free Energy Calculations

The Binding Energy Distribution Analysis Method (BEDAM) is employed to compute the standard binding free energies of a series of ligands to a FK506 binding protein (FKBP12) with implicit solvation. Binding free energy estimates are in reasonably good agreement with experimental affinities. The conformations of the complexes identified by the simulations are in good agreement with crystallographic data, which was not used to restrain ligand orientations. The BEDAM method is based on λ -hopping Hamiltonian parallel Replica Exchange (HREM) molecular dynamics conformational sampling, the OPLS-AA/AGBNP2 effective potential, and multi-state free energy estimators (MBAR). Achieving converged and accurate results depends on all of these elements of the calculation. Convergence of the binding free energy is tied to the level of convergence of binding energy distributions at critical intermediate states where bound and unbound states are at equilibrium, and where the rate of binding/unbinding conformational transitions is maximal. This finding mirrors similar observations in the context of order/disorder transitions as for example in protein folding. Insights concerning the physical mechanism of ligand binding and unbinding are obtained. Convergence for the largest FK506 ligand is achieved only after imposing strict conformational restraints, which however require accurate prior structural knowledge of the structure of the complex. The analytical AGBNP2 model is found to underestimate the magnitude of the hydrophobic driving force towards binding in these systems characterized by loosely packed protein-ligand binding interfaces. Rescoring of the binding energies using a numerical surface area model corrects this deficiency. This study illustrates the complex interplay between energy models, exploration of conformational space, and free energy estimators needed to obtain robust estimates from binding free energy calculations.     

AGBNP: an analytic implicit solvent model suitable for molecular dynamics simulations and high-resolution modeling

We have developed an implicit solvent effective potential (AGBNP) that is suitable for molecular dynamics simulations and high-resolution modeling. It is based on a novel implementation of the pairwise descreening Generalized Born model for the electrostatic component and a new nonpolar hydration free energy estimator. The nonpolar term consists of an estimator for the solute-solvent van der Waals dispersion energy designed to mimic the continuum solvent solute-solvent van der Waals interaction energy, in addition to a surface area term corresponding to the work of cavity formation. AGBNP makes use of a new parameter-free algorithm to calculate the scaling coefficients used in the pairwise descreening scheme to take into account atomic overlaps. The same algorithm is also used to calculate atomic surface areas. We show that excellent agreement is achieved for the GB self-energies and surface areas in comparison to accurate, but much more expensive, numerical evaluations. The parameter-free approach used in AGBNP and the sensitivity of the AGBNP model with respect to large and small conformational changes makes the model suitable for high-resolution modeling of protein loops and receptor sites as well as high-resolution prediction of the structure and thermodynamics of protein-ligand complexes. We present illustrative results for these kinds of benchmarks. The model is fully analytical with first derivatives and is computationally efficient. It has been incorporated into the IMPACT molecular simulation program.     

Can a physics‐based,all‐atom potential find a protein's native structure among misfolded structures? I. Large scale AMBER benchmarking

Recent work has shown that physics-based, all-atom energy functions (AMBER, CHARMM, OPLS-AA) and local minimization, when used in scoring, are able to discriminate among native and decoy structures. Yet, there have been only few instances reported of the successful use of physics based potentials in the actual refinement of protein models from a starting conformation to one that ends in structures, which are closer to the native state. An energy function that has a global minimum energy in the protein's native state and a good correlation between energy and native-likeness should be able to drive model structures closer to their native structure during a conformational search. Here, the possible reasons for the discrepancy between the scoring and refinement results for the case of AMBER potential are examined. When the conformational search via molecular dynamics is driven by the AMBER potential for a large set of 150 nonhomologous proteins and their associated decoys, often the native minimum does not appear to be the lowest free energy state. Ways of correcting the potential function in order to make it more suitable for protein model refinement are proposed.     

A combined treatment of hydration and dynamical effects for the modeling of host–guest binding thermodynamics: the SAMPL5 blinded challenge

As part of the SAMPL5 blinded experiment, we computed the absolute binding free energies of 22 host–guest complexes employing a novel approach based on the BEDAM single-decoupling alchemical free energy protocol with parallel replica exchange conformational sampling and the AGBNP2 implicit solvation model specifically customized to treat the effect of water displacement as modeled by the Hydration Site Analysis method with explicit solvation. Initial predictions were affected by the lack of treatment of ionic charge screening, which is very significant for these highly charged hosts, and resulted in poor relative ranking of negatively versus positively charged guests. Binding free energies obtained with Debye–Hückel treatment of salt effects were in good agreement with experimental measurements. Water displacement effects contributed favorably and very significantly to the observed binding affinities; without it, the modeling predictions would have grossly underestimated binding. The work validates the implicit/explicit solvation approach employed here and it shows that comprehensive physical models can be effective at predicting binding affinities of molecular complexes requiring accurate treatment of conformational dynamics and hydration.     

