Computational Molecular Biophysics Today: A Confluence of Methodical Advances and Complex Biomolecular Applications

1 Introduction


These are exciting times for macromolecular scientists. The efficiency and resolution of experimental techniques is improving rapidly, bringing to bear tour-de-force static and dynamic views of large polymeric systems. Recent examples of such systems include the crystallographically-determined nucleosome core particle -- an essential building block of the DNA/protein spools that make up the chromosomal material [1]; unusual conformations of overstretched DNA and proteins as seen in force-versus-extension measurements by clever single-molecule manipulations [2] (also reviewed in [3] for DNA and [4] for the giant muscle molecule titin); competing unfolding pathways for the small protein barstar -- as obtained by kinetic studies using spectroscopic probes [5]; folding kinetics of a catalytic RNA -- as visualized by millisecond time-resolved free radical cleavage and detected by clever design of mutant fast folders for which the kinetic trap has been alleviated (reviewed in [6]); and long buckyball nanotubes -- giant linear fullerene chains that can sustain enormous elastic deformations [7].

Theoretical modeling is thriving on increased computer power, new algorithmic ideas from various disciplines, rapid and tantalizing three-dimensional graphics, and parallel architectural opportunities. Perhaps more than any other Grand Challenge application today, modeling large biological polymers -- proteins, nucleic acids, and lipids -- is a truly multidisciplinary enterprise. In a synergistic fashion, biologists describe the cellular picture; chemists fill in the atomic and molecular details; physicists extend these views to the electronic level and the underlying forces; mathematicians analyze and formulate appropriate numerical models and algorithms; and computer scientists and engineers provide the crucial implementational support for running large computer programs on high-speed and extended-communication platforms.

The immense appeal and interdisciplinary nature of computational molecular biophysics is largely driven by the many important practical applications in the field, from drug design to biomedical engineering to food chemistry. And computational scientists at large are intrigued by the wealth of real problems that demand sophisticated yet practical algorithms. Such challenging problems arise, for example, in protein folding prediction, biomolecular dynamics simulations, genome analysis, and drug design, and involve global and combinatorial optimization, Hamiltonian and stochastic dynamics, numerical linear algebra, multivariate statistical analysis, and dynamic programming.

Recognizing the broad theoretical underpinnings and wide application scope of biomolecular modeling and simulations, the Journal of Computational Physics welcomes contributions which develop and apply important computational tools to problems of biological importance. To mark this interest, this special volume in computational molecular biophysics is dedicated to the field. Although selective in content and representing only a small number of research groups, many exciting and active research areas are sampled. These include various methodologies in molecular dynamics (MD) and quantum-classical MD simulations (long-time integration schemes, fast electrostatic summation), configurational sampling and analysis techniques, quantum-mechanical approaches, and implementation of large simulation programs on high-speed computers. Various intriguing applications are also included in this volume, involving thermodynamic and dynamic studies of proteins and various biomolecular complexes, as found in membrane assemblies, between DNA and proteins, and between DNA and environmental carcinogens.

2 Algorithmic Advances

The first ten articles of the volume describe various methodological advances. We open with a general overview and field perspective by Schlick et al., who focus on algorithmic advances in dynamic simulations (integration, fast electrostatics) and structure refinement of experimental models. Included in this review are illustrative macromolecular applications and discussion of practical implementation of large molecular dynamics programs on parallel architectures.

2.1 Molecular Dynamics Simulations

MD integration methods are the focus of the four articles. Multiple timestep (MTS) methods, in particular, are effective techniques for integrating the classical equations of motion for systems with disparate timescales. They employ a hierarchical approach for updating the various force components so that slower forces (e.g., electrostatics) are computed less frequently than the more rapidly-varying terms (e.g., bonded interactions). This approach can produce substantial computational savings, since evaluation of the long-range, slowly-varying terms dominates the force computations. However, since the force components are intricately coupled, the numerical and physical behavior of the resulting trajectories depends sensitively on the force partitioning employed as well as the merging of solution components for the different force classes. In fact, trajectory-corrupting resonance artifacts can result at certain protocols (timestep and scheme combinations). Symplectic methods, for example, have been advocated since they guarantee that for small timesteps the numerical solution (trajectory) is the exact solution of a `nearby' Hamiltonian system, a consequence of their strong preservation of geometric properties of the dynamic flow. However, developing methods that are accurate and stable over long times and at the same time produce large computational savings is a current area of active research.

