Mechanistic Insights into Autoinhibition of the Oncogenic Chromatin Remodeler ALC1.

Human ALC1 is an oncogene-encoded chromatin-remodeling enzyme required for DNA repair that possesses a poly(ADP-ribose) (PAR)-binding macro domain. Its engagement with PARylated PARP1 activates ALC1 at sites of DNA damage, but the underlying mechanism remains unclear. Here, we establish a dual role for the macro domain in autoinhibition of ALC1 ATPase activity and coupling to nucleosome mobilization. In the absence of DNA damage, an inactive conformation of the ATPase is maintained by juxtaposition of the macro domain against predominantly the C-terminal ATPase lobe through conserved electrostatic interactions. Mutations within this interface displace the macro domain, constitutively activate the ALC1 ATPase independent of PARylated PARP1, and alter the dynamics of ALC1 recruitment at DNA damage sites. Upon DNA damage, binding of PARylated PARP1 by the macro domain induces a conformational change that relieves autoinhibitory interactions with the ATPase motor, which selectively activates ALC1 remodeling upon recruitment to sites of DNA damage.

An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation.

Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchmarks. While such benchmarks are rather common in the fields of quantum chemistry, this is not the case for empirical FFs. That is, few benchmarks are reused to evaluate FFs, and development teams rather use their own training and test sets. Here we present an overview of currently available tests and benchmarks for computational chemistry, focusing on organic compounds, including halogens and common ions, as FFs for these are the most common ones. We argue that many of the benchmark data sets from quantum chemistry can in fact be reused for evaluating FFs, but new gas phase data is still needed for compounds containing phosphorus and sulfur in different valence states. In addition, more nonequilibrium interaction energies and forces, as well as molecular properties such as electrostatic potentials around compounds, would be beneficial. For the condensed phases there is a large body of experimental data available, and tools to utilize these data in an automated fashion are under development. If FF developers, as well as researchers in artificial intelligence, would adopt a number of these data sets, it would become easier to compare the relative strengths and weaknesses of different models and to, eventually, restore the balance in the force.

Can molecular dynamics be used to simulate biomolecular recognition?

There are many problems in biochemistry that are difficult to study experimentally. Simulation methods are appealing due to direct availability of atomic coordinates as a function of time. However, direct molecular simulations are challenged by the size of systems and the time scales needed to describe relevant motions. In theory, enhanced sampling algorithms can help to overcome some of the limitations of molecular simulations. Here, we discuss a problem in biochemistry that offers a significant challenge for enhanced sampling methods and that could, therefore, serve as a benchmark for comparing approaches that use machine learning to find suitable collective variables. In particular, we study the transitions LacI undergoes upon moving between being non-specifically and specifically bound to DNA. Many degrees of freedom change during this transition and that the transition does not occur reversibly in simulations if only a subset of these degrees of freedom are biased. We also explain why this problem is so important to biologists and the transformative impact that a simulation of it would have on the understanding of DNA regulation.

Binding Networks Identify Targetable Protein Pockets for Mechanism-Based Drug Design.

The human genome codes only a few thousand druggable proteins, mainly receptors and enzymes. While this pool of available drug targets is limited, there is an untapped potential for discovering new drug-binding mechanisms and modes. For example, enzymes with long binding cavities offer numerous prerequisite binding sites that may be visited by an inhibitor during migration from a bulk solution to the destination site. Drug design can use these prerequisite sites as new structural targets. However, identifying these ephemeral sites is challenging. Here, we introduce a new method called NetBinder for the systematic identification and classification of prerequisite binding sites at atomic resolution. NetBinder is based on atomistic simulations of the full inhibitor binding process and provides a networking framework on which to select the most important binding modes and uncover the entire binding mechanism, including previously undiscovered events. NetBinder was validated by a study of the binding mechanism of blebbistatin (a potent inhibitor) to myosin 2 (a promising target for cancer chemotherapy). Myosin 2 is a good test enzyme because, like other potential targets, it has a long internal binding cavity that provides blebbistatin with numerous potential prerequisite binding sites. The mechanism proposed by NetBinder of myosin 2 structural changes during blebbistatin binding shows excellent agreement with experimentally determined binding sites and structural changes. While NetBinder was tested on myosin 2, it may easily be adopted to other proteins with long internal cavities, such as G-protein-coupled receptors or ion channels, the most popular current drug targets. NetBinder provides a new paradigm for drug design by a network-based elucidation of binding mechanisms at an atomic resolution.

