The future of molecular dynamics simulations in drug discovery

Design of Polyphosphate Inhibitors: A Molecular Dynamics Investigation on Polyethylene Glycol-Linked Cationic Binding Groups

Inorganic polyphosphate (polyP) released by human platelets has recently been shown to activate blood clotting and identified as a potential target for the development of novel antithrombotics. Recent studies have shown that polymers with cationic binding groups (CBGs) inhibit polyP and attenuate thrombosis. However, a good molecular-level understanding of the binding mechanism is lacking for further drug development. While molecular dynamics (MD) simulation can provide molecule-level information, the time scale required to simulate these large biomacromolecules makes classical MD simulation impractical. To overcome this challenge, we employed metadynamics simulations with both all-atom and coarse-grained force fields. The force field parameters for polyethylene glycol (PEG) conjugated CBGs and polyP were developed to carry out coarse-grained MD simulations, which enabled simulations of these large biomacromolecules in a reasonable time scale. We found that the length of the PEG tail does not impact the interaction between the (PEG) _n-CBG and polyP. As expected, increasing the number of the charged tertiary amine groups in the head group strengthens its binding to polyP. Our simulation shows that (PEG) _n-CBG initially form aggregates, mostly with the PEG in the core and the hydrophilic CBG groups pointing toward water; then the aggregates approach the polyP and sandwich the polyP to form a complex. We found that the binding of (PEG) _n-CBG remains intact against various lengths of polyP. Binding thermodynamics for two of the (PEG) _n-CBG/polyP systems simulated were measured by isothermal titration calorimetry to confirm the key finding of the simulations that the length PEG tail does not influence ligand binding to polyP.

In vitro models and systems for evaluating the dynamics of drug delivery to the healthy and diseased brain

The blood-brain barrier (BBB) plays a crucial role in maintaining brain homeostasis and transport of drugs to the brain. The conventional animal and Transwell BBB models along with emerging microfluidic-based BBB-on-chip systems have provided fundamental functionalities of the BBB and facilitated the testing of drug delivery to the brain tissue. However, developing biomimetic and predictive BBB models capable of reasonably mimicking essential characteristics of the BBB functions is still a challenge. In addition, detailed analysis of the dynamics of drug delivery to the healthy or diseased brain requires not only biomimetic BBB tissue models but also new systems capable of monitoring the BBB microenvironment and dynamics of barrier function and delivery mechanisms. This review provides a comprehensive overview of recent advances in microengineering of BBB models with different functional complexity and mimicking capability of healthy and diseased states. It also discusses new technologies that can make the next generation of biomimetic human BBBs containing integrated biosensors for real-time monitoring the tissue microenvironment and barrier function and correlating it with the dynamics of drug delivery. Such integrated system addresses important brain drug delivery questions related to the treatment of brain diseases. We further discuss how the combination of in vitro BBB systems, computational models and nanotechnology supports for characterization of the dynamics of drug delivery to the brain.

Systematic Testing of Belief-Propagation Estimates for Absolute Free Energies in Atomistic Peptides and Proteins

Motivated by the extremely high computing costs associated with estimates of free energies for biological systems using molecular simulations, we further the exploration of existing "belief propagation" (BP) algorithms for fixed-backbone peptide and protein systems. The precalculation of pairwise interactions among discretized libraries of side-chain conformations, along with representation of protein side chains as nodes in a graphical model, enables direct application of the BP approach, which requires only ∼1 s of single-processor run time after the precalculation stage. We use a "loopy BP" algorithm, which can be seen as an approximate generalization of the transfer-matrix approach to highly connected (i.e., loopy) graphs, and it has previously been applied to protein calculations. We examine the application of loopy BP to several peptides as well as the binding site of the T4 lysozyme L99A mutant. The present study reports on (i) the comparison of the approximate BP results with estimates from unbiased estimators based on the Amber99SB force field; (ii) investigation of the effects of varying library size on BP predictions; and (iii) a theoretical discussion of the discretization effects that can arise in BP calculations. The data suggest that, despite their approximate nature, BP free-energy estimates are highly accurate-indeed, they never fall outside confidence intervals from unbiased estimators for the systems where independent results could be obtained. Furthermore, we find that libraries of sufficiently fine discretization (which diminish library-size sensitivity) can be obtained with standard computing resources in most cases. Altogether, the extremely low computing times and accurate results suggest the BP approach warrants further study.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or catalytic enzyme is known experimentally. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to inhibit a particular protein interaction or biological activity. The virtual ligand screening process often relies on docking methods which allow predicting the binding of a molecule into a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of protein flexibility information, no solvation effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions and even in membrane-like environments, and ranking complexes with more accurate binding energy calculations. In this chapter we describe the up-to-date MD protocols that are mandatory supporting tools in the virtual ligand screening (VS) process. Using docking in combination with MD is one of the best computer-aided drug design protocols nowadays. It has proved its efficiency through many examples, described below.