Reich discusses multiple-timestepping schemes for classical MD and extensions to quantum-classical dynamic models, which treat selected segments of the molecular system by the time-dependent Schrödinger equation. In this context, the quantum degrees of freedom are considered fast, and the classical degrees of freedom are the slow forces. As in classical molecular dynamics integration, these force components are tightly coupled and cannot be simply separated. Reich shows that multiple time-stepping schemes can be defined in a natural way for such quantum-classical MD simulations. Reich also points to the dangerous consequences of using too large timesteps in such simulations and concludes that further work is required to design optimal MTS schemes in this context.

Sandu and Schlick present an extension of MTS schemes to stochastic dynamics based on the simple Langevin equation. They analyze how a stochastic MTS treatment can mask resonance artifacts and how best to choose the scheme's parameters so as to balance accuracy, stability, and computational speed. An application of the resulting LN method to a solvated protein suggests that the method can be applied to conformational sampling problems. Further, the stochastic coupling can be minimized to approximate Newtonian dynamics as closely as possible. The stochastic formulation of LN yields significant computational speedup in comparison to symplectic, Newtonian MTS schemes, and in theory generates the same equilibrium distributions as obtained from Newtonian trajectories.

Bond, Laird, and Leimkuhler describe a symplectic MD integrator for simulations performed in the canonical (constant temperature) ensemble. Their method is based on a Poincaré time transformation of the extended Hamiltonian model of Nosé. The new method (Nosé-Poincaré) requires little more computational effort than standard (microcanonical or constant energy) simulations and samples from the canonical distribution when the time evolution is ergodic. The Nosé-Poincaré method is applicable to constrained and rigid-body models, and it can also be used for Nosé-chains.

Lynch, Perkyns, and Pettitt examine the ability of grand canonical ensemble MD (constant temperature, volume, and chemical potential) based on an extended Lagrangian approach to predict thermodynamic quantities from microscopic information. Their study analyzes corresponding number and number fluctuation averages in the context of the Kirkwood-Buff thermodynamic theory in statistical mechanics. Results are presented for three water models and suggest that such Kirkwood-Buff thermodynamic estimates, such as of free energies, can be obtained readily from constant chemical-potential ensembles and exploited in the study of biological systems. (See also the article by Jayaram et al. on free-energy calculations).

2.2 Conformational Analyses


Sampling configuration space efficiently is a major, general challenge in simulations of biomolecules, at least as important as following fast processes closely via accurate trajectories of the Hamiltonian. Though in theory MD simulations can span the large range of thermally accessible states, computational cost limits the total simulation length that can be followed. For example, only the nanosecond timeframe is routinely accessible today for macromolecular systems, a timeframe still very short in comparison to timescales of large-scale deformations of biological interest. Various Monte Carlo, hybrid Monte Carlo, and many other sampling procedures have been developed with this goal, but the key challenge is obtaining good sampling performance on systems with many degrees of freedom.

Schütte et al. develop a hybrid Monte Carlo procedure for following essential features of the dynamic evolution of a Hamiltonian system. The characterization of `essential conformations' and their stability is formulated in terms of statistical mechanics rather than molecular geometry, specifically as an almost invariant subset in position space. These conformational subsets are defined via the discretized eigenvalue problem for a statistically appropriate spatial transition Markov operator that replaces the Frobenius-Perron operator (which describes the transition probabilities within a dynamical system). An appropriate discretization of this operator can make the approach tractable to molecular systems, as demonstrated for a small RNA system.

