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Physical models have a long and important role in exploring and communicating scientific and medical concepts and structures. One of the earliest physical scientific visualizations was the orrery, a mechanical model of the solar system, which first appeared in Greece around 150 BC. Early anatomical models, such as those made from Papier-Mache were developed in the early 19th Century. The earliest molecular models were those produced in the mid 19th century by chemists (such as Hoffman and Kekule) to visualize the nature of chemical composition. These models brought abstract or unseen structures into the context of human experience.

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers. This article is protected by copyright. All rights reserved.

Models of insulin secretory vesicles from pancreatic beta cells have been created using the cellPACK suite of tools to research, curate, construct and visualise the current state of knowledge. The model integrates experimental information from proteomics, structural biology, cryoelectron microscopy and X-ray tomography, and is used to generate models of mature and immature vesicles. A new method was developed to generate a confidence score that reconciles inconsistencies between three available proteomes using expert annotations of cellular localisation. The models are used to simulate soft X-ray tomograms, allowing quantification of features that are observed in experimental tomograms, and in turn, allowing interpretation of X-ray tomograms at the molecular level.

We have identified novel HIV-1 capsid inhibitors targeting the PF74 binding site. Acting as the building block of the HIV-1 capsid core, the HIV-1 capsid protein plays an important role in the viral life cycle and is an attractive target for antiviral development. A structure-based virtual screening workflow for hit identification was employed, which includes docking 1.6 million commercially-available drug-like compounds from the ZINC database to the capsid dimer, followed by applying two absolute binding free energy (ABFE) filters on the 500 top-ranked molecules from docking. The first employs the Binding Energy Distribution Analysis Method (BEDAM) in implicit solvent. The top-ranked compounds are then refined using the Double Decoupling method in explicit solvent. Both docking and BEDAM refinement were carried out on the IBM World Community Grid as part of the FightAIDS@Home project. Using this virtual screening workflow, we identified 24 molecules with calculated binding free energies between - 6 and - 12 kcal/mol. We performed thermal shift assays on these molecules to examine their potential effects on the stability of HIV-1 capsid hexamer and found that two compounds, ZINC520357473 and ZINC4119064 increased the melting point of the latter by 14.8 °C and 33 °C, respectively. These results support the conclusion that the two ZINC compounds are primary hits targeting the capsid dimer interface. Our simulations also suggest that the two hit molecules may bind at the capsid dimer interface by occupying a new sub-pocket that has not been exploited by existing CA inhibitors. The possible causes for why other top-scored compounds suggested by ABFE filters failed to show measurable activity are discussed.

CellPAINT is an interactive digital tool that allows non-expert users to create illustrations of the molecular structure of cells and viruses. We present a new release with several key enhancements, including the ability to generate custom ingredients from structure information in the Protein Data Bank, and interaction, grouping, and locking functions that streamline the creation of assemblies and illustration of large, complex scenes. An example of CellPAINT as a tool for hypothesis generation in the interpretation of cryoelectron tomograms is presented. CellPAINT is freely available at http://ccsb.scripps.edu/cellpaint.

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), kills 1.6 million people annually. To bridge the gap between structure- and cell-based drug discovery strategies, we are pionee...

This work was funded under the ILLVISATION grant by WWTF (VRG11-010). It is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2019-CPF-4108 and BAS/1/1680-01-01. The Scripps Research Institute researchers acknowledge support from the National Institutes of Health under the grant R01-GM120604. This paper was partly written in collaboration with the VRVis Competence Center. VRVis is funded by BMVIT, BMWFW, Styria, SFG and Vienna Business Agency in the scope of COMET - Competence Centers for Excellent Technologies (854174), which is managed by FFG. The authors would like to thank Nanographics GmbH (nanographics.at) for providing the Marion Software Framework.

The structural study of icosahedral viruses has a long and impactful history in both crystallographic methodology and molecular biology. The evolution of the Protein Data Bank has paralleled and supported these studies providing readily accessible formats dealing with novel features associated with viral particle symmetries and subunit interactions. This overview describes the growth in size and complexity of icosahedral viruses from the first early studies of small RNA plant viruses and human picornaviruses up to the larger and more complex bacterial phage, insect, and human disease viruses such as Zika, hepatitis B, Adeno and Polyoma virus. The analysis of icosahedral viral capsid protein domain folds has shown striking similarities, with the beta jelly roll motif observed across multiple evolutionarily divergent species. The icosahedral symmetry of viruses drove the development of noncrystallographic symmetry averaging as a powerful phasing method, and the constraints of maintaining this symmetry resulted in the concept of quasi-equivalence in viral structures. Symmetry also played an important early role in demonstrating the power of cryo-electron microscopy as an alternative to crystallography in generating atomic resolution structures of these viruses. The Protein Data Bank has been a critical resource for assembling and disseminating these structures to a wide community, and the virus particle explorer (VIPER) was developed to enable users to easily generate and view complete viral capsid structures from their asymmetric building blocks. Finally, we share a personal perspective on the early use of computer graphics to communicate the intricacies, interactions, and beauty of these virus structures.