Systematic exploration of multiple drug binding sites

Background

Targets with multiple (prerequisite or allosteric) binding sites have an increasing importance in drug design. Experimental determination of atomic resolution structures of ligands weakly bound to multiple binding sites is often challenging. Blind docking has been widely used for fast mapping of the entire target surface for multiple binding sites. Reliability of blind docking is limited by approximations of hydration models, simplified handling of molecular flexibility, and imperfect search algorithms.

Results

To overcome such limitations, the present study introduces Wrap ‘n’ Shake (WnS), an atomic resolution method that systematically “wraps” the entire target into a monolayer of ligand molecules. Functional binding sites are extracted by a rapid molecular dynamics shaker. WnS is tested on biologically important systems such as mitogen-activated protein, tyrosine-protein kinases, key players of cellular signaling, and farnesyl pyrophosphate synthase, a target of antitumor agents.

Electronic supplementary material

The online version of this article (10.1186/s13321-017-0255-6) contains supplementary material, which is available to authorized users.

Structure based drug discovery for designing leads for the non-toxic metabolic targets in multi drug resistant Mycobacterium tuberculosis

Background

The problem of drug resistance and bacterial persistence in tuberculosis is a cause of global alarm. Although, the UN’s Sustainable Development Goals for 2030 has targeted a Tb free world, the treatment gap exists and only a few new drug candidates are in the pipeline. In spite of large information from medicinal chemistry to ‘omics’ data, there has been a little effort from pharmaceutical companies to generate pipelines for the development of novel drug candidates against the multi drug resistant Mycobacterium tuberculosis.

Methods

In the present study, we describe an integrated methodology; utilizing systems level information to optimize ligand selection to lower the failure rates at the pre-clinical and clinical levels. In the present study, metabolic targets (Rv2763c, Rv3247c, Rv1094, Rv3607c, Rv3048c, Rv2965c, Rv2361c, Rv0865, Rv0321, Rv0098, Rv0390, Rv3588c, Rv2244, Rv2465c and Rv2607) in M. tuberculosis, identified using our previous Systems Biology and data-intensive genome level analysis, have been used to design potential lead molecules, which are likely to be non-toxic. Various in silico drug discovery tools have been utilized to generate small molecular leads for each of the 15 targets with available crystal structures.

Results

The present study resulted in identification of 20 novel lead molecules including 4 FDA approved drugs (droxidropa, tetroxoprim, domperidone and nemonapride) which can be further taken for drug repurposing. This comprehensive integrated methodology, with both experimental and in silico approaches, has the potential to not only tackle the MDR form of Mtb but also the most important persister population of the bacterium, with a potential to reduce the failures in the Tb drug discovery.

Conclusion

We propose an integrated approach of systems and structural biology for identifying targets that address the high attrition rate issue in lead identification and drug development We expect that this system level analysis will be applicable for identification of drug candidates to other pathogenic organisms as well.

Electronic supplementary material

The online version of this article (10.1186/s12967-017-1363-9) contains supplementary material, which is available to authorized users.

Sublimation Entropy and Dissociation Constants Prediction by Quantitative Evaluation of Molecular Mobility in Crystals

Prediction of binding free energies (or dissociation constants) is a crucial challenge for computational biochemistry. One of the main problems here consists in fast and accurate evaluation of binding entropy, which is far more time-consuming than evaluation of binding enthalpy. Here, we offer a fast and rather accurate approach to evaluate the sublimation entropy (i.e., entropy of binding of a vapor molecule to a crystal, taken with the opposite sign) from the average range of molecular movements in the solid state. To estimate this range (and the corresponding amplitude), we considered equilibrium sublimation of small organic molecules from molecular crystals. The calculations were based on experimental data on the sublimation enthalpy, pressure of saturated vapor, and structural characteristics of the molecule in question. The resulting average amplitude (0.17 ± 0.01 Å) of molecular movements in crystals was used to predict sublimation entropies and dissociation constants for sublimation of 28 molecular crystals. The results of these predictions are in close agreement with experimental values.