Essential conformational features are also examined by Dauber-Osguthorpe et al. for a small protein (chymotrypsin-like serine protease) by comparing signal-processed data from MD trajectories to normal-mode analyses. The latter technique has been used to characterize a system's motion around equilibrium on the basis of the same empirical potentials that define the MD forces. Though normal-mode analysis can describe collective modes of the system, it is restricted to one region of phase space. In theory, MD trajectories traverse phase space and offer more information, but good analytical techniques are needed to extract details from the voluminous data generated. Through Fourier transforming atomic trajectories and focusing on frequency ranges of interest, digital processing techniques can reveal important structural and dynamic information. The current study attempts to compare results from normal-mode analysis to MD simulations, to compare theoretical results to experiment, and to characterize the major motion of the protein that facilitates its binding to a substrate. An overall qualitatively similar picture of the protein motion is found from both MD and normal-mode analysis, suggesting a robustness in the protein motion that is easily captured, specifically a movement of one hairpin loop on the protein's surface.

Gullingsrud, Braun, and Schulten describe a time series analysis method for the reconstruction of potentials of mean force from MD trajectories. Steered Molecular Dynamics (SMD) simulations are used to study protein/ligand binding and unfolding of proteins at atomic-level detail by mimicking single-molecule manipulation experiments. Experiment and simulation occur on much different timescales; hence potential reconstruction is important for relating simulation results to experimental force versus extension measurements. Three methods for analyzing time series involving displacement and force data are investigated and tested on model systems; the most promising method involves minimization of a Onsager-Machlup functional. Analysis of SMD data for a phospholipid membrane monolayer system by this method shows the correct reproduction of the adhesion forces of lipids in membranes, with detailed structural insights emerging.

2.3 Fast Electrostatics

Boschitsch, Fenley, and Olson describe important algorithmic work in another area of MD, namely fast evaluation of electrostatic energies and forces in biomolecular simulations. Various divide-and-conquer approaches have been developed in the last decade to reduce the direct,  complexity associated with an N-body problem to near linear complexity. To date, however, most fast multipole methods have focused on purely Coulombic potentials. These researchers develop a fast multipole expansion based on spherical modified Bessel functions, which are appropriate for the screened Coulomb interactions (i.e., Yukawa or Debye-Huckel potentials) used in modeling polyelectrolytes at various salt concentrations. The performance of the resulting fast adaptive multipole algorithm (using an octree group procedure) is evaluated in terms of accuracy and computational time for various charged systems. Results show the nearly linear scaling complexity and the robust performance of the method for various charged systems at different salt concentrations.

2.4 Quantum Mechanical Simulations

The electronic description in quantum mechanics is necessary when there is a change in electronic structure, such as in chemical reactions in enzyme reactions. Linear scaling quantum mechanical methods are very promising in meeting the challenge.

Lewis, Liu, Lee, and Yang describe and apply a divide-and-conquer semi-empirical quantum mechanical technique with linear scaling complexity. The divide-and-conquer is the first of linear-scaling methods that have made possible quantum applications to large biological systems. The application to the enzyme cytidine deaminase examines the reaction pathway of catalysis, involving a sequence of structural rearrangements that depend on ligand binding. Structural predictions of the active site under a variety of conditions are detailed as the enzyme traverses the pathway from ground state to transition state to product.

3 Design of Molecular Dynamics Programs

Besides new algorithmic developments, efficient implementation of MD programs for macromolecules is another important area of research. Huber and McCammon describe an object-oriented library termed OOMPAA in the C++ programming language for molecular modeling and simulation software. This more modular and flexible design -- an elaboration of traditional subroutine-oriented code structure -- facilitates code modification and expansion. Their performance evaluation on pairwise summation computations indicates competitiveness with respect to traditional Fortran codes.