The propagation of gaseous detonations through curved channels has a number of practical engineering applications that range from combustion initiation concepts to informing the design of rotating detonation engines. Understanding the failure mechanisms that do not allow a steadily propagating cellular detonation to traverse a curved segment of channel leads directly into these applications.

Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.

Visualization has been a key technology in the progress of structural molecular biology for as long as the field has existed. This perspective describes the nature of the visualization process in structural studies, how it has evolved over the years, and its relationship to the changes in technology that have supported and driven it. It focuses on how technical advances have changed the way we look at and interact with molecular structure, and how structural biology has fostered and challenged that technology.

We present the first approach to integrative structural modeling of the biological mesoscale within an interactive visual environment. These complex models can comprise up to millions of molecules with defined atomic structures, locations, and interactions. Their construction has previously been attempted only within a non-visual and non-interactive environment. Our solution unites the modeling and visualization aspect, enabling interactive construction of atomic resolution mesoscale models of large portions of a cell. We present a novel set of GPU algorithms that build the basis for the rapid construction of complex biological structures. These structures consist of multiple membrane-enclosed compartments including both soluble molecules and fibrous structures. The compartments are defined using volume voxelization of triangulated meshes. For membranes, we present an extension of the Wang Tile concept that populates the bilayer with individual lipids. Soluble molecules are populated within compartments distributed according to a Halton sequence. Fibrous structures, such as RNA or actin filaments, are created by self-avoiding random walks. Resulting overlaps of molecules are resolved by a forced-based system. Our approach opens new possibilities to the world of interactive construction of cellular compartments. We demonstrate its effectiveness by showcasing scenes of different scale and complexity that comprise blood plasma, mycoplasma, and HIV.

Visualizations of hierarchical data can often be explored interactively. For example, in geographic visualization, there are continents, which can be subdivided into countries, states, counties and cities. Similarly, in models of viruses or bacteria at the highest level are the compartments, and below that are macromolecules, secondary structures (such as α‐helices), amino‐acids, and on the finest level atoms. Distinguishing between items can be assisted through the use of color at all levels. However, currently, there are no hierarchical and adaptive color mapping techniques for very large multi‐scale visualizations that can be explored interactively. We present a novel, multi‐scale, color‐mapping technique for adaptively adjusting the color scheme to the current view and scale. Color is treated as a resource and is smoothly redistributed. The distribution adjusts to the scale of the currently observed detail and maximizes the color range utilization given current viewing requirements. Thus, we ensure that the user is able to distinguish items on any level, even if the color is not constant for a particular feature. The coloring technique is demonstrated for a political map and a mesoscale structural model of HIV. The technique has been tested by users with expertise in structural biology and was overall well received.

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), kills 1.6 million people annually. To bridge the gap between structure- and cell-based drug discovery strategies, we are pioneering a computer-aided discovery paradigm that merges structure-based virtual screening with ligand-based, machine learning methods trained with cell-based data. This approach successfully identified N-(3-methoxyphenyl)-7-nitrobenzo[c][1,2,5]oxadiazol-4-amine (JSF-2164) as an inhibitor of purified InhA with whole-cell efficacy versus in vitro cultured M. tuberculosis. When the intrabacterial drug metabolism (IBDM) platform was leveraged, mechanistic studies demonstrated that JSF-2164 underwent a rapid F(420)H(2)-dependent biotransformation within M. tuberculosis to afford intrabacterial nitric oxide and two amines, identified as JSF-3616 and JSF-3617. Thus, metabolism of JSF-2164 obscured the InhA inhibition phenotype within cultured M. tuberculosis. This study demonstrates a new docking/Bayesian computational strategy to combine cell- and target-based drug screening and the need to probe intrabacterial metabolism when clarifying the antitubercular mechanism of action.

Precise assembly and disassembly of the HIV-1 capsid core are key to the success of viral replication. The forces that govern capsid core formation and dissociation involve intricate interactions between pentamers and hexamers formed by HIV-1 CA. We identified one particular interaction between E28 of one CA and K30′ of the adjacent CA that appears more frequently in pentamers than in hexamers and that is important for capsid assembly. Targeting the corresponding site could lead to the development of antivirals which disrupt this interaction and affect capsid assembly., HIV-1 capsid protein (CA) plays critical roles in both early and late stages of the viral replication cycle. Mutagenesis and structural experiments have revealed that capsid core stability significantly affects uncoating and initiation of reverse transcription in host cells. This has led to efforts in developing antivirals targeting CA and its assembly, although none of the currently identified compounds are used in the clinic for treatment of HIV infection. A specific interaction that is primarily present in pentameric interfaces in the HIV-1 capsid core was identified and is reported to be important for CA assembly. This is shown by multidisciplinary characterization of CA site-directed mutants using biochemical analysis of virus-like particle formation, transmission electron microscopy of in vitro assembly, crystallographic studies, and molecular dynamic simulations. The data are consistent with a model where a hydrogen bond between CA residues E28 and K30′ from neighboring N-terminal domains (CANTDs) is important for CA pentamer interactions during core assembly. This pentamer-preferred interaction forms part of an N-terminal domain interface (NDI) pocket that is amenable to antiviral targeting.

