Improved sampling methods for molecular simulation

Simulated Tempering-Enhanced Umbrella Sampling Improves Convergence of Free Energy Calculations of Drug Membrane Permeation

Molecular dynamics simulations have been widely used to study solute permeation across biological membranes. The potential of mean force (PMF) for solute permeation is typically computed using enhanced sampling techniques such as umbrella sampling (US). For bulky drug-like permeants, however, obtaining converged PMFs remains challenging and often requires long simulation times, resulting in an unacceptable computational cost. Here, we augmented US with simulated tempering (ST), an extended-ensemble technique that consists in varying the temperature of the system along a pre-defined temperature ladder. Simulated tempering-enhanced US (STeUS) was employed to improve the convergence of PMF calculations for the permeation of methanol and three common drug molecules. To obtain sufficient sampling of the umbrella histograms, which were computed only from the ground temperature, we modified the simulation time fraction spent at the ground temperature between 1/K and 50%, where K is the number of ST temperature states. We found that STeUS accelerates convergence, when compared to standard US, and that the benefit of STeUS is system-dependent. For bulky molecules, for which standard US poorly converged, the application of ST was highly successful, leading to a more than fivefold accelerated convergence of the PMFs. For the small methanol solute, for which conventional US converges moderately, the application of ST is only beneficial if 50% of the STeUS simulation time is spent at the ground temperature. This study establishes STeUS as an efficient and simple method for PMF calculations, thereby strongly reducing the computational cost of routine high-throughput studies of drug permeability.

Accelerating atomistic simulations of proteins using multiscale enhanced sampling with independent tempering

Efficient sampling of the conformational space is essential for quantitative simulations of proteins. The multiscale enhanced sampling (MSES) method accelerates atomistic sampling by coupling it to a coarse-grained (CG) simulation. Bias from coupling to the CG model is removed using Hamiltonian replica exchange, such that one could benefit simultaneously from the high accuracy of atomistic models and fast dynamics of CG ones. Here, we extend MSES to allow independent control of the effective temperatures of atomistic and CG simulations, by directly scaling the atomistic and CG Hamiltonians. The new algorithm, named MSES with independent tempering (MSES-IT), supports more sophisticated Hamiltonian and temperature replica exchange protocols to further improve the sampling efficiency. Using a small but nontrivial β-hairpin, we show that setting the effective temperature of CG model in all conditions to its melting temperature maximizes structural transition rates at the CG level and promotes more efficient replica exchange and diffusion in the condition space. As the result, MSES-IT drive faster reversible transitions at the atomic level and leads to significant improvement in generating converged conformational ensembles compared to the original MSES scheme.

Mapping enzyme-substrate interactions: its potential to study the mechanism of enzymes

With the increase of the need to use more sustainable processes for the industry in our society, the modeling of enzymes has become crucial to fully comprehend their mechanism of action and use this knowledge to enhance and design their properties. A lot of methods to study enzymes computationally exist and they have been classified on sequence-based, structure-based, and the more new artificial intelligence-based ones. Albeit the abundance of methods to help predict the function of an enzyme, molecular modeling is crucial when trying to understand the enzyme mechanism, as they aim to correlate atomistic information with experimental data. Among them, methods that simulate the system dynamics at a molecular mechanics level of theory (classical force fields) have shown to offer a comprehensive study. In this book chapter, we will analyze these techniques, emphasizing the importance of precise modeling of enzyme-substrate interactions. In the end, a brief explanation of the transference of the information from research studies to the industry is given accompanied with two examples of family enzymes where their modeling has helped their exploitation.

Relating circular dichroism to atomic structure by means of MD simulations and computed CD spectra with α-peptoids as an example

Accurate TD-DFT calculations of electronic circular dichroism have been performed to characterise the 3D structure of α-peptoids.