The Illinois program NAMD2 described by Kalé et al. is specifically tailored to parallel computing platforms. Through a multidisciplinary collaboration, its features are continuously adapted to the application needs of biophysicists. NAMD2 is also implemented using a modular C++ design, and uses data-driven objects, and object-migration based load balancing supported in Charm++, a parallel C++ library. NAMD2 uses spatial decomposition combined with force decomposition to enhance scalability on various parallel architectures. The performance efficiency of NAMD2 on 220 processors (namely parallel speedup of 180, or around 80% efficiency) is impressive and might further be improved through communication-overhead reduction.

4 Biomolecular Applications

To make biomolecular simulation studies physically relevant, modeling ingenuity and algorithmic tailoring is often required to treat large complex systems. The five biomolecular applications presented next in the volume require such tailoring: efficient configurational sampling guided by experimental data, efficient global optimization procedures for protein folding, free-energy simulation protocols for molecular complexes, simulation protocols for supramolecular systems, and homology-based protocols for protein structure prediction.

4.1 Biomolecular Interactions and Complexes

Broyde and Hingerty describe the development of effective conformational search techniques for identifying novel geometries of DNA bound covalently to environmental aromatic carcinogens (carcinogen-DNA adducts). Understanding energetic and dynamic features of such systems is important for correlating adduct type with mutagenic and tumorigenic tendencies on the basis of structural deformations at the DNA level. The search techniques summarized in this review involve wide-scale conformational searches with systematic buildup, and minimization in torsion-angle rather than Cartesian space, thereby reducing markedly the number of free conformational variables. Penalty functions are applied optionally to selected distances to locate structures within the bounds of experimentally-derived NMR interproton values and to search for selected DNA hydrogen bonding patterns. Predictions employing these approaches are in agreement with high-resolution NMR studies in solution. Moreover, the geometric features discerned for distinct classes of carcinogen-bound DNAs have suggested how to relate structural effects with the complex biological pathways involved in mutagenesis and tumorigenesis. In particular, the predicted and observed phenomenon of opposite orientation along the DNA in adducts stemming from chiral chemical reactants with different tumorigenic potencies has established a structure/function theme that applies more generally.


Characterizing binding interactions among biomolecular systems is another important goal in computational molecular biophysics. Jayaram et al. detail a thermodynamic and functional analysis of a protein/DNA complex (restriction enzyme EcoRI endonuclease bound to its cognate DNA in solution) using free-energy simulation methodology with a careful electrostatic treatment. Free-energy simulations attempt to follow a conformational change or reaction by defining a specific reaction coordinate and are generally subject to large errors and protocol sensitivity. With care, however, as done in this work, insights into the molecular process which cannot be obtained by other techniques can emerge. These researchers analyzed the calculated free energy of binding in terms of various energetic contributions (electrostatics, van der Waals, hydrophobic effects) and their influence on the binding process (favoring or disfavoring complexation) at the residue level (nucleotide and amino acid). The energy of solvation in particular is found to be important, with van der Waals interactions and water release favoring binding, and electrostatic interactions (intramolecular and solvation) proving unfavorable to the reaction.

Duong, Mehler and Weinstein propose an appropriate MD protocol for a complex membrane bilayer system by considering various electrostatic treatments and trajectory ensembles (microcanonical vs. isothermal-isobaric, i.e., constant energy and volume vs. constant temperature and pressure). The nanosecond-range simulations reveal detailed structural and dynamic characteristics of the system's components (lipid hydrocarbon chains, polar head groups, water) that compare favorably with experimental data. Of the simulation protocols tested, the microcanonical ensemble/truncated electrostatics combination, with sufficient equilibration, yielded a membrane structure with properties in best agreement with experiment. The dynamic behavior of a membrane/peptide complex is then explored to probe atomic details of the segment's stabilization by the membrane. The modeled peptide is one transmembrane segment of a G protein coupled receptor that is important for signal transduction processes. The model incorporates a proline-kink that perturbs the -helical structure, exposing polar groups to the membrane environment. Analysis of the flexibility of the bend angle at the junction between the two -helical domains flanking the proline in the transmembrane segment and of the hydrogen-bonding organization reveals the strong topological and energetic boundary forces produced by the lipid bilayer. It also demonstrates the importance of the membrane-penetrating water molecules in stabilizing the polar segments of the perturbed -helical peptide in the membrane environment.

