MSEED: A program for the rapid analytical determination of accessible surface areas and their derivatives

Role of Thylakoid Lipids in Protochlorophyllide Oxidoreductase Activation: Allosteric Mechanism Elucidated by a Computational Study

Light-dependent protochlorophyllide oxidoreductase (LPOR) is a chlorophyll synthetase that catalyzes the reduction of protochlorophyllide (Pchlide) to chlorophyllide (Chlide) with indispensable roles in regulating photosynthesis processes. A recent study confirmed that thylakoid lipids (TL) were able to allosterically enhance modulator-induced LPOR activation. However, the allosteric modulation mechanism of LPOR by these compounds remains unclear. Herein, we integrated multiple computational approaches to explore the potential cavities in the Arabidopsis thaliana LPOR and an allosteric site around the helix-G region where high affinity for phosphatidyl glycerol (PG) was identified. Adopting accelerated molecular dynamics simulation for different LPOR states, we rigorously analyzed binary LPOR/PG and ternary LPOR/NADPH/PG complexes in terms of their dynamics, energetics, and attainable allosteric regulation. Our findings clarify the experimental observation of increased NADPH binding affinity for LPOR with PGs. Moreover, the simulations indicated that allosteric regulators targeting LPOR favor a mechanism involving lid opening upon binding to an allosteric hinge pocket mechanism. This understanding paves the way for designing novel LPOR activators and expanding the applications of LPOR.

Analytical calculation of the solvent-accessible surface area and its nuclear gradients by stereographic projection: A general approach for molecules, polymers, nanotubes, helices, and surfaces

In this article, we explore an alternative to the analytical Gauss-Bonnet approach for computing the solvent-accessible surface area (SASA) and its nuclear gradients. These two key quantities are required to evaluate the nonelectrostatic contribution to the solvation energy and its nuclear gradients in implicit solvation models. We extend a previously proposed analytical approach for finite systems based on the stereographic projection technique to infinite periodic systems such as polymers, nanotubes, helices, or surfaces and detail its implementation in the Crystal code. We provide the full derivation of the SASA nuclear gradients, and introduce an iterative perturbation scheme of the atomic coordinates to stabilize the gradients calculation for certain difficult symmetric systems. An excellent agreement of computed SASA with reference analytical values is found for finite systems, while the SASA size-extensivity is verified for infinite periodic systems. In addition, correctness of the analytical gradients is confirmed by the excellent agreement obtained with numerical gradients and by the translational invariance achieved, both for finite and infinite periodic systems. Overall therefore, the stereographic projection approach appears as a general, simple, and efficient technique to compute the key quantities required for the calculation of the nonelectrostatic contribution to the solvation energy and its nuclear gradients in implicit solvation models applicable to both finite and infinite periodic systems.

Efficient minimization of multipole electrostatic potentials in torsion space

The development of models of macromolecular electrostatics capable of delivering improved fidelity to quantum mechanical calculations is an active field of research in computational chemistry. Most molecular force field development takes place in the context of models with full Cartesian coordinate degrees of freedom. Nevertheless, a number of macromolecular modeling programs use a reduced set of conformational variables limited to rotatable bonds. Efficient algorithms for minimizing the energies of macromolecular systems with torsional degrees of freedom have been developed with the assumption that all atom-atom interaction potentials are isotropic. We describe novel modifications to address the anisotropy of higher order multipole terms while retaining the efficiency of these approaches. In addition, we present a treatment for obtaining derivatives of atom-centered tensors with respect to torsional degrees of freedom. We apply these results to enable minimization of the Amoeba multipole electrostatics potential in a system with torsional degrees of freedom, and validate the correctness of the gradients by comparison to finite difference approximations. In the interest of enabling a complete model of electrostatics with implicit treatment of solvent-mediated effects, we also derive expressions for the derivative of solvent accessible surface area with respect to torsional degrees of freedom.

TRIFORCE: Tessellated Semianalytical Solvent Exposed Surface Areas and Derivatives

We present a new approach to the calculation of solvent-accessible surface areas of molecules with potential application to surface area based methods for determination of solvation free energies. As in traditional analytical and statistical approaches, this new algorithm, called TRIFORCE, reports both component areas and derivatives as a function of the atomic coordinates and radii. Unique to TRIFORCE are the rapid and scalable approaches for the determination of sphere intersection points and numerical estimation of the surface areas, derivatives, and other properties that can be associated with the surface area facets. The algorithm performs a special tessellation and semianalytical integration that uses a precomputed look-up table. This provides a simple way to balance numerical accuracy and memory usage. TRIFORCE calculates derivatives in the same manner, enabling application in force-dependent activities such as molecular geometry minimization. TRIFORCE is available free of charge for academic purposes as both a C++ library, which can be directly interfaced to existing molecular simulation packages, and a web-accessible application.

Measuring the shapes of macromolecules - and why it matters

The molecular basis of life rests on the activity of biological macromolecules, mostly nucleic acids and proteins. A perhaps surprising finding that crystallized over the last handful of decades is that geometric reasoning plays a major role in our attempt to understand these activities. In this paper, we address this connection between geometry and biology, focusing on methods for measuring and characterizing the shapes of macromolecules. We briefly review existing numerical and analytical approaches that solve these problems. We cover in more details our own work in this field, focusing on the alpha shape theory as it provides a unifying mathematical framework that enable the analytical calculations of the surface area and volume of a macromolecule represented as a union of balls, the detection of pockets and cavities in the molecule, and the quantification of contacts between the atomic balls. We have shown that each of these quantities can be related to physical properties of the molecule under study and ultimately provides insight on its activity. We conclude with a brief description of new challenges for the alpha shape theory in modern structural biology.

