Molecular simulation as an aid to experimentalists

In silico and in vivo analysis reveal impact of c-Myc tag in FMC63 scFv-CD19 protein interface and CAR-T cell efficacy

Anti-CD19 CAR-T cell therapy represents a breakthrough in the treatment of B-cell malignancies, and it is expected that this therapy modality will soon cover a range of solid tumors as well. Therefore, a universal cheap and sensitive method to detect CAR expression is of foremost importance. One possibility is the use of epitope tags such as c-Myc, HA or FLAG tags attached to the CAR extracellular domain, however, it is important to determine whether these tags can influence binding of the CAR with its target molecule. Here, we conducted in-silico structural modelling of an FMC63-based anti-CD19 single-chain variable fragment (scFv) with and without a c-Myc peptide tag added to the N-terminus portion and performed molecular dynamics simulation of the scFv with the CD19 target. We show that the c-Myc tag presence in the N-terminus portion does not affect the scFv's structural equilibrium and grants more stability to the scFv. However, intermolecular interaction potential (IIP) analysis reveals that the tag can approximate the complementarity-determining regions (CDRs) present in the scFv and cause steric impediment, potentially disturbing interaction with the CD19 protein. We then tested this possibility with CAR-T cells generated from human donors in a Nalm-6 leukemia model, showing that CAR-T cells with the c-Myc tag have overall worse antitumor activity, which was also observed when the tag was added to the C-terminus position. Ultimately, our results suggest that tag addition is an important aspect of CAR design and can influence CAR-T cell function, therefore its use should be carefully considered.

Simulating Bacterial Membrane Models at the Atomistic Level: A Force Field Comparison

Molecular dynamics (MD) simulations are currently an indispensable tool to understand both the dynamic and nanoscale organization of cell membrane models. A large number of quantitative parameters can be extracted from these simulations, but their reliability is determined by the quality of the employed force field and the simulation parameters. Much of the work on parametrizing and optimizing force fields for biomembrane modeling has been focused on homogeneous bilayers with a single phospholipid type. However, these may not perform effectively or could even be unsuitable for lipid mixtures commonly employed in membrane models. This work aims to fill this gap by comparing MD simulation results of several bacterial membrane models using different force fields and simulation parameters, namely, CHARMM36, Slipids, and GROMOS-CKP. Furthermore, the hydrogen isotope exchange (HIE) method, combined with GROMOS-CKP (GROMOS-H2Q), was also tested to check for the impact of this acceleration strategy on the performance of the force field. A common set of simulation parameters was employed for all of the force fields in addition to those corresponding to the original parametrization of each of them. Furthermore, new experimental order parameter values determined from NMR of several lipid mixtures are also reported to compare them with those determined from MD simulations. Our results reveal that most of the calculated physical properties of bacterial membrane models from MD simulations are substantially force field and lipid composition dependent. Some lipid mixtures exhibit nearly ideal behaviors, while the interaction of different lipid types in other mixtures is highly synergistic. None of the employed force fields seem to be clearly superior to the other three, each having its own strengths and weaknesses. Slipids are notably effective at replicating the order parameters for all acyl chains, including those in lipid mixtures, but they offer the least accurate results for headgroup parameters. Conversely, CHARMM provides almost perfect estimates for the order parameters of the headgroups but tends to overestimate those of the lipid tails. The GROMOS parametrizations deliver reasonable order parameters for entire lipid molecules, including multicomponent bilayers, although they do not reach the accuracy of Slipids for tails or CHARMM for headgroups. Importantly, GROMOS-H2Q stands out for its computational efficiency, being at least 3 times faster than GROMOS, which is already faster than both CHARMM and Slipids. In turn, GROMOS-H2Q yields much higher compressibilities compared to all other parametrizations.

Methods for Classical-Mechanical Molecular Simulation in Chemistry: Achievements, Limitations, Perspectives

More than a half century ago it became feasible to simulate, using classical-mechanical equations of motion, the dynamics of molecular systems on a computer. Since then classical-physical molecular simulation has become an integral part of chemical research. It is widely applied in a variety of branches of chemistry and has significantly contributed to the development of chemical knowledge. It offers understanding and interpretation of experimental results, semiquantitative predictions for measurable and nonmeasurable properties of substances, and allows the calculation of properties of molecular systems under conditions that are experimentally inaccessible. Yet, molecular simulation is built on a number of assumptions, approximations, and simplifications which limit its range of applicability and its accuracy. These concern the potential-energy function used, adequate sampling of the vast statistical-mechanical configurational space of a molecular system and the methods used to compute particular properties of chemical systems from statistical-mechanical ensembles. During the past half century various methodological ideas to improve the efficiency and accuracy of classical-physical molecular simulation have been proposed, investigated, evaluated, implemented in general simulation software or were abandoned. The latter because of fundamental flaws or, while being physically sound, computational inefficiency. Some of these methodological ideas are briefly reviewed and the most effective methods are highlighted. Limitations of classical-physical simulation are discussed and perspectives are sketched.

