The future of molecular dynamics simulations in drug discovery

Antihistamine response: a dynamically refined function at the host-tick interface

Background

Ticks counteract host inflammatory responses by secreting proteins from their saliva that compete for histamine binding. Among these tick salivary proteins are lipocalins, antiparallel beta-barrel proteins that sequester small molecules. A tick salivary lipocalin has been structurally resolved and experimentally shown to efficiently compete for histamine with its native receptor (e.g., H1 histamine receptor). To date, molecular dynamics simulations focus on protein-protein and protein-ligand interactions, but there are currently no studies for simultaneous ligand exploration between two competing proteins.

Methods

Aided by state-of-the-art, high-throughput computational methods, the current study simulated and analyzed the dynamics of competitive histamine binding at the tick-host interface using the available crystal structures of both the tick salivary lipocalin histamine-binding protein from Rhipicephalus appendiculatus and the human histamine receptor 1.

Results

The attraction towards the tick salivary lipocalin seems to depend on the protonated (adding a hydrogen ion) state of histamine since the current study shows that as histamine becomes more protonated it increases its exploration for the tick salivary lipocalin. This implies that during tick feeding, histamine may need to be protonated for the tick salivary lipocalin to efficiently sequester it in order to counteract inflammation. Additionally, the beta-hairpin loops (at both ends of the tick salivary lipocalin barrel) were reported to have a functional role in sequestering histamine and the results in the current study concur and provide evidence for this hypothesis. These beta-hairpin loops of the tick salivary lipocalin possess more acidic residues than a structurally similar but functionally unrelated lipocalin from the butterfly, Pieris brassicae; comparative results indicate these acidic residues may be responsible for the ability of the tick lipocalin to out-compete the native (H1) receptor for histamine.

Conclusions

Three explanatory types of data can be obtained from the current study: (i) the dynamics of multiple binding sites, (ii) competition between two proteins for a ligand, and (iii) the intrinsic molecular components involved in the competition. These data can provide further insight at the atomic level of the host-tick interface that cannot be experimentally determined. Additionally, the methods used in this study can be applied in rationally designing drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-014-0491-9) contains supplementary material, which is available to authorized users.

DOCLASP - Docking ligands to target proteins using spatial and electrostatic congruence extracted from a known holoenzyme and applying simple geometrical transformations

The ability to accurately and effectively predict the interaction between proteins and small drug-like compounds has long intrigued researchers for pedagogic, humanitarian and economic reasons. Protein docking methods (AutoDock, GOLD, DOCK, FlexX and Glide to name a few) rank a large number of possible conformations of protein-ligand complexes using fast algorithms. Previously, it has been shown that structural congruence leading to the same enzymatic function necessitates the congruence of electrostatic properties (CLASP). The current work presents a methodology for docking a ligand into a target protein, provided that there is at least one known holoenzyme with ligand bound - DOCLASP (Docking using CLASP). The contact points of the ligand in the holoenzyme defines a motif, which is used to query the target enzyme using CLASP. If there are significant matches, the holoenzyme and the target protein are superimposed based on congruent atoms. The same linear and rotational transformations are also applied to the ligand, thus creating a unified coordinate framework having the holoenzyme, the ligand and the target enzyme. In the current work, the dipeptidyl peptidase-IV inhibitor vildagliptin was docked to the PI-PLC structure complexed with myo-inositol using DOCLASP. Also, corroboration of the docking of phenylthiourea to the modelled structure of polyphenol oxidase (JrPPO1) from walnut is provided based on the subsequently solved structure of JrPPO1 (PDBid:5CE9). Analysis of the binding of the antitrypanosomial drug suramin to nine non-homologous proteins in the PDB database shows a diverse set of binding motifs, and multiple binding sites in the phospholipase A2-likeproteins from the Bothrops genus of pitvipers. The conformational changes in the suramin molecule on binding highlights the challenges in docking flexible ligands into an already 'plastic' binding site. Thus, DOCLASP presents a method for 'soft docking' ligands to proteins with low computational requirements.