The structure of CO2 and CH4 at the interface of a poly(urethane urea) oligomer model from the microscopic point of view.

The world desperately needs new technologies and solutions for gas capture and separation. To make this possible, molecular modeling is applied here to investigate the structural, thermodynamic, and dynamical properties of a model for the poly(urethane urea) (PUU) oligomer model to selectively capture CO2 in the presence of CH4. In this work, we applied a well-known approach to derive atomic partial charges for atoms in a polymer chain based on self-consistent sampling using quantum chemistry and stochastic dynamics. The interactions of the gases with the PUU model were studied in a pure gas based system as well as in a gas mixture. A detailed structure characterization revealed high interaction of CO2 molecules with the hard segments of the PUU. Therefore, the structural and energy properties explain the reasons for the greater CO2 sorption than CH4. We find that the CO2 sorption is higher than the CH4 with a selectivity of 7.5 at 298 K for the gas mixture. We characterized the Gibbs dividing surface for each system, and the CO2 is confined for a long time at the gas-oligomer model interface. The simulated oligomer model showed performance above the 2008 Robeson's upper bound and may be a potential material for CO2/CH4 separation. Further computational and experimental studies are needed to evaluate the material.

Toward a Computational Ecotoxicity Assay.

Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.

Making Soup: Preparing and Validating Models of the Bacterial Cytoplasm for Molecular Simulation.

Biomolecular crowding affects the biophysical and biochemical behavior of macromolecules compared with the dilute environment in experiments on isolated proteins. Computational modeling and simulation are useful tools to study how crowding affects the structural dynamics and biological properties of macromolecules. With increases in computational power, modeling and simulation of large-scale all-atom explicit-solvent models of the prokaryote cytoplasm have now become possible. In this work, we built an atomistic model of the cytoplasm of Escherichia coli composed of 1.5 million atoms and submitted it to a total of 3 µs of molecular dynamics simulations. The model consisted of eight different proteins representing about 50% of the cytoplasmic proteins and one type of t-RNA molecule. Properties of biomolecules under crowding conditions were compared with those from simulations of the individual compounds under dilute conditions. The simulation model was found to be consistent with experimental data about the diffusion coefficient and stability of macromolecules under crowded conditions. In order to stimulate further work, we provide a Python script and a set of files to enable other researchers to build their own E. coli cytoplasm models to address questions related to crowding.

A potential for molecular simulation of compounds with linear moieties.

The harmonic angle bending potential is used in many force fields for (bio)molecular simulation. The force associated with this potential is discontinuous at angles close to 180°, which can lead to numeric instabilities. Angle bending of linear groups, such as alkynes or nitriles, or linear molecules, such as carbon dioxide, can be treated by a simple harmonic potential if we describe the fluctuations as a deviation from a reference position of the central atom, the position of which is determined by the flanking atoms. The force constant for the linear angle potential can be derived analytically from the corresponding force constant in the traditional potential. The new potential is tested on the properties of alkynes, nitriles, and carbon dioxide. We find that the angles of the linear groups remain about 2° closer to 180° using the new potential. The bond and angle force constants for carbon dioxide were tuned to reproduce the experimentally determined frequencies. An interesting finding was that simulations of liquid carbon dioxide under pressure with the new flexible model were stable only when explicitly modeling the long-range Lennard-Jones (LJ) interactions due to the very long-range nature of the LJ interactions (>1.7 nm). In the other tested liquids, we find that a Lennard-Jones cutoff of 1.1 nm yields similar results as the particle mesh Ewald algorithm for LJ interactions. Algorithmic factors influencing the stability of liquid simulations are discussed as well. Finally, we demonstrate that the linear angle potential can be used in free energy perturbation calculations.

Influence of Na+ and Mg2+ ions on RNA structures studied with molecular dynamics simulations.

The structure of ribonucleic acid (RNA) polymers is strongly dependent on the presence of, in particular Mg2+ cations to stabilize structural features. Only in high-resolution X-ray crystallography structures can ions be identified reliably. Here, we perform molecular dynamics simulations of 24 RNA structures with varying ion concentrations. Twelve of the structures were helical and the others complex folded. The aim of the study is to predict ion positions but also to evaluate the impact of different types of ions (Na+ or Mg2+) and the ionic strength on structural stability and variations of RNA. As a general conclusion Mg2+ is found to conserve the experimental structure better than Na+ and, where experimental ion positions are available, they can be reproduced with reasonable accuracy. If a large surplus of ions is present the added electrostatic screening makes prediction of binding-sites less reproducible. Distinct differences in ion-binding between helical and complex folded structures are found. The strength of binding (ΔG‡ for breaking RNA atom-ion interactions) is found to differ between roughly 10 and 26 kJ/mol for the different RNA atoms. Differences in stability between helical and complex folded structures and of the influence of metal ions on either are discussed.