Geometric algorithms for the conformational analysis of long protein loops

The efficient filtering of unfeasible conformations would considerably benefit the exploration of the conformational space when searching for minimum energy structures or during molecular simulation. The most important conditions for filtering are the maintenance of molecular chain integrity and the avoidance of steric clashes. These conditions can be seen as geometric constraints on a molecular model. In this article, we discuss how techniques issued from recent research in robotics can be applied to this filtering. Two complementary techniques are presented: one for conformational sampling and another for computing conformational changes satisfying such geometric constraints. The main interest of the proposed techniques is their application to the structural analysis of long protein loops. First experimental results demonstrate the efficacy of the approach for studying the mobility of loop 7 in amylosucrase from Neisseria polysaccharea. The supposed motions of this 17-residue loop would play an important role in the activity of this enzyme.     

An improved OPLS-AA force field for carbohydrates

This work describes an improved version of the original OPLS-all atom (OPLS-AA) force field for carbohydrates (Damm et al., J Comp Chem 1997, 18, 1955). The improvement is achieved by applying additional scaling factors for the electrostatic interactions between 1,5- and 1,6-interactions. This new model is tested first for improving the conformational energetics of 1,2-ethanediol, the smallest polyol. With a 1,5-scaling factor of 1.25 the force field calculated relative energies are in excellent agreement with the ab initio-derived data. Applying the new 1,5-scaling makes it also necessary to use a 1,6-scaling factor for the interactions between the C4 and C6 atoms in hexopyranoses. After torsional parameter fitting, this improves the conformational energetics in comparison to the OPLS-AA force field. The set of hexopyranoses included in the torsional parameter derivation consists of the two anomers of D-glucose, D-mannose, and D-galactose, as well as of the methyl-pyranosides of D-glucose, D-mannose. Rotational profiles for the rotation of the exocyclic group and of different hydroxyl groups are also compared for the two force fields and at the ab initio level of theory. The new force field reduces the overly high barriers calculated using the OPLS-AA force field. This leads to better sampling, which was shown to produce more realistic conformational behavior for hexopyranoses in liquid simulation. From 10-ns molecular dynamics (MD) simulations of alpha-D-glucose and alpha-D-galactose the ratios for the three different conformations of the hydroxymethylene group and the average (3)J(H,H) coupling constants are derived and compared to experimental values. The results obtained for OPLS-AA-SEI force field are in good agreement with experiment whereas the properties derived for the OPLS-AA force field suffer from sampling problems. The undertaken investigations show that the newly derived OPLS-AA-SEI force field will allow simulating larger carbohydrates or polysaccharides with improved sampling of the hydroxyl groups.     

Paramagnetic-based NMR restraints lift residual dipolar coupling degeneracy in multidomain detergent-solubilized membrane proteins

Residual dipolar couplings (RDCs) are widely used as orientation-dependent NMR restraints to improve the resolution of the NMR conformational ensemble of biomacromolecules and define the relative orientation of multidomain proteins and protein complexes. However, the interpretation of RDCs is complicated by the intrinsic degeneracy of analytical solutions and protein dynamics that lead to ill-defined orientations of the structural domains (ghost orientations). Here, we illustrate how restraints from paramagnetic relaxation enhancement (PRE) experiments lift the orientational ambiguity of multidomain membrane proteins solubilized in detergent micelles. We tested this approach on monomeric phospholamban (PLN), a 52-residue membrane protein, which is composed of two helical domains connected by a flexible loop. We show that the combination of classical solution NMR restraints (NOEs and dihedral angles) with RDC and PRE constraints resolves topological ambiguities, improving the convergence of the PLN structural ensemble and giving the depth of insertion of the protein within the micelle. The combination of RDCs with PREs will be necessary for improving the accuracy and precision of membrane protein conformational ensembles, where three-dimensional structures are dictated by interactions with the membrane-mimicking environment rather than compact tertiary folds common in globular proteins.     