DOCLASP - Docking ligands to target proteins using spatial and electrostatic congruence extracted from a known holoenzyme and applying simple geometrical transformations

The ability to accurately and effectively predict the interaction between proteins and small drug-like compounds has long intrigued researchers for pedagogic, humanitarian and economic reasons. Protein docking methods (AutoDock, GOLD, DOCK, FlexX and Glide to name a few) rank a large number of possible conformations of protein-ligand complexes using fast algorithms. Previously, it has been shown that structural congruence leading to the same enzymatic function necessitates the congruence of electrostatic properties (CLASP). The current work presents a methodology for docking a ligand into a target protein, provided that there is at least one known holoenzyme with ligand bound - DOCLASP (Docking using CLASP). The contact points of the ligand in the holoenzyme defines a motif, which is used to query the target enzyme using CLASP. If there are significant matches, the holoenzyme and the target protein are superimposed based on congruent atoms. The same linear and rotational transformations are also applied to the ligand, thus creating a unified coordinate framework having the holoenzyme, the ligand and the target enzyme. In the current work, the dipeptidyl peptidase-IV inhibitor vildagliptin was docked to the PI-PLC structure complexed with myo-inositol using DOCLASP. Also, corroboration of the docking of phenylthiourea to the modelled structure of polyphenol oxidase (JrPPO1) from walnut is provided based on the subsequently solved structure of JrPPO1 (PDBid:5CE9). Analysis of the binding of the antitrypanosomial drug suramin to nine non-homologous proteins in the PDB database shows a diverse set of binding motifs, and multiple binding sites in the phospholipase A2-likeproteins from the Bothrops genus of pitvipers. The conformational changes in the suramin molecule on binding highlights the challenges in docking flexible ligands into an already ’plastic’ binding site. Thus, DOCLASP presents a method for ’soft docking’ ligands to proteins with low computational requirements.

Computer-aided targeting of the PI3K/Akt/mTOR pathway: toxicity reduction and therapeutic opportunities

The PI3K/Akt/mTOR pathway plays an essential role in a wide range of biological functions, including metabolism, macromolecular synthesis, cell growth, proliferation and survival. Its versatility, however, makes it a conspicuous target of many pathogens; and the consequential deregulations of this pathway often lead to complications, such as tumorigenesis, type 2 diabetes and cardiovascular diseases. Molecular targeted therapy, aimed at modulating the deregulated pathway, holds great promise for controlling these diseases, though side effects may be inevitable, given the ubiquity of the pathway in cell functions. Here, we review a variety of factors found to modulate the PI3K/Akt/mTOR pathway, including gene mutations, certain metabolites, inflammatory factors, chemical toxicants, drugs found to rectify the pathway, as well as viruses that hijack the pathway for their own synthetic purposes. Furthermore, this evidence of PI3K/Akt/mTOR pathway alteration and related pathogenesis has inspired the exploration of computer-aided targeting of this pathway to optimize therapeutic strategies. Herein, we discuss several possible options, using computer-aided targeting, to reduce the toxicity of molecularly-targeted therapy, including mathematical modeling, to reveal system-level control mechanisms and to confer a low-dosage combination therapy, the potential of PP2A as a therapeutic target, the formulation of parameters to identify patients who would most benefit from specific targeted therapies and molecular dynamics simulations and docking studies to discover drugs that are isoform specific or mutation selective so as to avoid undesired broad inhibitions. We hope this review will stimulate novel ideas for pharmaceutical discovery and deepen our understanding of curability and toxicity by targeting the PI3K/Akt/mTOR pathway.

Molecular dynamics simulations: insight into molecular phenomena at interfaces

Molecular dynamics simulations, when aptly devised, can enhance our fundamental understanding of a system, set up a platform for testing theoretical predictions, and provide insight and a framework for further experimental studies. This feature article highlights the importance of molecular dynamics simulations in understanding interfacial phenomena using three case studies involving liquid-liquid and solid-liquid interfaces. After briefly reviewing molecular dynamics methods, we discuss velocity slip at a liquid-liquid interface, the coalescence of liquid drops in suspension and in free space, and the behavior of colloidal nanoparticles at a liquid-liquid interface. We emphasize the utility of simple intermolecular potentials and generic liquids. The case studies exemplify the significant insight gained through the molecular modeling approach regarding the interfacial phenomena studied. We conclude the highlight with a brief discussion illustrating potential shortcomings and pitfalls of molecular dynamics simulations.