Characterization of void space in polydisperse sphere packings: Applications to hard-sphere packings and to protein structure analysis

The implementation of a method for the exact evaluation of the volume and surface area of cavities and free volumes in polydisperse sphere packings is described. The generalization of an algorithm for Voronoi tessellation by Tanemura et al. is presented, employing the radical plane construction, as a part of the method. We employ this method to calculate the equation of state for monodisperse and polydisperse hard-sphere fluids, crystals, and for the metastable amorphous branch up to random close packing or jamming densities. We compute the distribution of free volumes, and compare with previous results employing a heuristic definition of free volume. We show the efficacy of the method for analyzing protein structure, by computing various quantities such as the distribution of sizes of buried cavities and pockets, the scaling of solvent accessible area to the corresponding occupied volume, the composition of residues lining cavities, etc.

Beta-decomposition for the volume and area of the union of three-dimensional balls and their offsets

Given a set of spherical balls, called atoms, in three-dimensional space, its mass properties such as the volume and the boundary area of the union of the atoms are important for many disciplines, particularly for computational chemistry/biology and structural molecular biology. Despite many previous studies, this seemingly easy problem of computing mass properties has not been well-solved. If the mass properties of the union of the offset of the atoms are to be computed as well, the problem gets even harder. In this article, we propose algorithms that compute the mass properties of both the union of atoms and their offsets both correctly and efficiently. The proposed algorithms employ an approach, called the Beta-decomposition, based on the recent theory of the beta-complex. Given the beta-complex of an atom set, these algorithms decompose the target mass property into a set of primitives using the simplexes of the beta-complex. Then, the molecular mass property is computed by appropriately summing up the mass property corresponding to each simplex. The time complexity of the proposed algorithm is O(m) in the worst case where m is the number of simplexes in the beta-complex that can be efficiently computed from the Voronoi diagram of the atoms. It is known in ℝ(3) that m = O(n) on average for biomolecules and m = O(n(2)) in the worst case for general spheres where n is the number of atoms. The theory is first introduced in ℝ(2) and extended to ℝ(3). The proposed algorithms were implemented into the software BetaMass and thoroughly tested using molecular structures available in the Protein Data Bank. BetaMass is freely available at the Voronoi Diagram Research Center web site.

Geometric measures of large biomolecules: surface, volume, and pockets

Geometry plays a major role in our attempts to understand the activity of large molecules. For example, surface area and volume are used to quantify the interactions between these molecules and the water surrounding them in implicit solvent models. In addition, the detection of pockets serves as a starting point for predictive studies of biomolecule-ligand interactions. The alpha shape theory provides an exact and robust method for computing these geometric measures. Several implementations of this theory are currently available. We show however that these implementations fail on very large macromolecular systems. We show that these difficulties are not theoretical; rather, they are related to the architecture of current computers that rely on the use of cache memory to speed up calculation. By rewriting the algorithms that implement the different steps of the alpha shape theory such that we enforce locality, we show that we can remediate these cache problems; the corresponding code, UnionBall has an apparent O(n) behavior over a large range of values of n (up to tens of millions), where n is the number of atoms. As an example, it takes 136 sec with UnionBall to compute the contribution of each atom to the surface area and volume of a viral capsid with more than five million atoms on a commodity PC. UnionBall includes functions for computing analytically the surface area and volume of the intersection of two, three and four spheres that are fully detailed in an appendix. UnionBall is available as an OpenSource software.

On-the-fly Numerical Surface Integration for Finite-Difference Poisson-Boltzmann Methods

Most implicit solvation models require the definition of a molecular surface as the interface that separates the solute in atomic detail from the solvent approximated as a continuous medium. Commonly used surface definitions include the solvent accessible surface (SAS), the solvent excluded surface (SES), and the van der Waals surface. In this study, we present an efficient numerical algorithm to compute the SES and SAS areas to facilitate the applications of finite-difference Poisson-Boltzmann methods in biomolecular simulations. Different from previous numerical approaches, our algorithm is physics-inspired and intimately coupled to the finite-difference Poisson-Boltzmann methods to fully take advantage of its existing data structures. Our analysis shows that the algorithm can achieve very good agreement with the analytical method in the calculation of the SES and SAS areas. Specifically, in our comprehensive test of 1,555 molecules, the average unsigned relative error is 0.27% in the SES area calculations and 1.05% in the SAS area calculations at the grid spacing of 1/2Å. In addition, a systematic correction analysis can be used to improve the accuracy for the coarse-grid SES area calculations, with the average unsigned relative error in the SES areas reduced to 0.13%. These validation studies indicate that the proposed algorithm can be applied to biomolecules over a broad range of sizes and structures. Finally, the numerical algorithm can also be adapted to evaluate the surface integral of either a vector field or a scalar field defined on the molecular surface for additional solvation energetics and force calculations.

Empirical prediction of protein pKa values with residue mutation

A fast, empirical method, Mut-pKa, is presented for predicting the pKa values of ionizable residues in proteins based on mutation. The method compares the effect of mutating each residue that may act as a hydrogen bond donor or acceptor for the ionizable residue. The energetic effect of each type of mutation, along with a desolvation measure and the overall background charge, is fit against pKa data for histidine and carboxyl residues. A total of 214 residues from 35 different proteins were used in the dataset. Using 11 parameters for each type of ionizable residue, a root mean squared error (RMSE) of 0.78 and 1.12 pH units were obtained for carboxyl and histidines residues, respectively, using leave one out cross validation (LOOCV). The results were particularly promising for buried residues, which had RMSE values of 0.99 and 1.13 for carboxyl and histidine residues, respectively. A number of desolvation measures were tested. The simplest measure, the number of atoms surrounding the residue, was found to work best. The effect of using dynamics was also studied using short molecular dynamics runs, followed by minimization of the structures. Mut-pKa has significantly fewer parameters than, but similar performance to, other empirical methods. Because of this and the LOOCV results, we believe the model is robust and that overfitting is not a problem.