Labeling is intrinsically important for exploring and understanding complex environments and models in a variety of domains. We present a method for interactive labeling of crowded 3D scenes containing very many instances of objects spanning multiple scales in size. In contrast to previous labeling methods, we target cases where many instances of dozens of types are present and where the hierarchical structure of the objects in the scene presents an opportunity to choose the most suitable level for each placed label. Our solution builds on and goes beyond labeling techniques in medical 3D visualization, cartography, and biological illustrations from books and prints. In contrast to these techniques, the main characteristics of our new technique are: 1) a novel way of labeling objects as part of a bigger structure when appropriate, 2) visual clutter reduction by labeling only representative instances for each type of an object, and a strategy of selecting those. The appropriate level of label is chosen by analyzing the scene’s depth buffer and the scene objects’ hierarchy tree. We address the topic of communicating the parent-children relationship between labels by employing visual hierarchy concepts adapted from graphic design. Selecting representative instances considers several criteria tailored to the character of the data and is combined with a greedy optimization approach. We demonstrate the usage of our method with models from mesoscale biology where these two characteristics—multi-scale and multi-instance—are abundant, along with the fact that these scenes are extraordinarily dense.

HIV-1 integrase (IN) is essential for virus replication and represents an important multifunctional therapeutic target. Recently discovered quinoline-based allosteric IN inhibitors (ALLINIs) potently impair HIV-1 replication and are currently in clinical trials. ALLINIs exhibit a multimodal mechanism of action by inducing aberrant IN multimerization during virion morphogenesis and by competing with IN for binding to its cognate cellular cofactor LEDGF/p75 during early steps of HIV-1 infection. However, quinoline-based ALLINIs impose a low genetic barrier for the evolution of resistant phenotypes, which highlights a need for discovery of second-generation inhibitors. Using crystallographic screening of a library of 971 fragments against the HIV-1 IN catalytic core domain (CCD) followed by a fragment expansion approach, we have identified thiophenecarboxylic acid derivatives that bind at the CCD-CCD dimer interface at the principal lens epithelium-derived growth factor (LEDGF)/p75 binding pocket. The most active derivative (5) inhibited LEDGF/p75-dependent HIV-1 IN activity in vitro with an IC50 of 72 μm and impaired HIV-1 infection of T cells at an EC50 of 36 μm. The identified lead compound, with a relatively small molecular weight (221 Da), provides an optimal building block for developing a new class of inhibitors. Furthermore, although structurally distinct thiophenecarboxylic acid derivatives target a similar pocket at the IN dimer interface as the quinoline-based ALLINIs, the lead compound, 5, inhibited IN mutants that confer resistance to quinoline-based compounds. Collectively, our findings provide a plausible path for structure-based development of second-generation ALLINIs.

Small molecules are powerful tools for investigating protein function and can serve as leads for new therapeutics. Most human proteins, however, lack small-molecule ligands, and entire protein classes are considered “undruggable” 1,2. Fragment-based ligand discovery (FBLD) can identify small-molecule probes for proteins that have proven difficult to target using high-throughput screening of complex compound libraries 1,3. Although reversibly binding ligands are commonly pursued, covalent fragments provide an alternative route to small-molecule probes 4–10, including those that can access regions of proteins that are difficult to access through binding affinity alone 5,10,11. In this manuscript, we report a quantitative analysis of cysteine-reactive small-molecule fragments screened against thousands of proteins. Covalent ligands were identified for >700 cysteines found in both druggable proteins and proteins deficient in chemical probes, including transcription factors, adaptor/scaffolding proteins, and uncharacterized proteins. Among the atypical ligand-protein interactions discovered were compounds that react preferentially with pro- (inactive) caspases. We used these ligands to distinguish extrinsic apoptosis pathways in human cell lines versus primary human T-cells, showing that the former is largely mediated by caspase-8 while the latter depends on both caspase-8 and −10. Fragment-based covalent ligand discovery provides a greatly expanded portrait of the ligandable proteome and furnishes compounds that can illuminate protein functions in native biological systems.

Computational docking can be used to predict bound conformations and free energies of binding for small molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction, and docking with explicit hydration. The entire protocol will require approximately 5 hours.

Mesoscale molecular modeling is providing a new window into the inner workings of living cells. Modeling of genomes, however, remains a technical challenge, due to their large size and complexity. We describe a lattice method for rapid generation of bacterial nucleoid models that integrates experimental data from a variety of biophysical techniques and provides a starting point for simulation and hypothesis generation. The current method builds models of a circular bacterial genome with supercoiled plectonemes, packed within the small space of the bacterial cell. Lattice models are generated for Mycoplasma genitalium and Escherichia coli nucleoids, and used to simulate interaction data. The method is rapid enough to allow generation of multiple models when analyzing structure/function relationships, and we demonstrate use of the lattice models in creation of an all-atom representation of an entire cell.