Polymerase-Based Signal Delay for Temporally Regulating DNA Involved Reactions, Programming Dynamic Molecular Systems, and Biomimetic Sensing

Complex temporal molecular signals play a pivotal role in the intricate biological pathways of living organisms, and cells exhibit the ability to transmit and receive information by intricately managing the temporal dynamics of their signaling molecules. Although biomimetic molecular networks are successfully engineered outside of cells, the capacity to precisely manipulate temporal behaviors remains limited. In this study, the catalysis activity of isothermal DNA polymerase (DNAP) through combined use of molecular dynamics simulation analysis and fluorescence assays is first characterized. DNAP-driven delay in signal strand release ranged from 100 to 102 min, which is achieved through new strategies including the introduction of primer overhangs, utilization of inhibitory reagents, and alteration of DNA template lengths. The results provide a deeper insight into the underlying mechanisms of temporal control DNAP-mediated primer extension and DNA strand displacement reactions. Then, the regulated DNAP catalysis reactions are applied in temporal modulation of downstream DNA-involved reactions, the establishment of dynamic molecular signals, and the generation of barcodes for multiplexed detection of target genes. The utility of DNAP-based signal delay as a dynamic DNA nanotechnology extends beyond theoretical concepts and achieves practical applications in the fields of cell-free synthetic biology and bionic sensing.

Delineating the Antiapoptotic Property of Apigenin as an Antitumor Agent: A Computational and In Vitro Study on HeLa Cells

Apigenin, a flavonoid, is reported to have multiple health benefits including cancer prevention; this study evaluates the drug likeliness and Swiss ADME properties of apigenin. Apoptosis, which is a key hallmark of cancer, is associated with the deregulation of the balance between proapoptotic proteins and antiapoptotic proteins such as BCL-2,BCL-xl, BFL-1, BCL-w, BRAG-16, and MCL-1. The docking studies of apigenin with the mentioned proteins was performed to identify the interactions between the ligand and proteins, which suggested that apigenin was able to bind to most of the proteins similar to the inhibitory molecules of its native structure. A remarkable reduction in the total energy after energy minimization of apigenin-antiapoptotic protein complexes suggested increased stability of the docked complexes. The same complexes were found to be stable over a 10 ns period of molecular simulation at 300 K. These findings advocated the study to evaluate apigenin's potential to inhibit the HeLa cells at 5, 10, and 15 μM concentrations in the clonogenic assay. Apigenin inhibited the colony-forming ability of HeLa cells in a dose-dependent manner over a fortnight. Light microscopy of the treated cells displayed the morphological evidence characteristic of apoptosis in HeLa cells such as blebbing, spike formation, cytoplasmic oozing, and nuclear fragmentation. Thus, these results clearly indicate that apigenin may be used as a potential chemopreventive agent in cervical cancer management.

Thermodynamic Insights into Variation in Thermomechanical and Physical Properties of Isotactic Polypropylene: Effect of Shear and Cooling Rates

In order to elucidate the effect of shear and cooling process on structural, thermomechanical, and physical properties of polymer melt, excess entropy, a thermodynamic quantity is calculated from radial distribution function generated from equilibrated parts of the molecular simulation trajectories. The structural properties are calculated, which includes the density of polypropylene melt, end to end distance, radius of gyration of the polypropylene polymer chain, and monomer-monomer radial distribution function. Non-equilibrium molecular dynamics simulation was employed to investigate the role of the applied shear rate on the properties of polypropylene. Furthermore, a range of cooling rates were employed to cool the melt. Thermomechanical properties, such as Young's modulus, and physical properties, such as glass transition temperature, were determined for different cases. Results showed that slow cooling and high shear substantially improved the Young's modulus and glass transition temperature of the i-PP. Furthermore, a two-body contribution to the excess entropy was used to elucidate the structure-property relationships in the polymer melt as well as the glassy state and the dependence of shear and cooling rate on these properties. We have used the Rosenfeld excess entropy-viscosity relationship to calculate the viscous behavior of the polymer under a steady shear condition.

Experimentally Observed Conformational Changes in Antibodies Due to Binding and Paratope-epitope Asymmetries

Understanding binding related changes in antibody conformations is important for epitope prediction and antibody refinement. The increase of available data in the PDB allowed a more detailed investigation of the conformational landscape for free and bound antibodies. A dataset containing a total of 835 unique PDB entries of antibodies that were crystallized in complex with their antigen and in a free state was constructed. It was examined for binding related conformation changes. We present further evidence supporting the theory of a pre-existing-equilibrium in experimental data. Multiple sequence alignments did not show binding induced tendencies in the solvent accessibility of residues in any specific position. Evaluating the changes in solvent accessibility per residue revealed a certain binding induced increase for several amino acids. Antibody-antigen interaction statistics were established and quantify a significant directional asymmetry between many interacting antibody and antigen residue pairs, especially a richness in tyrosine in the antibody epitope compared to its paratope. This asymmetry could potentially facilitate an increase in the success rate of computationally guided antibody refinement.