Advances in implicit models of water solvent to compute conformational free energy and molecular dynamics of proteins at constant pH

Modern implicit solvent models for macromolecular simulations in water-proton bath are considered. The fundamental quantity that implicit models approximate is the solute potential of mean force, which is obtained by averaging over solvent degrees of freedom. The implicit solvent models suggest practical ways to calculate free energies of macromolecular conformations taking into account equilibrium interactions with water solvent and proton bath, while the explicit solvent approach is unable to do that due to the need to account for a large number of solvent degrees of freedom. The most advanced realizations of the implicit continuum models by different research groups are discussed, their accuracy are examined, and some applications of the implicit solvent models to macromolecular modeling, such as free energy calculations, protein folding, and constant pH molecular dynamics are highlighted.

Interactive visualization of molecular surface dynamics

Molecular dynamics simulations of proteins play a growing role in various fields such as pharmaceutical, biochemical and medical research. Accordingly, the need for high quality visualization of these protein systems raises. Highly interactive visualization techniques are especially needed for the analysis of time-dependent molecular simulations. Beside various other molecular representations the surface representations are of high importance for these applications. So far, users had to accept a trade-off between rendering quality and performance--particularly when visualizing trajectories of time-dependent protein data. We present a new approach for visualizing the Solvent Excluded Surface of proteins using a GPU ray casting technique and thus achieving interactive frame rates even for long protein trajectories where conventional methods based on precomputation are not applicable. Furthermore, we propose a semantic simplification of the raw protein data to reduce the visual complexity of the surface and thereby accelerate the rendering without impeding perception of the protein's basic shape. We also demonstrate the application of our Solvent Excluded Surface method to visualize the spatial probability density for the protein atoms over the whole period of the trajectory in one frame, providing a qualitative analysis of the protein flexibility.

Joint neighbors approximation of macromolecular solvent accessible surface area

A new method for approximate analytical calculations of solvent accessible surface area (SASA) for arbitrary molecules and their gradients with respect to their atomic coordinates was developed. This method is based on the recursive procedure of pairwise joining of neighboring atoms. Unlike other available methods of approximate SASA calculations, the method has no empirical parameters, and therefore can be used with comparable accuracy in calculations of SASA in folded and unfolded conformations of macromolecules of any chemical nature. As shown by tests with globular proteins in folded conformations, average errors in absolute atomic surface area is around 1 A2, while for unfolded protein conformations it varies from 1.65 to 1.87 A2. Computational times of the method are comparable with those by GETAREA, one of the fastest exact analytical methods available today.

Simple Physics-Based Analytical Formulas for the Potentials of Mean Force for the Interaction of Amino Acid Side Chains in Water. 1. Approximate Expression for the Free Energy of Hydrophobic Association Based on a Gaussian-Overlap Model

A physics-based model is proposed to derive approximate analytical expressions for the cavity component of the free energy of hydrophobic association of spherical and spheroidal solutes in water. The model is based on the difference between the number and context of the water molecules in the hydration sphere of a hydrophobic dimer and of two isolated hydrophobic solutes. It is assumed that the water molecules touching the convex part of the molecular surface of the dimer and those in the hydration spheres of the monomers contribute equally to the free energy of solvation, and those touching the saddle part of the molecular surface of the dimer result in a more pronounced increase in free energy because of their more restricted mobility (entropy loss) and fewer favorable electrostatic interactions with other water molecules. The density of water in the hydration sphere around a single solute particle is approximated by the derivative of a Gaussian centered on the solute molecule with respect to its standard deviation. On the basis of this approximation, the number of water molecules in different parts of the hydration sphere of the dimer is expressed in terms of the first and the second mixed derivatives of the two Gaussians centered on the first and second solute molecules, respectively, with respect to the standard deviations of these Gaussians, and plausible analytical expressions for the cavity component of the hydrophobic-association energy of spherical and spheroidal solutes are introduced. As opposed to earlier hydration-shell models, our expressions reproduce the desolvation maxima in the potentials of mean force of pairs of nonpolar solutes in water, and their advantage over the models based on molecular-surface area is that they have continuous gradients in the coordinates of solute centers.

Conformational energy landscape of the acyl pocket loop in acetylcholinesterase: a Monte Carlo-generalized Born model study

The X-ray crystal structure of the reaction product of acetylcholinesterase (AChE) with the inhibitor diisopropylphosphorofluoridate (DFP) showed significant structural displacement in a loop segment of residues 287-290. To understand this conformational selection, a Monte Carlo (MC) simulation study was performed of the energy landscape for the loop segment. A computational strategy was applied by using a combined simulated annealing and room temperature Metropolis sampling approach with solvent polarization modeled by a generalized Born (GB) approximation. Results from thermal annealing reveal a landscape topology of broader basin opening and greater distribution of energies for the displaced loop conformation, while the ensemble average of conformations at 298 K favored a shift in populations toward the native by a free-energy difference in good agreement with the estimated experimental value. Residue motions along a reaction profile of loop conformational reorganization are proposed where Arg-289 is critical in determining electrostatic effects of solvent interaction versus Coulombic charging.

A new analytical method for computing solvent-accessible surface area of macromolecules and its gradients

In the calculation of thermodynamic properties and three-dimensional structures of macromolecules, such as proteins, it is important to have an efficient algorithm for computing the solvent-accessible surface area of macromolecules. Here, we propose a new analytical method for this purpose. In the proposed algorithm we consider the transformation that maps the spherical circles formed by intersection of the atomic surfaces in three-dimensional space onto the circles on a two-dimensional plane, and the problem of computing the solvent-accessible surface area is reduced to the problem of computing the corresponding curve integrals on the plane. This allows to consider only the integrals along the circular trajectories on the plane. The algorithm is suitable for parallelization. Testings on many proteins as well as the comparison to the other analogous algorithms have shown that our method is accurate and efficient.