We describe two methods of automated covalent docking using Autodock4: the two-point attractor method and the flexible side chain method. Both methods were applied to a training set of 20 diverse protein-ligand covalent complexes, evaluating their reliability in predicting the crystallographic pose of the ligands. The flexible side chain method performed best, recovering the pose in 75% of cases, with failures for the largest inhibitors tested. Both methods are freely available at the AutoDock website (http://autodock.scripps.edu).

Isoniazid (INH) is usually administered to treat latent Mycobacterium tuberculosis (Mtb) infections, and is used in combination therapy to treat active tuberculosis disease (TB). Unfortunately, resistance to this drug is hampering its clinical effectiveness. INH is a prodrug that must be activated by Mtb catalase peroxidase (KatG) before it can inhibit InhA (Mtb enoyl-acyl-carrier-protein reductase). Isoniazid-resistant cases of TB found in clinical settings usually involve mutations in or deletion of katG, which abrogate INH activation. Compounds that inhibit InhA without requiring prior activation by KatG would not be affected by this resistance mechanism and hence would display continued potency against these drug-resistant isolates of Mtb. Virtual screening experiments versus InhA in the GO Fight Against Malaria project (GO FAM) were designed to discover new scaffolds that display base stacking interactions with the NAD cofactor. GO FAM experiments included targets from other pathogens, including Mtb, when they had structural similarity to a malaria target. Eight of the sixteen soluble compounds identified by docking against InhA plus visual inspection were modest inhibitors and did not require prior activation by KatG. The best two inhibitors discovered are both fragment-sized compounds and displayed Ki values of 54 and 59 μM, respectively. Importantly, the novel inhibitors discovered have low structural similarity to known InhA inhibitors and, thus, help expand the number of chemotypes on which future medicinal chemistry efforts can be focused. These new fragment hits could eventually help advance the fight against INH-resistant Mtb strains, which pose a significant global health threat.

HIV-1 is rare among viruses for having a low number of envelope glycoprotein (Env) spikes per virion, i.e., ∼7 to 14. This exceptional feature has been associated with avoidance of humoral immunity, i.e., B cell activation and antibody neutralization. Virus-like particles (VLPs) with increased density of Env are being pursued for vaccine development; however, these typically require protein engineering that alters Env structure. Here, we used instead a strategy that targets the producer cell. We employed fluorescence-activated cell sorting (FACS) to sort for cells that are recognized by trimer cross-reactive broadly neutralizing antibody (bnAb) and not by nonneutralizing antibodies. Following multiple iterations of FACS, cells and progeny virions were shown to display higher levels of antigenically correct Env in a manner that correlated between cells and cognate virions ( P = 0.027). High-Env VLPs, or hVLPs, were shown to be monodisperse and to display more than a 10-fold increase in spikes per particle by electron microscopy (average, 127 spikes; range, 90 to 214 spikes). Sequencing revealed a partial truncation in the C-terminal tail of Env that had emerged in the sort; however, iterative rounds of “cell factory” selection were required for the high-Env phenotype. hVLPs showed greater infectivity than standard pseudovirions but largely similar neutralization sensitivity. Importantly, hVLPs also showed superior activation of Env-specific B cells. Hence, high-Env HIV-1 virions, obtained through selection of producer cells, represent an adaptable platform for vaccine design and should aid in the study of native Env. IMPORTANCE The paucity of spikes on HIV is a unique feature that has been associated with evasion of the immune system, while increasing spike density has been a goal of vaccine design. Increasing the density of Env by modifying it in various ways has met with limited success. Here, we focused instead on the producer cell. Cells that stably express HIV spikes were screened on the basis of high binding by bnAbs and low binding by nonneutralizing antibodies. Levels of spikes on cells correlated well with those on progeny virions. Importantly, high-Env virus-like particles (hVLPs) were produced with a manifest array of well-defined spikes, and these were shown to be superior in activating desirable B cells. Our study describes HIV particles that are densely coated with functional spikes, which should facilitate the study of HIV spikes and their development as immunogens.

cellPACK assembles computational models of the biological mesoscale, an intermediate scale (10-100 nm) between molecular and cellular biology scales. cellPACK's modular architecture unites existing and novel packing algorithms to generate, visualize and analyze comprehensive three-dimensional models of complex biological environments that integrate data from multiple experimental systems biology and structural biology sources. cellPACK is available as open-source code, with tools for validation of models and with 'recipes' and models for five biological systems: blood plasma, cytoplasm, synaptic vesicles, HIV and a mycoplasma cell. We have applied cellPACK to model distributions of HIV envelope protein to test several hypotheses for consistency with experimental observations. Biologists, educators and outreach specialists can interact with cellPACK models, develop new recipes and perform packing experiments through scripting and graphical user interfaces at http://cellPACK.org/.

