Preliminary study on the use of synthetic substrate for juvenile stage production of the yabby, Cherax destructor (Clark) (Decapoda: Parastacidae)

Juvenile Cherax destructor (commonly called the yabby) (mean weight 48.3 mg) were cultured intensively (stocking density 360/m²) under controlled conditions for 48 days. The animals were provided with a combination of food (high protein pellets and/or natural feed organisms attached to a conditioned synthetic substrate) and refuge. Fastest growth and highest yield was recorded when both pellets and the conditioned synthetic material were provided. Although the yabbies sheltered in the synthetic substrate, it did not increase survival. Juvenile yabbies (< 200 mg) were able to graze on small organisms attached to the synthetic material but this ability appeared to decline as the yabbies grew to a larger size. The use of artificial substrates in the intensive nursery phase production of juvenile freshwater crayfish is discussed.

Probing the fibrate binding specificity of rat liver fatty acid binding protein

Liver-fatty acid binding protein (L-FABP) is found in high levels in enterocytes and is involved in cytosolic solubilization of fatty acids. In addition, L-FABP has been shown to bind endogenous and exogenous lipophilic compounds, suggesting that it may also play a role in modulating their absorption and disposition within enterocytes. Previously, we have described binding of L-FABP to a range of drugs, including a series of fibrates. In the present study, we have generated structural models of L-FABP-fibrate complexes and undertaken thermodynamic analysis of the binding of fibrates containing either a carboxylic acid or ester functionality. Analysis of the current data reveals that both the location and the energetics of binding are different for fibrates that contain a carboxylate compared to those that do not. As such, the data presented in this study suggest potential mechanisms that underpin molecular recognition and dictate specificity in the interaction between fibrates and L-FABP.

Irreversible and bivalent ligands for selected G-protein coupled receptors

Derivatives of pharmaceutical compounds that could potentially bind irreversibly to a hypertension controlling receptor were synthesised to provide tools for studying this receptor. Also compounds that simultaneously activate two cardiovascular receptors with opposing cellular mechanisms were synthesised. This resulted in a desired partial reduction in the activity of one receptor.

A new rodent model to assess blood stage immunity to the plasmodium falciparum antigen merozoite surface protein 119 reveals a protective role for invasion inhibitory antibodies

Antibodies capable of inhibiting the invasion of Plasmodium merozoites into erythrocytes are present in individuals that are clinically immune to the malaria parasite. Those targeting the 19-kD COOH-terminal domain of the major merozoite surface protein (MSP)-119 are a major component of this inhibitory activity. However, it has been difficult to assess the overall relevance of such antibodies to antiparasite immunity. Here we use an allelic replacement approach to generate a rodent malaria parasite (Plasmodium berghei) that expresses a human malaria (Plasmodium falciparum) form of MSP-119. We show that mice made semi-immune to this parasite line generate high levels of merozoite inhibitory antibodies that are specific for P. falciparum MSP-119. Importantly, protection from homologous blood stage challenge in these mice correlated with levels of P. falciparum MSP-119–specific inhibitory antibodies, but not with titres of total MSP-119–specific immunoglobulins. We conclude that merozoite inhibitory antibodies generated in response to infection can play a significant role in suppressing parasitemia in vivo. This study provides a strong impetus for the development of blood stage vaccines designed to generate invasion inhibitory antibodies and offers a new animal model to trial P. falciparum MSP-119 vaccines.

New insights into protein export in malaria parasites

In order to survive and promote its virulence the malaria parasite must export hundreds of its proteins beyond an encasing vacuole and membrane into the host red blood cell. In the last few years, several major advances have been made that have significantly contributed to our understanding of this export process. These include: (i) the identification of sequences that direct protein export (a signal sequence and a motif termed PEXEL), which have allowed predictions of the exportomes of Plasmodium species that are the cause of malaria, (ii) the recognition that the fate of proteins destined for export is already decided within the parasite's endoplasmic reticulum and involves the PEXEL motif being recognized and cleaved by the aspartic protease plasmepsin V and (iii) the discovery of the Plasmodium translocon of exported proteins (PTEX) that is responsible for the passage of proteins across the vacuolar membrane. We review protein export in Plasmodium and these latest developments in the field that have now provided a new platform from which trafficking of malaria proteins can be dissected.