Predicting peptide binding to MHC pockets via molecular modeling, implicit solvation, and global optimization

Development of a computational prediction method based on molecular modeling, global optimization, and implicit solvation has produced accurate structure and relative binding affinity predictions for peptide amino acids binding to five pockets of the MHC molecule HLA-DRB1*0101. Because peptide binding to MHC molecules is essential to many immune responses, development of such a method for understanding and predicting the forces that drive binding is crucial for pharmaceutical design and disease treatment. Underlying the development of this prediction method are two hypotheses. The first is that pockets formed by the peptide binding groove of MHC molecules are independent, separating the prediction of peptide amino acids that bind within individual pockets from those that bind between pockets. The second hypothesis is that the native state of a system composed of an amino acid bound to a protein pocket corresponds to the system's lowest free energy. The prediction method developed from these hypotheses uses atomistic-level modeling, deterministic global optimization, and three methods of implicit solvation: solvent-accessible area, solvent-accessible volume, and Poisson-Boltzmann electrostatics. The method predicts relative binding affinities of peptide amino acids for pockets of HLA-DRB1*0101 by determining computationally an amino acid's global minimum energy conformation. Prediction results from the method are in agreement with X-ray crystallography data and experimental binding assays.

Performance of hybrid methods for large-scale unconstrained optimization as applied to models of proteins

Energy minimization plays an important role in structure determination and analysis of proteins, peptides, and other organic molecules; therefore, development of efficient minimization algorithms is important. Recently, Morales and Nocedal developed hybrid methods for large-scale unconstrained optimization that interlace iterations of the limited-memory BFGS method (L-BFGS) and the Hessian-free Newton method (Computat Opt Appl 2002, 21, 143-154). We test the performance of this approach as compared to those of the L-BFGS algorithm of Liu and Nocedal and the truncated Newton (TN) with automatic preconditioner of Nash, as applied to the protein bovine pancreatic trypsin inhibitor (BPTI) and a loop of the protein ribonuclease A. These systems are described by the all-atom AMBER force field with a dielectric constant epsilon = 1 and a distance-dependent dielectric function epsilon = 2r, where r is the distance between two atoms. It is shown that for the optimal parameters the hybrid approach is typically two times more efficient in terms of CPU time and function/gradient calculations than the two other methods. The advantage of the hybrid approach increases as the electrostatic interactions become stronger, that is, in going from epsilon = 2r to epsilon = 1, which leads to a more rugged and probably more nonlinear potential energy surface. However, no general rule that defines the optimal parameters has been found and their determination requires a relatively large number of trial-and-error calculations for each problem.

The weighted-volume derivative of a space-filling diagram

Computing the volume occupied by individual atoms in macromolecular structures has been the subject of research for several decades. This interest has grown in the recent years, because weighted volumes are widely used in implicit solvent models. Applications of the latter in molecular mechanics simulations require that the derivatives of these weighted volumes be known. In this article, we give a formula for the volume derivative of a molecule modeled as a space-filling diagram made up of balls in motion. The formula is given in terms of the weights, radii, and distances between the centers as well as the sizes of the facets of the power diagram restricted to the space-filling diagram. Special attention is given to the detection and treatment of singularities as well as discontinuities of the derivative.

MASKER: improved solvent-excluded molecular surface area estimations using Boolean masks

A fast algorithm for computing the solvent-accessible molecular surface area (SAS) using Boolean masks [Le Grand,S.M. and Merz,K.M.J. (1993). J. Comput. Chem., 14, 349-352) has been modified to estimate the solvent-excluded molecular surface area (SES), including contact, toroidal and re-entrant surface components. Numerical estimates of arc lengths of intersecting atomic SAS are used to estimate the toroidal surface and intersections between those arcs are used to estimate the re-entrant surface area. The new method is compared with an exact analytical method. Boolean molecular surface areas are continuous and pairwise differentiable and should be useful for molecular dynamics simulations, especially as the basis for an implicit solvent model.

MIAX: a new paradigm for modeling biomacromolecular interactions and complex formation in condensed phases

A new paradigm is proposed for modeling biomacromolecular interactions and complex formation in solution (protein-protein interactions so far in this report) that constitutes the scaffold of the automatic system MIAX (acronym for Macromolecular Interaction Assessment X). It combines in a rational way a series of computational methodologies, the goal being the prediction of the most native-like protein complex that may be formed when two isolated (unbound) protein monomers interact in a liquid environment. The overall strategy consists of first inferring putative precomplex structures by identification of binding sites or epitopes on the proteins surfaces and a simultaneous rigid-body docking process using geometric instances alone. Precomplex configurations are defined here as all those decoys the interfaces of which comply substantially with the inferred binding sites and whose free energy values are lower. Retaining all those precomplex configurations with low energies leads to a reasonable number of decoys for which a flexible treatment is amenable. A novel algorithm is introduced here for automatically inferring binding sites in proteins given their 3-D structure. The procedure combines an unsupervised learning algorithm based on the self-organizing map or Kohonen network with a 2-D Fourier spectral analysis. To model interaction, the potential function proposed here plays a central role in the system and is constituted by empirical terms expressing well-characterized factors influencing biomacromolecular interaction processes, essentially electrostatic, van der Waals, and hydrophobic. Each of these procedures is validated by comparing results with observed instances. Finally, the more demanding process of flexible docking is performed in MIAX embedding the potential function in a simulated annealing optimization procedure. Whereas search of the entire configuration hyperspace is a major factor precluding hitherto systems from efficiently modeling macromolecular interaction modes and complex structures, the paradigm presented here may constitute a step forward in the field because it is shown that a rational treatment of the information available from the 3-D structure of the interacting monomers combined with conveniently selected computational techniques can assist to elude search of regions of low probability in configuration space and indeed lead to a highly efficient system oriented to solve this intriguing and fundamental biologic problem.

