Herbicides and plants

Summary

Herbicide activity depends upon the inherent ability of the active ingredient (AI) to interact with the target enzyme(s) and the efficiency of its delivery at the target site(s). In this paper consideration is given to the factors which influence effective target site delivery and activity of foliage and soil-applied compounds. In the case of foliage- applied herbicides, the efficiency of retention and cuticle penetration is influenced by the stage and habit of growth of the plant, leaf age and surface characteristics, the molecular and formulation features of the AI, and the environmental conditions before, during, or after spraying. These factors may influence the efficiency of uptake, translocation and metabolism of AI en route to the target sites. The action of soil-applied compounds is influenced by the physico-chemical properties of the AI, its adsorption/desorption on the clay-humus colloidal complex, and the absorption, transport and metabolism en route to the target sites. In particular the water solubility of the AI, its formulation and the climate conditions subsequent to spraying, may influence selectivity and environmental fate. Finally, the ability of plants to acquire herbicide tolerance is considered in relation to both crop and weed with particular reference to the mechanisms which can be used to induce tolerance in crop plants.     

Affinity Selection and Mass Spectrometry-Based Strategies to Identify Lead Compounds in Combinatorial Libraries

The screening of diverse libraries of small molecules created by combinatorial synthetic methods is a recent development which has the potential to accelerate the identification of lead compounds in drug discovery. We have developed a direct and rapid method to identify lead compounds in libraries involving affinity selection and mass spectrometry. In our strategy, the receptor or target molecule of interest is used to isolate the active components from the library physically, followed by direct structural identification of the active compounds bound to the target molecule by mass spectrometry. In a drug design strategy, structurally diverse libraries can be used for the initial identification of lead compounds. Once lead compounds have been identified, libraries containing compounds chemically similar to the lead compound can be generated and used to optimize the binding characteristics. These strategies have also been adopted for more detailed studies of protein–ligand interactions.     

From in silico target prediction to multi-target drug design: current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.     

Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease

Background

We consider the possibility of engineering metabolic pathways in a chassis organism in order to synthesize novel target compounds that are heterologous to the chassis. For this purpose, we model metabolic networks through hypergraphs where reactions are represented by hyperarcs. Each hyperarc represents an enzyme-catalyzed reaction that transforms set of substrates compounds into product compounds. We follow a retrosynthetic approach in order to search in the metabolic space (hypergraphs) for pathways (hyperpaths) linking the target compounds to a source set of compounds.

Results

To select the best pathways to engineer, we have developed an objective function that computes the cost of inserting a heterologous pathway in a given chassis organism. In order to find minimum-cost pathways, we propose in this paper two methods based on steady state analysis and network topology that are to the best of our knowledge, the first to enumerate all possible heterologous pathways linking a target compounds to a source set of compounds. In the context of metabolic engineering, the source set is composed of all naturally produced chassis compounds (endogenuous chassis metabolites) and the target set can be any compound of the chemical space. We also provide an algorithm for identifying precursors which can be supplied to the growth media in order to increase the number of ways to synthesize specific target compounds.

Conclusions

We find the topological approach to be faster by several orders of magnitude than the steady state approach. Yet both methods are generally scalable in time with the number of pathways in the metabolic network. Therefore this work provides a powerful tool for pathway enumeration with direct application to biosynthetic pathway design.     

Computational ligand-based rational design: Role of conformational sampling and force fields in model development

A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.     

Antimalarial drugs inhibiting hemozoin (beta-hematin) formation: a mechanistic update

Digestion of hemoglobin in the food vacuole of the malaria parasite produces very high quantities of redox active toxic free heme. Hemozoin (beta-hematin) formation is a unique process adopted by Plasmodium sp. to detoxify free heme. Hemozoin formation is a validated target for most of the well-known existing antimalarial drugs and considered to be a suitable target to develop new antimalarials. Here we discuss the possible mechanisms of free heme detoxification in the malaria parasite and the mechanistic details of compounds, which offer antimalarial activity by inhibiting hemozoin formation. The chemical nature of new antimalarial compounds showing antimalarial activity through the inhibition of hemozoin formation has also been incorporated, which may help to design future antimalarials with therapeutic potential against multi-drug resistant malaria.     