Mechanistic role of nucleobases in self-cleavage catalysis of hairpin ribozyme at ambient versus high-pressure conditions

Ribozymes catalyze the site-specific self-cleavage of intramolecular phosphodiester bonds. Initially thought to act as metalloenzymes, they are now known to be functional even in the absence of divalent metal ions and specific nucleobases directly participate in the self-cleavage reaction. Here, we use extensive replica exchange molecular dynamics simulations to probe the precise mechanistic role of nucleobases by simulating precatalytic reactant and active precursor states of a hairpin ribozyme along its reaction path at ambient as well as high-pressure conditions. The results provide novel key insights into the self-cleavage of ribozymes. We find that deprotonation of the hydroxyl group is crucial and might be the penultimate step to the self-cleavage. The G8 nucleobase is found to stabilize the activated precursor into inline arrangement for facile nucleophilic attack of the scissile phosphate only after deprotonation of the hydroxyl group. The protonated A38 nucleobase, in contrast, mainly acts a proton donor to the O5'-oxygen leaving group that eventually leads to the self-cleavage. Indeed, systematic high-pressure simulations of catalytically relevant states confirm these findings and, moreover, provide support to the role of ribozymes as piezophilic biocatalysts with regard to their relevance in early life under extreme conditions in the realm of RNA world hypothesis.

Probing chirality recognition of protonated glutamic acid dimers by gas-phase vibrational spectroscopy and first-principles simulations

We characterize stereospecific aspects of homochiral and heterochiral dimers of glutamic acid by infrared spectroscopy and first-principles molecular dynamics simulations.

N-Terminal Acetylation Preserves α-Synuclein from Oligomerization by Blocking Intermolecular Hydrogen Bonds

The abnormal aggregation of α-synuclein (α-Syn) is closely associated with Parkinson's disease. Different post-translational modifications of α-Syn have been identified and contribute distinctly in α-Syn aggregation and cytotoxicity. Recently, α-Syn was reported to be N-terminally acetylated in cells, yet the functional implication of this modification, especially in α-Syn oligomerization, remains unclear. By using a solid-state nanopore system, we found that N-terminal acetylation can significantly decrease α-Syn oligomerization. Replica-exchange molecular dynamics simulations further revealed that addition of an acetyl group at the N-terminus disrupts intermolecular hydrogen bonds, which slows down the initial α-Syn oligomerization. Our finding highlights the essential role of N-terminal acetylation of α-Syn in preserving its native conformation against pathological aggregation.

Identification of the key interactions in structural transition pathway of FtsZ from Staphylococcus aureus

The tubulin-homolog protein FtsZ is essential for bacterial cell division. FtsZ polymerizes to form protofilaments that assemble into a contractile ring-shaped structure in the presence of GTP. Recent studies showed that FtsZ treadmilling coupled with the GTPase activity drives cell wall synthesis and bacterial cell division. The treadmilling caused by assembly and disassembly of FtsZ links to a conformational change of the monomer from a tense (T) to a relaxed (R) state, but considerable controversy still remains concerning the mechanism. In this study, we report crystal structures of FtsZ from Staphylococcus aureus corresponding to the T and R state conformations in the same crystal, indicating the structural equilibrium of the two state. The two structures identified a key residue Arg29, whose importance was also confirmed by our modified MD simulations. Crystal structures of the R29A mutant showed T and R state-like conformations with slight but important structural changes compared to those of wild-type. Collectively, these data provide new insights for understanding how intramolecular interactions are related to the structural transition of FtsZ.

Enhanced Sampling of an Atomic Model with Hybrid Nonequilibrium Molecular Dynamics-Monte Carlo Simulations Guided by a Coarse-Grained Model

Molecular dynamics (MD) trajectories based on a classical equation of motion provide a straightforward, albeit somewhat inefficient approach, to explore and sample the configurational space of a complex molecular system. While a broad range of techniques can be used to accelerate and enhance the sampling efficiency of classical simulations, only algorithms that are consistent with the Boltzmann equilibrium distribution yield a proper statistical mechanical computational framework. Here, a multiscale hybrid algorithm relying simultaneously on all-atom fine-grained (FG) and coarse-grained (CG) representations of a system is designed to improve sampling efficiency by combining the strength of nonequilibrium molecular dynamics (neMD) and Metropolis Monte Carlo (MC). This CG-guided hybrid neMD-MC algorithm comprises six steps: (1) a FG configuration of an atomic system is dynamically propagated for some period of time using equilibrium MD; (2) the resulting FG configuration is mapped onto a simplified CG model; (3) the CG model is propagated for a brief time interval to yield a new CG configuration; (4) the resulting CG configuration is used as a target to guide the evolution of the FG system; (5) the FG configuration (from step 1) is driven via a nonequilibrium MD (neMD) simulation toward the CG target; (6) the resulting FG configuration at the end of the neMD trajectory is then accepted or rejected according to a Metropolis criterion before returning to step 1. A symmetric two-ends momentum reversal prescription is used for the neMD trajectories of the FG system to guarantee that the CG-guided hybrid neMD-MC algorithm obeys microscopic detailed balance and rigorously yields the equilibrium Boltzmann distribution. The enhanced sampling achieved with the method is illustrated with a model system with hindered diffusion and explicit-solvent peptide simulations. Illustrative tests indicate that the method can yield a speedup of about 80 times for the model system and up to 21 times for polyalanine and (AAQAA)₃ in water.