A novel approach for identifying the surface atoms of macromolecules

A significant number of atoms lie buried beneath the "molecular surface" of proteins and other biologic macromolecules. Interactions between ligands and these macromolecules are dominated by interactions with the "surface atoms". Although interactions with the "buried" or interior atoms of the macromolecule certainly contribute to the total intermolecular interaction energy, many computer-assisted drug design (CADD) strategies can benefit from the identification of those atoms "on the surface" of proteins and other macromolecules. We have developed a simple, yet novel method to distinguish the surface atoms of macromolecules from the interior atoms which is based on computing the atomic contributions to the solvent-accessible surface (SAS) area. This report describes that method and demonstrates that it compares very favorably with four alternative methods.

Application of the frozen atom approximation to the GB/SA continuum model for solvation free energy

The generalized Born/surface area (GB/SA) continuum model for solvation free energy is a fast and accurate alternative to using discrete water molecules in molecular simulations of solvated systems. However, computational studies of large solvated molecular systems such as enzyme-ligand complexes can still be computationally expensive even with continuum solvation methods simply because of the large number of atoms in the solute molecules. Because in such systems often only a relatively small portion of the system such as the ligand binding site is under study, it becomes less attractive to calculate energies and derivatives for all atoms in the system. To curtail computation while still maintaining high energetic accuracy, atoms distant from the site of interest are often frozen; that is, their coordinates are made invariant. Such frozen atoms do not require energetic and derivative updates during the course of a simulation. Herein we describe methodology and results for applying the frozen atom approach to both the generalized Born (GB) and the solvent accessible surface area (SASA) parts of the GB/SA continuum model for solvation free energy. For strictly pairwise energetic terms, such as the Coulombic and van-der-Waals energies, contributions from pairs of frozen atoms can be ignored. This leaves energetic differences unaffected for conformations that vary only in the positions of nonfrozen atoms. Due to the nonlocal nature of the GB analytical form, however, excluding such pairs from a GB calculation leads to unacceptable inaccuracies. To apply a frozen-atom scheme to GB calculations, a buffer region within the frozen-atom zone is generated based on a user-definable cutoff distance from the nonfrozen atoms. Certain pairwise interactions between frozen atoms in the buffer region are retained in the GB computation. This allows high accuracy in conformational GB comparisons to be maintained while achieving significant savings in computational time compared to the full (nonfrozen) calculation. A similar approach for using a buffer region of frozen atoms is taken for the SASA calculation. The SASA calculation is local in nature, and thus exact SASA energies are maintained. With a buffer region of 8 A for the frozen-atom cases, excellent agreement in differences in energies for three different conformations of cytochrome P450 with a bound camphor ligand are obtained with respect to the nonfrozen cases. For various minimization protocols, simulations run 2 to 10.5 times faster and memory usage is reduced by a factor of 1.5 to 5. Application of the frozen atom method for GB/SA calculations thus can render computationally tractable biologically and medically important simulations such as those used to study ligand-receptor binding conformations and energies in a solvated environment.

Determination of conformational equilibrium of peptides in solution by NMR spectroscopy and theoretical conformational analysis: application to the calibration of mean-field solvation models

Peptides occur in solution as ensembles of conformations rather than in a fixed conformation. The existing energy functions are usually inadequate to predict the conformational equilibrium in solution, because of failure to account properly for solvation, if the solvent is not considered explicitly (which is usually prohibitively expensive). NMR data are therefore widely incorporated into theoretical conformational analysis. Because of conformational flexibility, restrained molecular dynamics (with restraints derived from NMR data), which is usually applied to determine protein conformation is of limited use in the case of peptides. Instead, (a) the restraints are averaged within predefined time windows during molecular dynamics (MD) simulations (time averaging), (b) multiple-copy MD simulations are carried out and the restraints are averaged over the copies (ensemble averaging), or (c) a representative ensemble of sterically feasible conformations is generated and the weights of the conformations are then fitted so that the computed average observables match the experimental data (weight fitting). All these approaches are briefly discussed in this article. If an adequate force field is used, conformations with large statistical weights obtained from the weight-fitting procedure should also have low energies, which can be implemented in force field calibration. Such a procedure is particularly attractive regarding the parameterization of the solvation energy in nonaqueous solvents, e.g., dimethyl sulfoxide, for which thermodynamic solvation data are scarce. A method for calibration of solvation parameters in dimethyl sulfoxide, which is based on this principle was recently proposed by C. Baysal and H. Meirovitch (Journal of the American Chemical Society, 1998, Vol. 120, pp. 800--812), in which the energy gap between the conformations compatible with NMR data and the alternative conformations is maximized. In this work we propose an alternative method based on the principle that the best-fitting statistical weights of conformations should match the Boltzmann weights computed with the force field applied. Preliminary results obtained using three test peptides of varying conformational mobility: H-Ser(1)-Pro(2)-Lys(3)-Leu(4)-OH, Ac-Tyr(1)-D-Phe(2)-Ser(3)-Pro(4)-Lys(5)-Leu(6)-NH(2), and cyclo(Tyr(1)-D-Phe(2)-Ser(3)-Pro(4)-Lys(5)-Leu(6)) are presented.

Are proteins well-packed?

The average packing density inside proteins is as high as in crystalline solids. Does this mean proteins are well-packed? We go beyond average densities, and look at the full distribution functions of free volumes inside proteins. Using a new and rigorous Delaunay triangulation method for parsing space into empty and filled regions, we introduce formal definitions of interior and surface packing densities. Although proteins look like organic crystals by the criterion of average density, they look more like liquids and glasses by the criterion of their free volume distributions. The distributions are broad, and the scalings of volume-to-surface, volume-to-cluster-radius, and numbers of void versus volume show that the interiors of proteins are more like randomly packed spheres near their percolation threshold than like jigsaw puzzles. We find that larger proteins are packed more loosely than smaller proteins. And we find that the enthalpies of folding (per amino acid) are independent of the packing density of a protein, indicating that van der Waals interactions are not a dominant component of the folding forces.