Data-Driven Elucidation of Flavor Chemistry

Flavor molecules are commonly used in the food industry to enhance product quality and consumer experiences but are associated with potential human health risks, highlighting the need for safer alternatives. To address these health-associated challenges and promote reasonable application, several databases for flavor molecules have been constructed. However, no existing studies have comprehensively summarized these data resources according to quality, focused fields, and potential gaps. Here, we systematically summarized 25 flavor molecule databases published within the last 20 years and revealed that data inaccessibility, untimely updates, and nonstandard flavor descriptions are the main limitations of current studies. We examined the development of computational approaches (e.g., machine learning and molecular simulation) for the identification of novel flavor molecules and discussed their major challenges regarding throughput, model interpretability, and the lack of gold-standard data sets for equitable model evaluation. Additionally, we discussed future strategies for the mining and designing of novel flavor molecules based on multi-omics and artificial intelligence to provide a new foundation for flavor science research.

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic

Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex data sets that capture the 3D spatial positions, dynamics, and interactions of thousands of molecules. Analyzing MD data sets is key for understanding and predicting emergent phenomena and in identifying key drivers and tuning design knobs of such phenomena. In this work, we show that the Euler characteristic (EC) provides an effective topological descriptor that facilitates MD analysis. The EC is a versatile, low-dimensional, and easy-to-interpret descriptor that can be used to reduce, analyze, and quantify complex data objects that are represented as graphs/networks, manifolds/functions, and point clouds. Specifically, we show that the EC is an informative descriptor that can be used for machine learning and data analysis tasks such as classification, visualization, and regression. We demonstrate the benefits of the proposed approach through case studies that aim to understand and predict the hydrophobicity of self-assembled monolayers and the reactivity of complex solvent environments.

Binding affinity estimation from restrained umbrella sampling simulations

The protein-ligand binding affinity quantifies the binding strength between a protein and its ligand. Computer modeling and simulations can be used to estimate the binding affinity or binding free energy using data- or physics-driven methods or a combination thereof. Here we discuss a purely physics-based sampling approach based on biased molecular dynamics simulations. Our proposed method generalizes and simplifies previously suggested stratification strategies that use umbrella sampling or other enhanced sampling simulations with additional collective-variable-based restraints. The approach presented here uses a flexible scheme that can be easily tailored for any system of interest. We estimate the binding affinity of human fibroblast growth factor 1 to heparin hexasaccharide based on the available crystal structure of the complex as the initial model and four different variations of the proposed method to compare against the experimentally determined binding affinity obtained from isothermal titration calorimetry experiments.

Identification of potential phytochemicals from Citrus Limon against main protease of SARS-CoV-2: molecular docking, molecular dynamic simulations and quantum computations

The outbreak of coronavirus disease (COVID-19) caused by a novel RNA virus emerged at the end of 2019. Most of the patient's symptoms are mild to moderate, and influenza, acute respiratory distress syndrome (ARDS) and multi-organ failure are common. The disease is mild to moderate in most patients and is reported in many cases such as pneumonia, ARDS and multi-organ dysfunction. This study's objective is to evaluate 25 natural compounds from Citrus limon (CL) used by comprehensive molecular docking, density functional theory (DFT) and molecular dynamics analysis against SARS-CoV-2 main protease (M^pro). Among all the experimental compounds, diosmetin has shown the best docking values against the M^pro of SARS-CoV-2 compared to the standard antiviral drug. In DFT calculations, the order associated with biochemical reactivity is as follows: eriodictoyl > quercetin > spinacetin > diosmetin > luteolin > apigenin, whereas the regions of oxygen and hydrogen atoms from the selected isolated compounds are appropriate for electrophilic and nucleophilic attacks, respectively. Also, HOMO-LUMO and global descriptors values indicated a promising result of these compounds. Moreover, a molecular dynamics simulation study revealed the stable conformation and binding pattern in a stimulating environment of natural compounds CL. Considering molecular docking, simulation, and DFT analysis of the selected compounds, notably eriodictoyl, quercetin, and diosmetin showed good potential against SARS-CoV-2 M^pro. Our in silico study revealed promising antiviral activity, which may be considered a potential key factor or a therapeutic target for COVID-19.Communicated by Ramaswamy H. Sarma.

Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics

Markov state models (MSMs) play a key role in studying protein conformational dynamics. A sliding count window with a fixed lag time is widely used to sample sub-trajectories for transition counting and MSM construction. However, sub-trajectories sampled with a fixed lag time may not perform well under different selections of lag time, which requires strong prior practice and leads to less robust estimation. To alleviate it, we propose a novel stochastic method from a Poisson process to generate perturbative lag time for sub-trajectory sampling and utilize it to construct a Markov chain. Comprehensive evaluations on the double-well system, WW domain, BPTI, and RBD-ACE2 complex of SARS-CoV-2 reveal that our algorithm significantly increases the robustness and power of a constructed MSM without disturbing the Markovian properties. Furthermore, the superiority of our algorithm is amplified for slow dynamic modes in complex biological processes.

The concentration-dependent effect of hydrocortisone on the structure of model lung surfactant monolayer by using an in silico approach

Understanding the adsorption mechanism of corticosteroids in the lung surfactant requires the knowledge of corticosteroid molecular interactions with lung surfactant monolayer (LSM). We employed coarse-grained molecular dynamics simulation to explore the action of hydrocortisone on an LSM comprised of a phospholipid, cholesterol and surfactant protein. The structural and dynamical morphology of the lung surfactant monolayer at different surface tensions were investigated to assess the monolayer compressibility. The simulations were also conducted at the two extreme ends of breathing cycles: exhalation (0 mN m-1 surface tension) and inhalation (20 mN m-1 surface tension). The impact of surface tension and hydrocortisone concentration on the monolayer compressibility and stability are significant, resulting the monolayer expansion at higher surface tension. However, at low surface tension, the highly compressed monolayer induces monolayer instability in the presence of the drug due to the accumulation of surfactant protein and drug. The constant area per lipid simulation results demonstrate that the surface pressure-area isotherms show a decrease in area-per-lipid with increased drug concentration. The drug-induced expansion causes considerable instability in the monolayer after a specific drug concentration is attained at inhalation breathing condition, whereas, for exhalation breathing, the monolayer gets more compressed, causing the LSM to collapse. The monolayer collapse occurs for inhalation due to the higher drug concentration, whereas for exhalation due to the accumulation of surfactant proteins and drugs. The findings from this study will aid in enhancing the knowledge of molecular interactions of corticosteroid drugs with lung surfactants to treat respiratory diseases.

Computational Tools for Accurate Binding Free-Energy Prediction

A quantitative thermodynamic understanding of the noncovalent association of (bio)molecules is of central importance in molecular life sciences. An important quantity characterizing (bio)molecular association is the binding affinity or absolute binding free energy. In recent years, the computational prediction of absolute binding free energies has evolved considerably in terms of accuracy, computational speed, and user-friendliness. In this chapter, we first give an overview of how absolute free energies are defined and how they can be determined with computational means. We proceed with an outline of the theoretical basis of the two most reliable methods, potential of mean force, and double decoupling calculations. In particular, we describe how the sampling problem can be alleviated by application of restraints. Finally, we provide step-by-step instructions of how to set up corresponding molecular simulations with a commonly employed molecular dynamics simulation engine.

Fighting Against Bacterial Lipopolysaccharide-Caused Infections through Molecular Dynamics Simulations: A Review

Lipopolysaccharide (LPS) is the primary component of the outer leaflet of Gram-negative bacterial outer membranes. LPS elicits an overwhelming immune response during infection, which can lead to life-threatening sepsis or septic shock for which no suitable treatment is available so far. As a result of the worldwide expanding multidrug-resistant bacteria, the occurrence and frequency of sepsis are expected to increase; thus, there is an urge to develop novel strategies for treating bacterial infections. In this regard, gaining an in-depth understanding about the ability of LPS to both stimulate the host immune system and interact with several molecules is crucial for fighting against LPS-caused infections and allowing for the rational design of novel antisepsis drugs, vaccines and LPS sequestration and detection methods. Molecular dynamics (MD) simulations, which are understood as being a computational microscope, have proven to be of significant value to understand LPS-related phenomena, driving and optimizing experimental research studies. In this work, a comprehensive review on the methods that can be combined with MD simulations, recently applied in LPS research, is provided. We focus especially on both enhanced sampling methods, which enable the exploration of more complex systems and access to larger time scales, and free energy calculation approaches. Thereby, apart from outlining several strategies for surmounting LPS-caused infections, this work reports the current state-of-the-art of the methods applied with MD simulations for moving a step forward in the development of such strategies.

