Structure Of Target Protein Research Articles

Ligand-Induced Conformational Changes: Improved Predictions of Ligand Binding Conformations and Affinities

A computational docking strategy using multiple conformations of the target protein is discussed and evaluated. A series of low molecular weight, competitive, nonpeptide protein tyrosine phosphatase inhibitors are considered for which the x-ray crystallographic structures in complex with protein tyrosine phosphatase 1B (PTP1B) are known. To obtain a quantitative measure of the impact of conformational changes induced by the inhibitors, these were docked to the active site region of various structures of PTP1B using the docking program FlexX. Firstly, the inhibitors were docked to a PTP1B crystal structure cocrystallized with a hexapeptide. The estimated binding energies for various docking modes as well as the RMS differences between the docked compounds and the crystallographic structure were calculated. In this scenario the estimated binding energies were not predictive inasmuch as docking modes with low estimated binding energies corresponded to relatively large RMS differences when aligned with the corresponding crystal structure. Secondly, the inhibitors were docked to their parent protein structures in which they were cocrystallized. In this case, there was a good correlation between low predicted binding energy and a correct docking mode. Thirdly, to improve the predictability of the docking procedure in the general case, where only a single target protein structure is known, we evaluate an approach which takes possible protein side-chain conformational changes into account. Here, side chains exposed to the active site were considered in their allowed rotamer conformations and protein models containing all possible combinations of side-chain rotamers were generated. To evaluate which of these modeled active sites is the most likely binding site conformation for a certain inhibitor, the inhibitors were docked against all active site models. The receptor rotamer model corresponding to the lowest estimated binding energy is taken as the top candidate. Using this protocol, correct inhibitor binding modes could successfully be discriminated from proposed incorrect binding modes. Moreover, the ranking of the estimated ligand binding energies was in good agreement with experimentally observed binding affinities.

Construction of hypothetical three-dimensional structure of P2Y1 receptor based on Fourier transform analysis.

G protein-coupled receptors constitute a large family of homologous transmembrane proteins that represents one of the most important classes of confirmed drug targets. For novel drug discovery, the 3D structure of target protein is indispensable. To construct hypothetical 3D structures of G protein-coupled receptors, several prediction methods have been proposed. But none of the them has confirmed a correct ligand binding site. In this study we constructed the 3D structure of bovine rhodopsin using the prediction method proposed by Donnelly et al., with some modification. We found that our 3D model showed a good agreement with the reported retinal binding site. Using the similar method, we constructed the 3D structure of the P2Y1 receptor; one of the G protein-coupled receptors, and showed a binding site of an endogenous ligand, ADP, on the basis of the 3D model and in vitro experimental data. These results should be valuable for design of a specific antagonist for P2Y1 receptor.

Structural Proteomics: Methods in Deriving Protein Structural Information and Issues in Data Management

Structural proteomics is an emerging paradigm that is gaining importance in the post-genomic era as a valuable discipline to process the protein target information being deciphered. The field plays a crucial role in assigning function to sequenced proteins, defining pathways in which the targets are involved, and understanding structure-function relationships of the protein targets. A key component of this research sector is accessing the three-dimensional structures of protein targets by both experimental and theoretical methods. This then leads to the question of how to store, retrieve, and manipulate vast amounts of sequence (1-D) and structural (3-D) information in a relational format so that extensive data analysis can be achieved. We at SBI have addressed both of these fundamental requirements of structural proteomics. We have developed an extensive collection of three-dimensional protein structures from sequence data and have implemented a relational architecture for data management. In this article we will discuss our approaches to structural proteomics and the tools that life science researchers can use in their discovery efforts.