Optimization of solvation models for predicting the structure of surface loops in proteins

A novel procedure for optimizing the atomic solvation parameters (ASPs) sigma(i) developed recently for cyclic peptides is extended to surface loops in proteins. The loop is free to move, whereas the protein template is held fixed in its X-ray structure. The energy is E(tot) = E(FF)(epsilon = nr) + summation operator sigma(i)A(i), where E(FF)(epsilon = nr) is the force-field energy of the loop-loop and loop-template interactions, epsilon = nr is a distance-dependent dielectric constant, and n is an additional parameter to be optimized. A(i) is the solvent-accessible surface area of atom i. The optimal sigma(i) and n are those for which the loop structure with the global minimum of E(tot)(n, sigma(i)) becomes the experimental X-ray structure. Thus, the ASPs depend on the force field and are optimized in the protein environment, unlike commonly used ASPs such as those of Wesson and Eisenberg (Protein Sci 1992;1:227-235). The latter are based on the free energy of transfer of small molecules from the gas phase to water and have been traditionally combined with various force fields without further calibration. We found that for loops the all-atom AMBER force field performed better than OPLS and CHARMM22. Two sets of ASPs [based on AMBER (n = 2)], optimized independently for loops 64-71 and 89-97 of ribonuclease A, were similar and thus enabled the definition of a best-fit set. All these ASPs were negative (hydrophilic), including those for carbon. Very good (i.e., small) root-mean-square-deviation values from the X-ray loop structure were obtained with the three sets of ASPs, suggesting that the best-fit set would be transferable to loops in other proteins as well. The structure of loop 13-24 is relatively stretched and was insensitive to the effect of the ASPs.

Prediction of a 12-residue loop in bovine pancreatic trypsin inhibitor: effects of buried water

The x-ray conformations of 5-, 7-, 9-, and 12-residue loops in bovine pancreatic trypsin inhibitor (BPTI) were predicted by the use of multiple independent Monte Carlo simulating annealing (MCSA) runs starting from random conformations. Four buried water molecules interacted with a 12-residue loop that started at residue 8 and ended at residue 19, and that included the binding region. The final conformation at the end of an MCSA run was characterized. Solvation free energy based on the solvent accessible surface area was included in the energy function at low simulated annealing temperatures. Conformational states were interactively separated by a recently developed algorithm. Computed loops were characterized in terms of total energy, and backbone and side chain root mean square deviations (RMSDs) between computed native loop conformations and the x-ray conformation. The 12-residue loop was computed with and without buried water [called WL12(8-19) and L12(8-19), respectively]. The backbone was reliably and reproducibly computed to within 1.1 A in L12(8-19) and 0.9 A in WL12(8-19). L12(8-19) required significantly more MCSA runs to achieve the same level of reproducibility as WL12(8-19). Based on the size of the cluster of low energy native loop conformations, and the computational effort, WL12(8-19) had greater entropy. In calculations of 7-, 9-, and 12-residue loops without buried water, the effects of buried water became obvious in the 12-residue loop calculation, which interacted with all four buried water molecules. Nearly all conformations of the native loop conformer had a hydrogen bond between the Lys 15 side chain and the backbone of Gly 12, Pro 13, and Cys 14, which may have implications in the rate of exchange of buried water with bulk solvent and in protein folding. The present version of MCSA program was more efficient than earlier versions.

[Software development for calculation of molecular surface area and its application to hydrophobic interaction]

A novel method of calculating the water-accessible molecular surface area from the number of points generated on the molecular surface was developed. This method yielded a molecular surface area with high accuracy and speed. The molecular surface area of lecithin shows an excellent linear correlation with the logarithm of the critical micelle concentration for many lecithins having different acyl chains. The solution structure of oxyphenonium bromide estimated from the molecular surface area approach was close to that obtained from NMR. Furthermore, the change of molecular surface area, delta S(HG), with docking of host and guest was defined and its calculation method was developed. Because both the host and the guest generally consist of hydrophilic and hydrophobic atomic groups, delta S(HG) was divided into such four terms as delta Soo(HG), delta Sow(HG), delta Swo(HG), and delta Sww(HG). For instance, delta Soo(HG) is the decrease in surface area with contact between the hydrophobic surfaces of the host and the guest. When the guest molecule was moved along the symmetry axis of cyclodextrin (CyD), the structure of a complex having the maximum value of delta Soo(HG) corresponds with the crystal structure. The solution structures of several inclusion systems were predicted by this method. For various systems including alpha-CyD, beta-CyD, gamma-CyD, and aromatic and aliphatic guests, the maximum values of delta Soo(HG) showed a good correlation with the logarithms of the binding constants. This relationship will be used for the prediction of the binding constants for CyD and other host-guest systems.

Physical reasons for the unusual alpha-helix stabilization afforded by charged or neutral polar residues in alanine-rich peptides

We have carried out conformational energy calculations on alanine-based copolymers with the sequence Ac-AAAAAXAAAA-NH(2) in water, where X stands for lysine or glutamine, to identify the underlying source of stability of alanine-based polypeptides containing charged or highly soluble polar residues in the absence of charge-charge interactions. The results indicate that ionizable or neutral polar residues introduced into the sequence to make them soluble sequester the water away from the CO and NH groups of the backbone, thereby enabling them to form internal hydrogen bonds. This solvation effect dictates the conformational preference and, hence, modifies the conformational propensity of alanine residues. Even though we carried out simulations for specific amino acid sequences, our results provide an understanding of some of the basic principles that govern the process of folding of these short sequences independently of the kind of residues introduced to make them soluble. In addition, we have investigated through simulations the effect of the bulk dielectric constant on the conformational preferences of these peptides. Extensive conformational Monte Carlo searches on terminally blocked 10-mer and 16-mer homopolymers of alanine in the absence of salt were carried out assuming values for the dielectric constant of the solvent epsilon of 80, 40, and 2. Our simulations show a clear tendency of these oligopeptides to augment the alpha-helix content as the bulk dielectric constant of the solvent is lowered. This behavior is due mainly to a loss of exposure of the CO and NH groups to the aqueous solvent. Experimental evidence indicates that the helical propensity of the amino acids in water shows a dramatic increase on addition of certain alcohols, such us trifluoroethanol. Our results provide a possible explanation of the mechanism by which alcohol/water mixtures affect the free energy of helical alanine oligopeptides relative to nonhelical ones.