CHARMM-GUI 10 years for biomolecular modeling and simulation

CHARMM-GUI, http://www.charmm-gui.org, is a web-based graphical user interface that prepares complex biomolecular systems for molecular simulations. CHARMM-GUI creates input files for a number of programs including CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Since its original development in 2006, CHARMM-GUI has been widely adopted for various purposes and now contains a number of different modules designed to set up a broad range of simulations: (1) PDB Reader & Manipulator, Glycan Reader, and Ligand Reader & Modeler for reading and modifying molecules; (2) Quick MD Simulator, Membrane Builder, Nanodisc Builder, HMMM Builder, Monolayer Builder, Micelle Builder, and Hex Phase Builder for building all-atom simulation systems in various environments; (3) PACE CG Builder and Martini Maker for building coarse-grained simulation systems; (4) DEER Facilitator and MDFF/xMDFF Utilizer for experimentally guided simulations; (5) Implicit Solvent Modeler, PBEQ-Solver, and GCMC/BD Ion Simulator for implicit solvent related calculations; (6) Ligand Binder for ligand solvation and binding free energy simulations; and (7) Drude Prepper for preparation of simulations with the CHARMM Drude polarizable force field. Recently, new modules have been integrated into CHARMM-GUI, such as Glycolipid Modeler for generation of various glycolipid structures, and LPS Modeler for generation of lipopolysaccharide structures from various Gram-negative bacteria. These new features together with existing modules are expected to facilitate advanced molecular modeling and simulation thereby leading to an improved understanding of the structure and dynamics of complex biomolecular systems. Here, we briefly review these capabilities and discuss potential future directions in the CHARMM-GUI development project. © 2016 Wiley Periodicals, Inc.

Exploring the Pivotal Role of the CK2 Hinge Region Sub-Pocket in Binding with Tricyclic Quinolone Analogues by Computational Analysis

Protein kinase CK2 has been considered as an attractive therapeutic target of cancer therapy. The tricyclic quinoline compound CX-4945 is the first representative of CK2 inhibitors used in human clinical trials. The binding of non-2,6-naphtyridine substituted compounds 27e (IC₅₀ > 500 nM) and 27h (IC₅₀ > 1000 nM) to CK2 is abolished. However, the unbinding mechanisms due to the key pharmacophore group replacement of compounds 27e and 27h are unveiled. In the present work, combined computational analysis was performed to investigate the underlying structural basis of the low-affinity of two systems. As indicated in the results, the loss of hydrogen bonds between the non-2,6-naphtyridine and the hinge region destroyed the proper recognition of the two complexes. Besides, the allosteric mechanisms between the deviated ligands and the changed regions (G-loop, C-loop and β4/β5 loop) are proposed. Furthermore, energetic analysis was evaluated by detailed energy calculation and residue-based energy decomposition. More importantly, the summary of known polar pharmacophore groups elucidates the pivotal roles of hinge region sub-pocket in the binding of CK2 inhibitors. These results provide rational clues to the fragment-based design of more potent CK2 inhibitors.

Predicting a Drug's Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data

Membrane permeability is a key property to consider during the drug design process, and particularly vital when dealing with small molecules that have intracellular targets as their efficacy highly depends on their ability to cross the membrane. In this work, we describe the use of umbrella sampling molecular dynamics (MD) computational modeling to comprehensively assess the passive permeability profile of a range of compounds through a lipid bilayer. The model was initially calibrated through in vitro validation studies employing a parallel artificial membrane permeability assay (PAMPA). The model was subsequently evaluated for its quantitative prediction of permeability profiles for a series of custom synthesized and closely related compounds. The results exhibited substantially improved agreement with the PAMPA data, relative to alternative existing methods. Our work introduces a computational model that underwent progressive molding and fine-tuning as a result of its synergistic collaboration with numerous in vitro PAMPA permeability assays. The presented computational model introduces itself as a useful, predictive tool for permeability prediction.

Computer-Aided Antibody Design: An Overview

The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.

Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge

As the length of molecular dynamics (MD) trajectories grows with increasing computational power, so does the importance of clustering methods for partitioning trajectories into conformational bins. Of the methods available, the vast majority require users to either have some a priori knowledge about the system to be clustered or to tune clustering parameters through trial and error. Here we present non-parametric uses of two modern clustering techniques suitable for first-pass investigation of an MD trajectory. Being non-parametric, these methods require neither prior knowledge nor parameter tuning. The first method, HDBSCAN, is fast-relative to other popular clustering methods-and is able to group unstructured or intrinsically disordered systems (such as intrinsically disordered proteins, or IDPs) into bins that represent global conformational shifts. HDBSCAN is also useful for determining the overall stability of a system-as it tends to group stable systems into one or two bins-and identifying transition events between metastable states. The second method, iMWK-Means, with explicit rescaling followed by K-Means, while slower than HDBSCAN, performs well with stable, structured systems such as folded proteins and is able to identify higher resolution details such as changes in relative position of secondary structural elements. Used in conjunction, these clustering methods allow a user to discern quickly and without prior knowledge the stability of a simulated system and identify both local and global conformational changes.

Mechanisms for the inhibition of amyloid aggregation by small ligands

This work investigates by biochemical, biophysical and MD techniques the opposite anti-amyloid properties of resveratrol and rosmarinic acid on the aggregation of hen egg white lysozyme (HEWL). Differences in association energy and contact maps were found that explain the different behaviours.