Molecular modeling of the amino-terminal domain of the mGluR1 glutamate metabotropic receptor by the threading method

The glutamatergic system plays a key role in the functioning of the human central nervous system; therefore, knowledge of its structure at the molecular level is important not only for the understanding of its operation but also for the target-directed design of new drugs, in particular, new neuroprotective drugs based on selective agonists and antagonists of glutamate receptors. Two main approaches are currently used in the comparative modeling of protein structures. The first and the most widely used homology method is based on comparison of the amino-acid sequences of the protein being modeled with those of a protein whose spatial structure is known from experiments [1]. The main limitation of this approach is the requirement that at least 30% of the amino-acid sequences in the target and template proteins be homologous, which is usually fulfilled only in the series of evolutionally and functionally related proteins [2]. The use of this approach allowed us to model the closed forms of the glutamate [3] and glycine [4] sites of the ionotropic NMDA receptor; the ionic channel of the NMDA receptor with phencyclidine binding sites [5]; and the open form of the aminoterminal domain (ATD) of the first subtype of the metabotropic mGluR glutamate receptor [6]. In the last-mentioned case, we were able to demonstrate on the basis of multiple alignment that R78 is involved in binding of the distal carboxy group of agonists. Models of the open and closed forms of ATD of the mGluR1 receptor made by comparative analysis were also considered in [7, 8]. The second approach, called threading, is based on the idea that even unrelated proteins with non-homologous amino acid sequences can have identical spatial structures [9‐10]. In terms of this approach, a “thread” corresponding to the sequence of the target protein is pulled through a fold determined by the template protein and the position of the thread is found to maximize the function of the generalized empirical potential. The main advantage of this approach over the above-considered one is that it is often possible to model rather reliably the spatial structures of proteins even in the absence of any homology of the primary sequence to the template protein. The purpose of this work is to construct the spatial models for the open and closed forms of the amino acid domain of the mGluR1 receptor by the threading method and to estimate the potential of this method for the cases of low homology of amino acid sequences. The search performed using the Threader2 program [9], which contains more than 2000 different folds in the database, made it possible to identify suitable template proteins for the modeling of open and closed forms of these receptors. The results were confirmed by statistic tests, which are also implemented in the Threader2 program. As in the case of homology modeling, the template proteins are LBP (leucine-binding proteins) for the open form and PEA (amide-binding proteins) for the closed form. The homology of the amino acid sequences is about 17% in both cases. The probability that the spatial structure of the target protein is similar to that of the template protein is estimated quantitatively by a statistic value called the Z factor. The table presents the calculated Z factors for all sub

Bioinformatics and Rational Drug Design: Tools for Discovery and Better Understanding of Biological Targets and Mode of Action of Drugs

Background : With the advent of modern high throughput technologies in both genomics and biological screening, and at the same time the enormous advances in computer technology, it is now feasible to use these tools in rational approaches in the search for new medicines. The role of bioinformatics in the search for new medicines is discussed. Methods : Discussion of the author's own work on bioinformatics in drug research in future perspective. Results : The emerging discipline of Bioinformatics plays a central role in the concert of technologies of the 'biological revolution' because it allows for handling of the enormous data load that comes with sequencing efforts and subsequent analyses of whole genomes, with mRNA profiling techniques and, last but not least, at a later stage of drug discovery the up-to-date application of rational drug design techniques to 3D structures of target proteins. This article covers and explains parts of the steps used in modern pharmaceutical research by means of a small number of examples. Conclusion : Bioinformatics is likely to play a pivotal role in the rational approaches for the search of new medicines.

High-throughput crystallography for lead discovery in drug design.

Knowledge of the three-dimensional structures of protein targets now emerging from genomic data has the potential to accelerate drug discovery greatly. X-ray crystallography is the most widely used technique for protein structure determination, but technical challenges and time constraints have traditionally limited its use primarily to lead optimization. Here, we describe how significant advances in process automation and informatics have aided the development of high-throughput X-ray crystallography, and discuss the use of this technique for structure-based lead discovery.

Automated docking to multiple target structures: incorporation of protein mobility and structural water heterogeneity in AutoDock.

Protein motion and heterogeneity of structural waters are approximated in ligand-docking simulations, using an ensemble of protein structures. Four methods of combining multiple target structures within a single grid-based lookup table of interaction energies are tested. The method is evaluated using complexes of 21 peptidomimetic inhibitors with human immunodeficiency virus type 1 (HIV-1) protease. Several of these structures show motion of an arginine residue, which is essential for binding of large inhibitors. A structural water is also present in 20 of the structures, but it must be absent in the remaining one for proper binding. Mean and minimum methods perform poorly, but two weighted average methods permit consistent and accurate ligand docking, using a single grid representation of the target protein structures.