Gay-Berne and electrostatic multipole based coarse-grain potential in implicit solvent

A general, transferable coarse-grain (CG) framework based on the Gay-Berne potential and electrostatic point multipole expansion is presented for polypeptide simulations. The solvent effect is described by the Generalized Kirkwood theory. The CG model is calibrated using the results of all-atom simulations of model compounds in solution. Instead of matching the overall effective forces produced by atomic models, the fundamental intermolecular forces such as electrostatic, repulsion-dispersion, and solvation are represented explicitly at a CG level. We demonstrate that the CG alanine dipeptide model is able to reproduce quantitatively the conformational energy of all-atom force fields in both gas and solution phases, including the electrostatic and solvation components. Replica exchange molecular dynamics and microsecond dynamic simulations of polyalanine of 5 and 12 residues reveal that the CG polyalanines fold into "alpha helix" and "beta sheet" structures. The 5-residue polyalanine displays a substantial increase in the "beta strand" fraction relative to the 12-residue polyalanine. The detailed conformational distribution is compared with those reported from recent all-atom simulations and experiments. The results suggest that the new coarse-graining approach presented in this study has the potential to offer both accuracy and efficiency for biomolecular modeling.     

Conformational sampling of bioactive molecules: a comparative study

The necessity to generate conformations that sample the entire conformational space accessible to a given molecule is ubiquitous in the field of computer-aided drug design. Protein-ligand docking, 3D database searching, and 3D QSAR are three commonly used techniques that depend critically upon the quality and diversity of the generated conformers. Although there are a wide range of conformational search algorithms available, the extent to which they sample conformational space is often unclear. To address this question, we conducted a robust comparison of the search algorithms implemented in several widely used molecular modeling packages, including Catalyst, Macromodel, Omega, MOE, and Rubicon as well as our own method, stochastic proximity embedding (SPE). We found that SPE used in conjunction with conformational boosting, a heuristic for biasing conformational search toward more extended or compact geometries, along with Catalyst, are significantly more effective in sampling the full range of conformational space compared to the other methods, which show distinct preferences for either more extended or more compact geometries.     

Biomolecular modeling: Goals, problems, perspectives

Computation based on molecular models is playing an increasingly important role in biology, biological chemistry, and biophysics. Since only a very limited number of properties of biomolecular systems is actually accessible to measurement by experimental means, computer simulation can complement experiment by providing not only averages, but also distributions and time series of any definable quantity, for example, conformational distributions or interactions between parts of systems. Present day biomolecular modeling is limited in its application by four main problems: 1) the force-field problem, 2) the search (sampling) problem, 3) the ensemble (sampling) problem, and 4) the experimental problem. These four problems are discussed and illustrated by practical examples. Perspectives are also outlined for pushing forward the limitations of biomolecular modeling.     

Efficiency of tabu‐search‐based conformational search algorithms

Efficient conformational search or sampling approaches play an integral role in molecular modeling, leading to a strong demand for even faster and more reliable conformer search algorithms. This article compares the efficiency of a molecular dynamics method, a simulated annealing method, and the basin hopping (BH) approach (which are widely used in this field) with a previously suggested tabu‐search‐based approach called gradient only tabu search (GOTS). The study emphasizes the success of the GOTS procedure and, more importantly, shows that an approach which combines BH and GOTS outperforms the single methods in efficiency and speed. We also show that ring structures built by a hydrogen bond are useful as starting points for conformational search investigations of peptides and organic ligands with biological activities, especially in structures that contain multiple rings. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011     

Homology model directed alignment selection for comparative molecular field analysis: Application to photosystem II inhibitors

The use of a computational docking protocol in conjunction with a protein homology model to derive molecular alignments for Comparative Molecular Field Analysis (CoMFA) was examined. In particular, the DOCK program and a model of the herbicidal target site, photosystem II (PSII), was used to derive alignments for two PSII inhibitor training sets, a set of benzo- and napthoquinones and a set of butenanilides. The protein design software in the QUANTA molecular modeling package was used to develop a homology model of spinach PSII based on the reported amino acid sequence and the X-ray crystal structure of the purple bacterium reaction center. The model is very similar to other reported PSII protein homology models. DOCK was then used to derive alignments for CoMFA modeling by docking the inhibitors in the PSII binding pocket. The molecular alignments produced from docking yielded highly predictive CoMFA models. As a comparison, the more traditional atom-atom alignments of the same two training sets failed to produce predictive CoMFA models. The general utilities of this application for homology model refinement and as an alternative scoring method are discussed.     