The formation of amyloid aggregates is the hallmark of systemic and neurodegenerative disorders, also known as amyloidoses. Many proteins have been found to aggregate into amyloid-like fibrils and this process is recognized as a general tendency of polypeptides. Lysozyme, an antibacterial protein, is a well-studied model since it is associated in human with systemic amyloidosis and that is widely available from chicken eggs (HEWL, hen egg white lysozyme). In the present study we investigated the mechanism of interaction of aggregating HEWL with rosmarinic acid and resveratrol, that we verified to be effective and ineffective, respectively, in inhibiting aggregate formation. We used a multidisciplinary strategy to characterize such effects, combining biochemical and biophysical methods with molecular dynamics (MD) simulations on the HEWL peptide 49–64 to gain insights into the mechanisms and energy variations associated to amyloid formation and inhibition. MD revealed that neither resveratrol nor rosmarinic acid were able to compete with the initial formation of the β-sheet structure. We then tested the association of two β-sheets, representing the model of an amyloid core structure. MD showed that rosmarinic acid displayed an interaction energy and a contact map comparable to that of sheet pairings. On the contrary, resveratrol association energy was found to be much lower and its contact map largely different than that of sheet pairings. The overall characterization elucidated a possible mechanism explaining why, in this model, resveratrol is inactive in blocking fibril formation, whereas rosmarinic acid is instead a powerful inhibitor.

Overview of the SAMPL5 host-guest challenge: Are we doing better?

The ability to computationally predict protein-small molecule binding affinities with high accuracy would accelerate drug discovery and reduce its cost by eliminating rounds of trial-and-error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can be used to rigorously test new computational methods. Although protein-ligand data are clearly important for this purpose, their size and complexity make it difficult to obtain well-converged results and to troubleshoot computational methods. Host-guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition but are far smaller and simpler. As a consequence, host-guest systems have been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to being blinded, and thus avoiding biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may be compared and ideas exchanged. The present paper provides an overview of the host-guest component of SAMPL5, which centers on three different hosts, two octa-acids and a glycoluril-based molecular clip, and two different sets of guest molecules, in aqueous solution. A range of methods were applied, including electronic structure calculations with implicit solvent models; methods that combine empirical force fields with implicit solvent models; and explicit solvent free energy simulations. The most reliable methods tend to fall in the latter class, consistent with results in prior SAMPL rounds, but the level of accuracy is still below that sought for reliable computer-aided drug design. Advances in force field accuracy, modeling of protonation equilibria, electronic structure methods, and solvent models, hold promise for future improvements.

Substrate prediction of Ixodes ricinus salivary lipocalins differentially expressed during Borrelia afzelii infection

Evolution has provided ticks with an arsenal of bioactive saliva molecules that counteract host defense mechanisms. This salivary pharmacopoeia enables blood-feeding while enabling pathogen transmission. High-throughput sequencing of tick salivary glands has thus become a major focus, revealing large expansion within protein encoding gene families. Among these are lipocalins, ubiquitous barrel-shaped proteins that sequester small, typically hydrophobic molecules. This study was initiated by mining the Ixodes ricinus salivary gland transcriptome for specific, uncharacterized lipocalins: three were identified. Differential expression of these I. ricinus lipocalins during feeding at distinct developmental stages and in response to Borrelia afzelii infection suggests a role in transmission of this Lyme disease spirochete. A phylogenetic analysis using 803 sequences places the three I. ricinus lipocalins with tick lipocalins that sequester monoamines, leukotrienes and fatty acids. Both structural analysis and biophysical simulations generated robust predictions showing these I. ricinus lipocalins have the potential to bind monoamines similar to other tick species previously reported. The multidisciplinary approach employed in this study characterized unique lipocalins that play a role in tick blood-feeding and transmission of the most important tick-borne pathogen in North America and Eurasia.

Designing molecular complexes using free-energy derivatives from liquid-state integral equation theory

Complex formation between molecules in solution is the key process by which molecular interactions are translated into functional systems. These processes are governed by the binding or free energy of association which depends on both direct molecular interactions and the solvation contribution. A design goal frequently addressed in pharmaceutical sciences is the optimization of chemical properties of the complex partners in the sense of minimizing their binding free energy with respect to a change in chemical structure. Here, we demonstrate that liquid-state theory in the form of the solute-solute equation of the reference interaction site model provides all necessary information for such a task with high efficiency. In particular, computing derivatives of the potential of mean force (PMF), which defines the free-energy surface of complex formation, with respect to potential parameters can be viewed as a means to define a direction in chemical space toward better binders. We illustrate the methodology in the benchmark case of alkali ion binding to the crown ether 18-crown-6 in aqueous solution. In order to examine the validity of the underlying solute-solute theory, we first compare PMFs computed by different approaches, including explicit free-energy molecular dynamics simulations as a reference. Predictions of an optimally binding ion radius based on free-energy derivatives are then shown to yield consistent results for different ion parameter sets and to compare well with earlier, orders-of-magnitude more costly explicit simulation results. This proof-of-principle study, therefore, demonstrates the potential of liquid-state theory for molecular design problems.