Characterization of the three-dimensional free energy manifold for the uracil ribonucleoside from asynchronous replica exchange simulations

Replica exchange molecular dynamics has emerged as a powerful tool for efficiently sampling free energy landscapes for conformational and chemical transitions. However, daunting challenges remain in efficiently getting such simulations to scale to the very large number of replicas required to address problems in state spaces beyond two dimensions. The development of enabling technology to carry out such simulations is in its infancy, and thus it remains an open question as to which applications demand extension into higher dimensions. In the present work, we explore this problem space by applying asynchronous Hamiltonian replica exchange molecular dynamics with a combined quantum mechanical/molecular mechanical potential to explore the conformational space for a simple ribonucleoside. This is done using a newly developed software framework capable of executing >3,000 replicas with only enough resources to run 2,000 simultaneously. This may not be possible with traditional synchronous replica exchange approaches. Our results demonstrate 1.) the necessity of high dimensional sampling simulations for biological systems, even as simple as a single ribonucleoside, and 2.) the utility of asynchronous exchange protocols in managing simultaneous resource requirements expected in high dimensional sampling simulations. It is expected that more complicated systems will only increase in computational demand and complexity, and thus the reported asynchronous approach may be increasingly beneficial in order to make such applications available to a broad range of computational scientists.

Characterizing the Conformational Landscape of Flavivirus Fusion Peptides via Simulation and Experiment

Conformational changes in the envelope proteins of flaviviruses help to expose the highly conserved fusion peptide (FP), a region which is critical to membrane fusion and host cell infection, and which represents a significant target for antiviral drugs and antibodies. In principle, extended timescale atomic-resolution simulations may be used to characterize the dynamics of such peptides. However, the resultant accuracy is critically dependent upon both the underlying force field and sufficient conformational sampling. In the present study, we report a comprehensive comparison of three simulation methods and four force fields comprising a total of more than 40 μs of sampling. Additionally, we describe the conformational landscape of the FP fold across all flavivirus family members. All investigated methods sampled conformations close to available X-ray structures, but exhibited differently populated ensembles. The best force field / sampling combination was sufficiently accurate to predict that the solvated peptide fold is less ordered than in the crystallographic state, which was subsequently confirmed via circular dichroism and spectrofluorometric measurements. Finally, the conformational landscape of a mutant incapable of membrane fusion was significantly shallower than wild-type variants, suggesting that dynamics should be considered when therapeutically targeting FP epitopes.

Dynamic Docking of Conformationally Constrained Macrocycles: Methods and Applications

Many natural products consist of large and flexible macrocycles that engage their targets via multiple contact points. This combination of contained flexibility and large contact area often allows natural products to bind at target surfaces rather than deep pockets, making them attractive scaffolds for inhibiting protein-protein interactions and other challenging therapeutic targets. The increasing ability to manipulate such compounds either biosynthetically or via semisynthetic modification means that these compounds can now be considered as starting points for medchem campaigns rather than solely as ends. Modern medchem benefits substantially from rational improvements made on the basis of molecular docking. As such, docking methods have been enhanced in recent years to deal with the complicated binding modalities and flexible scaffolds of macrocyclic natural products and natural product-like structures. Here, we comprehensively review methods for treating and docking these large macrocyclic scaffolds and discuss some of the resulting advances in medicinal chemistry.

Going clean: structure and dynamics of peptides in the gas phase and paths to solvation

The gas phase is an artificial environment for biomolecules that has gained much attention both experimentally and theoretically due to its unique characteristic of providing a clean room environment for the comparison between theory and experiment. In this review we give an overview mainly on first-principles simulations of isolated peptides and the initial steps of their interactions with ions and solvent molecules: a bottom up approach to the complexity of biological environments. We focus on the accuracy of different methods to explore the conformational space, the connections between theory and experiment regarding collision cross section evaluations and (anharmonic) vibrational spectra, and the challenges faced in this field.

