Protein crystallography and examples of its applications in medicinal chemistry

The field of protein crystallography inspires and enthrals, whether it be for the beauty and symmetry of a perfectly formed protein crystal, the unlocked secrets of a novel protein fold, or the precise atomic-level detail yielded from a protein-ligand complex. Since 1958, when the first protein structure was solved, there have been tremendous advances in all aspects of protein crystallography, from protein preparation and crystallisation through to diffraction data measurement and structure refinement. These advances have significantly reduced the time required to solve protein crystal structures, while at the same time substantially improving the quality and resolution of the resulting structures. Moreover, the technological developments have induced researchers to tackle ever more complex systems, including ribosomes and intact membrane-bound proteins, with a reasonable expectation of success. In this review, the steps involved in determining a protein crystal structure are described and the impact of recent methodological advances identified. Protein crystal structures have proved to be extraordinarily useful in medicinal chemistry research, particularly with respect to inhibitor design. The precise interaction between a drug and its receptor can be visualised at the molecular level using protein crystal structures, and this information then used to improve the complementarity and thus increase the potency and selectivity of an inhibitor. The use of protein crystal structures in receptor-based drug design is highlighted by (i) HIV protease, (ii) influenza virus neuraminidase and (iii) prostaglandin H-2-synthetase. These represent, respectively, examples of protein crystal structures that (i) influenced the design of drugs currently approved for use in the treatment of HIV infection, (ii) led to the design of compounds currently in clinical trials for the treatment of influenza infection and (iii) could enable the design of highly specific non-steroidal anti-inflammatory drugs that lack the common side-effects of this drug class.

Catalytically active Dengue virus NS3 protease forms aggregates that are separable by size exclusion chromatography

An active form of the Dengue virus protease NS3 (CF40.Gly.NS3pro) was expressed in Escherichia coli. This construct consists of a critical 40 amino acid cofactor domain from NS2B fused to the N-terminal 184 amino acid protease domain of NS3 via a flexible, covalent linker (Gly(4)SerGly(4)). The recombinantly produced protein is soluble and has a hexa-histidine tag engineered at the N-terminus for ease of purification using metal affinity chromatography. However, the presence of lower molecular weight impurities after affinity chromatography indicated the need for additional purification steps. The consistent appearance of these impurities suggested that they may be the products of proteolysis and/or auto-proteolysis. The latter possibility was subsequently excluded by the observation of the same impurities in a purified, catalytically inactive form of the recombinant protease (CF40.Gly.NS3pro.SA). Further analysis indicated that these impurities may represent premature translation termination products. Regardless of their origin, they were shown to form various sized aggregates with full-length CF40.Gly.NS3pro that can be separated by size exclusion chromatography, yielding fractions of active protease of sufficient purity for crystallisation trials. The ultimate goal of these studies is to obtain a crystal structure of a catalytically active form of the Dengue virus NS3 protease for structure-based drug design. (C) 2002 Elsevier Science (USA). All rights reserved.

The influence of substrate kinetics on the microbial community structure in granular anaerobic biomass

The development of a strong, active granular sludge bed is necessary for optimal operation of upflow anaerobic sludge blanket reactors. The microbial and mechanical structure of the granules may have a strong influence on desirable properties such as growth rate, settling velocity and shear strength. Theories have been proposed for granule microbial structure based on the relative kinetics of substrate degradation, but contradict some observations from both modelling and microscopic studies. In this paper, the structures of four granule types were examined from full-scale UASB reactors, treating wastewater from a cannery, a slaughterhouse, and two breweries. Microbial structure was determined using fluorescence in situ hybridisation probing with 16S rRNA-directed oligonucleotide probes, and superficial structure and microbial density (volume occupied by cells and microbial debris) assessed using scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The granules were also modelled using a distributed parameter biofilm model, with a previously published biochemical model structure, biofilm modelling approach, and model parameters. The model results reflected the trophic structures observed, indicating that the structures were possibly determined by kinetics. Of particular interest were results from simulations of the protein grown granules, which were predicted to have slow growth rates, low microbial density, and no trophic layers, the last two of which were reflected by microscopic observations. The primary cause of this structure, as assessed by modelling, was the particulate nature of the wastewater, and the slow rate of particulate hydrolysis, rather than the presence of proteins in the wastewater. Because solids hydrolysis was rate limiting, soluble substrate concentrations were very low (below Monod half saturation concentration), which caused low growth rates. (C) 2003 Elsevier Ltd. All rights reserved.