Zinc is present in a wide variety of proteins and is important in the metabolism of most organisms. Zinc metalloenzymes are therapeutically relevant targets in diseases such as cancer, heart disease, bacterial infection, and Alzheimer’s disease. In most cases a drug molecule targeting such enzymes establishes an interaction that coordinates with the zinc ion. Thus, accurate prediction of the interaction of ligands with zinc is an important aspect of computational docking and virtual screening against zinc containing proteins. We have extended the AutoDock force field to include a specialized potential describing the interactions of zinc-coordinating ligands. This potential describes both the energetic and geometric components of the interaction. The new force field, named AutoDock4Zn, was calibrated on a data set of 292 crystal complexes containing zinc. Redocking experiments show that the force field provides significant improvement in performance in both free energy of binding estimation as well as in root-mean-square deviation from the crystal structure pose. The new force field has been implemented in AutoDock without modification to the source code.

As part of the SAMPL4 blind challenge, filtered AutoDock Vina ligand docking predictions and large scale binding energy distribution analysis method binding free energy calculations have been applied to the virtual screening of a focused library of candidate binders to the LEDGF site of the HIV integrase protein. The computational protocol leveraged docking and high level atomistic models to improve enrichment. The enrichment factor of our blind predictions ranked best among all of the computational submissions, and second best overall. This work represents to our knowledge the first example of the application of an all-atom physics-based binding free energy model to large scale virtual screening. A total of 285 parallel Hamiltonian replica exchange molecular dynamics absolute protein-ligand binding free energy simulations were conducted starting from docked poses. The setup of the simulations was fully automated, calculations were distributed on multiple computing resources and were completed in a 6-weeks period. The accuracy of the docked poses and the inclusion of intramolecular strain and entropic losses in the binding free energy estimates were the major factors behind the success of the method. Lack of sufficient time and computing resources to investigate additional protonation states of the ligands was a major cause of mispredictions. The experiment demonstrated the applicability of binding free energy modeling to improve hit rates in challenging virtual screening of focused ligand libraries during lead optimization.

The small program Illustrate generates non-photorealistic images of biological molecules for use in dissemination, outreach, and education. The method has been used as part of the "Molecule of the Month," an ongoing educational column at the RCSB Protein Data Bank (http://rcsb.org). Insights from 20 years of application of the program are presented, and the program has been released both as open-source Fortran at GitHub and through an interactive web-based interface.

The fragment indole-6-carboxylic acid (1F1), previously identified as a flap site binder in a fragment-based screen against HIV protease (PR), has been co-crystallized with pepstatin-inhibited PR and with apo-PR. Another fragment, 3-indolepropionic acid (1F1-N), predicted by AutoDock calculations and confirmed in a novel ‘inhibition of nucleation’ crystallization assay, exploits the same interactions in the flap site in two crystal structures. Both 1F1 and 1F1-N bind to the closed form of apo-PR and to pepstatin:PR. In solution, 1F1 and 1F1-N raise the Tm of apo-PR by 3.5–5 °C as assayed by differential scanning fluorimetry (DSF), and show equivalent low-micromolar binding constants to both apo-PR and pepstatin:PR, assayed by backscattering interferometry (BSI). The observed signal intensities in BSI are greater for each fragment upon binding to apo-PR than to pepstatin-bound PR, consistent with greater conformational change in the former binding event. Together, these data indicate that fragment binding in the flap site favors a closed conformation of HIV PR.

Structural molecular biology is now becoming part of high school science curriculum thus posing a challenge for teachers who need to convey three-dimensional (3D) structures with conventional text and pictures. In many cases even interactive computer graphics does not go far enough to address these challenges. We have developed a flexible model of the polypeptide backbone using 3D printing technology. With this model we have produced a polypeptide assembly kit to create an idealized model of the Triosephosphate isomerase mutase enzyme (TIM), which forms a structure known as TIM barrel. This kit has been used in a laboratory practical where students perform a step-by-step investigation into the nature of protein folding, starting with the handedness of amino acids to the formation of secondary and tertiary structure. Based on the classroom evidence we collected, we conclude that these models are valuable and inexpensive resource for teaching structural molecular biology.

We describe ADChemCast, a method for using results from virtual screening to create a richer representation of a target binding site, which may be used to improve ranking of compounds and characterize the determinants of ligand-receptor specificity. ADChemCast clusters docked conformations of ligands based on shared pairwise receptor-ligand interactions within chemically similar structural fragments, building a set of attributes characteristic of binders and non-binders. Machine learning is then used to build rules from the most informational attributes for use in reranking of compounds. In this report, we use ADChemCast to improve the ranking of compounds in 11 diverse proteins from the Database of Useful Decoys-Enhanced (DUD-E), and demonstrate the utility of the method for characterizing relevant binding attributes in HIV reverse transcriptase.

SummaryIncreasingly complex research has made it more difficult to prepare data for publication, education, and outreach. Many scientists must also wade through black-box code to interface computational algorithms from diverse sources to supplement their bench work. To reduce these barriers we have developed an open-source plug-in, embedded Python Molecular Viewer (ePMV), that runs molecular modeling software directly inside of professional 3D animation applications (hosts) to provide simultaneous access to the capabilities of these newly connected systems. Uniting host and scientific algorithms into a single interface allows users from varied backgrounds to assemble professional quality visuals and to perform computational experiments with relative ease. By enabling easy exchange of algorithms, ePMV can facilitate interdisciplinary research, smooth communication between broadly diverse specialties, and provide a common platform to frame and visualize the increasingly detailed intersection(s) of cellular and molecular biology.