Effect of the Force Field on Molecular Dynamics Simulations of the Multidrug Efflux Protein P-Glycoprotein

Molecular dynamics (MD) simulations have been used extensively to study P-glycoprotein (P-gp), a flexible multidrug transporter that is a key player in the development of multidrug resistance to chemotherapeutics. A substantial body of literature has grown from simulation studies that have employed various simulation conditions and parameters, including AMBER, CHARMM, OPLS, GROMOS, and coarse-grained force fields, drawing conclusions from simulations spanning hundreds of nanoseconds. Each force field is typically parametrized and validated on different data and observables, usually of small molecules and peptides; there have been few comparisons of force field performance on large protein-membrane systems. Here we compare the conformational ensembles of P-gp embedded in a POPC/cholesterol bilayer generated over 500 ns of replicate simulation with five force fields from popular biomolecular families: AMBER 99SB-ILDN, CHARMM 36, OPLS-AA/L, GROMOS 54A7, and MARTINI. We find considerable differences among the ensembles with little conformational overlap, although they correspond to similar extents to structural data obtained from electron paramagnetic resonance and cross-linking studies. Moreover, each trajectory was still sampling new conformations at a high rate after 500 ns of simulation, suggesting the need for more sampling. This work highlights the need to consider known limitations of the force field used (e.g., biases toward certain secondary structures) and the simulation itself (e.g., whether sufficient sampling has been achieved) when interpreting accumulated results of simulation studies of P-gp and other transport proteins.

Molecular Simulations Guidelines for Biological Nanomaterials: From Peptides to Membranes

In studying biological processes and focusing on the molecular mechanisms at the basis of these, molecular dynamics (MD) simulations have demonstrated to be a very useful tool for the past 50 years. This suite of computational methods calculates the time-dependent evolution of a molecular system using physics-based first principles. In this chapter, we give a brief introduction to the theory and practical use of molecular dynamics simulations, highlighting the different models and algorithms that have been developed to tackle specific problems, with a special focus on classical force fields. Some examples of how simulations have been used in the past will help the reader in discerning their power, limitations, and significance.

Mechanistic Understanding From Molecular Dynamics Simulation in Pharmaceutical Research 1: Drug Delivery

In this review, we outline the growing role that molecular dynamics simulation is able to play as a design tool in drug delivery. We cover both the pharmaceutical and computational backgrounds, in a pedagogical fashion, as this review is designed to be equally accessible to pharmaceutical researchers interested in what this new computational tool is capable of and experts in molecular modeling who wish to pursue pharmaceutical applications as a context for their research. The field has become too broad for us to concisely describe all work that has been carried out; many comprehensive reviews on subtopics of this area are cited. We discuss the insight molecular dynamics modeling has provided in dissolution and solubility, however, the majority of the discussion is focused on nanomedicine: the development of nanoscale drug delivery vehicles. Here we focus on three areas where molecular dynamics modeling has had a particularly strong impact: (1) behavior in the bloodstream and protective polymer corona, (2) Drug loading and controlled release, and (3) Nanoparticle interaction with both model and biological membranes. We conclude with some thoughts on the role that molecular dynamics simulation can grow to play in the development of new drug delivery systems.

Recent Open Issues in Coarse Grained Force Fields

This viewpoint intends to show recent open issues of using coarse grained models in molecular dynamics simulation. It reviews the current knowledge of the comparison between experimental and simulation data of structural and physical chemical properties that depend on the hydrophilic and hydrophobic behavior of the molecule.

In Vitro-In Silico Modeling Approach to Rationally Designed Simple and Versatile Drug Delivery Systems

Rational design and development of a nanosystem usually relies on empirical approaches as well as a fair degree of serendipity. Understanding how nanosystems behave at the molecular level is of great importance for potential biomedical applications. In this work, we describe a nanosystem composed of two natural compounds, vitamin E and sphingomyelin, prepared by spontaneous emulsification (vitamin E-sphingomyelin nanosystems (VSNs)). Extensive characterization revealed suitable physicochemical properties, very high biocompatibility in vitro and in vivo, and colloidal stability during storage and in biological media, all relevant properties for clinical translation. We have additionally pursued a computational approach to gain an improved understanding of the assembling, structure, dynamics, and drug-loading capacity of VSNs, using both small molecules and biomolecules (resveratrol, curcumin, gemcitabine, and two peptides). The spontaneous formation of compartmentalized VSNs starting from completely disassembled molecules, observed here for the first time, was accurately assessed from the computational molecular dynamics trajectories. We describe here a synergistic in silico/in vitro approach showing the predictive power of computational simulations for VSNs' structural characterization and description of internal interaction mechanisms responsible for the association of bioactive molecules, representing a paradigm shift in the rational design of nanotechnologies as drug delivery systems for advanced personalized medicine.