Gene therapy for the management of pain: part II: molecular targets.

TREATMENT of chronic pain, particularly of neuropathic etiology, is extremely difficult and resistant to many available pharmacologic therapies. Current analgesic agents may be limited with regard to analgesic efficacy or side effects. Newer and experimental pharmacologic agents may also have significant limitations. By targeting a specific receptor or other specific protein targets, a gene therapy approach to the treatment of pain may provide greater analgesic efficacy without the limitations associated with current pharmacotherapy. Advances in the field of gene therapy, along with significant increases in our understanding of the neurobiology of nociception and knowledge of the fundamental genetic structure of many nociceptive targets, have made gene therapy for the management of pain a conceivable reality. In part I of this review, we introduced the basic concepts of gene therapy with an emphasis on the available tools (e.g., viral vectors and antisense oligonucleotides) and strategies for upregulating antinociceptive or downregulating pronociceptive targets. In part II, we summarize current knowledge regarding the nociceptive role, molecular biology, and antisense and knockout data of several novel nociceptive targets for gene therapy. We base our selection of the targets included in this review on the three aforementioned criteria. The targets selected are the best characterized and, in our opinion, most likely amenable to the gene therapeutic approach. A simple but feasible strategy and potential gene therapy targets for the management of pain are summarized in figure 1. However, the list is admittedly incomplete, and the readers are referred to other recent reviews cited in part I of this review for a broader perspective on potential targets for the management of pain.

Use of mutation spectra analysis software.

The study and comparison of mutation(al) spectra is an important problem in molecular biology, because these spectra often reflect on important features of mutations and their fixation. Such features include the interaction of DNA with various mutagens, the function of repair/replication enzymes, and properties of target proteins. It is known that mutability varies significantly along nucleotide sequences, such that mutations often concentrate at certain positions, called "hotspots," in a sequence. In this paper, we discuss in detail two approaches for mutation spectra analysis: the comparison of mutation spectra with a HG-PUBL program, (FTP: sunsite.unc.edu/pub/academic/biology/dna-mutations/hyperg) and hotspot prediction with the CLUSTERM program (www.itba.mi.cnr.it/webmutation; ftp.bionet.nsc.ru/pub/biology/dbms/clusterm.zip). Several other approaches for mutational spectra analysis, such as the analysis of a target protein structure, hotspot context revealing, multiple spectra comparisons, as well as a number of mutation databases are briefly described. Mutation spectra in the lacI gene of E. coli and the human p53 gene are used for illustration of various difficulties of such analysis.

How many amino-acid sequences can fold to a given protein structure?

Information about the set of amino-acid sequences that will fold to a given target protein structure can be predicted without assuming complete knowledge of the possible conformational space they can occupy.

A new approach to the design of uniquely folded thermally stable proteins

A new computer program (CORE) is described that predicts core hydrophobic sequences of predetermined target protein structures. A novel scoring function is employed, which for the first time incorporates parameters directly correlated to free energies of unfolding (deltaGu), melting temperatures (Tm), and cooperativity. Metropolis-driven simulated annealing and low-temperature Monte Carlo sampling are used to optimize this score, generating sequences predicted to yield uniquely folded, stable proteins with cooperative unfolding transitions. The hydrophobic core residues of four natural proteins were predicted using CORE with the backbone structure and solvent exposed residues as input. In the two smaller proteins tested (Gbeta1, 11 core amino acids; 434 cro, 10 core amino acids), the native sequence was regenerated as well as the sequence of known thermally stable variants that exhibit cooperative denaturation transitions. Previously designed sequences of variants with lower thermal stability and weaker cooperativity were not predicted. In the two larger proteins tested (myoglobin, 32 core amino acids; methionine aminopeptidase, 63 core amino acids), sequences with corresponding side-chain conformations remarkably similar to that of native were predicted.