Yhh1p/Cft1p directly links poly(A) site recognition and RNA polymerase II transcription termination

RNA polymerase II (pol II) transcription termination requires co-transcriptional recognition of a functional polyadenylation signal, but the molecular mechanisms that transduce this signal to pol II remain unclear. We show that Yhh1p/Cft1p, the yeast homologue of the mammalian AAUAAA interacting protein CPSF 160, is an RNA-binding protein and provide evidence that it participates in poly(A) site recognition. Interestingly, RNA binding is mediated by a central domain composed of predicted -propeller-forming repeats, which occurs in proteins of diverse cellular functions. We also found that Yhh1p/Cft1p bound specifically to the phosphorylated C-terminal domain (CTD) of pol II in vitro and in a two-hybrid test in vivo. Furthermore, transcriptional run-on analysis demonstrated that yhh1 mutants were defective in transcription termination, suggesting that Yhh1p/Cft1p functions in the coupling of transcription and 3'-end formation. We propose that direct interactions of Yhh1p/Cft1p with both the RNA transcript and the CTD are required to communicate poly(A) site recognition to elongating pol II to initiate transcription termination.

Protein fibre powder and new applications

Protein fibers such as silk and wool have been used as textile fibers for centuries. It is only in recent years that these fibers have been converted into fine powder forms for non-textile applications. This presentation will cover our recent research in protein fiber powders. Ultra-fine powders from different protein fibers have been produced using a combination of media and non media milling techniques. New application examples of these fine powders are discussed. These applications include hybrid fibers combining the advantages of natural and synthetic polymer fibers, tissue engineering composite scaffolds with enhanced biomechanical properties, and metal ion absorption.

Synthesis of ethyl oleate employing synthetic hydrogel-immobilized lipase of Bacillus coagulans MTCC-6375

Ten polymeric hydrogels were chemically synthesized by varying the concentrations of copolymer (DMA) and cross-linker (MBAm) molecules. An alkaline lipase of Bacillus coagulans MTCC-6375 was immobilized onto a poly (MAc-co-DMA-cl-MBAm)-hydrogel support at pH 8.5 and temperature 55ºC in 16 h. The bound lipase possessed 7.6 U.g⁻¹ (matrix) lipase activity with a specific activity of 18 U.mg⁻¹ protein. Hydrogel bound-lipase catalyzed esterification of oleic acid and ethanol to synthesize ethyl oleate in n-nonane. Various kinetic parameters were optimized to produce ethyl oleate using immobilized lipase. The optimal parameters were bound enzyme/substrate (E/S) ratio 0.62 mg/mM, ethanol/oleic acid 100 mM:75 mM or 100 mM:100 mM, incubation time 18 h and reaction temperature 55ºC that resulted in approximately 53% conversion of reactants into ethyl oleate in n-nonane. However, addition of a molecular sieve to the reaction mixture promoted the conversion to 58% in 18 h in n-nonane, which was equivalent to 55 mM of ethyl oleate produced.

Dimensionality reduction of protein mass spectrometry data using random projection

Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Unfortunately, classification performance may degrade owing to the enormously high dimensionality of the data. This paper investigates the use of Random Projection in protein MS data dimensionality reduction. The effectiveness of Random Projection (RP) is analyzed and compared against Principal Component Analysis (PCA) by using three classification algorithms, namely Support Vector Machine, Feed-forward Neural Networks and K-Nearest Neighbour. Three real-world cancer data sets are employed to evaluate the performances of RP and PCA. Through the investigations, RP method demonstrated better or at least comparable classification performance as PCA if the dimensionality of the projection matrix is sufficiently large. This paper also explores the use of RP as a pre-processing step prior to PCA. The results show that without sacrificing classification accuracy, performing RP prior to PCA significantly improves the computational time.

Targeting viral hepatitis using natural milk protein and traditional medicinal herbs

Hepatitis is a major health related disease spread worldwide with frequent occurrence of epidemics. It is a zoonotic disease which leads to jaundice, anorexia, malaise and death. Although, vaccines have been developed against hepatitis A and hepatitis B, it is a challenge to generate vaccines against other prevalent forms of hepatitis which are equally harmful and spread worldwide. Natural products that are obtained from living organisms and found freely in nature have proven to be effective against several types of hepatitis due to presence of pharmacologically important bioactive compounds. Since they are natural products they do not cause much harm to body and can be easily applied or consumed. Our main focus is on hepatitis E virus (HEV) which is an opportunistic pathogen and leads to acute jaundice. This virus is mainly present in developing countries with poor sanitation facilities and effects individuals having weak immune response, mainly children, old people, organ transplant patients and pregnant women. HEV infection makes the patient more susceptible to infections from other viruses as well as HIV. In this review, we discussed about the natural protein known as lactoferrin which is isolated from milk colostrum and extracts of some medicinal plants that have proven to be effective against various forms of hepatitis. Such form of natural therapies forms the basis of modern medicine and major pharmaceutical discoveries.

Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in practice. While inherently insensitive to visible spectrum illumination changes, IR data introduces specific challenges of its own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, when comparing two thermal IR images of faces, we mutually normalize their poses and facial expressions by using an active appearance model (AAM) to generate synthetic images of the two faces with a neutral facial expression and in the same view (the average of the two input views). This is achieved by piecewise affine warping which follows AAM fitting. A major contribution of our work is the use of an AAM ensemble in which each AAM is specialized to a particular range of poses and a particular region of the thermal IR face space. Combined with the contributions from our previous work which addressed the problem of reliable AAM fitting in the thermal IR spectrum, and the development of a person-specific representation robust to transient changes in the pattern of facial temperature emissions, the proposed ensemble framework accurately matches faces across the full range of yaw from frontal to profile, even in the presence of scale variation (e.g. due to the varying distance of a subject from the camera). The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces and a newly acquired data set of thermal IR motion videos. Our approach achieved perfect recognition performance on both data sets, significantly outperforming the current state of the art methods even when they are trained with multiple images spanning a range of head views.

Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR

Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in the real world. While inherently insensitive to visible spectrum illumination changes, IR images introduce specific challenges of their own, most notably sensitivity to factors which affect facial heat emission patterns, e.g. emotional state, ambient temperature, and alcohol intake. In addition, facial expression and pose changes are more difficult to correct in IR images because they are less rich in high frequency detail which is an important cue for fitting any deformable model. In this paper we describe a novel method which addresses these major challenges. Specifically, to normalize for pose and facial expression changes we generate a synthetic frontal image of a face in a canonical, neutral facial expression from an image of the face in an arbitrary pose and facial expression. This is achieved by piecewise affine warping which follows active appearance model (AAM) fitting. This is the first publication which explores the use of an AAM on thermal IR images; we propose a pre-processing step which enhances detail in thermal images, making AAM convergence faster and more accurate. To overcome the problem of thermal IR image sensitivity to the exact pattern of facial temperature emissions we describe a representation based on reliable anatomical features. In contrast to previous approaches, our representation is not binary; rather, our method accounts for the reliability of the extracted features. This makes the proposed representation much more robust both to pose and scale changes. The effectiveness of the proposed approach is demonstrated on the largest public database of thermal IR images of faces on which it achieved 100% identification rate, significantly outperforming previously described methods

Specific recognition of the HIV-1 genomic RNA by the Gag precursor

During assembly of HIV-1 particles in infected cells, the viral Pr55(Gag) protein (or Gag precursor) must select the viral genomic RNA (gRNA) from a variety of cellular and viral spliced RNAs. However, there is no consensus on how Pr55(Gag) achieves this selection. Here, by using RNA binding and footprinting assays, we demonstrate that the primary Pr55(Gag) binding site on the gRNA consists of the internal loop and the lower part of stem-loop 1 (SL1), the upper part of which initiates gRNA dimerization. A double regulation ensures specific binding of Pr55(Gag) to the gRNA despite the fact that SL1 is also present in spliced viral RNAs. The region upstream of SL1, which is present in all HIV-1 RNAs, prevents binding to SL1, but this negative effect is counteracted by sequences downstream of SL4, which are unique to the gRNA.

Predicting protein function using multiple kernels

High-throughput experimental techniques provide a wide variety of heterogeneous proteomic data sources. To exploit the information spread across multiple sources for protein function prediction, these data sources are transformed into kernels and then integrated into a composite kernel. Several methods first optimize the weights on these kernels to produce a composite kernel, and then train a classifier on the composite kernel. As such, these approaches result in an optimal composite kernel, but not necessarily in an optimal classifier. On the other hand, some approaches optimize the loss of binary classifiers and learn weights for the different kernels iteratively. For multi-class or multi-label data, these methods have to solve the problem of optimizing weights on these kernels for each of the labels, which are computationally expensive and ignore the correlation among labels. In this paper, we propose a method called Predicting Protein Function using Multiple K ernels (ProMK). ProMK iteratively optimizes the phases of learning optimal weights and reduces the empirical loss of multi-label classifier for each of the labels simultaneously. ProMK can integrate kernels selectively and downgrade the weights on noisy kernels. We investigate the performance of ProMK on several publicly available protein function prediction benchmarks and synthetic datasets. We show that the proposed approach performs better than previously proposed protein function prediction approaches that integrate multiple data sources and multi-label multiple kernel learning methods. The codes of our proposed method are available at https://sites.google.com/site/guoxian85/promk.

Multi-output interval type-2 fuzzy logic system for protein secondary structure prediction

A new multi-output interval type-2 fuzzy logic system (MOIT2FLS) is introduced for protein secondary structure prediction in this paper. Three outputs of the MOIT2FLS correspond to three structure classes including helix, strand (sheet) and coil. Quantitative properties of amino acids are employed to characterize twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Three clustering tasks are performed using the adaptive vector quantization method to construct an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS. The genetic fitness function is designed based on the Q3 measure. Experimental results demonstrate the dominance of the proposed approach against the traditional methods that are Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.