Algal Toxin Goniodomin A Binds Potassium Ion Selectively to Yield a Conformationally Altered Complex with Potential Biological Consequences

The marine toxin goniodomin A (GDA) is a polycyclic macrolide containing a spiroacetal and three cyclic ethers as part of the macrocycle backbone. GDA is produced by three species of the Alexandrium genus of dinoflagellates, blooms of which are associated with "red tides", which are widely dispersed and can cause significant harm to marine life. The toxicity of GDA has been attributed to stabilization of the filamentous form of the actin group of structural proteins, but the structural basis for its binding is not known. Japanese workers, capitalizing on the assumed rigidity of the heavily substituted macrolide ring, assigned the relative configuration and conformation by relying on NMR coupling constants and NOEs; the absolute configuration was assigned by degradation to a fragment that was compared with synthetic material. We have confirmed the absolute structure and broad features of the conformation by X-ray crystallography but have found GDA to complex with alkali metal ions in spite of two of the heterocyclic rings facing outward. Such an arrangement would have been expected to impair the ability of GDA to form a crown-ether-type multidentate complex. GDA shows preference for K⁺, Rb⁺, and Cs⁺ over Li⁺ and Na⁺ in determinations of relative affinities by TLC on metal-ion-impregnated silica gel plates and by electrospray mass spectrometry. NMR studies employing the K⁺ complex of GDA, formed from potassium tetrakis[pentafluorophenyl]borate (KBArF₂₀), reveal a major alteration of the conformation of the macrolide ring. These observations argue against the prior assumption of rigidity of the ring. Alterations in chemical shifts, coupling constants, and NOEs indicate the involvement of most of the molecule other than ring F. Molecular mechanics simulations suggest K⁺ forms a heptacoordinate complex involving O_A, O_B, O_C, O_D, O_E, and the C-26 and C-27 hydroxy groups. We speculate that complexation of K⁺ with GDA electrostatically stabilizes the complex of GDA with filamentous actin in marine animals due to the protein being negatively charged at physiological pH. GDA may also cause potassium leakage through cell membranes. This study provides insight into the structural features and chemistry of GDA that may be responsible for significant ecological damage associated with the GDA-producing algal blooms.

Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix

The refinement of biomolecular crystallographic models relies on geometric restraints to help to address the paucity of experimental data typical in these experiments. Limitations in these restraints can degrade the quality of the resulting atomic models. Here, an integration of the full all-atom Amber molecular-dynamics force field into Phenix crystallographic refinement is presented, which enables more complete modeling of biomolecular chemistry. The advantages of the force field include a carefully derived set of torsion-angle potentials, an extensive and flexible set of atom types, Lennard-Jones treatment of nonbonded interactions and a full treatment of crystalline electrostatics. The new combined method was tested against conventional geometry restraints for over 22 000 protein structures. Structures refined with the new method show substantially improved model quality. On average, Ramachandran and rotamer scores are somewhat better, clashscores and MolProbity scores are significantly improved, and the modeling of electrostatics leads to structures that exhibit more, and more correct, hydrogen bonds than those refined using traditional geometry restraints. In general it is found that model improvements are greatest at lower resolutions, prompting plans to add the Amber target function to real-space refinement for use in electron cryo-microscopy. This work opens the door to the future development of more advanced applications such as Amber-based ensemble refinement, quantum-mechanical representation of active sites and improved geometric restraints for simulated annealing.

Molecular simulations minimally restrained by experimental data

One popular approach to incorporating experimental data into molecular simulations is to restrain the ensemble average of observables to their experimental values. Here, I derive equations for the equilibrium distributions generated by restrained ensemble simulations and the corresponding expected values of observables. My results suggest a method to restrain simulations so that they generate distributions that are minimally perturbed from the unbiased distributions while reproducing the experimental values of the observables within their measurement uncertainties.

Conformational states of Zika virus non-structural protein 3 determined by molecular dynamics simulations with small-angle X-Ray scattering data

Zika virus (ZIKV) has become a great public health emergency. Its non-structural protein 3 (NS3) is a key enzyme in viral replication and has been considered as a potential therapeutic target. A conformational characterization of ZIKV NS3 is critical for a comprehensive understanding of its molecular interactions and functions. However, the high conformational flexibility of solution NS3 obstacles the structural characterization of NS3 solely from the experimental observable that averages over its heterogeneous conformations. Here, we employed replica exchange with solute tempering (REST) method to simulate the di-domain protein ZIKV NS3. Three independent MD simulations identified a conserved conformational ensemble of NS3, consisting of a major conformational state and several minor states from compact to loose conformations. The major state agrees well with the scattering profile from small-angle X-ray scattering (SAXS) experiments. Moreover, the simulated ensemble is supported by a direct data-fitting result that requires both short- and long-range structural contacts to recover the experimental data. We discussed the interplay between simulation and experiment in ensemble construction of flexible biomolecules and shed light on the physically derived conformational ensembles.

Targeting Difficult Protein-Protein Interactions with Plain and General Computational Approaches

Investigating protein-protein interactions (PPIs) holds great potential for therapeutic applications, since they mediate intricate cell signaling networks in physiological and disease states. However, their complex and multifaceted nature poses a major challenge for biochemistry and medicinal chemistry, thereby limiting the druggability of biological partners participating in PPIs. Molecular Dynamics (MD) provides a solid framework to study the reciprocal shaping of proteins’ interacting surfaces. Here, we review successful applications of MD-based methods developed in our group to predict interfacial areas involved in PPIs of pharmaceutical interest. We report two interesting examples of how structural, dynamic and energetic information can be combined into efficient strategies which, complemented by experiments, can lead to the design of new small molecules with promising activities against cancer and infections. Our advances in targeting key PPIs in angiogenic pathways and antigen-antibody recognition events will be discussed for their role in drug discovery and chemical biology.