New directions in pharmacology

mum p harmacology, classically concemed with observation and analysis of drug effects on complex living systems, is now taking many new directions. Of particular importance are new molecular techniques to describe the structure of target proteins, an improved understanding of the biochemical mechanisms of diseases to allow the identification of~new drug targets and the development of disease models, technological advances such as combinatorial chemistry and high throughput screening, and the emergence of completely new types of drugs. The availability of molecular techniques permits for the first time, I believe, a complete understanding of drug effects. By complete I mean an understanding at different levels: identification of the primary target, usually a protein such as a receptor, ion channel, or enzyme; molecular and structural understanding of the binding of a drug to this target; the biochemical mechanisms that are initiated or affected; the cellular responses; the alterations in complex organ functions in vitro and in vivo; and the knowledge whether all these changes actually benefit the patient. There are very few drugs for which such a complete understanding has been achieved. Aspirin, developed just over 100 years ago, is probably the best-known example, for which we seem to know everything, from the three-dimensional structure of the target cyclo-oxygenases to the many therapeutic benefits. And yet our knowledge is still limited, because it is not certain which cyclo-oxygenase isoform is the relevant one in aspirin's actions. The first drug that was developed on the basis of a known target structure is the carbonic anhydrase inhibitor dorzolamide for the topical treatment of glaucoma. Other structure-based developments have followed, notably New directions in pharmacology

Solid-Phase SNAr Macrocyclizations To Give Turn-Extended-Turn Peptidomimetics

An optimized set of conditions was developed for the synthesis ofβ-turn mimics 1 by SNAr macrocyclizations. These allow efficient production of molecules containing two amino acids (to mimic part of a turn region) fused with a turn-inducing fragment. The turn-inducing fragment is formed from amino acids with nucleophilic side chains and the SNAr electrophile. This methodology facilitates production of focussed libraries based on target protein structures.

Energy functions for protein design

Recent successes in protein design have illustrated the promise of computational approaches. These methods rely on energy expressions to evaluate the quality of different amino acid sequences for target protein structures. The force fields optimized for design differ from those typically used in molecular mechanics and molecular dynamics calculations.

Assigning amino acid sequences to 3‐dimensional protein folds

With the advent of genome sequencing projects, the amino acid sequences of thousands of proteins are determined every year. Each of these protein sequences must be identified with its function and its 3-dimensional structure for us to gain a full understanding of the molecular biology of organisms. To meet this challenge, new methods are being developed for fold recognition, the computational assignment of newly determined amino acid sequences to 3-dimensional protein structures. These methods start with a library of known 3-dimensional target protein structures. The new probe sequence is then aligned to each target protein structure in the library and the compatibility of the sequence for that structure is scored. If a target structure is found to have a significantly high compatibility score, it is assumed that the probe sequence folds in much the same way as the target structure. The fundamental assumptions of this approach are that many different sequences fold in similar ways and there is a relatively high probability that a new sequence possesses a previously observed fold. We review various approaches to fold recognition and break down the process into its main steps: creation of a library of target folds; representation of the folds; alignment of the probe sequence to a target fold using a sequence-to-structure compatibility scoring function; and assessment of significance of compatibility. We emphasize that even though this new field of fold recognition has made rapid progress, technical problems remain to be solved in most of the steps. Standard benchmarks may help identify the problem steps and find solutions to the problems.

Designing amino acid sequences to fold with good hydrophobic cores.

We present two methods for designing amino acid sequences of proteins that will fold to have good hydrophobic cores. Given the coordinates of the desired target protein or polymer structure, the methods generate sequences of hydrophobic (H) and polar (P) monomers that are intended to fold to these structures. One method designs hydrophobic inside, polar outside; the other minimizes an energy function in a sequence evolution process. The sequences generated by these methods agree at the level of 60-80% of the sequence positions in 20 proteins in the Protein Data Bank. A major challenge in protein design is to create sequences that can fold uniquely, i.e. to a single conformation rather than to many. While an earlier lattice-based sequence evolution method was shown not to design unique folders, our method generates unique folders in lattice model tests. These methods may also be useful in designing other types of foldable polymer not based on amino acids.