Development of 7TM receptor-ligand complex models using ligand-biased, semi-empirical helix-bundle repacking in torsion space: application to the agonist interaction of the human dopamine D2 receptor

Prediction of 3D structures of membrane proteins, and of G-protein coupled receptors (GPCRs) in particular, is motivated by their importance in biological systems and the difficulties associated with experimental structure determination. In the present study, a novel method for the prediction of 3D structures of the membrane-embedded region of helical membrane proteins is presented. A large pool of candidate models are produced by repacking of the helices of a homology model using Monte Carlo sampling in torsion space, followed by ranking based on their geometric and ligand-binding properties. The trajectory is directed by weak initial restraints to orient helices towards the original model to improve computation efficiency, and by a ligand to guide the receptor towards a chosen conformational state. The method was validated by construction of the β₁ adrenergic receptor model in complex with (S)-cyanopindolol using bovine rhodopsin as template. In addition, models of the dopamine D₂ receptor were produced with the selective and rigid agonist (R)-N-propylapomorphine ((R)-NPA) present. A second quality assessment was implemented by evaluating the results from docking of a library of 29 ligands with known activity, which further discriminated between receptor models. Agonist binding and recognition by the dopamine D₂ receptor is interpreted using the 3D structure model resulting from the approach. This method has a potential for modeling of all types of helical transmembrane proteins for which a structural template with sequence homology sufficient for homology modeling is not available or is in an incorrect conformational state, but for which sufficient empirical information is accessible.     

A method for conformational sampling of loops in proteins based on adiabatic decoupling and temperature or force scaling

A method for conformational Boltzmann sampling of loops in proteins in aqueous solution is presented that is based on adiabatic decoupling molecular dynamics (MD) simulation with temperature or force scaling. To illustrate the enhanced sampling, the loop from residues 33 to 43 in the bovine protein ribonuclease A is adiabatically decoupled from the rest of the protein and the solvent with a mass scaling factor s(m) =1000 and the sampling is enhanced with a scaling of the temperature using s(T) =2 or of the force using s(V) =0.667. Over 5 ns of simulation the secondary structure of the protein remains unaltered while a combined dihedral-angle conformational cluster analysis shows an increase of conformations outside the first most populated cluster of loop conformations for adiabatic decoupling MD with temperature scaling using s(T) =2 or force scaling using s(V) =0.667 compared to the standard MD simulation. The atom-positional root-mean-square fluctuations of the C(α) atoms of the loop show an increase in the movement of the loop as well, indicating that adiabatic decoupling MD with upscaling of the temperature or downscaling of the force is a promising method for conformational Boltzmann sampling.     

Sampling multiple scoring functions can improve protein loop structure prediction accuracy

Accurately predicting loop structures is important for understanding functions of many proteins. In order to obtain loop models with high accuracy, efficiently sampling the loop conformation space to discover reasonable structures is a critical step. In loop conformation sampling, coarse-grain energy (scoring) functions coupling with reduced protein representations are often used to reduce the number of degrees of freedom as well as sampling computational time. However, due to implicitly considering many factors by reduced representations, the coarse-grain scoring functions may have potential insensitivity and inaccuracy, which can mislead the sampling process and consequently ignore important loop conformations. In this paper, we present a new computational sampling approach to obtain reasonable loop backbone models, so-called the Pareto optimal sampling (POS) method. The rationale of the POS method is to sample the function space of multiple, carefully selected scoring functions to discover an ensemble of diversified structures yielding Pareto optimality to all sampled conformations. The POS method can efficiently tolerate insensitivity and inaccuracy in individual scoring functions and thereby lead to significant accuracy improvement in loop structure prediction. We apply the POS method to a set of 4-12-residue loop targets using a function space composed of backbone-only Rosetta and distance-scale finite ideal-gas reference (DFIRE) and a triplet backbone dihedral potential developed in our lab. Our computational results show that in 501 out of 502 targets, the model sets generated by POS contain structure models are within subangstrom resolution. Moreover, the top-ranked models have a root mean square deviation (rmsd) less than 1 A in 96.8, 84.1, and 72.2% of the short (4-6 residues), medium (7-9 residues), and long (10-12 residues) targets, respectively, when the all-atom models are generated by local optimization from the backbone models and are ranked by our recently developed Pareto optimal consensus (POC) method. Similar sampling effectiveness can also be found in a set of 13-residue loop targets.     