The reliability of molecular dynamics simulations of the multidrug transporter P-glycoprotein in a membrane environment

Despite decades of research, the mechanism of action of the ABC multidrug transporter P-glycoprotein (P-gp) remains elusive. Due to experimental limitations, many researchers have turned to molecular dynamics simulation studies in order to investigate different aspects of P-gp function. However, such studies are challenging and caution is required when interpreting the results. P-gp is highly flexible and the time scale on which it can be simulated is limited. There is also uncertainty regarding the accuracy of the various crystal structures available, let alone the structure of the protein in a physiologically relevant environment. In this study, three alternative structural models of mouse P-gp (3G5U, 4KSB, 4M1M), all resolved to 3.8 Å, were used to initiate sets of simulations of P-gp in a membrane environment in order to determine: a) the sensitivity of the results to differences in the starting configuration; and b) the extent to which converged results could be expected on the times scales commonly simulated for this system. The simulations suggest that the arrangement of the nucleotide binding domains (NBDs) observed in the crystal structures is not stable in a membrane environment. In all simulations, the NBDs rapidly associated (within 10 ns) and changes within the transmembrane helices were observed. The secondary structure within the transmembrane domain was best preserved in the 4M1M model under the simulation conditions used. However, the extent to which replicate simulations diverged on a 100 to 200 ns timescale meant that it was not possible to draw definitive conclusions as to which structure overall was most stable, or to obtain converged and reliable results for any of the properties examined. The work brings into question the reliability of conclusions made in regard to the nature of specific interactions inferred from previous simulation studies on this system involving similar sampling times. It also highlights the need to demonstrate the statistical significance of any results obtained in simulations of large flexible proteins, especially where the initial structure is uncertain.

Validation of Molecular Simulation: An Overview of Issues

Computer simulation of molecular systems enables structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic, or supra-molecular level. To interpret results of such simulations appropriately, the quality of the calculated properties must be evaluated. This depends on the way the simulations are performed and on the way they are validated by comparison to values Q^exp of experimentally observable quantities Q. One must consider 1) the accuracy of Q^exp , 2) the accuracy of the function Q(r^N ) used to calculate a Q-value based on a molecular configuration r^N of N particles, 3) the sensitivity of the function Q(r^N ) to the configuration r^N , 4) the relative time scales of the simulation and experiment, 5) the degree to which the calculated and experimental properties are equivalent, and 6) the degree to which the system simulated matches the experimental conditions. Experimental data is limited in scope and generally corresponds to averages over both time and space. A critical analysis of the various factors influencing the apparent degree of (dis)agreement between simulations and experiment is presented and illustrated using examples from the literature. What can be done to enhance the validation of molecular simulation is also discussed.

Interpretation of Seemingly Contradictory Data: Low NMR S2 Order Parameters Observed in Helices and High NMR S2 Order Parameters in Disordered Loops of the Protein hGH at Low pH

At low pH, human growth hormone (hGH) adopts a partially folded state, in which the native helices are maintained, but the long loop regions and side-chain packing become disordered. Some of the S² order parameters for backbone N-H vectors derived from NMR relaxation measurements on hGH at low pH initially seem contradictory. Three isolated residues (15, 20, and 171) in helices A and D exhibit low order parameter values (<0.5) indicating flexibility, whereas residue 143 in the centre of a long flexible loop region has a high order parameter (0.82). Using S² order parameter restraining MD simulations, this paradox has been resolved. Low S² values in helices are due to the presence of a mixture of 3₁₀ -helical and α-helical hydrogen bonds. High S² values in relatively disordered parts of a protein may be due to fluctuating networks of hydrogen bonds between the backbone and the side chains, which restrict the motion of N-H bond vectors.

Modeling Functional Motions of Biological Systems by Customized Natural Moves

Simulating the functional motions of biomolecular systems requires large computational resources. We introduce a computationally inexpensive protocol for the systematic testing of hypotheses regarding the dynamic behavior of proteins and nucleic acids. The protocol is based on natural move Monte Carlo, a highly efficient conformational sampling method with built-in customization capabilities that allows researchers to design and perform a large number of simulations to investigate functional motions in biological systems. We demonstrate the use of this protocol on both a protein and a DNA case study. Firstly, we investigate the plasticity of a class II major histocompatibility complex in the absence of a bound peptide. Secondly, we study the effects of the epigenetic mark 5-hydroxymethyl on cytosine on the structure of the Dickerson-Drew dodecamer. We show how our customized natural moves protocol can be used to investigate causal relationships of functional motions in biological systems.