Molten alkali halides - temperature dependence of structure, dynamics and thermodynamics.

The renewed interest in molten salts in the energy industry fuels the need of a thorough understanding of their physicochemical properties. Alkali halide melts are perhaps the simplest ionic liquids, but they are used as electrolytes in batteries or for thermal energy storage. Although their structure is considered to be well documented and understood, a systematic evaluation of experimental structural data reveals significant discrepancies, while there is only limited experimental information on dynamic properties. Here, we investigate structure, dynamics and thermodynamic properties of pure alkali halide melts using state-of-the-art simulation models at different temperatures. The simulations provide a consistent picture of the structure of alkali halide melts with coordination numbers that lie in between experimental numbers. The simulations reveal a strengthening of the cation-anion bonds with increasing temperature that, somewhat counter-intuitively, coincides with faster dynamics in the melts. The thermodynamic analysis unveils that structure breaking proceeds on the picosecond timescale through an associative substitution mechanism as signified by a negative entropy of activation. The results on ion pair lifetimes contribute to an improved understanding of the microscopic origin of dynamical properties, such as e.g. conductivity of salt melts. The structural analysis provided here contributes to a more coherent picture of the coordination numbers in alkali halides than what is currently available from experimental data.

Force Field Benchmark of Amino Acids: I. Hydration and Diffusion in Different Water Models.

Thermodynamic and kinetic properties are of critical importance for the applicability of computational models to biomolecules such as proteins. Here we present an extensive evaluation of the Amber ff99SB-ILDN force field for modeling of hydration and diffusion of amino acids with three-site (SPC, SPC/E, SPC/Eb, and TIP3P), four-site (TIP4P, TIP4P-Ew, and TIP4P/2005), and five-site (TIP5P and TIP5P-Ew) water models. Hydration free energies (HFEs) of neutral amino acid side chain analogues have little dependence on the water model, with a root-mean-square error (RMSE) of ∼1 kcal/mol from experimental observations. On the basis of the number of interacting sites in the water model, HFEs of charged side chains can be putatively classified into three groups, of which the group of three-site models lies between those of four- and five-site water models; for each group, the water model dependence is greatly eliminated when the solvent Galvani potential is considered. Some discrepancies in the location of the first hydration peak ( RRDF) in the ion-water radial distribution function between experimental and calculated observations were detected, such as a systematic underestimation of the acetate (Asp side chain) ion. The RMSE of calculated diffusion coefficients of amino acids from experiment increases linearly with the increasing diffusion coefficients of the solvent water models at infinite dilution. TIP3P has the fastest diffusivity, in line with literature findings, while the "FB" and "OPC" water model families as well as TIP4P/2005 perform well, within a relative error of 5%, and TIP4P/2005 yields the most accurate estimate for the water diffusion coefficient. All of the tested water models overestimate amino acid diffusion coefficients by approximately 40% (TIP4P/2005) to 200% (TIP3P). Scaling of protein-water interactions with TIP4P/2005 in the Amber ff99SBws and ff03ws force fields leads to more negative HFEs but has little influence on the diffusion of amino acids. The most recent FF/water combinations of ff14SB/OPC3, ff15ipq/SPC/Eb, and fb15/TIP3P-FB do not show obvious improvements in accuracy for the tested quantities. These findings here establish a benchmark that may aid in the development and improvement of classical force fields to accurately model protein dynamics and thermodynamics.

Water Determines the Structure and Dynamics of Proteins.

Water is an essential participant in the stability, structure, dynamics, and function of proteins and other biomolecules. Thermodynamically, changes in the aqueous environment affect the stability of biomolecules. Structurally, water participates chemically in the catalytic function of proteins and nucleic acids and physically in the collapse of the protein chain during folding through hydrophobic collapse and mediates binding through the hydrogen bond in complex formation. Water is a partner that slaves the dynamics of proteins, and water interaction with proteins affect their dynamics. Here we provide a review of the experimental and computational advances over the past decade in understanding the role of water in the dynamics, structure, and function of proteins. We focus on the combination of X-ray and neutron crystallography, NMR, terahertz spectroscopy, mass spectroscopy, thermodynamics, and computer simulations to reveal how water assist proteins in their function. The recent advances in computer simulations and the enhanced sensitivity of experimental tools promise major advances in the understanding of protein dynamics, and water surely will be a protagonist.