Chitinase from Thermomyces lanuginosus SSBP and its biotechnological applications

Chitinases are ubiquitous class of extracellular enzymes, which have gained attention in the past few years due to their wide biotechnological applications. The effectiveness of conventional insecticides is increasingly compromised by the occurrence of resistance; thus, chitinase offers a potential alternative to the use of chemical fungicides. The thermostable enzymes from thermophilic microorganisms have numerous industrial, medical, environmental and biotechnological applications due to their high stability for temperature and pH. Thermomyces lanuginosus produced a large number of chitinases, of which chitinase I and II are successfully cloned and purified recently. Molecular dynamic simulations revealed that the stability of these enzymes are maintained even at higher temperature. In this review article we have focused on chitinases from different sources, mainly fungal chitinase of T. lanuginosus and its industrial application.

Sensitivity in Binding Free Energies Due to Protein Reorganization

Tremendous recent improvements in computer hardware, coupled with advances in sampling techniques and force fields, are now allowing protein-ligand binding free energy calculations to be routinely used to aid pharmaceutical drug discovery projects. However, despite these recent innovations, there are still needs for further improvement in sampling algorithms to more adequately sample protein motion relevant to protein-ligand binding. Here, we report our work identifying and studying such clear and remaining needs in the apolar cavity of T4 lysozyme L99A. In this study, we model recent experimental results that show the progressive opening of the binding pocket in response to a series of homologous ligands.1 Even while using enhanced sampling techniques, we demonstrate that the predicted relative binding free energies (RBFE) are sensitive to the initial protein conformational state. Particularly, we highlight the importance of sufficient sampling of protein conformational changes and demonstrate how inclusion of three key protein residues in the "hot" region of the FEP/REST simulation improves the sampling and resolves this sensitivity, given enough simulation time.

Peptides design based on transmembrane Escherichia coli's OmpA protein through molecular dynamics simulations in water-dodecane interfaces

Recent research efforts have focused on the production of environmentally nonthreatening products, including identifying biosurfactants that can replace conventional surfactants. In order to utilize biosurfactants in different industries such as cosmetic, food or petroleum, it is necessary to understand the underpinnings behind the interactions that could take place for biosurfactants which display potential for interface activity. This work aimed to use molecular dynamics simulations to understand the interactions of rationally obtained peptide sequences from the original sequence of the OmpA gene in Escherichia coli, based on the free energy change (ΔG) during peptide insertion at the water-dodecane interface. Seventeen OmpA-based peptide sequences were selected and analyzed based on their hydropathy index profiles. We found that free energy change due to Columbic interactions and SASA (ΔGCoul/SASA), total free energy change and MW (ΔG/MW), and free energy change due to Coulombic and van der Waals interactions (ΔGCoul/ΔGvdW) ratios could provide a better understating in the contribution of the free energy decrease at the interface. The results indicated that the peptide sequences GKNHDTGVSPVFA and THENQLGAGAFG display biosurfactant potential based on low ΔG per square nanometer, high ΔGCoul/ΔGvdW ratio, clearly defined moieties along its hydrophobic surface and sequence, and the presence of charged residues in the polar head. Clearly defined moieties and SASA were determinant for electrostatic interactions between oil-water interfaces. Experimental validations exhibited that the emulsions prepared remained stable between 3 and 27h, respectively. Even though the peptide GKNHDTGVSPVFA displays strong interactions at the interface, stabilization times showed that the peptide THENQLGAGAFG exhibited the best performance suggesting that the stability can be better described by kinetic rather than thermodynamic criteria once the emulsion is formed.

Modern approaches to accelerate discovery of new antischistosomal drugs

INTRODUCTION

The almost exclusive use of only praziquantel for the treatment of schistosomiasis has raised concerns about the possible emergence of drug-resistant schistosomes. Consequently, there is an urgent need for new antischistosomal drugs. The identification of leads and the generation of high quality data are crucial steps in the early stages of schistosome drug discovery projects.

AREAS COVERED

Herein, the authors focus on the current developments in antischistosomal lead discovery, specifically referring to the use of automated in vitro target-based and whole-organism screens and virtual screening of chemical databases. They highlight the strengths and pitfalls of each of the above-mentioned approaches, and suggest possible roadmaps towards the integration of several strategies, which may contribute for optimizing research outputs and led to more successful and cost-effective drug discovery endeavors.