Effective Approximation of Molecular Volume Using Atom-Centered Dielectric Functions in Generalized Born Models

The generalized Born (GB) theory is a prime choice for implicit treatment of solvent that provides a favorable balance between efficiency and accuracy for reliable simulation of protein conformational equilibria. In GB, the dielectric boundary is a key physical property that needs to be properly described. While it is widely accepted that the molecular surface (MS) should provide the most physical description, most existing GB models are based on van der Waals (vdW)-like surfaces for computational simplicity and efficiency. A simple and effective approximation to molecular volume is explored here using atom-centered dielectric functions within the context of a generalized Born model with simple switching (GBSW). The new model, termed GBSW/MS2, is as efficient as the original vdW-like-surface-based GBSW model, but is able to reproduce the Born radii calculated from the "exact" Poisson-Boltzmann theory with a correlation of 0.95. More importantly, examination of the potentials of mean force of hydrogen-bonding and charge-charge interactions demonstrates that GBSW/MS2 correctly captures the first desolvation peaks, a key signature of true MS. Physical parameters including atomic input radii and peptide backbone torsion were subsequently optimized on the basis of solvation free energies of model compounds, potentials of mean force of their interactions, and conformational equilibria of a set of helical and β-hairpin model peptides. The resulting GBSW/MS2 protein force field reasonably recapitulates the structures and stabilities of these model peptides. Several remaining limitations and possible future developments are also discussed.

Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways

Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA was applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example. For the AdK transition, the new concept of a Hausdorff-pair map enabled us to extract the molecular structural determinants responsible for differences in pathways, namely a set of conserved salt bridges whose charge-charge interactions are fully modelled in DIMS MD but not in FRODA. PSA has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing conformational transitions.

Many proteins are nanomachines that perform mechanical or chemical work by changing their three-dimensional shape and cycle between multiple conformational states. Computer simulations of such conformational transitions provide mechanistic insights into protein function but such simulations have been challenging. In particular, it is not clear how to quantitatively compare current simulation methods or to assess their accuracy. To that end, we present a general and flexible computational framework for quantifying transition paths—by measuring mutual geometric similarity—that, compared with existing approaches, requires minimal a-priori assumptions and can take advantage of full atomic detail alongside heuristic information derived from intuition. Using our Path Similarity Analysis (PSA) framework in parallel with several existing quantitative approaches, we examine transitions generated for a toy model of a transition and two biological systems, the enzyme adenylate kinase and diphtheria toxin. Our results show that PSA enables the quantitative comparison of different path sampling methods and aids the identification of potentially important atomistic motions by exploiting geometric information in transition paths. The method has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing macromolecular conformational transitions.

An Analysis of Biomolecular Force Fields for Simulations of Polyglutamine in Solution

Polyglutamine (polyQ) peptides are a useful model system for biophysical studies of protein folding and aggregation, both for their intriguing aggregation properties and their own relevance to human disease. The genetic expansion of a polyQ tract triggers the formation of amyloid aggregates associated with nine neurodegenerative diseases. Several clearly identifiable and separable factors, notably the length of the polyQ tract, influence the mechanism of aggregation, its associated kinetics, and the ensemble of structures formed. Atomistic simulations are well positioned to answer open questions regarding the thermodynamics and kinetics of polyQ folding and aggregation. The additional, explicit representation of water permits deeper investigation of the role of solvent dynamics, and it permits a direct comparison of simulation results with infrared spectroscopy experiments. The generation of meaningful simulation results hinges on satisfying two essential criteria: achieving sufficient conformational sampling to draw statistically valid conclusions, and accurately reproducing the intermolecular forces that govern system structure and dynamics. In this work, we examine the ability of 12 biomolecular force fields to reproduce the properties of a simple, 30-residue polyQ peptide (Q30) in explicit water. In addition to secondary and tertiary structure, we consider generic structural properties of polymers that provide additional dimensions for analysis of the highly degenerate disordered states of the molecule. We find that the 12 force fields produce a wide range of predictions. We identify AMBER ff99SB, AMBER ff99SB*, and OPLS-AA/L to be most suitable for studies of polyQ folding and aggregation.

The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes

Among all tools available to design new drugs, molecular dynamics (MD) simulations have become an essential technique. Initially developed to investigate molecular models with a limited number of atoms, computers now enable investigations of large macromolecular systems with a simulation time reaching the microsecond range. The reviewed articles cover four years of research to give an overview on the actual impact of MD on the current medicinal chemistry landscape with a particular emphasis on studies of ligand-protein interactions. With a special focus on studies combining computational approaches with data gained from other techniques, this review shows how deeply embedded MD simulations are in drug design strategies and articulates what the future of this technique could be.