De novo design of biocatalysts

The challenging field of de novo enzyme design is beginning to produce exciting results. The application of powerful computational methods to functional protein design has recently succeeded at engineering target activities. In addition, efforts in directed evolution continue to expand the transformations that can be accomplished by existing enzymes. The engineering of completely novel catalytic activity requires traversing inactive sequence space in a fitness landscape, a feat that is better suited to computational design. Optimizing activity, which can include subtle alterations in backbone conformation and protein motion, is better suited to directed evolution, which is highly effective at scaling fitness landscapes towards maxima. Improved rational design efforts coupled with directed evolution should dramatically improve the scope of de novo enzyme design.     

Modeling and artificial intelligence approaches to enzyme systems

Modeling is a means of formulating and testing complex hypotheses. Useful modeling is now possible with biological laboratory microcomputers with which experimenters feel comfortable. Artificial intelligence (AI) is sufficiently similar to modeling that AI techniques, now becoming usable on microcomputers, are applicable to modeling. Microcomputer and AI applications to physiological system studies with multienzyme models and with kinetic models of isolated enzymes are described. Using an IBM PC microcomputer, we have been able to fit kinetic enzyme models; to extend this process to design kinetic experiments by determining the optimal conditions; and to construct an enzyme (hexokinase) kinetics data base. We have also used a PC to do most of the constructing of complex multienzyme models, initially with small simple BASIC programs; alternative methods with standard spreadsheet or data base programs have been defined. Formulating and solving differential equations in appropriate representational languages, and sensitivity analysis, are soon likely to be feasible with PCs. Much of the modeling process can be stated in terms of AI expert systems, using sets of rules for fitting and evaluating models and designing further experiments. AI techniques also permit critiquing and evaluating the data, experiments, and hypotheses being modeled, and can be extended to supervise the calculations involved.     

Filtering of ineffective siRNAs and improved siRNA design tool

MOTIVATION: Short interfering RNAs (siRNAs) can be used to suppress gene expression and possess many potential applications in therapy, but how to design an effective siRNA is still not clear. Based on the MPI (Max-Planck-Institute) basic principles, a number of siRNA design tools have been developed recently. The set of candidates reported by these tools is usually large and often contains ineffective siRNAs. In view of this, we initiate the study of filtering ineffective siRNAs. RESULTS: The contribution of this paper is 2-fold. First, we propose a fair scheme to compare existing design tools based on real data in the literature. Second, we attempt to improve the MPI principles and existing tools by an algorithm that can filter ineffective siRNAs. The algorithm is based on some new observations on the secondary structure, which we have verified by AI techniques (decision trees and support vector machines). We have tested our algorithm together with the MPI principles and the existing tools. The results show that our filtering algorithm is effective. AVAILABILITY: The siRNA design software tool can be found in the website http://www.cs.hku.hk/~sirna/ CONTACT: smyiu@cs.hku.hk     

Leveraging structure determination with fragment screening for infectious disease drug targets: MECP synthase from Burkholderia pseudomallei

As part of the Seattle Structural Genomics Center for Infectious Disease, we seek to enhance structural genomics with ligand-bound structure data which can serve as a blueprint for structure-based drug design. We have adapted fragment-based screening methods to our structural genomics pipeline to generate multiple ligand-bound structures of high priority drug targets from pathogenic organisms. In this study, we report fragment screening methods and structure determination results for 2C-methyl-D-erythritol-2,4-cyclo-diphosphate (MECP) synthase from Burkholderia pseudomallei, the gram-negative bacterium which causes melioidosis. Screening by nuclear magnetic resonance spectroscopy as well as crystal soaking followed by X-ray diffraction led to the identification of several small molecules which bind this enzyme in a critical metabolic pathway. A series of complex structures obtained with screening hits reveal distinct binding pockets and a range of small molecules which form complexes with the target. Additional soaks with these compounds further demonstrate a subset of fragments to only bind the protein when present in specific combinations. This ensemble of fragment-bound complexes illuminates several characteristics of MECP synthase, including a previously unknown binding surface external to the catalytic active site. These ligand-bound structures now serve to guide medicinal chemists and structural biologists in rational design of novel inhibitors for this enzyme.     

Recent advances in drug design methods: Where will they lead?