Insights from molecular dynamics simulations for computational protein design

A grand challenge in the field of structural biology is to design and engineer proteins that exhibit targeted functions. Although much success on this front has been achieved, design success rates remain low, an ever-present reminder of our limited understanding of the relationship between amino acid sequences and the structures they adopt. In addition to experimental techniques and rational design strategies, computational methods have been employed to aid in the design and engineering of proteins. Molecular dynamics (MD) is one such method that simulates the motions of proteins according to classical dynamics. Here, we review how insights into protein dynamics derived from MD simulations have influenced the design of proteins. One of the greatest strengths of MD is its capacity to reveal information beyond what is available in the static structures deposited in the Protein Data Bank. In this regard simulations can be used to directly guide protein design by providing atomistic details of the dynamic molecular interactions contributing to protein stability and function. MD simulations can also be used as a virtual screening tool to rank, select, identify, and assess potential designs. MD is uniquely poised to inform protein design efforts where the application requires realistic models of protein dynamics and atomic level descriptions of the relationship between dynamics and function. Here, we review cases where MD simulations was used to modulate protein stability and protein function by providing information regarding the conformation(s), conformational transitions, interactions, and dynamics that govern stability and function. In addition, we discuss cases where conformations from protein folding/unfolding simulations have been exploited for protein design, yielding novel outcomes that could not be obtained from static structures.

Deriving Structural Information from Experimentally Measured Data on Biomolecules

During the past half century, the number and accuracy of experimental techniques that can deliver values of observables for biomolecular systems have been steadily increasing. The conversion of a measured value Q^exp of an observable quantity Q into structural information is, however, a task beset with theoretical and practical problems: 1) insufficient or inaccurate values of Q^exp , 2) inaccuracies in the function Q(r→) used to relate the quantity Q to structure r→ , 3) how to account for the averaging inherent in the measurement of Q^exp , 4) how to handle the possible multiple-valuedness of the inverse r→(Q) of the function Q(r→) , to mention a few. These apply to a variety of observable quantities Q and measurement techniques such as X-ray and neutron diffraction, small-angle and wide-angle X-ray scattering, free-electron laser imaging, cryo-electron microscopy, nuclear magnetic resonance, electron paramagnetic resonance, infrared and Raman spectroscopy, circular dichroism, Förster resonance energy transfer, atomic force microscopy and ion-mobility mass spectrometry. The process of deriving structural information from measured data is reviewed with an eye to non-experts and newcomers in the field using examples from the literature of the effect of the various choices and approximations involved in the process. A list of choices to be avoided is provided.

Computational and experimental approaches for investigating nanoparticle-based drug delivery systems

Most therapeutic agents suffer from poor solubility, rapid clearance from the blood stream, a lack of targeting, and often poor translocation ability across cell membranes. Drug/gene delivery systems (DDSs) are capable of overcoming some of these barriers to enhance delivery of drugs to their right place of action, e.g. inside cancer cells. In this review, we focus on nanoparticles as DDSs. Complementary experimental and computational studies have enhanced our understanding of the mechanism of action of nanocarriers and their underlying interactions with drugs, biomembranes and other biological molecules. We review key biophysical aspects of DDSs and discuss how computer modeling can assist in rational design of DDSs with improved and optimized properties. We summarize commonly used experimental techniques for the study of DDSs. Then we review computational studies for several major categories of nanocarriers, including dendrimers and dendrons, polymer-, peptide-, nucleic acid-, lipid-, and carbon-based DDSs, and gold nanoparticles. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Demonstrating an Order-of-Magnitude Sampling Enhancement in Molecular Dynamics Simulations of Complex Protein Systems

Molecular dynamics (MD) simulations can describe protein motions in atomic detail, but transitions between protein conformational states sometimes take place on time scales that are infeasible or very expensive to reach by direct simulation. Enhanced sampling methods, the aim of which is to increase the sampling efficiency of MD simulations, have thus been extensively employed. The effectiveness of such methods when applied to complex biological systems like proteins, however, has been difficult to establish because even enhanced sampling simulations of such systems do not typically reach time scales at which convergence is extensive enough to reliably quantify sampling efficiency. Here, we obtain sufficiently converged simulations of three proteins to evaluate the performance of simulated tempering, a member of a widely used class of enhanced sampling methods that use elevated temperature to accelerate sampling. Simulated tempering simulations with individual lengths of up to 100 μs were compared to (previously published) conventional MD simulations with individual lengths of up to 1 ms. With two proteins, BPTI and ubiquitin, we evaluated the efficiency of sampling of conformational states near the native state, and for the third, the villin headpiece, we examined the rate of folding and unfolding. Our comparisons demonstrate that simulated tempering can consistently achieve a substantial sampling speedup of an order of magnitude or more relative to conventional MD.