4.2 In Silico Protein Folding

Protein structure prediction is a well known goal in molecular biophysics, with research advances even making headlines in The New York Times and The Wall Street Journal. Sánchez and Sali review the state-of-the-art in homology-based structure prediction. Such comparative modeling works -- structure prediction based on sequence similarity -- are becoming increasingly valuable with the rapidly growing information on genomic sequences. This class of methods appears to be the only one at present among theoretical prediction techniques that can yield sufficient accuracy of protein models (e.g., 2 Å resolution) to be useful for biological applications (e.g., ligand design, protein engineering). In general, a large sequence homology (e.g., > 40%) usually implies great structure similarity (as characterized by a similar overall fold).

The authors describe available methods for homology-based protein structure prediction, discuss prospects for their automation, and advocate that a focused, large-scale structural genomics effort (experimental and theoretical) is needed to reliably determine three-dimensional (3D) protein structures from the wealth of rising sequence information. In particular, they advocate that target sequences for structure determination by both experimentalists and theorists should be selected based on their potential to produce new structural motifs (folds), so that eventually these cataloged patterns -- the number of which is thought be be finite -- could be nearly exhausted.


Abagyan and Totrov describe alternative prediction methods for polypeptide and protein 3D structure termed ab initio. Such methods essentially attempt to predict 3D structures from sequence alone; they employ an empirical objective function representing interaction energies (and sometimes statistical information) in combination with selected sampling strategies. The energy function used by Abagyan and Totrov is based on an all-atom vacuo potential developed by Scheraga and co-workers (ECEPP) with added terms to account for the solvation free energy (on the basis of solvent accessible surfaces) and entropy (based on residue burial entropies). For efficient sampling of conformation space, the researchers advocate stochastic global optimization techniques, involving full local minimization after each random move. Specifically, they describe an ``Optimal-bias Monte Carlo minimization'' algorithm in dihedral-variable (rather than Cartesian) space that employs biased moves predetermined according to local probability distributions of the conformational variables. The method is shown to perform efficiently on several peptides and to find the global minimum for a 23-residue peptide.

5 On The Horizon

The methodological and application studies collected in this volume reflect only a tip of giant iceberg that continuously grows. With the exponential increase in genomic information, multidisciplinary collaborations are needed more than ever to tackle the technological and scientific challenges that are emerging in our era of overwhelming biological information. Solving these many challenges in macromolecular structure will constitute a giant leap in our ability to design new pharmaceuticals and materials, and to exploit functional properties of new gene products for virtually every aspect of our lives. One can readily envision as a result the development of designer foods that fight diseases, engineering of improved computer chips or new architectural components (e.g., for bridges) that can sustain enormous structural deformations, and powerful vaccines to stave off human foes.

Will we eventually succeed in reliably predicting 3D structures of proteins from sequence? If so, will this `Holy Grail' be solved by an accurate physical description of the underlying forces, a wealth of experimental information, or some combination? Will methodological advances eventually conquer the sampling problem, both in space (global optimization) and time (as in molecular dynamics) for large-scale chaotic systems?

In only twenty years the field has evolved impressively from simulations of simplified biomolecular systems represented by only a few dozen atoms covering a fraction of a picosecond to those following intricate biomolecular complexes approaching one million atoms over timescales that are 4 and 5 orders of magnitude longer. Yet the underlying empirical nature of the governing energy function cannot be ignored; even the fastest computers and the most exhaustive conformational searches may not lead to perfect answers to make the experimentalist's workbench obsolete. Yet perfection is not a realistic goal for computational biomolecular scientists. Rather, good approximations to complex real problems can be far more insightful in this field than exact solutions to ideal questions. Thus, at this crucial juncture between the rapidly improving theoretical and experimental techniques, reserved optimism is prudent. I hesitate to predict what a journal volume of this nature will contain a decade from now, but I certainly look forward to the discoveries.

Tamar Schlick


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