•A new self-assisting physical assembly kit of protein backbone structure is described•The model demonstrates emergent properties during the formation of protein structure•The use of the model for teaching principles of protein structure is described•Electronic files for creating the model components for 3D printing are made availableStructural 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.Download high-res image (239KB)Download full-size image

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.

To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein–ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new “Rank Difference Ratio” metric. The first stage of SAMPL4 involved using virtual screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: (1) AutoDock Vina (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4’s very challenging compound library that displayed a significantly lower amount of structural diversity than most libraries that are conventionally employed in prospective virtual screens, these approaches produced hit rates of 24, 25, 34, and 27 %, respectively, on a set with 19 % declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly created, multi-institution, NIH-funded center called the “HIV Interaction and Viral Evolution Center”.

In modeling ligand–protein interactions, the representation and role of water are of great importance. We introduce a force field and hydration docking method that enables the automated prediction of waters mediating the binding of ligands with target proteins. The method presumes no prior knowledge of the apo or holo protein hydration state and is potentially useful in the process of structure-based drug discovery. The hydration force field accounts for the entropic and enthalpic contributions of discrete waters to ligand binding, improving energy estimation accuracy and docking performance. The force field has been calibrated and validated on a total of 417 complexes (197 training set; 220 test set), then tested in cross-docking experiments, for a total of 1649 ligand–protein complexes evaluated. The method is computationally efficient and was used to model up to 35 waters during docking. The method was implemented and tested using unaltered AutoDock4 with new force field tables.

Simulating protein flexibility is a major issue in the docking-based drug-design process for which a single methodological solution does not exist. In our search of new anti-Alzheimer ligands, we were faced with the challenge of including receptor plasticity in a virtual screening campaign aimed at finding new β-secretase inhibitors. To this aim, we incorporated protein flexibility in our simulations by using an ensemble of static X-ray enzyme structures to screen the National Cancer Institute database. A unified description of the protein motion was also generated by computing and combining a set of grid maps using an energy weighting scheme. Such a description was used in an energy-weighted virtual screening experiment on the same molecular database. Assessment of the enrichment factors from these two virtual screening approaches demonstrated comparable predictive powers, with the energy-weighted method being faster than the ensemble method. The in vitro evaluation demonstrated that out of the 32 tested ligands, 17 featured the predicted enzyme inhibiting property. Such an impressive success rate (53.1%) demonstrates the enhanced power of the two methodologies and suggests that energy-weighted virtual screening is a more than valid alternative to ensemble virtual screening given its reduced computational demands and comparable performance.

Tuberculosis is among the world’s deadliest infectious diseases. APS reductase catalyzes the first committed step in bacterial sulfate reduction and is a validated drug target against latent tuberculosis infection. We performed a virtual screening to identify APSR inhibitors. These inhibitors represent the first non-phosphate-based molecules to inhibit APSR. Common chemical features lay the foundation for the development of agents that could shorten the duration of chemotherapy by targeting the latent stage of TB infection.

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.

Stable structures of icosahedral symmetry can serve numerous functional roles, including chemical microencapsulation and delivery of drugs and biomolecules, epitope presentation to allow for an efficient immunization process, synthesis of nanoparticles of uniform size, observation of encapsulated reactive intermediates, formation of structural elements for supramolecular constructs, and molecular computing. By examining physical models of spherical virus assembly we have arrived at a general synthetic strategy for producing chemical capsids at size scales between fullerenes and spherical viruses. Such capsids can be formed by self-assembly from a class of molecules developed from a symmetric pentagonal core. By designing chemical complementarity into the five interface edges of the molecule, we can produce self-assembling stable structures of icosahedral symmetry. We considered three different binding mechanisms: hydrogen bonding, metal binding, and formation of disulfide bonds. These structures can be designed to assemble and disassemble under controlled environmental conditions. We have conducted molecular dynamics simulation on a class of corannulene-based molecules to demonstrate the characteristics of self-assembly and to aid in the design of the molecular subunits. The edge complementarities can be of diverse structure, and they need not reflect the fivefold symmetry of the molecular core. Thus, self-assembling capsids formed from coded subunits can serve as addressable nanocontainers or custom-made structural elements.

Increasingly 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.Graphical AbstractDownload high-res image (401KB)Download full-size imageHighlights► ePMV provides molecular viewer functionality inside of professional animation software ► ePMV interoperates molecular modeling tools on the same data and from a single interface ► ePMV enables production of high-quality molecular visualizations, both static and dynamic ► ePMV facilitates communication between researchers and scientific visualization specialists

•Structural characterization is key to our understanding of biomolecular mechanisms•Modeling of biomolecules and their interactions has been rapidly progressing•The current focus is shifting toward larger systems, up to the level of a cell•The review describes structural approaches to cell modeling and future developmentComputational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field.Download high-res image (140KB)Download full-size image

Human immunodeficiency virus type 1 (HIV-1) integrase is one of three virally encoded enzymes essential for replication and, therefore, a rational choice as a drug target for the treatment of HIV-1-infected individuals. In 2007, raltegravir became the first integrase inhibitor approved for use in the treatment of HIV-infected patients, more than a decade since the approval of the first protease inhibitor (saquinavir, Hoffman La-Roche, 1995) and two decades since the approval of the first reverse transcriptase inhibitor (retrovir, GlaxoSmithKline, 1987). The slow progress toward a clinically effective HIV-1 integrase inhibitor can at least in part be attributed to a poor structural understanding of this key viral protein.Here we describe the development of a restrained molecular dynamics protocol that produces a more accurate model of the active site of this drug target. This model provides an advance on previously described models as it ensures that the catalytic DDE motif makes correct, monodentate interactions with the two active-site magnesium ions. Dynamic restraints applied to this coordination state create models with the correct solvation sphere for the metal ion complex and highlight the coordination sites available for metal-binding ligands. Application of appropriate dynamic flexibility to the core domain allowed the inclusion of multiple conformational states in subsequent docking studies.These models have allowed us to (1) explore the effects of key drug resistance mutations on the dynamic flexibility and conformational preferences of HIV integrase and to (2) study raltegravir binding in the context of these dynamic models of both wild type and the G140S/Q148H drug-resistant enzyme.