A molecular simulation protocol to avoid sampling redundancy and discover new states

BACKGROUND

For biomacromolecules or their assemblies, experimental knowledge is often restricted to specific states. Ambiguity pervades simulations of these complex systems because there is no prior knowledge of relevant phase space domains, and sampling recurrence is difficult to achieve. In molecular dynamics methods, ruggedness of the free energy surface exacerbates this problem by slowing down the unbiased exploration of phase space. Sampling is inefficient if dwell times in metastable states are large.

METHODS

We suggest a heuristic algorithm to terminate and reseed trajectories run in multiple copies in parallel. It uses a recent method to order snapshots, which provides notions of "interesting" and "unique" for individual simulations. We define criteria to guide the reseeding of runs from more "interesting" points if they sample overlapping regions of phase space.

RESULTS

Using a pedagogical example and an α-helical peptide, the approach is demonstrated to amplify the rate of exploration of phase space and to discover metastable states not found by conventional sampling schemes. Evidence is provided that accurate kinetics and pathways can be extracted from the simulations.

CONCLUSIONS

The method, termed PIGS for Progress Index Guided Sampling, proceeds in unsupervised fashion, is scalable, and benefits synergistically from larger numbers of replicas. Results confirm that the underlying ideas are appropriate and sufficient to enhance sampling.

GENERAL SIGNIFICANCE

In molecular simulations, errors caused by not exploring relevant domains in phase space are always unquantifiable and can be arbitrarily large. Our protocol adds to the toolkit available to researchers in reducing these types of errors. This article is part of a Special Issue entitled "Recent developments of molecular dynamics".

Folding of fourteen small proteins with a residue-specific force field and replica-exchange molecular dynamics

Ab initio protein folding via physical-based all-atom simulation is still quite challenging. Using a recently developed residue-specific force field (RSFF1) in explicit solvent, we are able to fold a diverse set of 14 model proteins. The obtained structural features of unfolded state are in good agreement with previous observations. The replica-exchange molecular dynamics simulation is found to be efficient, resulting in multiple folding events for each protein. Transition path time is found to be significantly reduced under elevated temperature.

Investigations of α-helix↔β-sheet transition pathways in a miniprotein using the finite-temperature string method

A parallel implementation of the finite-temperature string method is described, which takes into account the invariance of coordinates with respect to rigid-body motions. The method is applied to the complex α-helix↔β-sheet transition in a β-hairpin miniprotein in implicit solvent, which exhibits much of the complexity of conformational changes in proteins. Two transition paths are considered, one derived from a linear interpolant between the endpoint structures and the other derived from a targeted dynamics simulation. Two methods for computing the conformational free energy (FE) along the string are compared, a restrained method, and a tessellation method introduced by E. Vanden-Eijnden and M. Venturoli [J. Chem. Phys. 130, 194103 (2009)]. It is found that obtaining meaningful free energy profiles using the present atom-based coordinates requires restricting sampling to a vicinity of the converged path, where the hyperplanar approximation to the isocommittor surface is sufficiently accurate. This sampling restriction can be easily achieved using restraints or constraints. The endpoint FE differences computed from the FE profiles are validated by comparison with previous calculations using a path-independent confinement method. The FE profiles are decomposed into the enthalpic and entropic contributions, and it is shown that the entropy difference contribution can be as large as 10 kcal/mol for intermediate regions along the path, compared to 15-20 kcal/mol for the enthalpy contribution. This result demonstrates that enthalpic barriers for transitions are offset by entropic contributions arising from the existence of different paths across a barrier. The possibility of using systematically coarse-grained representations of amino acids, in the spirit of multiple interaction site residue models, is proposed as a means to avoid ad hoc sampling restrictions to narrow transition tubes.