Nucleocytoplasmic transport of macromolecules is a fundamental process of eukaryotic cells. Translocation of proteins and many RNAs between the nucleus and cytoplasm is carried out by shuttling receptors of the β-karyopherin family, also called importins and exportins. Leptomycin B, a small molecule inhibitor of the exportin CRM1, has proved to be an invaluable tool for cell biologists, but up to now no small molecule inhibitors of nuclear import have been described. We devised a microtiter plate based permeabilized cell screen for small molecule inhibitors of the importin α/β pathway. By analyzing peptidomimetic libraries, we identified β-turn and α-helix peptidomimetic compounds that selectively inhibit nuclear import by importin α/β but not by transportin. Structure-activity relationship analysis showed that large aromatic residues and/or a histidine side chain are required for effective import inhibition by these compounds. Our validated inhibitors can be useful for in vitro studies of nuclear import, and can also provide a framework for synthesis of higher potency nuclear import inhibitors.

In the present study, Lamarckian genetic algorithm (LGA) based docking (AutoDock 4.0), k-nearest neighbor molecular field analysis (kNN-MFA) based 3D-QSAR, fragment and knowledge based approach were used to generate and screen a virtual library. Three analogues of β-phenylethylidene hydrazine (PEH) were designed as novel GABA-AT inhibitors. The docking interactions of N-[(1E) 2-phenylethylidene]pyridin-2-amine (S 4 ), (2E)-1-phenyl-2-(2-phenylethylidene)hydrazine (S 8 ) and (1E)-1-ethylidene-2-phenylhydrazine (S 21 ) with Lys 329 appears to be in close proximity and explains the high GABA-AT selectivity observed.

Importance of the field: Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of protein and nucleic acid structures, virtual screening continues to grow as an effective method for the discovery of new inhibitors and drug molecules. Areas covered in this review: We describe virtual screening methods that are available in the AutoDock suite of programs and several of our successes in using AutoDock virtual screening in pharmaceutical lead discovery. What the reader will gain: A general overview of the challenges of virtual screening is presented, along with the tools available in the AutoDock suite of programs for addressing these challenges. Take home message: Virtual screening is an effective tool for the discovery of compounds for use as leads in drug discovery, and the free, open source program AutoDock is an effective tool for virtual screening. © 2010 Informa UK Ltd.

Structural biology is rapidly accumulating a wealth of detailed information about protein function, binding sites, RNA, large assemblies and molecular motions. These data are increasingly of interest to a broader community of life scientists, not just structural experts. Visualization is a primary means for accessing and using these data, yet visualization is also a stumbling block that prevents many life scientists from benefiting from three-dimensional structural data. In this review, we focus on key biological questions where visualizing three-dimensional structures can provide insight and describe available methods and tools. Supplementary information The online version of this article (doi:10.1038/nmeth.1427) contains supplementary material, which is available to authorized users.

We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique.

α-Cobratoxin (Cbtx), the neurotoxin isolated from the venom of the Thai cobra Naja kaouthia, causes paralysis by preventing acetylcholine (ACh) binding to nicotinic acetylcholine receptors (nAChRs). In the current study, the region of the Cbtx molecule that is directly involved in binding to nAChRs is used as the target for anticobratoxin drug design. The crystal structure (1YI5) of Cbtx in complex with the acetylcholine binding protein (AChBP), a soluble homolog of the extracellular binding domain of nAChRs, was selected to prepare an α-cobratoxin active binding site for docking. The amino acid residues (Ser182-Tyr192) of the AChBP structure, the binding site of Cbtx, were used as the positive control to validate the prepared Cbtx active binding site (root mean square deviation < 1.2 A). Virtual screening of the National Cancer Institute diversity set, a library of 1990 compounds with nonredundant pharmacophore profiles, using AutoDock against the Cbtx active site, revealed 39 potential inhibitor candidates. The adapted in vitro radioligand competition assays using [ 3 H]epibatidine and [ 125 I]bungarotoxin against the AChBPs from the marine species, Aplysia californica (Ac), and from the freshwater snails, Lymnaea stagnalis (Ls) and Bolinus truncates (Bt), revealed 4 compounds from the list of inhibitor candidates that had micromolar to nanomolar interferences for the toxin binding to AChBPs. Three hits (NSC42258, NSC121865, and NSC134754) can prolong the survival time of the mice if administered 30 min before injection with Cbtx, but only NSC121865 and NSC134754 can prolong the survival time if injected immediately after injection with Cbtx. These inhibitors serve as novel templates/scaffolds for the development of more potent and specific anticobratoxin. (Journal of Biomolecular Screening 2009:1109-1118)