Lead discovery and in silico 3D structure modeling of tumorigenic FAM72A (p17)

FAM72A (p17) is a novel neuronal protein that has been linked to tumorigenic effects in non-neuronal tissue. Using state of the art in silico physicochemical analyses (e.g., I-TASSER, RaptorX, and Modeller), we determined the three-dimensional (3D) protein structure of FAM72A and further identified potential ligand-protein interactions. Our data indicate a Zn(2+)/Fe(3+)-containing 3D protein structure, based on a 3GA3_A model template, which potentially interacts with the organic molecule RSM ((2s)-2-(acetylamino)-N-methyl-4-[(R)-methylsulfinyl] butanamide). The discovery of RSM may serve as potential lead for further anti-FAM72A drug screening tests in the pharmaceutical industry because interference with FAM72A's activities via RSM-related molecules might be a novel option to influence the tumor suppressor protein p53 signaling pathways for the treatment of various types of cancers.

Computational Insights into Substrate and Site Specificities, Catalytic Mechanism, and Protonation States of the Catalytic Asp Dyad of β -Secretase

In this review, information regarding substrate and site specificities, catalytic mechanism, and protonation states of the catalytic Asp dyad of β-secretase (BACE1) derived from computational studies has been discussed. BACE1 catalyzes the rate-limiting step in the generation of Alzheimer amyloid beta peptide through the proteolytic cleavage of the amyloid precursor protein. Due to its biological functioning, this enzyme has been considered as one of the most important targets for finding the cure for Alzheimer's disease. Molecular dynamics (MD) simulations suggested that structural differences in the key regions (inserts A, D, and F and the 10s loop) of the enzyme are responsible for the observed difference in its activities towards the WT- and SW-substrates. The modifications in the flap, third strand, and insert F regions were found to be involved in the alteration in the site specificity of the glycosylphosphatidylinositol bound form of BACE1. Our QM and QM/MM calculations suggested that BACE1 hydrolyzed the SW-substrate more efficiently than the WT-substrate and that cleavage of the peptide bond occurred in the rate-determining step. The results from molecular docking studies showed that the information concerning a single protonation state of the Asp dyad is not enough to run an in silico screening campaign.

A method to predict blood-brain barrier permeability of drug-like compounds using molecular dynamics simulations

The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (Peff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R(2) = 0.94) and logPS (R(2) = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.

A computational approach predicting CYP450 metabolism and estrogenic activity of an endocrine disrupting compound (PCB-30)

Endocrine disrupting chemicals influence growth and development through interactions with the hormone system, often through binding to hormone receptors such as the estrogen receptor. Computational methods can predict endocrine disrupting chemical activity of unmodified compounds, but approaches predicting activity following metabolism are lacking. The present study uses a well-known environmental contaminant, PCB-30 (2,4,6-trichlorobiphenyl), as a prototype endocrine disrupting chemical and integrates predictive (computational) and experimental methods to determine its metabolic transformation by cytochrome P450 3A4 (CYP3A4) and cytochrome P450 2D6 (CYP2D6) into estrogenic byproducts. Computational predictions suggest that hydroxylation of PCB-30 occurs at the 3- or 4-phenol positions and leads to metabolites that bind more strongly than the parent molecule to the human estrogen receptor alpha (hER-α). Gas chromatography-mass spectrometry experiments confirmed that the primary metabolite for CYP3A4 and CYP2D6 is 4-hydroxy-PCB-30, and the secondary metabolite is 3-hydroxy-PCB-30. Cell-based bioassays (bioluminescent yeast expressing hER-α) confirmed that hydroxylated metabolites are more estrogenic than PCB-30. These experimental results support the applied model's ability to predict the metabolic and estrogenic fate of PCB-30, which could be used to identify other endocrine disrupting chemicals involved in similar pathways.

More than the sum of its parts: coarse-grained peptide-lipid interactions from a simple cross-parametrization

Interfacial systems are at the core of fascinating phenomena in many disciplines, such as biochemistry, soft-matter physics, and food science. However, the parametrization of accurate, reliable, and consistent coarse-grained (CG) models for systems at interfaces remains a challenging endeavor. In the present work, we explore to what extent two independently developed solvent-free CG models of peptides and lipids--of different mapping schemes, parametrization methods, target functions, and validation criteria--can be combined by only tuning the cross-interactions. Our results show that the cross-parametrization can reproduce a number of structural properties of membrane peptides (for example, tilt and hydrophobic mismatch), in agreement with existing peptide-lipid CG force fields. We find encouraging results for two challenging biophysical problems: (i) membrane pore formation mediated by the cooperative action of several antimicrobial peptides, and (ii) the insertion and folding of the helix-forming peptide WALP23 in the membrane.