Drug design methods have made significant new advances over the last ten years, mainly in the areas of molecular modelling. In more recent times important developments in theory have led to a different type of modelling becoming possible, the so-called de novo or automated design algorithms. In this new method the programs perform much of the chemist's thinking, in finding appropriately sized chemical groups to fit into a target site. However this is a combinatoric problem which has no general analytical solution; it is ripe for optimization. Other advances, such as combinatorial chemical synthesis and screening, will dramatically influence the search for new lead structures for target sites, which at present are poorly understood. Already these methods are being applied to peptide libraries. Peptides do not make good drug compounds because of their poor bioavailability; further, their flexibility reduces their affinity. In some cases peptide backbones can be removed and replaced with rigid non-peptide scaffolds.     

基于深度学习的蛋白质建模与设计

随着蛋白质序列及结构数据的大量累积,在获得了大量描述性信息之后如何有效利用海量数据,从已有数据中高效提取信息并且应用到下游任务当中就成为了研究者亟待解决的问题。蛋白质的设计可使新蛋白的研发不再受限于实验条件,这对药物靶点预测、新药研发和材料设计等领域具有重要意义。深度学习作为一种高效的数据特征提取方法,可以通过它对蛋白质数据进行建模,进而加入先验信息对蛋白质进行设计。故此基于深度学习的蛋白质设计就成为一个具有广阔前景的研究领域。文中主要阐述基于深度学习的蛋白质序列与结构数据的建模和设计方法。详述该方法的策略、原理、适用范围、应用实例。讨论了深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

Treadmill control protocols for arbitrary work rate profiles combining simultaneous nonlinear changes in speed and angle

Treadmill training and testing applications combine changes in speed and angle to achieve a target work rate profile. Here we derive new methods which increase the flexibility available in the design of such protocols.Current methods of computing speed and angle are first reformulated in terms of work rate, rather than time, so that the existing limitation to work rate ramps is removed: the new methods allow arbitrary changes in work rate over time. New solutions are derived which allow the speed and angle functions to be positioned arbitrarily within the available speed and angle envelopes. It is shown that the methods can be easily specialised to the particular case of an incremental exercise test where work rate is defined to increase linearly with respect to time.The new methods facilitate the definition of arbitrary, possibly discontinuous, work rate profiles while permitting complete flexibility in the design of speed and angle profiles for treadmill training and testing applications.     

Diazen-1-ium-1,2-diolated nitric oxide donor ester prodrugs of 5-(4-hydroxymethylphenyl)-1-(4-aminosulfonylphenyl)-3-trifluoromethyl-1H-pyrazole and its methanesulfonyl analog: synthesis, biological evaluation and nitric oxide release studies

A new class of hybrid nitric oxide-releasing anti-inflammatory (AI) ester prodrugs (NONO-coxibs 12a-b) wherein an O(2)-acetoxymethyl 1-(2-carboxypyrrolidin-1-yl)diazen-1-ium-1,2-diolate (11, O(2)-acetoxymethyl PROLI/NO) NO-donor moiety was covalently coupled to the bromomethyl group of 5-(4-bromomethylphenyl)-1-(4-aminosulfonylphenyl)-3-trifluoromethyl-1H-pyrazole (9a), and its methanesulfonyl analog (9b), were synthesized. The diazen-1-ium-1,2-diolate compounds 12a-b released a low amount of NO upon incubation with phosphate buffer (PBS) at pH 7.4 (6.1-8.2% range). In comparison, the percentage NO released was significantly higher (76-77% of the theoretical maximal release of two molecules of NO/molecule of the parent hybrid ester prodrug) when the diazen-1-ium-1,2-diolate ester prodrugs 12a-b were incubated in the presence of rat serum. These incubation studies suggest that both NO and the anti-inflammatory 5-(4-hydroxymethylphenyl)-1-(4-aminosulfonylphenyl)-3-trifluoromethyl-1H-pyrazole (10a), and its methanesulfonyl analog (10b), would be released from the parent NONO-coxib 12a or 12b upon in vivo cleavage by non-specific serum esterases. The hydroxymethyl compounds 10a-b were weak inhibitors of the cyclooxygenase-1 (COX-1) and COX-2 isozymes (IC(50)=3.7-10.5 microM range). However, the hydroxymethyl compounds 10a-b and the parent NONO-coxibs 12a-b exhibited good AI activities (ED(50)=76.7-111.6 micromol/kg po range) that were greater than that exhibited by the reference drugs aspirin (ED(50)=710 micromol/kg po) and ibuprofen (ED(50)=327 micromol/kg po), but less than that of celecoxib (ED(50)=30.9mumol/kg po). These studies indicate hybrid ester AI/NO-donor prodrugs (NONO-coxibs) constitutes a plausible drug design concept targeted toward the development of selective COX-2 inhibitory AI drugs that are devoid of adverse cardiovascular effects.     