Rediocides A and G, the principle components of Trigonostemon reidioides (Kurz) Craib, which is known as Lotthanong in Thai, were investigated for a detoxification mechanism against Naja kaouthia venom by in silico, in vitro, and in vivo methods. Molecular dockings of alpha-cobratoxin with rediocides A and G were performed, and the binding energies were found to be -14.17 and -14.14 kcal/mol, respectively. Rediocides bind to alpha-cobratoxin at the same location as alpha-cobratoxin binds to the nicotinic acetylcholine receptor (nAChR), i.e., at the Asp27, Phe29, Arg33, Gly34, Lys35, and Val37 residues. alpha-Cobratoxin cannot bind to nAChR, because some of its binding sites are occupied with rediocides. From in vitro SDS-PAGE, it was found that rediocides can diminish the bands of alpha-cobratoxin. In the presence of acetylcholine-binding protein (AChBP), it was apparent that rediocides can bind both alpha-cobratoxin and AChBP. From an in vivo test, it was found that injection of rediocides at 0.5 mg/kg immediately after an alpha-cobratoxin dose of three times LD(50) cannot prolong the survival time of mice. However, rediocide can prolong the survival time, if it is injected 30 min before the injection of alpha-cobratoxin. The in vitro SDS-PAGE and the in vivo results support the in silico detoxification mechanism of rediocides against cobra venom at a molecular level.

Many human cancers show constitutive or amplified expression of the transcriptional regulator and oncoprotein Myc, making Myc a potential target for therapeutic intervention. Here we report the down-regulation of Myc activity by reducing the availability of Max, the essential dimerization partner of Myc. Max is expressed constitutively and can form unstable homodimers. We have isolated stabilizers of the Max homodimer by applying virtual ligand screening (VLS) to identify specific binding pockets for small molecule interactors. Candidate compounds found by VLS were screened by fluorescence resonance energy transfer, and from these screens emerged a potent, specific stabilizer of the Max homodimer. In vitro binding assays demonstrated that the stabilizer enhances the formation of the Max-Max homodimer and interferes with the heterodimerization of Myc and Max in a dose-dependent manner. Furthermore, this compound interferes with Myc-induced oncogenic transformation, Myc-dependent cell growth, and Myc-mediated transcriptional activation. The Max-Max stabilizer can be considered a lead compound for the development of inhibitors of the Myc network.

Signaling of the tissue factor-FVIIa complex regulates angiogenesis, tumor growth, and inflammation. TF-FVIIa triggers cell signaling events by cleavage of protease activated receptor (PAR2) at the Arg36-Ser37 scissile bond. The recognition of PAR2 by the FVIIa protease domain is poorly understood. We perform molecular modeling and dynamics simulations to derive the PAR2-FVIIa interactions. Docking of the PAR2 Arg36-Ser37 scissile bond to the S1 site and subsequent molecular dynamics leads to interactions of the PAR2 ectodomain with P and P′ sites of the FVIIa catalytic cleft as well as to electrostatic interactions between a stably folded region of PAR2 and a cluster of basic residues remote from the catalytic cleft of FVIIa. To address the functional significance of this interaction for PAR2 cleavage, we employed two antibodies with epitopes previously mapped to this cluster of basic residues. Although these antibodies do not block the catalytic cleft, both antibodies completely abrogated PAR2 activation by TF-FVIIa. Our simulations indicate a conformation of the PAR2 ectodomain that limits the cleavage site to no more than 33 A from its membrane proximal residue. Since the active site of FVIIa in the TF-FVIIa complex is ∼75 A above the membrane, cleavage of the folded conformation of PAR2 would require tilting of the TF-FVIIa complex toward the membrane, indicating that additional cellular factors may be required to properly align the scissile bond of PAR2 with TF-FVIIa. Proteins 2009. © 2009 Wiley-Liss, Inc.

Phage-displayed peptides that selectively bind to aldolase catalytic antibody 93F3 when bound to a particular 1,3-diketone hapten derivative have been developed using designed selection strategies with libraries containing 7–12 randomized amino acid residues. These phage-displayed peptides discriminated the particular 93F3–diketone complex from ligand-free 93F3 and from 93F3 bound to other 1,3-diketone hapten derivatives. By altering the selection procedures, phage-displayed peptides that bind to antibody 93F3 in the absence of 1,3-diketone hapten derivatives have also been developed. With using these phage-displayed peptides, ligand-bound states of the antibody were distinguished from each other. A docking model of one of the peptides bound to the antibody 93F3–diketone complex was created using a sequential divide-and-conquer peptide docking strategy; the model suggests that the peptide interacts with both the antibody and the ligand through a delicate hydrogen bonding network.

The results from reiterated docking experiments may be used to evaluate an empirical vibrational en- tropy of binding in ligand-protein complexes. We have tested several methods for evaluating the vibrational contri- bution to binding of 22 nucleotide analogues to the enzyme APS reductase. These include two cluster size methods that measure the probability of finding a particular conformation, a method that estimates the extent of the local energetic well by looking at the scatter of conformations within clustered results, and an RMSD-based method that uses the overall scatter and clustering of all conformations. We have also directly characterized the local energy landscape by randomly sampling around docked conformations. The simple cluster size method shows the best per- formance, improving the identification of correct conformations in multiple docking experiments.