Molecular dynamics studies of the protein-protein interactions in inhibitor of κB kinase-β

Activation of the inhibitor of κB kinase subunit β (IKKβ) oligomer initiates a cascade that results in the translocation of transcription factors involved in mediating immune responses. Dimerization of IKKβ is required for its activation. Coarse-grained and atomistic molecular dynamics simulations were used to investigate the conformation-activity and structure-activity relationships within the oligomer assembly of IKKβ that are impacted upon activation, mutation, and binding of ATP. Intermolecular interactions, free energies, and conformational changes were compared among several conformations, including a monomer, two different dimers, and the tetramer. Modifications to the activation segment induce conformational changes that disrupt dimerization and suggest that the multimeric assembly mediates a global stability for the enzyme that influences the activity of IKKβ.

Multiscale Free Energy Simulations: An Efficient Method for Connecting Classical MD Simulations to QM or QM/MM Free Energies Using Non-Boltzmann Bennett Reweighting Schemes

The reliability of free energy simulations (FES) is limited by two factors: (a) the need for correct sampling and (b) the accuracy of the computational method employed. Classical methods (e.g., force fields) are typically used for FES and present a myriad of challenges, with parametrization being a principle one. On the other hand, parameter-free quantum mechanical (QM) methods tend to be too computationally expensive for adequate sampling. One widely used approach is a combination of methods, where the free energy difference between the two end states is computed by, e.g., molecular mechanics (MM), and the end states are corrected by more accurate methods, such as QM or hybrid QM/MM techniques. Here we report two new approaches that significantly improve the aforementioned scheme; with a focus on how to compute corrections between, e.g., the MM and the more accurate QM calculations. First, a molecular dynamics trajectory that properly samples relevant conformational degrees of freedom is generated. Next, potential energies of each trajectory frame are generated with a QM or QM/MM Hamiltonian. Free energy differences are then calculated based on the QM or QM/MM energies using either a non-Boltzmann Bennett approach (QM-NBB) or non-Boltzmann free energy perturbation (NB-FEP). Both approaches are applied to calculate relative and absolute solvation free energies in explicit and implicit solvent environments. Solvation free energy differences (relative and absolute) between ethane and methanol in explicit solvent are used as the initial test case for QM-NBB. Next, implicit solvent methods are employed in conjunction with both QM-NBB and NB-FEP to compute absolute solvation free energies for 21 compounds. These compounds range from small molecules such as ethane and methanol to fairly large, flexible solutes, such as triacetyl glycerol. Several technical aspects were investigated. Ultimately some best practices are suggested for improving methods that seek to connect MM to QM (or QM/MM) levels of theory in FES.

FOUNDATIONS FOR MODELING THE DYNAMICS OF GENE REGULATORY NETWORKS: A MULTILEVEL-PERSPECTIVE REVIEW

A promising alternative for unraveling the principles under which the dynamic interactions among genes lead to cellular phenotypes relies on mathematical and computational models at different levels of abstraction, from the molecular level of protein-DNA interactions to the system level of functional relationships among genes. This review article presents, under a bottom–up perspective, a hierarchy of approaches to modeling gene regulatory network dynamics, from microscopic descriptions at the single-molecule level in the spatial context of an individual cell to macroscopic models providing phenomenological descriptions at the population-average level. The reviewed modeling approaches include Molecular Dynamics, Particle-Based Brownian Dynamics, the Master Equation approach, Ordinary Differential Equations, and the Boolean logic abstraction. Each of these frameworks is motivated by a particular biological context and the nature of the insight being pursued. The setting of gene network dynamic models from such frameworks involves assumptions and mathematical artifacts often ignored by the non-specialist. This article aims at providing an entry point for biologists new to the field and computer scientists not acquainted with some recent biophysically-inspired models of gene regulation. The connections promoting intuition between different abstraction levels and the role that approximations play in the modeling process are highlighted throughout the paper.