Predicting function from structure: 3D structure studies of the mammalian golgi complex

3D electron tomography studies of the structure of the mammalian Golgi complex have led to four functional predictions (1). The sorting and exit site from the Golgi comprises two or three distinct trans-cisternae (2). The docking of vesicular-tubular clusters at the cis-face and the fragmentation of trans-cisternae are coordinated (3). The mechanisms of transport through, and exit from, the Golgi vary with physiological state, and in different cells and tissues (4). Specialized trans-ER functions in the delivery of ceramide to sphingomyelin synthase in the trans-Golgi membrane, for the regulated sorting via sphingolipid-cholesterol-rich domains. These structure-based predictions can now be tested using a variety of powerful cell and molecular tools.

Teaching principles of genetics through SNP analysis

An understanding of inheritance requires comprehension of genetic processes at all levels, from molecules to populations. Frequently genetics courses are separated into molecular and organismal genetics and students may fail to see the relationships between them. This is particularly true with human genetics, because of the difficulties in designing experimental approaches which are consistent with ethical restrictions, student abilities and background knowledge, and available time and materials. During 2005 we used analysis of single nucleotide polymorphisms (SNPs) in two genetic regions to enhance student learning and provide a practical experience in human genetics. Students scanned databases to discover SNPs in a gene of interest, used software to design PCR primers and a restriction enzyme based assay for the alleles, and carried out an analysis of the SNP on anonymous individual and family DNAs. The project occupied eight to ten hours per week for one semester, with some time spent in the laboratory and some spent in database searching, reading and writing the report. In completing their projects, students acquired a knowledge of Mendel’s first law (through looking at inheritance patterns), Mendel’s second law and the exceptions (the concepts of linkage and linkage disequilibrium), DNA structure (primer design and restriction enzyme analysis) and function (SNPs in coding and non-coding regions), population genetics and the statistical analysis of allele frequencies, genomics, bioinformatics and the ethical issues associated with the use of human samples. They also developed skills in presentation of results by publication and conference participation. Deficiencies in their understanding (for example of inheritance patterns, gene structure, statistical approaches and report writing) were detected and guidance given during the project. SNP analysis was found to be a powerful approach to enhance and integrate student understanding of genetic concepts.

Molecular recognition of macrocyclic peptidomimetic inhibitors by HIV-1 protease

High-resolution crystal structures are described for seven macrocycles complexed with HIV-1 protease (HIVPR). The macrocycles possess two amides and an aromatic group within 15-17 membered rings designed to replace N- or C-terminal tripeptides from peptidic inhibitors of HIVPR. Appended to each macrocycle is a transition state isostere and either an acyclic peptide, nonpeptide, or another macrocycle. These cyclic analogues are potent inhibitors of HIVPR, and the crystal structures show them to be structural mimics of acyclic peptides, binding in the active site of HIVPR via the same interactions. Each macrocycle is restrained to adopt a P-strand conformation which is preorganized for protease binding. An unusual feature of the binding of C-terminal macrocyclic inhibitors is the interaction between a positively charged secondary amine and a catalytic aspartate of HIVPR. A bicyclic inhibitor binds similarly through its secondary amine that lies between its component N-terminal and C-terminal macrocycles. In contrast, the corresponding tertiary amine of the N-terminal macrocycles does not interact with the catalytic aspartates. The amine-aspartate interaction induces a 1.5 Angstrom N-terminal translation of the inhibitors in the active site and is accompanied by weakened interactions with a water molecule that bridges the ligand to the enzyme, as well as static disorder in enzyme flap residues. This flexibility may facilitate peptide cleavage and product dissociation during catalysis. Proteases [Aba(67,95)]HIVPR and [Lys(7),Ile(33),Aba(67,95)]- HIVPR used in this work were shown to have very similar crystal structures.