Achieving Rigorous Accelerated Conformational Sampling in Explicit Solvent

Molecular dynamics simulations can provide valuable atomistic insights into biomolecular function. However, the accuracy of molecular simulations on general-purpose computers depends on the time scale of the events of interest. Advanced simulation methods, such as accelerated molecular dynamics, have shown tremendous promise in sampling the conformational dynamics of biomolecules, where standard molecular dynamics simulations are nonergodic. Here we present a sampling method based on accelerated molecular dynamics in which rotatable dihedral angles and nonbonded interactions are boosted separately. This method (RaMD-db) is a different implementation of the dual-boost accelerated molecular dynamics, introduced earlier. The advantage is that this method speeds up sampling of the conformational space of biomolecules in explicit solvent, as the degrees of freedom most relevant for conformational transitions are accelerated. We tested RaMD-db on one of the most difficult sampling problems - protein folding. Starting from fully extended polypeptide chains, two fast folding α-helical proteins (Trpcage and the double mutant of C-terminal fragment of Villin headpiece) and a designed β-hairpin (Chignolin) were completely folded to their native structures in very short simulation time. Multiple folding/unfolding transitions could be observed in a single trajectory. Our results show that RaMD-db is a promisingly fast and efficient sampling method for conformational transitions in explicit solvent. RaMD-db thus opens new avenues for understanding biomolecular self-assembly and functional dynamics occurring on long time and length scales.

Accelerate Sampling in Atomistic Energy Landscapes Using Topology-Based Coarse-Grained Models

We describe a multiscale enhanced sampling (MSES) method where efficient topology-based coarse-grained models are coupled with all-atom ones to enhance the sampling of atomistic protein energy landscape. The bias from the coupling is removed by Hamiltonian replica exchange, thus allowing one to benefit simultaneously from faster transitions of coarse-grained modeling and accuracy of atomistic force fields. The method is demonstrated by calculating the conformational equilibria of several small but nontrivial β-hairpins with varied stabilities.

Accelerated Molecular Dynamics Simulations with the AMOEBA Polarizable Force Field on Graphics Processing Units

The accelerated molecular dynamics (aMD) method has recently been shown to enhance the sampling of biomolecules in molecular dynamics (MD) simulations, often by several orders of magnitude. Here, we describe an implementation of the aMD method for the OpenMM application layer that takes full advantage of graphics processing units (GPUs) computing. The aMD method is shown to work in combination with the AMOEBA polarizable force field (AMOEBA-aMD), allowing the simulation of long time-scale events with a polarizable force field. Benchmarks are provided to show that the AMOEBA-aMD method is efficiently implemented and produces accurate results in its standard parametrization. For the BPTI protein, we demonstrate that the protein structure described with AMOEBA remains stable even on the extended time scales accessed at high levels of accelerations. For the DNA repair metalloenzyme endonuclease IV, we show that the use of the AMOEBA force field is a significant improvement over fixed charged models for describing the enzyme active-site. The new AMOEBA-aMD method is publicly available (http://wiki.simtk.org/openmm/VirtualRepository) and promises to be interesting for studying complex systems that can benefit from both the use of a polarizable force field and enhanced sampling.

Effects of hypoxanthine substitution in peptide nucleic acids targeting KRAS2 oncogenic mRNA molecules: theory and experiment

Genetic disorders can arise from single base substitutions in a single gene. A single base substitution for wild type guanine in the twelfth codon of KRAS2 mRNA occurs frequently to initiate lung, pancreatic, and colon cancer. We have observed single base mismatch specificity in radioimaging of mutant KRAS2 mRNA in tumors in mice by in vivo hybridization with radiolabeled peptide nucleic acid (PNA) dodecamers. We hypothesized that multimutant specificity could be achieved with a PNA dodecamer incorporating hypoxanthine, which can form Watson-Crick base pairs with adenine, cytosine, thymine, and uracil. Using molecular dynamics simulations and free energy calculations, we show that hypoxanthine substitutions in PNAs are tolerated in KRAS2 RNA:PNA duplexes where wild type guanine is replaced by mutant uracil or adenine in RNA. To validate our predictions, we synthesized PNA dodecamers with hypoxanthine, and then measured the thermal stability of RNA:PNA duplexes. Circular dichroism thermal melting results showed that hypoxanthine-containing PNAs are more stable in duplexes where hypoxanthine-adenine and hypoxanthine-uracil base pairs are formed than single mismatch duplexes or duplexes containing hypoxanthine-guanine opposition.