On the transferability of atomic solvation parameters: Ab initio structural prediction of cyclic heptapeptides in DMSO

A statistical mechanics methodology for predicting the solution structures and populations of peptides developed recently is based on a novel method for optimizing implicit solvation models, which was applied initially to a cyclic hexapeptide in DMSO (C. Baysal and H. Meirovitch, Journal of American Chemical Society, 1998, vol. 120, pp. 800-812). Thus, the molecule has been described by the simplified energy function E(tot) = E(GRO) + summation operator(k) sigma(k)A(k), where E(GRO) is the GROMOS force-field energy, sigma(k) and A(k) are the atomic solvation parameter (ASP) and the solvent accessible surface area of atom k, respectively. In a more recent study, these ASPs have been found to be transferable to the cyclic pentapeptide cyclo(D-Pro(1)-Ala(2)-Ala(3)-Ala(4)-Ala(5)) in DMSO (C. Baysal and H. Meirovitch, Biopolymers, 2000, vol. 53, pp. 423-433). In the present paper, our methodology is applied to the cyclic heptapeptides axinastatin 2 [cyclo(Asn(1)-Pro(2)-Phe(3)-Val(4)-Leu(5)-Pro(6)-Val(7))] and axinastatin 3 [cyclo(Asn(1)-Pro(2)-Phe(3)-Ile(4)-Leu(5)-Pro(6)-Val(7))], in DMSO, which were studied by nmr by Mechnich et al. (Helvetica Chimica Acta, 1997, vol. 80, pp. 1338-1354). The calculations for axinastatin 2 show that special ASPs should be optimized for the partially charged side-chain atoms of Asn while the rest of the atoms take their values derived in our previous work; this suggests that similar optimization might be needed for other side chains as well. The solution structures of these peptides are obtained ab initio (i.e., without using experimental restraints) by an extensive conformational search based on E(GRO) alone and E(*)(tot), which consists of the new set of ASPs. For E(*)(tot), the theoretical values of proton-proton distances, (3)J coupling constants, and other properties are found to agree very well with the nmr results, and they are always better than those based on E(GRO).

Computational analysis of two similar neuropeptides yields distinct conformational ensembles

Conformational states and thermodynamic properties for two similar neuropeptides, GDPFLRF-NH(2) and GYPFLRF-NH(2), have been computed by Monte Carlo simulated annealing (MCSA) conformational searches and Metropolis Monte Carlo (MMC) calculations. These peptides were recently shown to have dramatically different conformations in solution by NMR [Edison et al., J Neuroscience 1999;19:6318-6326]. Final conformations of multiple independent MCSA runs were the starting points for MMC calculations, and conformations saved at intervals during MMC runs were characterized in terms of total energy, configuration entropy, side-chain fraction population, and ensemble average inter-nuclear distances. Without the use of any NMR data-generated pseudo-potentials, the present calculations were in excellent qualitative agreement with all previous NMR experimental data and provided a foundation by which to more quantitatively interpret the experimental NMR results. Proteins 2000;40:367-377.

Photodecarboxylation of ketoprofen in aqueous solution. A time-resolved laser-induced optoacoustic study

The photodecarboxylation reaction of 2-(3-benzoylphenyl)propionate (ketoprofen anion, KP-) was studied in water and in 0.1 M phosphate buffer solutions in the pH range 5.7-11.0 by laser-induced optoacoustic spectroscopy (LIOAS, T range 9.5-31.6 degrees C). Upon exciting KP- with 355 nm laser pulses under anaerobic conditions, two components in the LIOAS signals with well-separated lifetimes were found (tau 1 < 20 ns; 250 < tau 2 < 500 ns) in the whole pH range, whereas a long-lived third component (4 < tau 3 < 10 microseconds) was only detected at pH < or = 6.1. The heat and structural volume changes accompanying the first step did not depend on pH or on the presence of buffer. The carbanion resulting from prompt decarboxylation within the nanosecond pulse (< 10 ns) drastically reduces its molar volume ([-18.9 +/- 2.0] cm3/mol) with respect to KP- and its enthalpy content is (256 +/- 10) kJ/mol. At acid pH (ca 6), a species is formed with a lifetime in the hundreds of ns. The enthalpy and structural volume change for this species with respect to KP- are (181 +/- 15) kJ/mol and (+0.6 +/- 2.0) cm3/mol, respectively. This species is most likely a neutral biradical formed by protonation of the decarboxylated carbanion, and decays to the final product 3-ethylbenzophenone in several microsecond. At basic pH (ca 11), direct formation of 3-ethylbenzophenone occurs in hundreds of ns involving a reaction with the solvent. The global decarboxylation reaction is endothermic ([45 +/- 15] kJ/mol) and shows an expansion of (+14.5 +/- 0.5) cm3/mol with respect to KP-. At low pH, the presence of buffer strongly affects the magnitude of the structural volume changes associated with intermolecular proton-transfer processes of the long-lived species due to reactions of the buffer anion with the decarboxylated ketoprofen anion.