DOCLASP - Docking ligands to target proteins using spatial and electrostatic congruence extracted from a known holoenzyme and applying simple geometrical transformations

The ability to accurately and effectively predict the interaction between proteins and small drug-like compounds has long intrigued researchers for pedagogic, humanitarian and economic reasons. Protein docking methods (AutoDock, GOLD, DOCK, FlexX and Glide to name a few) rank a large number of possible conformations of protein-ligand complexes using fast algorithms. Previously, it has been shown that structural congruence leading to the same enzymatic function necessitates the congruence of electrostatic properties (CLASP). The current work presents a methodology for docking a ligand into a target protein, provided that there is at least one known holoenzyme with ligand bound - DOCLASP (Docking using CLASP). The contact points of the ligand in the holoenzyme defines a motif, which is used to query the target enzyme using CLASP. If there are significant matches, the holoenzyme and the target protein are superimposed based on congruent atoms. The same linear and rotational transformations are also applied to the ligand, thus creating a unified coordinate framework having the holoenzyme, the ligand and the target enzyme. In the current work, the dipeptidyl peptidase-IV inhibitor vildagliptin was docked to the PI-PLC structure complexed with myo-inositol using DOCLASP. Also, corroboration of the docking of phenylthiourea to the modelled structure of polyphenol oxidase (JrPPO1) from walnut is provided based on the subsequently solved structure of JrPPO1 (PDBid:5CE9). Analysis of the binding of the antitrypanosomial drug suramin to nine non-homologous proteins in the PDB database shows a diverse set of binding motifs, and multiple binding sites in the phospholipase A2-likeproteins from the Bothrops genus of pitvipers. The conformational changes in the suramin molecule on binding highlights the challenges in docking flexible ligands into an already 'plastic' binding site. Thus, DOCLASP presents a method for 'soft docking' ligands to proteins with low computational requirements.

Effects of desolvation barriers and sidechains on local–nonlocal coupling and chevron behaviors in coarse-grained models of protein folding

Coarse-grained protein chain models with desolvation barriers or sidechains lead to stronger local–nonlocal coupling and more linear chevron plots.

Shaping up for structural glycomics: a predictive protocol for oligosaccharide conformational analysis applied to N-linked glycans

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Aqueous 10 μs simulations of N-linked mannosyl cores and sialyl Lewis (sLe) antennae are validated.

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Sequence dependent glycosidic linkage and pyranose ring μs motions are implicated in bioactivity.

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Stacked pyranoses in sLe^a and sLe^x are predicted to be atypically rigid on μs timescales.

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In a 25 μs simulation of sLe^x, all known conformers were sampled within the initial 10 μs of dynamics.

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Unbiased 10 μs simulations are proposed as a route to systematic and accurate glycomic 3D-analysis.

The human glycome comprises a vast untapped repository of 3D-structural information that holds the key to glycan recognition and a new era of rationally designed mimetic chemical probes, drugs, and biomaterials. Toward routine prediction of oligosaccharide conformational populations and exchange rates at thermodynamic equilibrium, we apply hardware-accelerated aqueous molecular dynamics to model μs motions in N-glycans that underpin inflammation and immunity. In 10 μs simulations, conformational equilibria of mannosyl cores, sialyl Lewis (sLe) antennae, and constituent sub-sequences agreed with prior refinements (X-ray and NMR). Glycosidic linkage and pyranose ring flexing were affected by branching, linkage position, and secondary structure, implicating sequence dependent motions in glycomic functional diversity. Linkage and ring conformational transitions that have eluded precise quantification by experiment and conventional (ns) simulations were predicted to occur on μs timescales. All rings populated non-chair shapes and the stacked galactose and fucose pyranoses of sLe^a and sLe^x were rigidified, suggesting an exploitable 3D-signature of cell adhesion protein binding. Analyses of sLe^x dynamics over 25 μs revealed that only 10 μs were sufficient to explore all aqueous conformers. This simulation protocol, which yields conformational ensembles that are independent of initial 3D-structure, is proposed as a route to understanding oligosaccharide recognition and structure–activity relationships, toward development of carbohydrate-based novel chemical entities.