Starting-structure dependence of nanosecond timescale intersubstate transitions and reproducibility of MD-derived order parameters

Factors affecting the accuracy of molecular dynamics (MD) simulations are investigated by comparing generalized order parameters for backbone NH vectors of the B3 immunoglobulin-binding domain of streptococcal protein G (GB3) derived from simulations with values obtained from NMR spin relaxation (Yao L, Grishaev A, Cornilescu G, Bax A, J Am Chem Soc 2010;132:4295-4309.). Choices for many parameters of the simulations, such as buffer volume, water model, or salt concentration, have only minor influences on the resulting order parameters. In contrast, seemingly minor conformational differences in starting structures, such as orientations of sidechain hydroxyl groups, resulting from applying different protonation algorithms to the same structure, have major effects on backbone dynamics. Some, but not all, of these effects are mitigated by increased sampling in simulations. Most discrepancies between simulated and experimental results occur for residues located at the ends of secondary structures and involve large amplitude nanosecond timescale transitions between distinct conformational substates. These transitions result in autocorrelation functions for bond vector reorientation that do not converge when calculated over individual simulation blocks, typically of length similar to the overall rotational diffusion time. A test for convergence before averaging the order parameters from different blocks results in better agreement between order parameters calculated from different sets of simulations and with NMR-derived order parameters. Thus, MD-derived order parameters are more strongly affected by transitions between conformational substates than by fluctuations within individual substates themselves, while conformational differences in the starting structures affect the frequency and scale of such transitions.

Tackling the challenges posed by target flexibility in drug design

IMPORTANCE OF THE FIELD

Current computational docking methods are often effective in predicting accurate drug-binding geometries in cases of relatively rigid target structures. However, binding of drug-like ligands to protein receptor molecules frequently involves or even requires conformational adaptation. Realistic prediction of ligand-receptor binding geometries and complex stability needs in many cases an appropriate inclusion of conformational changes, not only for the ligand, but also for the receptor molecule.

AREAS COVERED IN THIS REVIEW

Recent approaches to efficiently account for target receptor flexibility during docking simulations are reviewed.

WHAT THE READER WILL GAIN

The reader will gain insights into methods to efficiently treat protein side-chain flexibility and approaches for continuous adaptation of backbone conformations in pre-calculated essential or soft collective degrees of freedom. In addition, molecular dynamics or Monte Carlo based methods providing simultaneous inclusion of receptor and ligand flexibility are discussed as well as promising new developments to generate conformationally diverse ensembles of a protein structure. The large variety of possible conformational changes in proteins on ligand binding is illustrated for the enzyme reverse transcriptase of HIV-1, which is an important drug target.

TAKE HOME MESSAGE

If the backbone conformation of the binding site does not change, current docking programs can perform well by taking side-chain reorientations into account only. Future progress to account for full target flexibility in docking requires both accurate prediction of the essential modes of backbone motion and improvements in scoring to enhance selectivity. Thus, the scoring function should realistically cover energetic and particularly entropic contributions to binding, which would allow more realistic estimates of binding free energies.

Towards the physical basis of how intrinsic disorder mediates protein function

Intrinsically disordered proteins (IDPs) are an important class of functional proteins that is highly prevalent in biology and has broad association with human diseases. In contrast to structured proteins, free IDPs exist as heterogeneous and dynamical conformational ensembles under physiological conditions. Many concepts have been discussed on how such intrinsic disorder may provide crucial functional advantages, particularly in cellular signaling and regulation. Establishing the physical basis of these proposed phenomena requires not only detailed characterization of the disordered conformational ensembles, but also mechanistic understanding of the roles of various ensemble properties in IDP interaction and regulation. Here, we review the experimental and computational approaches that may be integrated to address many important challenges of establishing a "structural" basis of IDP function, and discuss some of the key emerging ideas on how the conformational ensembles of IDPs may mediate function, especially in coupled binding and folding interactions.

Computational models of protein kinematics and dynamics: beyond simulation

Physics-based simulation represents a powerful method for investigating the time-varying behavior of dynamic protein systems at high spatial and temporal resolution. Such simulations, however, can be prohibitively difficult or lengthy for large proteins or when probing the lower-resolution, long-timescale behaviors of proteins generally. Importantly, not all questions about a protein system require full space and time resolution to produce an informative answer. For instance, by avoiding the simulation of uncorrelated, high-frequency atomic movements, a larger, domain-level picture of protein dynamics can be revealed. The purpose of this review is to highlight the growing body of complementary work that goes beyond simulation. In particular, this review focuses on methods that address kinematics and dynamics, as well as those that address larger organizational questions and can quickly yield useful information about the long-timescale behavior of a protein.