EXPERT OPINION

Increasing partnerships and access to funding for drug discovery have strengthened the battle against schistosomiasis in recent years. However, the authors believe this battle also includes innovative strategies to overcome scientific challenges. In this context, significant advances of in vitro screening as well as computer-aided drug discovery have contributed to increase the success rate and reduce the costs of drug discovery campaigns. Although some of these approaches were already used in current antischistosomal lead discovery pipelines, the integration of these strategies in a solid workflow should allow the production of new treatments for schistosomiasis in the near future.

All-Atom Molecular Dynamics of Virus Capsids as Drug Targets

Virus capsids are protein shells that package the viral genome. Although their morphology and biological functions can vary markedly, capsids often play critical roles in regulating viral infection pathways. A detailed knowledge of virus capsids, including their dynamic structure, interactions with cellular factors, and the specific roles that they play in the replication cycle, is imperative for the development of antiviral therapeutics. The following Perspective introduces an emerging area of computational biology that focuses on the dynamics of virus capsids and capsid-protein assemblies, with particular emphasis on the effects of small-molecule drug binding on capsid structure, stability, and allosteric pathways. When performed at chemical detail, molecular dynamics simulations can reveal subtle changes in virus capsids induced by drug molecules a fraction of their size. Here, the current challenges of performing all-atom capsid-drug simulations are discussed, along with an outlook on the applicability of virus capsid simulations to reveal novel drug targets.

Amorphous solid dispersions: Rational selection of a manufacturing process

Amorphous products and particularly amorphous solid dispersions are currently one of the most exciting areas in the pharmaceutical field. This approach presents huge potential and advantageous features concerning the overall improvement of drug bioavailability. Currently, different manufacturing processes are being developed to produce amorphous solid dispersions with suitable robustness and reproducibility, ranging from solvent evaporation to melting processes. In the present paper, laboratorial and industrial scale processes were reviewed, and guidelines for a rationale selection of manufacturing processes were proposed. This would ensure an adequate development (laboratorial scale) and production according to the good manufacturing practices (GMP) (industrial scale) of amorphous solid dispersions, with further implications on the process validations and drug development pipeline.

Flexible CDOCKER: Development and application of a pseudo-explicit structure-based docking method within CHARMM

Protein-ligand docking is a commonly used method for lead identification and refinement. While traditional structure-based docking methods represent the receptor as a rigid body, recent developments have been moving toward the inclusion of protein flexibility. Proteins exist in an interconverting ensemble of conformational states, but effectively and efficiently searching the conformational space available to both the receptor and ligand remains a well-appreciated computational challenge. To this end, we have developed the Flexible CDOCKER method as an extension of the family of complete docking solutions available within CHARMM. This method integrates atomically detailed side chain flexibility with grid-based docking methods, maintaining efficiency while allowing the protein and ligand configurations to explore their conformational space simultaneously. This is in contrast to existing approaches that use induced-fit like sampling, such as Glide or Autodock, where the protein or the ligand space is sampled independently in an iterative fashion. Presented here are developments to the CHARMM docking methodology to incorporate receptor flexibility and improvements to the sampling protocol as demonstrated with re-docking trials on a subset of the CCDC/Astex set. These developments within CDOCKER achieve docking accuracy competitive with or exceeding the performance of other widely utilized docking programs.

Flap Dynamics in Aspartic Proteases: A Computational Perspective

Recent advances in biochemistry and drug design have placed proteases as one of the critical target groups for developing novel small-molecule inhibitors. Among all proteases, aspartic proteases have gained significant attention due to their role in HIV/AIDS, malaria, Alzheimer's disease, etc. The binding cleft is covered by one or two β-hairpins (flaps) which need to be opened before a ligand can bind. After binding, the flaps close to retain the ligand in the active site. Development of computational tools has improved our understanding of flap dynamics and its role in ligand recognition. In the past decade, several computational approaches, for example molecular dynamics (MD) simulations, coarse-grained simulations, replica-exchange molecular dynamics (REMD) and metadynamics, have been used to understand flap dynamics and conformational motions associated with flap movements. This review is intended to summarize the computational progress towards understanding the flap dynamics of proteases and to be a reference for future studies in this field.

Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis

Despite the tremendous improvement in overall global health heralded by the adoption of the Millennium Declaration in the year 2000, tropical infections remain a major health problem in the developing world. Recent estimates indicate that the major tropical infectious diseases, namely, malaria, tuberculosis, trypanosomiasis, and leishmaniasis, account for more than 2.2 million deaths and a loss of approximately 85 million disability-adjusted life years annually. The crucial role of chemotherapy in curtailing the deleterious health and economic impacts of these infections has invigorated the search for new drugs against tropical infectious diseases. The research efforts have involved increased application of computational technologies in mainstream drug discovery programs at the hit identification, hit-to-lead, and lead optimization stages. This review highlights various computer-aided drug discovery approaches that have been utilized in efforts to identify novel antimalarial, antitubercular, antitrypanosomal, and antileishmanial agents. The focus is largely on developments over the past 5 years (2010-2014).