Rapid parameterization of small molecules using the Force Field Toolkit

The inability to rapidly generate accurate and robust parameters for novel chemical matter continues to severely limit the application of molecular dynamics simulations to many biological systems of interest, especially in fields such as drug discovery. Although the release of generalized versions of common classical force fields, for example, General Amber Force Field and CHARMM General Force Field, have posited guidelines for parameterization of small molecules, many technical challenges remain that have hampered their wide-scale extension. The Force Field Toolkit (ffTK), described herein, minimizes common barriers to ligand parameterization through algorithm and method development, automation of tedious and error-prone tasks, and graphical user interface design. Distributed as a VMD plugin, ffTK facilitates the traversal of a clear and organized workflow resulting in a complete set of CHARMM-compatible parameters. A variety of tools are provided to generate quantum mechanical target data, setup multidimensional optimization routines, and analyze parameter performance. Parameters developed for a small test set of molecules using ffTK were comparable to existing CGenFF parameters in their ability to reproduce experimentally measured values for pure-solvent properties (<15% error from experiment) and free energy of solvation (±0.5 kcal/mol from experiment).

Reaching new levels of realism in modeling biological macromolecules in cellular environments

An increasing number of studies are aimed at modeling cellular environments in a comprehensive and realistic fashion. A major challenge in these efforts is how to bridge spatial and temporal scales over many orders of magnitude. Furthermore, there are additional challenges in integrating different aspects ranging from questions about biomolecular stability in crowded environments to the description of reactive processes on cellular scales. In this review, recent studies with models of biomolecules in cellular environments at different levels of detail are discussed in terms of their strengths and weaknesses. In particular, atomistic models, implicit representations of cellular environments, coarse-grained and spheroidal models of biomolecules, as well as the inclusion of reactive processes via reaction-diffusion models are described. Furthermore, strategies for integrating the different models into a comprehensive description of cellular environments are discussed.

On the foldability of tryptophan-containing tetra- and pentapeptides: an exhaustive molecular dynamics study

Short peptides serve as minimal model systems to decipher the determinants of foldability due to their simplicity arising from their smaller size, their ability to echo protein-like structural characteristics, and their direct implication in force field validation. Here, we describe an effort to identify small peptides that can still form stable structures in aqueous solutions. We followed the in silico folding of a selected set of 8640 tryptophan-containing tetra- and pentapeptides through 15 210 molecular dynamics simulations amounting to a total of 272.46 μs using explicit representation of the solute and full treatment of the electrostatics. The evaluation and sorting of peptides is achieved through scoring functions, which include terms based on interatomic vector distances, atomic fluctuations, and rmsd matrices between successive frames of a trajectory. Highly scored peptides are studied further via successive simulation rounds of increasing simulation length and using different empirical force fields. Our method suggested only a handful of peptides with strong foldability prognosis. The discrepancies between the predictions of the various force fields for such short sequences are also extensively discussed. We conclude that the vast majority of such short peptides do not adopt significantly stable structures in water solutions, at least based on our computational predictions. The present work can be utilized in the rational design and engineering of bioactive peptides with desired molecular properties.

Optimization of an elastic network augmented coarse grained model to study CCMV capsid deformation

The major protective coat of most viruses is a highly symmetric protein capsid that forms spontaneously from many copies of identical proteins. Structural and mechanical properties of such capsids, as well as their self-assembly process, have been studied experimentally and theoretically, including modeling efforts by computer simulations on various scales. Atomistic models include specific details of local protein binding but are limited in system size and accessible time, while coarse grained (CG) models do get access to longer time and length scales but often lack the specific local interactions. Multi-scale models aim at bridging this gap by systematically connecting different levels of resolution. Here, a CG model for CCMV (Cowpea Chlorotic Mottle Virus), a virus with an icosahedral shell of 180 identical protein monomers, is developed, where parameters are derived from atomistic simulations of capsid protein dimers in aqueous solution. In particular, a new method is introduced to combine the MARTINI CG model with a supportive elastic network based on structural fluctuations of individual monomers. In the parametrization process, both network connectivity and strength are optimized. This elastic-network optimized CG model, which solely relies on atomistic data of small units (dimers), is able to correctly predict inter-protein conformational flexibility and properties of larger capsid fragments of 20 and more subunits. Furthermore, it is shown that this CG model reproduces experimental (Atomic Force Microscopy) indentation measurements of the entire viral capsid. Thus it is shown that one obvious goal for hierarchical modeling, namely predicting mechanical properties of larger protein complexes from models that are carefully parametrized on elastic properties of smaller units, is achievable.