Evaluation of a high-throughput fluorescence assay method for HERG potassium channel inhibition

The number of projects in drug development that fail in late phases because of cardiac side effects such as QT prolongation can impede drug discovery and development of projects. The molecular target responsible for QT prolongation by a wide range of pharmaceutical agents is the myocardial hERG potassium channel. It is therefore desirable to screen for compound interactions with the hERG channel at an early stage of drug development. Here, the authors report a cell-based fluorescence assay using membrane potential-sensitive fluorescent dyes and stably transfected hERG channels from CHO cells. The assay allows semiautomated screening of compounds for hERG activity on 384-well plates and is sufficiently rapid for testing a large number of compounds. The assay is robust as indicated by a Z' factor larger than 0.6. The throughput is in the range of 10,000 data points per day, which is significantly higher than any other method presently available for hERG. The data obtained with the fluorescence assay were in qualitative agreement with those from patch-clamp electrophysiological analysis. There were no false-positive hits, and the rate of false-negative compounds is currently 12% but might be further reduced by testing compounds at higher concentration. Quantitative differences between fluorescence and electrophysiological methods may be due to the use- or voltage-dependent activity of the antagonists.     

应用化学基因组信息预测小分子化合物的潜在生物靶标的理论方法

在后基因组时代,化学基因组技术在药物作用靶点的确认、小分子化合物对通路的作用,以及小分子先导化合物的识别等方面都有着广泛的应用,为新药研发提供了新的技术方法。本文主要介绍了当前几种基于化学基因组信息来预测小分子化合物潜在生物靶标的理论方法（包括化学相似性搜索方法、反向分子对接方法、数据挖掘方法以及生物活性谱图分析方法）,并分析了这些方法的优缺点以及应用前景。     

Target identification of bioactive compounds

To fully understand the regulation of cellular events, functional analysis of each protein involved in the regulatory systems is required. Among a variety of methods to uncover protein function, chemical genetics is a remarkable approach in which small molecular compounds are used as probes to elucidate protein functions within signaling pathways. However, identifying the target of small molecular bioactive compounds isolated by cell-based assays represents a crucial hurdle that must be overcome before chemical genetic studies can commence. A variety of methods and technologies for identifying target proteins have been reported. This review therefore aims to describe approaches for identifying these molecular targets.     

Current topics in artificial insemination of sheep

There have been developments in several aspects of artificial insemination (AI) in recent years, some of which have been directly responsible for proliferation of AI in the sheep-breeding industries of several countries. The most notable advances have probably been associated with the development of intrauterine insemination by laparoscopy. There is potential for refinement of some of the related techniques, particularly in the area of control of ovulation and definition of appropriate times and optimum doses of spermatozoa for insemination. It is unlikely that laparoscopic AI will be developed sufficiently that it will become readily affordable, and therefore widely practised, by commercial producers. Unfortunately, there has been little progress in the past few years in improvement of the methods of cryopreservation of ram semen. There is considerable potential for AI to have a significant impact on the genetic improvement of sheep, though this has yet to be evaluated in practice. However, if the full potential of AI in sheep is to be realized, it will likely only happen when methods of freezing semen are improved sufficiently that cervical or even vaginal insemination can be widely used with frozen-thawed semen, or when practicable methods of deep cervical or intrauterine insemination through the cervix are developed.     

SARS病毒再测序DNA微阵列的探针设计

探针设计是SARS病毒再测序DNA微阵列制作的关键步骤,为了保证探针的杂交条件尽可能一致,采用了作者提出的两种等长变覆盖的探针设计方法,即基于Tm距离的算法和遗传算法。针对SAILS病毒基因组中的两段特异序列设计了一组探针,并与等长移位法和变长变覆盖法的设计结果进行了比较。等长变覆盖法得到的探针集在探针长度一致的情况下,探针的Tm值有较小的标准差和变化范围。结果表明,等长变覆盖法得到的探针具有更好的杂交条件一致性。