Systematic validation of protein force fields against experimental data

Molecular dynamics simulations provide a vehicle for capturing the structures, motions, and interactions of biological macromolecules in full atomic detail. The accuracy of such simulations, however, is critically dependent on the force field—the mathematical model used to approximate the atomic-level forces acting on the simulated molecular system. Here we present a systematic and extensive evaluation of eight different protein force fields based on comparisons of experimental data with molecular dynamics simulations that reach a previously inaccessible timescale. First, through extensive comparisons with experimental NMR data, we examined the force fields' abilities to describe the structure and fluctuations of folded proteins. Second, we quantified potential biases towards different secondary structure types by comparing experimental and simulation data for small peptides that preferentially populate either helical or sheet-like structures. Third, we tested the force fields' abilities to fold two small proteins—one α-helical, the other with β-sheet structure. The results suggest that force fields have improved over time, and that the most recent versions, while not perfect, provide an accurate description of many structural and dynamical properties of proteins.

Residual structures, conformational fluctuations, and electrostatic interactions in the synergistic folding of two intrinsically disordered proteins

To understand the interplay of residual structures and conformational fluctuations in the interaction of intrinsically disordered proteins (IDPs), we first combined implicit solvent and replica exchange sampling to calculate atomistic disordered ensembles of the nuclear co-activator binding domain (NCBD) of transcription coactivator CBP and the activation domain of the p160 steroid receptor coactivator ACTR. The calculated ensembles are in quantitative agreement with NMR-derived residue helicity and recapitulate the experimental observation that, while free ACTR largely lacks residual secondary structures, free NCBD is a molten globule with a helical content similar to that in the folded complex. Detailed conformational analysis reveals that free NCBD has an inherent ability to substantially sample all the helix configurations that have been previously observed either unbound or in complexes. Intriguingly, further high-temperature unbinding and unfolding simulations in implicit and explicit solvents emphasize the importance of conformational fluctuations in synergistic folding of NCBD with ACTR. A balance between preformed elements and conformational fluctuations appears necessary to allow NCBD to interact with different targets and fold into alternative conformations. Together with previous topology-based modeling and existing experimental data, the current simulations strongly support an “extended conformational selection” synergistic folding mechanism that involves a key intermediate state stabilized by interaction between the C-terminal helices of NCBD and ACTR. In addition, the atomistic simulations reveal the role of long-range as well as short-range electrostatic interactions in cooperating with readily fluctuating residual structures, which might enhance the encounter rate and promote efficient folding upon encounter for facile binding and folding interactions of IDPs. Thus, the current study not only provides a consistent mechanistic understanding of the NCBD/ACTR interaction, but also helps establish a multi-scale molecular modeling framework for understanding the structure, interaction, and regulation of IDPs in general.

Intrinsically disordered proteins (IDPs) are now widely recognized to play fundamental roles in biology and to be frequently associated with human diseases. Although the potential advantages of intrinsic disorder in cellular signaling and regulation have been widely discussed, the physical basis for these proposed phenomena remains sketchy at best. An integration of multi-scale molecular modeling and experimental characterization is necessary to uncover the molecular principles that govern the structure, interaction, and regulation of IDPs. In this work, we characterize the conformational properties of two IDPs involved in transcription regulation at the atomistic level and further examine the roles of these properties in their coupled binding and folding interactions. Our simulations suggest interplay among residual structures, conformational fluctuations, and electrostatic interactions that allows efficient synergistic folding of these two IDPs. In particular, we propose that electrostatic interactions might play an important role in facilitating rapid folding and binding recognition of IDPs, by enhancing the encounter rate and promoting efficient folding upon encounter.

Molecular dynamics applied in drug discovery: the case of HIV-1 protease

Molecular dynamics (MD) is a way to computationally simulate the movement of particles and it is widely used to provide a dynamic perspective on biomolecules. Nowadays, the ever-growing computer power and the improvement in methodology further strengthen the role of MD in drug discovery. In this chapter, an overview of MD's application in drug discovery will be given first, using HIV-1 protease as an example. Then, the underlying theories of MD will be briefly outlined. The second half of this chapter will provide a practical protocol on how to simulate a soluble protein in solvent. All-atom simulation with either implicit solvent or explicit solvent will be covered. The former samples global conformational change more efficiently, and post-processing including angle/distance measurement, structural deviation measurement, Ramachandran plot, and secondary structure analysis will be introduced. The latter is more realistic/expensive and is generally used to finely examine local conformational rearrangement and water-mediated interactions. Post-processing including water density analysis will be described.