Compass: A shape-based machine learning tool for drug design

Building predictive models for iterative drug design in the absence of a known target protein structure is an important challenge. We present a novel technique, Compass, that removes a major obstacle to accurate prediction by automatically selecting conformations and alignments of molecules without the benefit of a characterized active site. The technique combines explicit representation of molecular shape with neural network learning methods to produce highly predictive models, even across chemically distinct classes of molecules. We apply the method to predicting human perception of musk odor and show how the resulting models can provide graphical guidance for chemical modifications.

Structure-based drug design

There are now many successful examples of the design of new ligands based on knowledge of target protein structures. In most cases those ligands are unsuitable as drugs because of problems of toxicity, stability or bioavailability. The past twelve months have also seen the description of the structures of many proteins which are either known to be targets for existing drugs or have clear potential to be utilized in therapy.

Cytolytic T-lymphocyte responses to respiratory syncytial virus: effector cell phenotype and target proteins

Cytolytic T-lymphocyte (CTL) activity specific for respiratory syncytial (RS) virus was investigated after intranasal infection of mice with RS virus, after intraperitoneal infection of mice with a recombinant vaccinia virus expressing the F glycoprotein, and after intramuscular vaccination of mice with Formalin-inactivated RS virus or a chimeric glycoprotein, FG, expressed from a recombinant baculovirus. Spleen cell cultures from mice previously infected with live RS virus or the F-protein recombinant vaccinia virus had significant CTL activity after one cycle of in vitro restimulation with RS virus, and lytic activity was derived from a major histocompatibility complex-restricted, Lyt2.2+ (CD8+) subset. CTL activity was not restimulated in spleen cells from mice that received either the Formalin-inactivated RS virus or the purified glycoprotein, FG. The protein target structures for recognition by murine CD8+ CTL were identified by using target cells infected with recombinant vaccinia viruses that individually express seven structural proteins of RS virus. Quantitation of cytolytic activity against cells expressing each target structure suggested that 22K was the major target protein for CD8+ CTL, equivalent to recognition of cells infected with RS virus, followed by intermediate recognition of F or N, slight recognition of P, and no recognition of G, SH, or M. Repeated stimulation of murine CTL with RS virus resulted in outgrowth of CD4+ CTL which, over time, became the exclusive subset in culture. Murine CD4+ CTL were highly cytolytic for RS virus-infected cells, but they did not recognize target cells infected with any of the recombinant vaccinia viruses expressing the seven RS virus structural proteins. Finally, the CTL response in peripheral blood mononuclear cells of adult human volunteers was investigated. The detection of significant levels of RS virus-specific cytolytic activity in these cells was dependent on at least two restimulations with RS virus in vitro, and cytolytic activity was derived primarily from the CD4+ subset.

The emergence of beta-lactam resistance among strains of Enterobacter cloacae and Pseudomonas aeruginosa

The development of multiresistant bacterial strains associated with the use of the new beta-lactam antibiotic agents has prompted investigation into the mechanisms involved. Factors influencing beta-lactamase production, alterations of target-protein structure or production and decreases in bacterial outer membrane permeability have been identified as the three general mechanisms of beta-lactam resistance in aerobic Gram-negative bacteria. Because there is no DNA interaction by beta-lactam agents, emergence of resistance results from either spontaneous mutation or the presence of resistant subpopulations; the presence of antibiotics allows for overgrowth of resistant strains. Studies have indicated that there is a significant relationship between the rate of emerging resistance and the ratio of the minimum inhibitory concentration of a beta-lactam to the drug concentration used to select for resistance in vitro or achieved at the infection site in vivo. It is concluded that the use of synergistic combinations of agents should reduce the emergence of beta-lactam-resistant bacterial strains. Thus the choice of an appropriate therapeutic agent requires thorough evaluation of the specific pathogen for susceptibility to a variety of beta-lactam agents and aminoglycosides.