Ab initio prediction of the solution structures and populations of a cyclic pentapeptide in DMSO based on an implicit solvation model

Using a recently developed statistical mechanics methodology, the solution structures and populations of the cyclic pentapeptide cyclo(D-Pro(1)-Ala(2)-Ala(3)-Ala(4)-Ala(5)) in DMSO are obtained ab initio, i.e., without using experimental restraints. An important ingredient of this methodology is a novel optimization of implicit solvation parameters, which in our previous publication [Baysal, C.; Meirovitch, H. J Am Chem Soc 1998, 120, 800-812] has been applied to a cyclic hexapeptide in DMSO. The molecule has been described by the simplified energy function E(tot) = E(GRO) + summation operator(k) sigma(k)A(k), where E(GRO) is the GROMOS force-field energy, sigma(k) and A(k) are the atomic solvation parameter (ASP) and the solvent accessible surface area of atom k. This methodology, which relies on an extensive conformational search, Monte Carlo simulations, and free energy calculations, is applied here with E(tot) based on the ASPs derived in our previous work, and for comparison also with E(GRO) alone. For both models, entropy effects are found to be significant. For E(tot), the theoretical values of proton-proton distances and (3)J coupling constants agree very well with the NMR results [Mierke, D. F.; Kurz, M.; Kessler, H. J Am Chem Soc 1994, 116, 1042-1049], while the results for E(GRO) are significantly worse. This suggests that our ASPs might be transferrable to other cyclic peptides in DMSO as well, making our methodology a reliable tool for an ab initio structure prediction; obviously, if necessary, parts of this methodology can also be incorporated in a best-fit analysis where experimental restraints are used.

Approximate solvent-accessible surface areas from tetrahedrally directed neighbor densities

A fast analytical formula (TDND) has been derived for the calculation of approximate atomic and molecular solvent-accessible surface areas (SASA), as well as the first and second derivatives of these quantities with respect to atomic coordinates. Extending the work of Stouten et al. (Molecular Simulation, 1993, Vol. 10, pp. 97-120), as well as our own (Journal of Computational Chemistry, 1999, Vol. 20, pp. 586-596), the method makes use of a Gaussian function to calculate the neighbor density in four tetrahedral directions in three-dimensional space, sometimes twice with different orientations. SASA and first derivatives of the 2366 heavy atoms of penicillopepsin are computed in 0.13 s on an SGI R10000/194 MHz processor. When second derivatives are computed as well, the total time is 0.23 s. This is considerably faster than timings reported previously for other algorithms. Based on a parameterization set of nineteen compounds of different size (11-4346 atoms) and class (organics, proteins, DNA, and various complexes) consisting of a total 23,197 atoms, the method exhibits relative errors in the range 0.2-12.6% for total molecular surface areas and average absolute atomic surface area deviations in the range 1.7 to 3.6 A(2).

Free energy based populations of interconverting microstates of a cyclic peptide lead to the experimental NMR data

Analysis of nuclear Overhauser enhancement (NOE) intensities data of interconverting microstates of a peptide is a difficult problem in nmr. A new statistical mechanics methodology has been proposed recently, consisting of several steps: (1) potential energy wells on the energy surface of the molecule are identified (the corresponding regions are called wide microstates); (2) each wide microstate is then spanned by a Monte Carlo (MC) or molecular dynamics simulation starting from a representative structure, and the corresponding relative populations are obtained from the free energy calculated with the local states method; and (3) the overall NOEs and 3J coupling constants are obtained as averages over the corresponding contributions of the samples, weighted by the populations. Extending this methodology to cyclic peptides, we are treating here the hexapeptide cyclo(D-Pro1-Phe2-Ala3-Ser4-Phe5-Phe6) in DMSO, which was studied by Kessler et al. using nmr (Journal of the American Chemical Society, 1992, Vol. 114, pp. 4805-4818). They found that at least two structures are required to explain their NOE data, a conclusion also corroborated by our analysis (Journal of the American Chemical Society, 1998, Vol. 120, pp. 800-812) and led to a novel derivation of atomic solvation parameters (ASPs) for DMSO. Thus, the overall interactions within the peptide-solvent system are described approximately by Etot = EGRO + summation operator sigmaiAi, where EGRO is the energy of the GROMOS force field, Ai is the solvent-accessible surface area of atom i, and sigmai is the ASP. In the present paper the validity of these ASPs within the framework of the entire methodology is verified. This requires taking into account 23 microstates. A very good agreement is obtained between experimental and calculated NOEs and 3J coupling constants. The free energy based populations lead to the best results, which means that entropic effects should not be ignored. We have also studied the behavior of the internal angular fluctuations of the proton-proton vectors and discovered that they have a negligible effect on the calculated NOEs; this is due to the relatively concentrated wide microstates spanned by the MC simulations. The applicability of our ASPs to other cyclic peptides in DMSO is being studied in another work and preliminary results are discussed.

Description of hydration free energy density as a function of molecular physical properties

A method to calculate the solvation free energy density (SFED) at any point in the cavity surface or solvent volume surrounding a solute is proposed. In the special case in which the solvent is water, the SFED is referred to as the hydration free energy density (HFED). The HFED is described as a function of some physical properties of the molecules. These properties are represented by simple basis functions. The hydration free energy of a solute was obtained by integrating the HFED over the solvent volume surrounding the solute, using a grid model. Of 34 basis functions that were introduced to describe the HFED, only six contribute significantly to the HFED. These functions are representations of the surface area and volume of the solute, of the polarization and dispersion of the solute, and of two types of electrostatic interactions between the solute and its environment. The HFED is described as a linear combination of these basis functions, evaluated by summing the interaction energy between each atom of the solute with a grid point in the solvent, where each grid point is a representation of a finite volume of the solvent. The linear combination coefficients were determined by minimizing the error between the calculated and experimental hydration free energies of 81 neutral organic molecules that have a variety of functional groups. The calculated hydration free energies agree well with the experimental results. The hydration free energy of any other solute molecule can then be calculated by summing the product of the linear combination coefficients and the basis functions for the solute.