Limiting assumptions in molecular modeling: electrostatics

Molecular mechanics attempts to represent intermolecular interactions in terms of classical physics. Initial efforts assumed a point charge located at the atom center and coulombic interactions. It is been recognized over multiple decades that simply representing electrostatics with a charge on each atom failed to reproduce the electrostatic potential surrounding a molecule as estimated by quantum mechanics. Molecular orbitals are not spherically symmetrical, an implicit assumption of monopole electrostatics. This perspective reviews recent evidence that requires use of multipole electrostatics and polarizability in molecular modeling.

Protein dynamics via computational microscope

The purpose of this overview is to provide a concise introduction to the methodology and current advances in molecular dynamics (MD) simulations. MD simulations emerged as a powerful and popular tool to study dynamic behavior of proteins and macromolecule complexes at the atomic resolution. This approach can extend static structural data, such as X-ray crystallography, into dynamic domains with realistic timescales (up to millisecond) and high precision, therefore becoming a veritable computational microscope. This perspective covers current advances and methodology in the simulation of protein folding and drug design as illustrated by several important published examples. Overall, recent progress in the simulation field points to the direction that MD will have significant impact on molecular biology and pharmaceutical science.

Molecular simulations of drug–receptor complexes in anticancer research

Molecular modeling and computer simulation techniques have matured significantly in recent years and proved their value in the study of drug–DNA, drug–DNA–protein, drug–protein and protein–protein interactions. Evolution in this area has gone hand-in-hand with an increased availability of structural data on biological macromolecules, major advances in molecular mechanics force fields and considerable improvements in computer technologies, most significantly processing speeds, multiprocessor programming and data-storage capacity. The information derived from molecular simulations of drug–receptor complexes can be used to extract structural and energetic information that is usually beyond current experimental possibilities, provide independent accounts of experimentally observed behavior, help in the interpretation of biochemical or pharmacological results, and open new avenues for research by posing novel relevant questions that can guide the design of new experiments. As drug-screening tools, ligand- and fragment-docking platforms stand out as powerful techniques that can provide candidate molecules for hit and lead development. This review provides an overall perspective of the main methods and focuses on some selected applications to both classical and novel anticancer targets.

In silico identification of potent pancreatic triacylglycerol lipase inhibitors from traditional Chinese medicine

Pancreatic triacylglycerol lipase (PNLIP) are primary lipases that are critical for triacylglyceride digestion in human. Since reduced metabolism of triacylglyceride might be a plausible concept for weight loss, we screened for potential PNLIP inhibitors from traditional Chinese medicine (TCM) with the aim to identify weight loss candidate compounds. TCM candidates Aurantiamide, Cnidiadin, and 2-hexadecenoic acid exhibited higher Dock Scores than the commercial drug Orlistat, and were also predicted to have inhibitory characteristics against PNLIP using constructed MLR (R² = 0.8664) and SVM (R² = 0.9030) models. Molecular dynamics indicated that the TCM-PNLIP complexes formed were stable. We identified that the PNLIP binding site has several residues that can serve as anchors, and a hydrophobic corridor that provides additional stability to the complex. Aurantiamide, Cnidiadin, and 2-hexadecenoic acid all have features that correspond to these binding site features, indicating their potential as candidates for PNLIP inhibitors. The information presented in this study may provide helpful insights to designing novel weight-control drugs.

Molecular dynamics simulation in virus research

Virus replication in the host proceeds by chains of interactions between viral and host proteins. The interactions are deeply influenced by host immune molecules and anti-viral compounds, as well as by mutations in viral proteins. To understand how these interactions proceed mechanically and how they are influenced by mutations, one needs to know the structures and dynamics of the proteins. Molecular dynamics (MD) simulation is a powerful computational method for delineating motions of proteins at an atomic-scale via theoretical and empirical principles in physical chemistry. Recent advances in the hardware and software for biomolecular simulation have rapidly improved the precision and performance of this technique. Consequently, MD simulation is quickly extending the range of applications in biology, helping to reveal unique features of protein structures that would be hard to obtain by experimental methods alone. In this review, we summarize the recent advances in MD simulations in the study of virus–host interactions and evolution, and present future perspectives on this technique.