Binding and functional studies with the growth hormone receptor antagonist, B2036-PEG (pegvisomant), reveal effects of pegylation and evidence that it binds to a receptor dimers

GH actions are dependent on receptor dimerization. The GH receptor antagonist, B2036-PEG, has been developed for treating acromegaly. B2036 has mutations in site 1 to enhance receptor binding and in site 2 to block receptor dimerization. Pegylation (B2036-PEG) increases half-life and lowers immunogenicity, but high concentrations are required to control insulin-like growth factor-I levels. We examined antagonist structure and function and the impact of pegylation on biological efficacy. Unpegylated B2036 had a 4.5-fold greater affinity for GH binding protein (GHBP) than GH but similar affinity for membrane receptor. Pegylation substantially reduced membrane binding affinity and receptor antagonism, as assessed by a transcription assay, by 39- and 20-fold, respectively. GHBP reduced antagonist activity of unpegylated B2036 but did not effect antagonism by B2036-PEG. B2036 down-regulated receptors, and membrane binding sites doubled in the presence of dimerization-blocking antibodies, suggesting that B2036 binds to a receptor dimer. It is concluded that the high concentration requirement of B2036-PEG for clinical efficacy relates to pegylation, which decreases binding to membrane receptor but has the advantages of reduced clearance, immunogenicity, and interactions with GHBP. Our studies suggest that B2036 binds to a receptor dimer and induces internalization but not signaling.

Probing the effect of vehicles on topical delivery: Understanding the basic relationship between solvent and solute penetration using silicone membranes

Purpose. In the present study we examined the relationship between solvent uptake into a model membrane (silicone) with the physical properties of the solvents (e.g., solubility parameter, melting point, molecular weight) and its potential predictability. We then assessed the subsequent topical penetration and retention kinetics of hydrocortisone from various solvents to define whether modifications to either solute diffusivity or partitioning were dominant in increasing permeability through solvent-modified membranes. Methods. Membrane sorption of solvents was determined from weight differences following immersion in individual solvents, corrected for differences in density. Permeability and retention kinetics of H-3-hydrocortisone, applied as saturated solutions in the various solvents, were determined over 48 h in horizontal Franz-type glass diffusion cells. Results. Solvent sorption into the membrane could be related to differences in solubility parameters, MW and hydrogen bonding (r(2) = 0.76). The actual and predicted volume of solvent sorbed into the membrane was also found to be linearly related to Log hydrocortisone flux, with changes in both diffusivity and partitioning of hydrocortisone observed for the different solvent vehicles. Conclusions. A simple structure-based predictive model can be applied to the sorption of solvents into silicone membranes. Changes in solute diffusivity and partitioning appeared to contribute to the increased hydrocortisone flux observed with the various solvent vehicles. The application of this predictive model to the more complex skin membrane remains to be determined.

Briefing: Factored material properties and limit state loads-unlikely extreme or impossible pretense

In the limit state design (LSD) method each design criterion is formally stated and assessed using a performance function. The performance function defines the relationship between the design parameters and the design criterion. In practice, LSD involves factoring up loads and factoring down calculated strengths and material parameters. This provides a convenient way to carry out routine probabilistic-based design. The factors are statistically calculated to produce a design with an acceptably low probability of failure. Hence the ultimate load and the design material properties are mathematical concepts that have no physical interpretation. They may be physically impossible. Similarly, the appropriate analysis model is also defined by the performance function and may not describe the real behaviour at the perceived physical equivalent limit condition. These points must be understood to avoid confusion in the discussion and application of partial factor LSD methods.

