Reena Halai

Bio: Reena Halai is an academic researcher from University of Queensland. The author has contributed to research in topics: Receptor & Conus. The author has an hindex of 13, co-authored 19 publications receiving 948 citations.

Glycopeptide antibiotics: back to the future.

Abstract: Glycopeptide antibiotics have been a key weapon in the fight against bacterial infections for over half a century, with the progenitors, vancomycin (1) and teicoplanin (2), still used extensively. The increased occurrence of resistance and the effectiveness of these ‘last resort’ treatments for Gram-positive infections has led to the discovery and clinical development of second generation, semisynthetic lipoglycopeptide derivatives such as telavancin (3), dalbavancin (4) and oritavancin (5), which all possess broader spectra of activity and improved pharmacokinetic properties. Two of these new antibiotics, telavancin (3) and dalbavancin (4), were approved in the past 5 years and the third, oritavancin (5), is awaiting regulatory approval. In this review, the discovery, development and associated resistance of vancomycin (1) and teicoplanin (2), and semi-synthetic glycopeptides, telavancin (3), dalbavancin (4) and oritavancin (5), are detailed. The clinical implications of glycopeptide resistance, especially vancomycin (1), as well as the future prospects for current glycopeptide drugs and the development of new glycopeptides are discussed.

ConoServer, a database for conopeptide sequences and structures

Abstract: Summary: ConoServer is a new database dedicated to conopeptides, a large family of peptides found in the venom of marine snails of the genus Conus. These peptides have an exceptional diversity of sequences and chemical modifications and their ability to block ion channels makes them important as drug leads and tools for physiological studies. ConoServer uses standardized names and a genetic and structural classification scheme to present data retrieved from SwissProt, GenBank, the Protein DataBank and the literature. The ConoServer web site incorporates specialized features like the graphic display of post-translational modifications that are extensively present in conopeptides. Currently, ConoServer manages 1214 nucleic sequences (from 54 Conus species), 2258 proteic sequences (from 66 Conus species) and 99 3D structures. Availability: http://research1t.imb.uq.edu.au/conoserver/ Contact: d.craik@imb.uq.edu.au

Neutrophils--a key component of ischemia-reperfusion injury.

Abstract: Ischemia-reperfusion injury (IRI) is a common occurrence following myocardial infarction, transplantation, stroke, and trauma that can lead to multiple organ failure, which remains the foremost cause of death in critically ill patients. Current therapeutic strategies for IRI are mainly palliative, and there is an urgent requirement for a therapeutic that could prevent or reverse tissue damage caused by IRI. Neutrophils are the primary responders following ischemia and reperfusion and represent important components in the protracted inflammatory response and severity associated with IRI. Experimental studies demonstrate neutrophil infiltration at the site of ischemia and show that inducing neutropenia can protect organs from IRI. In this review, we highlight the mechanisms involved in neutrophil recruitment, activation, and adherence and how this contributes to disease severity in IRI. Inhibiting neutrophil mobilization, tissue recruitment, and ultimately neutrophil-associated activation of local and systemic inflammatory responses may have therapeutic potential in the amelioration of local and remote tissue damage following IRI.

A small-molecule inhibitor of the NLRP3 inflammasome for the treatment of inflammatory diseases

Abstract: The NOD-like receptor (NLR) family, pyrin domain-containing protein 3 (NLRP3) inflammasome is a component of the inflammatory process, and its aberrant activation is pathogenic in inherited disorders such as cryopyrin-associated periodic syndrome (CAPS) and complex diseases such as multiple sclerosis, type 2 diabetes, Alzheimer's disease and atherosclerosis. We describe the development of MCC950, a potent, selective, small-molecule inhibitor of NLRP3. MCC950 blocked canonical and noncanonical NLRP3 activation at nanomolar concentrations. MCC950 specifically inhibited activation of NLRP3 but not the AIM2, NLRC4 or NLRP1 inflammasomes. MCC950 reduced interleukin-1β (IL-1β) production in vivo and attenuated the severity of experimental autoimmune encephalomyelitis (EAE), a disease model of multiple sclerosis. Furthermore, MCC950 treatment rescued neonatal lethality in a mouse model of CAPS and was active in ex vivo samples from individuals with Muckle-Wells syndrome. MCC950 is thus a potential therapeutic for NLRP3-associated syndromes, including autoinflammatory and autoimmune diseases, and a tool for further study of the NLRP3 inflammasome in human health and disease.

The Future of Peptide‐based Drugs

Abstract: The suite of currently used drugs can be divided into two categories - traditional 'small molecule' drugs with typical molecular weights of 5000 Da that are not orally bioavailable and need to be delivered via injection. Due to their small size, conventional small molecule drugs may suffer from reduced target selectivity that often ultimately manifests in human side-effects, whereas protein therapeutics tend to be exquisitely specific for their targets due to many more interactions with them, but this comes at a cost of low bioavailability, poor membrane permeability, and metabolic instability. The time has now come to reinvestigate new drug leads that fit between these two molecular weight extremes, with the goal of combining advantages of small molecules (cost, conformational restriction, membrane permeability, metabolic stability, oral bioavailability) with those of proteins (natural components, target specificity, high potency). This article uses selected examples of peptides to highlight the importance of peptide drugs, some potential new opportunities for their exploitation, and some difficult challenges ahead in this field.

Taxonomy, Physiology, and Natural Products of Actinobacteria

Abstract: Actinobacteria are Gram-positive bacteria with high G+C DNA content that constitute one of the largest bacterial phyla, and they are ubiquitously distributed in both aquatic and terrestrial ecosystems. Many Actinobacteria have a mycelial lifestyle and undergo complex morphological differentiation. They also have an extensive secondary metabolism and produce about two-thirds of all naturally derived antibiotics in current clinical use, as well as many anticancer, anthelmintic, and antifungal compounds. Consequently, these bacteria are of major importance for biotechnology, medicine, and agriculture. Actinobacteria play diverse roles in their associations with various higher organisms, since their members have adopted different lifestyles, and the phylum includes pathogens (notably, species of Corynebacterium, Mycobacterium, Nocardia, Propionibacterium, and Tropheryma), soil inhabitants (e.g., Micromonospora and Streptomyces species), plant commensals (e.g., Frankia spp.), and gastrointestinal commensals (Bifidobacterium spp.). Actinobacteria also play an important role as symbionts and as pathogens in plant-associated microbial communities. This review presents an update on the biology of this important bacterial phylum.

In Silico Approach for Predicting Toxicity of Peptides and Proteins

Abstract: Background: Over the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins. Description: We obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins. Conclusion: ToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/ proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/ raghava/toxinpred/).