Fast evolutionary rates associated with functional loss in class I glucose transporters of Schistosoma mansoni

Background

The trematode parasite, Schistosoma mansoni, has evolved to switch from oxidative phosphorylation to glycolysis in the presence of glucose immediately after invading the human host. This metabolic switch is dependent on extracellular glucose concentration. Four glucose transporters are encoded in the genome of S. mansoni, however, only two were shown to facilitate glucose diffusion.

Results

By modeling the phase of human host infection, we showed that transporter transcript expression profiles of recently transformed schistosomula have two opposing responses to increased glucose concentrations. Concurring with the transcription profiles, our phylogenetic analyses revealed that S. mansoni glucose transporters belong to two separate clusters, one associated with class I glucose transporters from vertebrates and insects, and the other specific to parasitic Platyhelminthes. To study the evolutionary paths of both groups and their functional implications, we determined evolutionary rates, relative divergence times, genomic organization and performed structural analyses with the protein sequences. We finally used the modelled structures of the S. mansoni glucose transporters to biophysically (i) analyze the dynamics of key residues during glucose binding, (ii) test glucose stability within the active site, and (iii) demonstrate glucose diffusion. The two S. mansoni Platyhelminthes-specific glucose transporters, which seem to be younger than the other two, exhibit slower rates of molecular evolution, are encoded by intron-poor genes, and transport glucose. Interestingly, our molecular dynamic analyses suggest that S. mansoni class I glucose transporters are not able to transport glucose.

Conclusions

The glucose transporter family in S. mansoni exhibit different evolutionary histories. Our results suggested that S. mansoni class I glucose transporters lost their capacity to transport glucose and that this function evolved independently in the Platyhelminthes-specific glucose transporters. Finally, taking into account the differences in the dynamics of glucose transport of the Platyhelminthes-specific transporters of S. mansoni compared to that of humans, we conclude that S. mansoni glucose transporters may be targets for rationally designed drugs against schistosomiasis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2144-6) contains supplementary material, which is available to authorized users.

Molecular Dynamics: New Frontier in Personalized Medicine

The field of drug discovery has witnessed infinite development over the last decade with the demand for discovery of novel efficient lead compounds. Although the development of novel compounds in this field has seen large failure, a breakthrough in this area might be the establishment of personalized medicine. The trend of personalized medicine has shown stupendous growth being a hot topic after the successful completion of Human Genome Project and 1000 genomes pilot project. Genomic variant such as SNPs play a vital role with respect to inter individual's disease susceptibility and drug response. Hence, identification of such genetic variants has to be performed before administration of a drug. This process requires high-end techniques to understand the complexity of the molecules which might bring an insight to understand the compounds at their molecular level. To sustenance this, field of bioinformatics plays a crucial role in revealing the molecular mechanism of the mutation and thereby designing a drug for an individual in fast and affordable manner. High-end computational methods, such as molecular dynamics (MD) simulation has proved to be a constitutive approach to detecting the minor changes associated with an SNP for better understanding of the structural and functional relationship. The parameters used in molecular dynamic simulation elucidate different properties of a macromolecule, such as protein stability and flexibility. MD along with docking analysis can reveal the synergetic effect of an SNP in protein-ligand interaction and provides a foundation for designing a particular drug molecule for an individual. This compelling application of computational power and the advent of other technologies have paved a promising way toward personalized medicine. In this in-depth review, we tried to highlight the different wings of MD toward personalized medicine.

A Combined Molecular Docking/Dynamics Approach to Probe the Binding Mode of Cancer Drugs with Cytochrome P450 3A4

Cytarabine, daunorubicin, doxorubicin and vincristine are clinically used for combinatorial therapies of cancers in different combinations. However, the knowledge about the interaction of these drugs with the metabolizing enzyme cytochrome P450 is limited. Therefore, we utilized computational methods to predict and assess the drug-binding modes. In this study, we performed docking, MD simulations and free energy landscape analysis to understand the drug-enzyme interactions, protein domain motions and the most populated free energy minimum conformations of the docked protein-drug complexes, respectively. The outcome of docking and MD simulations predicted the productive, as well as the non-productive binding modes of the selected drugs. Based on these interaction studies, we observed that S119, R212 and R372 are the major drug-binding residues in CYP3A4. The molecular mechanics Poisson–Boltzmann surface area analysis revealed the dominance of hydrophobic forces in the CYP3A4-drug association. Further analyses predicted the residues that may contain favorable drug-specific interactions. The probable binding modes of the cancer drugs from this study may extend the knowledge of the protein-drug interaction and pave the way to design analogs with reduced toxicity. In addition, they also provide valuable insights into the metabolism of the cancer drugs.