Assessment of the ability to model proteins with leucine-rich repeats in light of the latest structural information

The three-dimensional structures of leucine-rich repeat (LRR) -containing proteins from five different families were previously predicted based on the crystal structure of the ribonuclease inhibitor. using an approach that combined homology-based modeling, structure-based sequence alignment of LRRs, and several rational assumptions. The structural models have been produced based on very limited sequence similarity, which, in general. cannot yield trustworthy predictions. Recently, the protein structures from three of these five families have been determined. In this report we estimate the quality of the modeling approach by comparing the models with the experimentally determined structures. The comparison suggests that the general architecture, curvature, interior/exterior orientations of side chains. and backbone conformation of the LRR structures can be predicted correctly. On the other hand. the analysis revealed that, in some cases. it is difficult to predict correctly the twist of the overall super-helical structure. Taking into consideration the conclusions from these comparisons, we identified a new family of bacterial LRR proteins and present its structural model. The reliability of the LRR protein modeling suggests that it would be informative to apply similar modeling approaches to other classes of solenoid proteins.

GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

Selecting optimal instantiations of data models - Theory and validation of an ex ante approach

The schema of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. Obtaining quickly the appropriate data increases the likelihood that an organization will make good decisions and respond adeptly to challenges. This research presents and validates a methodology for evaluating, ex ante, the relative desirability of alternative instantiations of a model of data. In contrast to prior research, each instantiation is based on a different formal theory. This research theorizes that the instantiation that yields the lowest weighted average query complexity for a representative sample of information requests is the most desirable instantiation for end-user queries. The theory was validated by an experiment that compared end-user performance using an instantiation of a data structure based on the relational model of data with performance using the corresponding instantiation of the data structure based on the object-relational model of data. Complexity was measured using three different Halstead metrics: program length, difficulty, and effort. For a representative sample of queries, the average complexity using each instantiation was calculated. As theorized, end users querying the instantiation with the lower average complexity made fewer semantic errors, i.e., were more effective at composing queries. (c) 2005 Elsevier B.V. All rights reserved.

An examination of learning processes during critical incident training: Implications for the development of adaptable trainees

Three experiments are reported that examined the process by which trainees learn decision-making skills during a critical incident training program. Formal theories of category learning were used to identify two processes that may be responsible for the acquisition of decision-making skills: rule learning and exemplar learning. Experiments I and 2 used the process dissociation procedure (L. L. Jacoby, 1998) to evaluate the contribution of these processes to performance. The results suggest that trainees used a mixture of rule and exemplar learning. Furthermore, these learning processes were influenced by different aspects of training structure and design. The goal of Experiment 3 was to develop training techniques that enable trainees to use a rule adaptively. Trainees were tested on cases that represented exceptions to the rule. Unexpectedly, the results suggest that providing general instruction regarding the kinds of conditions in which a decision rule does not apply caused them to fixate on the specific conditions mentioned and impaired their ability to identify other conditions in which the rule might not apply. The theoretical, methodological, and practical implications of the results are discussed.

Prediction of HLA-DQ3.2 beta ligands: evidence of multiple registers in class II binding peptides

Motivation: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. Methods: We describe a structure-based prediction model for modeling peptide-DQ3.2 beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2 beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2 beta binding and non-binding peptides obtained from biochemical and functional studies. Results: Our model predicts DQ3.2 beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2 beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.

A knowledge-based system for liquid retaining structure design with blackboard architecture

Owing to the high degree of vulnerability of liquid retaining structures to corrosion problems, there are stringent requirements in its design against cracking. In this paper, a prototype knowledge-based system is developed and implemented for the design of liquid retaining structures based on the blackboard architecture. A commercially available expert system shell VISUAL RULE STUDIO working as an ActiveX Designer under the VISUAL BASIC programming environment is employed. Hybrid knowledge representation approach with production rules and procedural methods under object-oriented programming are used to represent the engineering heuristics and design knowledge of this domain. It is demonstrated that the blackboard architecture is capable of integrating different knowledge together in an effective manner. The system is tailored to give advice to users regarding preliminary design, loading specification and optimized configuration selection of this type of structure. An example of application is given to illustrate the capabilities of the prototype system in transferring knowledge on liquid retaining structure to novice engineers. (C) 2004 Elsevier Ltd. All rights reserved.