Key issues in the computational simulation of GPCR function: representation of loop domains

Some key concerns raised by molecular modeling and computational simulation of functional mechanisms for membrane proteins are discussed and illustrated for members of the family of G protein coupled receptors (GPCRs). Of particular importance are issues related to the modeling and computational treatment of loop regions. These are demonstrated here with results from different levels of computational simulations applied to the structures of rhodopsin and a model of the 5-HT2A serotonin receptor, 5-HT2AR. First, comparative Molecular Dynamics (MD) simulations are reported for rhodopsin in vacuum and embedded in an explicit representation of the membrane and water environment. It is shown that in spite of a partial accounting of solvent screening effects by neutralization of charged side chains, vacuum MD simulations can lead to severe distortions of the loop structures. The primary source of the distortion appears to be formation of artifactual H-bonds, as has been repeatedly observed in vacuum simulations. To address such shortcomings, a recently proposed approach that has been developed for calculating the structure of segments that connect elements of secondary structure with known coordinates, is applied to 5-HT2AR to obtain an initial representation of the loops connecting the transmembrane (TM) helices. The approach consists of a simulated annealing combined with biased scaled collective variables Monte Carlo technique, and is applied to loops connecting the TM segments on both the extra-cellular and the cytoplasmic sides of the receptor. Although this initial calculation treats the loops as independent structural entities, the final structure exhibits a number of interloop interactions that may have functional significance. Finally, it is shown here that in the case where a given loop from two different GPCRs (here rhodopsin and 5-HT2AR) has approximately the same length and some degree of sequence identity, the fold adopted by the loops can be similar. Thus, in such special cases homology modeling might be used to obtain initial structures of these loops. Notably, however, all other loops in these two receptors appear to be very different in sequence and structure, so that their conformations can be found reliably only by ab initio, energy based methods and not by homology modeling.     

Incorporating dynamics in E. coli dihydrofolate reductase enhances structure-based drug discovery

Escherichia coli dihydrofolate reductase (DHFR) is a long-standing target for enzyme studies. The influence of protein motion on its catalytic cycle is significant, and the conformation of the M20 loop is of particular interest. We present receptor-based pharmacophore models-an equivalent of solvent-mapping of binding hotspots-based on ensembles of protein conformations from molecular dynamics simulations of DHFR.NADPH in both the closed and open conformation of the M20 loop. The optimal models identify DHFR inhibitors over druglike non-inhibitors; furthermore, high-affinity inhibitors of E. coli DHFR are preferentially identified over general DHFR inhibitors. As expected, models resulting from simulations with DHFR in the productive conformation with a closed M20 loop have better performance than those from the open-loop simulations. Model performance improves with increased dynamic sampling, indicating that including a greater degree of protein flexibility can enhance the quest for potent inhibitors. This was compared to the limited conformational sampling seen in crystal structures, which were suboptimal for this application.     

Hessian‐free low‐mode conformational search for large‐scale protein loop optimization: application to c‐jun N‐terminal kinase JNK3

A Hessian‐free low‐mode search algorithm has been developed for large‐scale conformational searching. The new method is termed LLMOD, and it utilizes the ARPACK package to compute low‐mode eigenvectors of a Hessian matrix that is only referenced implicitly, through its product with a series of vectors. The Hessian × vector product is calculated utilizing a finite difference formula based on gradients. LLMOD is the first conformational search method that can be applied to fully flexible, unconstrained protein structures for complex loop optimization problems. LLMOD has been tested on a particularly difficult model system, c‐jun N‐terminal kinase JNK3. We demonstrate that LLMOD was able to correct a P38/ERK2/HCL‐based homology model that grossly misplaced the crucial glycine‐rich loop in the ATP‐binding site. © 2000 John Wiley & Sons, Inc. J Comput Chem 22: 21–30, 2001     

Inactivity of YGL082W in vitro due to impairment of conformational change in the catalytic center loop

MINDY-1 is a recently discovered new family of deubiquitinating enzymes(DUB),but one of its yeast homologs,YGL082 W,does not show any DUB activity in vitro.Sequence alignment shows that YGL082 W possesses the correct catalytic triad,and yet did not catalyze either the hydrolysis of di-ubiquitin,crosslinking with C-terminally propargylated ubiquitin,or hydrolysis of ubiquitin-7-amino-4-methylcoumarin.After obtaining a crystal structure of the catalytic domain of YGL082 W,we identified an interesting difference between the catalytic center loop of YGL082 W and that of its human homolog MINDY-1.Because the conformation of the catalytic center loop was previously reported to be important for the deubiquitination activity of MINDY-1,we hypothesized that Glu27(instead of the corresponding Pro136 in MINDY-1) of the catalytic center loop of YGL082 W may impair the conformational change and account for the lack of activity.This hypothesis was supported by homology modeling and molecular dynamics simulations,which showed that the Pro-to-Glu mutation(P136 E mutation for MINDY-1) creates a hydrogen bond that inhibits the conformation change of the catalytic center loop of MINDY-1.Further experiments through site-directed mutation validated this hypothesis,showing that the P27 E mutation caused MIY1(a homologous active DUB from yeast) to lose activity.