We present a method, termed AutoLigand, for the prediction of ligand-binding sites in proteins of known structure. The method searches the space surrounding the protein and finds the contiguous envelope with the specified volume of atoms, which has the largest possible interaction energy with the protein. It uses a full atomic representation, with atom types for carbon, hydrogen, oxygen, nitrogen and sulfur (and others, if desired), and is designed to minimize the need for artificial geometry. Testing on a set of 187 diverse protein-ligand complexes has shown that the method is successful in predicting the location and approximate volume of the binding site in 73% of cases. Additional testing was performed on a set of 96 protein-ligand complexes with crystallographic structures of apo and holo forms, and AutoLigand was able to predict the binding site in 80% of the apo structures.

The inosine monophosphate cyclohydrolase (IMPCH) component (residues 1-199) of the bifunctional enzyme aminoimidazole-4-carboxamide ribonucleotide transformylase (AICAR Tfase, residues 200-593)/IMPCH (ATIC) catalyzes the final step in the de novo purine biosynthesis pathway that produces IMP. As a potential target for antineoplastic intervention, we designed IMPCH inhibitors, 1,5-dihydroimidazo[4,5-c][1,2,6]thiadiazin-4(3H)-one 2,2-dioxide (heterocycle, 1), the corresponding nucleoside (2), and the nucleoside monophosphate (nucleotide) (3), as mimics of the tetrahedral intermediate in the cyclization reaction. All compounds are competitive inhibitors against IMPCH (K(i) values = 0.13-0.23 microm) with the simple heterocycle 1 exhibiting the most potent inhibition (K(i) = 0.13 microm). Crystal structures of bifunctional ATIC in complex with nucleoside 2 and nucleotide 3 revealed IMPCH binding modes similar to that of the IMPCH feedback inhibitor, xanthosine 5'-monophosphate. Surprisingly, the simpler heterocycle 1 had a completely different IMPCH binding mode and was relocated to the phosphate binding pocket that was identified from previous xanthosine 5'-monophosphate structures. The aromatic imidazole ring interacts with a helix dipole, similar to the interaction with the phosphate moiety of 3. The crystal structures not only revealed the mechanism of inhibition of these compounds, but they now serve as a platform for future inhibitor improvements. Importantly, the nucleoside-complexed structure supports the notion that inhibitors lacking a negatively charged phosphate can still inhibit IMPCH activity with comparable potency to phosphate-containing inhibitors. Provocatively, the nucleotide inhibitor 3 also binds to the AICAR Tfase domain of ATIC, which now provides a lead compound for the design of inhibitors that simultaneously target both active sites of this bifunctional enzyme.

Here, we review some of the opportunities and challenges that we face in computational modeling of HIV therapeutic targets and structural biology, both in terms of methodology development and structure-based drug design (SBDD). Computational methods have provided fundamental support to HIV research since the initial structural studies, helping to unravel details of HIV biology. Computational models have proved to be a powerful tool to analyze and understand the impact of mutations and to overcome their structural and functional influence in drug resistance. With the availability of structural data, in silico experiments have been instrumental in exploiting and improving interactions between drugs and viral targets, such as HIV protease, reverse transcriptase, and integrase. Issues such as viral target dynamics and mutational variability, as well as the role of water and estimates of binding free energy in characterizing ligand interactions, are areas of active computational research. Ever-increasing computational resources and theoretical and algorithmic advances have played a significant role in progress to date, and we envision a continually expanding role for computational methods in our understanding of HIV biology and SBDD in the future.

Virtual molecular screening is used to dock small-molecule libraries to a macromolecule in order to find lead compounds with desired biological function. This in silico method is well known for its application in computer-aided drug design. This chapter describes how to perform small-molecule virtual screening by docking with PyRx, which is open-source software with an intuitive user interface that runs on all major operating systems (Linux, Windows, and Mac OS). Specific steps for using PyRx, as well as considerations for data preparation, docking, and data analysis, are also described.

Open image in new window Three-dimensional model of mature HIV. A cutaway view of mature HIV includes the capsid (gray, with pentamers in yellow) and nucleocapsid (red), matrix protein (green), accessory proteins (magenta), membrane (white), and envelope protein (blue). The model was generated using CellPACK by Graham Johnson

Building from the results of a computational screen of a range of triazole-containing compounds for binding efficiency to human immunodeficiency virus type 1 protease (HIV-1-Pr), a novel series of potent inhibitors has been developed. The copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC), which provides ready access to 1,4-disubstituted-1,2,3-triazoles, was used to unite a focused library of azide-containing fragments with a diverse array of functionalized alkyne-containing building blocks. In combination with direct screening of the crude reaction products, this method led to the rapid identification of a lead structure and readily enabled optimization of both azide and alkyne fragments. Replacement of the triazole with a range of alternative linkers led to greatly reduced protease inhibition; however, further functionalization of the triazoles at the 5-position gave a series of compounds with increased activity, exhibiting Ki values as low as 8 nM.