Small Molecule Thermochemistry: A Tool for Empirical Force Field Development.

Spectroscopic analysis of compounds is typically combined with density functional theory, for instance, for assigning vibrational frequencies, limiting application to relatively small compounds. Accurate classical force fields could, in principle, complement these quantum-chemical tools. A relatively simple way to validate vibrational frequencies is by computing thermochemical properties. We present such a validation for over 1800 small molecules using the harmonic approximation. Two popular empirical force fields (GAFF and CGenFF) are compared to experimental data and results from Gaussian-4 quantum-chemical calculations. Frequency scaling factors of 1.035 (CGenFF) and 1.018 (GAFF) are derived from the zero-point energies. The force field calculations have larger deviation from experiment than the G4 method for standard entropy, but for heat capacity the results are comparable. For internal thermal energy and zero-point energy the deviations from G4 are relatively small. The work suggests that with some tuning force fields could indeed complement DFT in spectroscopical applications.

Statistical efficiency of methods for computing free energy of hydration.

The hydration free energy (HFE) is a critical property for predicting and understanding chemical and biological processes in aqueous solution. There are a number of computational methods to derive HFE, generally classified into the equilibrium or non-equilibrium methods, based on the type of calculations used. In the present study, we compute the hydration free energies of 34 small, neutral, organic molecules with experimental HFE between +2 and -16 kcal/mol. The one-sided non-equilibrium methods Jarzynski Forward (JF) and Backward (JB), the two-sided non-equilibrium methods Jarzynski mean based on the average of JF and JB, Crooks Gaussian Intersection (CGI), and the Bennett Acceptance Ratio (BAR) are compared to the estimates from the two-sided equilibrium method Multistate Bennett Acceptance Ratio (MBAR), which is considered as the reference method for HFE calculations, and experimental data from the literature. Our results show that the estimated hydration free energies from all the methods are consistent with MBAR results, and all methods provide a mean absolute error of ∼0.8 kcal/mol and root mean square error of ∼1 kcal for the 34 organic molecules studied. In addition, the results show that one-sided methods JF and JB result in systematic deviations that cannot be corrected entirely. The statistical efficiency ε of the different methods can be expressed as the one over the simulation time times the average variance in the HFE. From such an analysis, we conclude that ε(MBAR) > ε(BAR) ≈ ε(CGI) > ε(JX), where JX is any of the Jarzynski methods. In other words, the non-equilibrium methods tested here for the prediction of HFE have lower computational efficiency than the MBAR method.

Transient isomers in the photodissociation of bromoiodomethane.

The photochemistry of halomethanes is fascinating for the complex cascade reactions toward either the parent or newly synthesized molecules. Here, we address the structural rearrangement of photodissociated CH2IBr in methanol and cyclohexane, probed by time-resolved X-ray scattering in liquid solution. Upon selective laser cleavage of the C-I bond, we follow the reaction cascade of the two geminate geometrical isomers, CH2I-Br and CH2Br-I. Both meta-stable isomers decay on different time scales, mediated by solvent interaction, toward the original parent molecule. We observe the internal rearrangement of CH2Br-I to CH2I-Br in cyclohexane by extending the time window up to 3 µs. We track the photoproduct kinetics of CH2Br-I in methanol solution where only one isomer is observed. The effect of the polarity of solvent on the geminate recombination pathways is discussed.

Mobility-based prediction of hydration structures of protein surfaces.

MOTIVATION: Hydration largely determines solubility, aggregation of proteins and influences interactions between proteins and drug molecules. Despite the importance of hydration, structural determination of hydration structure of protein surfaces is still challenging from both experimental and theoretical viewpoints. The precision of experimental measurements is often affected by fluctuations and mobility of water molecules resulting in uncertain assignment of water positions. RESULTS: Our method can utilize mobility as an information source for the prediction of hydration structure. The necessary information can be produced by molecular dynamics simulations accounting for all atomic interactions including water-water contacts. The predictions were validated and tested by comparison to more than 1500 crystallographic water positions in 20 hydrated protein molecules including enzymes of biomedical importance such as cyclin-dependent kinase 2. The agreement with experimental water positions was larger than 80% on average. The predictions can be particularly useful in situations where no or limited experimental knowledge is available on hydration structures of molecular surfaces. AVAILABILITY AND IMPLEMENTATION: The method is implemented in a standalone C program MobyWat released under the GNU General Public License, freely accessible with full documentation at http://www.mobywat.com.