Computer-aided Molecular Design of Compounds Targeting Histone Modifying Enzymes

Growing evidences show that epigenetic mechanisms play crucial roles in the genesis and progression of many physiopathological processes. As a result, research in epigenetic grew at a fast pace in the last decade. In particular, the study of histone post-translational modifications encountered an extraordinary progression and many modifications have been characterized and associated to fundamental biological processes and pathological conditions. Histone modifications are the catalytic result of a large set of enzyme families that operate covalent modifications on specific residues at the histone tails. Taken together, these modifications elicit a complex and concerted processing that greatly contribute to the chromatin remodeling and may drive different pathological conditions, especially cancer. For this reason, several epigenetic targets are currently under validation for drug discovery purposes and different academic and industrial programs have been already launched to produce the first pre-clinical and clinical outcomes. In this scenario, computer-aided molecular design techniques are offering important tools, mainly as a consequence of the increasing structural information available for these targets. In this mini-review we will briefly discuss the most common types of known histone modifications and the corresponding operating enzymes by emphasizing the computer-aided molecular design approaches that can be of use to speed-up the efforts to generate new pharmaceutically relevant compounds.

Multiscale approach to investigate self-assembly of telodendrimer based nanocarriers for anticancer drug delivery

Delivery of poorly soluble anticancer drugs can be achieved by employing polymeric drug delivery systems, capable of forming stable self-assembled nanocarriers with drug encapsulated within their hydrophobic cores. Computational investigations can aid the design of efficient drug-delivery platforms; however, simulations of nanocarrier self-assembly process are challenging due to high computational cost associated with the large system sizes (millions of atoms) and long time scales required for equilibration. In this work, we overcome this challenge by employing a multiscale computational approach in conjunction with experiments to analyze the role of the individual building blocks in the self-assembly of a highly tunable linear poly(ethylene glycol)-b-dendritic oligo(cholic acid) block copolymer called telodendrimer. The multiscale approach involved developing a coarse grained description of the telodendrimer, performing simulations over several microseconds to capture the self-assembly process, followed by reverse mapping of the coarse grained system to atomistic representation for structural analysis. Overcoming the computational bottleneck allowed us to run multiple self-assembly simulations and determine average size, drug-telodendrimer micellar stoichiometry, optimal drug loading capacity, and atomistic details such hydrogen-bonding and solvent accessible area of the nanocarrier. Computed results are in agreement with the experimental data, highlighting the success of the multiscale approach applied here.

Site of reactivity models predict molecular reactivity of diverse chemicals with glutathione

Drug toxicity is often caused by electrophilic reactive metabolites that covalently bind to proteins. Consequently, the quantitative strength of a molecule's reactivity with glutathione (GSH) is a frequently used indicator of its toxicity. Through cysteine, GSH (and proteins) scavenges reactive molecules to form conjugates in the body. GSH conjugates to specific atoms in reactive molecules: their sites of reactivity. The value of knowing a molecule's sites of reactivity is unexplored in the literature. This study tests the value of site of reactivity data that identifies the atoms within 1213 reactive molecules that conjugate to GSH and builds models to predict molecular reactivity with glutathione. An algorithm originally written to model sites of cytochrome P450 metabolism (called XenoSite) finds clear patterns in molecular structure that identify sites of reactivity within reactive molecules with 90.8% accuracy and separate reactive and unreactive molecules with 80.6% accuracy. Furthermore, the model output strongly correlates with quantitative GSH reactivity data in chemically diverse, external data sets. Site of reactivity data is nearly unstudied in the literature prior to our efforts, yet it contains a strong signal for reactivity that can be utilized to more accurately predict molecule reactivity and, eventually, toxicity.

Topological obstructions in the way of data-driven collective variables

Nonlinear dimensionality reduction (NLDR) techniques are increasingly used to visualize molecular trajectories and to create data-driven collective variables for enhanced sampling simulations. The success of these methods relies on their ability to identify the essential degrees of freedom characterizing conformational changes. Here, we show that NLDR methods face serious obstacles when the underlying collective variables present periodicities, e.g., arising from proper dihedral angles. As a result, NLDR methods collapse very distant configurations, thus leading to misinterpretations and inefficiencies in enhanced sampling. Here, we identify this largely overlooked problem and discuss possible approaches to overcome it. We also characterize the geometry and topology of conformational changes of alanine dipeptide, a benchmark system for testing new methods to identify collective variables.