Combinatorial Chemistry & High Throughput Screening - Volume 19, Issue 8, 2016

Nowadays, enzymes can be efficiently identified and screened from metagenomic resources or mutant libraries. A set of a few hundred new enzymes can be found using a simple substrate within few months. Hence, the establishment of collections of enzymes is no longer a big hurdle. However, a key problem is the relatively low rate of positive hits and that a timeline of several years from the identification of a gene to the development of a process is the reality rather than the exception. Major problems are related to the time-consuming and cost-intensive screening process that only very few enzymes finally pass. Accessing to the highest possible enzyme and mutant diversity by different, but complementary approaches is increasingly important. The aim of this review is to deliver state-of-art status of traditional and novel screening protocols for targeting lipases, esterases and phospholipases of industrial relevance, and that can be applied at high throughput scale (HTS) for at least 200 distinct substrates, at a speed of more than 105 – 108 clones/day. We also review fine-tuning sequence analysis pipelines and in silico tools, which can further improve enzyme selection by an unprecedent speed (up to 1030 enzymes). If the hit rate in an enzyme collection could be increased by HTS approaches, it can be expected that also the very further expensive and time-consuming enzyme optimization phase could be significantly shortened, as the processes of enzyme-candidate selection by such methods can be adapted to conditions most likely similar to the ones needed at industrial scale.

Feruloyl esterases (FAEs) are a diverse group of hydrolases widely distributed in plants and microorganisms which catalyzes the cleavage and formation of ester bonds between plant cell wall polysaccharides and phenolic acids. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing highadded value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production, characterization and classification of FAEs, however only a few reports of suitable High Throughput Screening assays for this kind of enzymes have been reported. This review is focused on a concise but complete revision of classical to High Throughput Screening methods for FAEs, highlighting its advantages and disadvantages, and finally suggesting future perspectives for this important research field.

High throughput screening (HTS) is a powerful tool in biotechnology. The search for new or improved enzymes with suitable biochemical properties for industrial processes, has resulted in high efforts and research activities to develop new methodologies for activity screening. In this context, important advances have been achieved for the screening of cellulases and xylanases activities from wild and recombinant microorganisms, and from sequence databases. These enzymes have a wide range of industrial applications, including food, animal feed, textile, pulp and paper industries and detergents. Cellulases and xylanases along with pectinases, represent 20% of the world enzyme market. Recently, cellulases and xylanases have been used on fermentable sugars recovered from lignocellulosic biomass for second-generation biorefineries, aimed to produce chemical and biofuel platforms. As a result, HTS methods for biomass or biomass-degrading enzymes are gaining importance. This article presents evidence of the studies carried out for HTS of cellulase and xylanase activities.

Proteases constitute one of the most important groups of industrial enzymes with a worldwide value expected to reach 2.7 billion US dollars by 2019. Proteases represent a group of enzymes that hydrolyze the peptide bonds of proteins, releasing polypeptides or free amino acids. These enzymes are used in cleaning products, production of leathers, textiles, food and dairy products, in the pharmaceutical and diagnostic industries and for water treatment. Another area of interest regarding proteases is the development of drugs that act as protease inhibitors. This review will briefly describe the general methods used in the detection of proteases and the few studies in the development of high throughput screening methods of proteases and protease inhibitors.

Rosiglitazone was restricted clinically due to the side effects such as edema, weight gain and cardiac failure mainly attributing to the single and selective PPARγ activation. Nowadays, multi-targeted PPARs agonists remained to be a hot topic in the antidiabetic medicinal chemistry field. In this paper, the cooperative PPARα/γ dual agonists were screened from Specs database via the flow chart of docking, ADMET prediction and molecular dynamics (MD) simulations. Representative compounds ZINC36517927 and ZINC13573581 displayed higher binding scores, better pharmacokinetic profiles and were predicted to display the best binding affinity with PPARα/γ. Complex-based pharmacophore (CBP) models showed the key interactions in the PPARα/γ active sites. 20 ns simulations performed to the PPAR-ligand complexes indicated a stable binding conformation. This work provided an approach to identify novel high-efficiency PPARα/γ dual agonists for the treatment of type 2 diabetes mellitus (T2DM).

O. sanctum L. (O. tenuiflorum) is an important sacred medicinal plant of India known as Holy Basil or Tulsi. The chemical composition of volatile oil is highly complex and comprises high ratio of phenylpropanoids and terpenes, and some phenolic compound or flavonoids such as orientin and vicenin. These minor flavonoids are known to be antioxidant and anticancer in nature. Orientin reported as potential anticancer agent due to anti-proliferative activity on human liver cancer cell line HepG2, but its mechanism of action is not fully explored. In the present work an in-silico structure-activity relationship study on orientin was performed and built a pharmacophore mapping and QSAR model to screen out the potential structurally similar analogues from chemical database of Discovery Studio (DSv3.5, Accelrys, USA) as potential anticancer agent. Analogue fenofibryl glucuronide was selected for in vitro cytotoxic/anticancer activity evaluation through MTT assay. Binding affinity and mode of action of orientin and its analogue were explored through molecular docking studies on quinone oxidoreductase, a potential target of flavonoids. Contrary to the assumption, in vitro results showed only 41% cell death at 202.389 μM concentration (at 96 hrs). Therefore, we concluded that the selected orientin analogue fenofibryl glucuronide was non-cytotoxic/non-anti-carcinogenic up to 100 μg/ml (202.389 μM) concentrations for a long term exposure i.e., till 96 hrs in human cancer cells of HepG2. We concluded that orientin and its analogue fenofibryl glucuronide as pure compound showed no activity or less cytotoxicity activity on liver cancer cell line HepG2.

Background: Tuberculosis is the second leading cause of death from an infectious disease worldwide after HIV, thus reasoning the expeditions in antituberculosis research. The rising number of cases of infection by resistant forms of M. tuberculosis has given impetus to the development of novel drugs that have different targets and mechanisms of action against the bacterium. Methods: In this study, we have used machine learning algorithms on the available high throughput screening data of inhibitors of fructose bisphosphate aldolase, an enzyme central to the glycolysis pathway in M. tuberculosis, to build predictive classification models to identify actives against Mycobacterium tuberculosis, the causative organism of tuberculosis. We used Naïve Bayes, Random Forest and C4.5 J48 algorithms available from Weka were used for building predictive classification models. Additionally, a set of most relevant attributes was selected using genetic search algorithm which offered improved model performance by avoiding over fitting and generating faster and cost effective models. Results: The model built using machine learning methods in this study provided good accuracy of classification of test compounds which suggests that in silico methods can be successfully used for screening of large datasets to identify potential drug leads. The substructure fragment analysis serves to further potentiate the M. tuberculosis drug development process as it would facilitate identification of structural fragments that are responsible for biological activity against this crucial glycolysis pathway target.

Quantitative structure – activity relationships (QSARs) are built up for three endpoints (i) blood-brain barrier permeability; (ii) butyrylcholinesterase (BChE) inhibitory activity; and (iii) for biological effect of antibacterial drugs. The models are based on utilization of the Monte Carlo technique. The CORAL software available on the Internet has been utilized for the calculations. The principles of validation of models together with principles of selection of potential therapeutic agents are suggested. An original version of the definition for the domain of applicability as well as the mechanistic interpretation of model calculated with the Monte Carlo technique are described. Advantages and disadvantages of the utilized approach are discussed.