Binding of Pollutants to Biomolecules: A Simulation Study.

A number of cases around the world have been reported where animals were found dead or dying with symptoms resembling a thiamine (vitamin B) deficiency, and for some of these, a link to pollutants has been suggested. Here, we investigate whether biomolecules involved in thiamin binding and transport could be blocked by a range of different pollutants. We used in silico docking of five compound classes (25 compounds in total) to each of five targets (prion protein, ECF-type ABC transporter, thi-box riboswitch receptor, thiamin pyrophosphokinase, and YKoF protein) and subsequently performed molecular dynamics (MD) simulations to assess the stability of the complexes. The compound classes were thiamin analogues (control), pesticides, veterinary medicines, polychlorinated biphenyls, and dioxins, all of which are prevalent in the environment to some extent. A few anthropogenic compounds were found to bind the ECF-type ABC transporter, but none binds stably to prion protein. For the riboswitch, most compounds remained in their binding pockets during 50 ns of MD simulation, indicating that RNA provides a promiscuous binding site. In both YKoF and thiamin pyrophosphokinase (TPK), most compounds remain tightly bound. However, TPK biomolecules undergo pollutant-induced conformational changes. Although most compounds are found to bind to some of these targets, a larger data set is needed along with more quantitative methods like free energy perturbation calculations before firm conclusions can be drawn. This study is in part a test bed for large-scale quantitative computational screening of interactions between biological entities and pollutant molecules.

Evaluation of Generalized Born Models for Large Scale Affinity Prediction of Cyclodextrin Host-Guest Complexes.

Binding affinity prediction with implicit solvent models remains a challenge in virtual screening for drug discovery. In order to assess the predictive power of implicit solvent models in docking techniques with Amber scoring, three generalized Born models (GBHCT, GBOBCI, and GBOBCII) available in Dock 6.7 were utilized, for determining the binding affinity of a large set of ß-cyclodextrin complexes with 75 neutral guest molecules. The results were compared to potential of mean force (PMF) free energy calculations with four GB models (GBStill, GBHCT, GBOBCI, and GBOBCII) and to experimental data. Docking results yield similar accuracy to the computationally demanding PMF method with umbrella sampling. Neither docking nor PMF calculations reproduce the experimental binding affinities, however, as indicated by a small Spearman rank order coefficient (∼0.5). The binding energies obtained from GB models were decomposed further into individual contributions of the binding partners and solvent environments and compared to explicit solvent simulations for five complexes allowing for rationalizing the difference between explicit and implicit solvent models. An important observation is that the explicit solvent screens the interaction between host and guest much stronger than GB models. In contrast, the screening in GB models is too strong in solutes, leading to overestimation of short-range interactions and too strong binding. It is difficult to envision a way of overcoming these two opposite effects.

Exploration of Interfacial Hydration Networks of Target-Ligand Complexes.

Interfacial hydration strongly influences interactions between biomolecules. For example, drug-target complexes are often stabilized by hydration networks formed between hydrophilic residues and water molecules at the interface. Exhaustive exploration of hydration networks is challenging for experimental as well as theoretical methods due to high mobility of participating water molecules. In the present study, we introduced a tool for determination of the complete, void-free hydration structures of molecular interfaces. The tool was applied to 31 complexes including histone proteins, a HIV-1 protease, a G-protein-signaling modulator, and peptide ligands of various lengths. The complexes contained 344 experimentally determined water positions used for validation, and excellent agreement with these was obtained. High-level cooperation between interfacial water molecules was detected by a new approach based on the decomposition of hydration networks into static and dynamic network regions (subnets). Besides providing hydration structures at the atomic level, our results uncovered hitherto hidden networking fundaments of integrity and stability of complex biomolecular interfaces filling an important gap in the toolkit of drug design and structural biochemistry. The presence of continuous, static regions of the interfacial hydration network was found necessary also for stable complexes of histone proteins participating in chromatin assembly and epigenetic regulation.