Nuclear estrogen receptor II (nER-II) is involved in the estrogen-dependent ribonucleoprotein transport in the goat uterus: II. Isolation and characterization of three small nuclear ribonucleoprotein proteins which bind to nER-II.

Three proteins of a goat uterine small nuclear ribonucleoprotein (snRNP) fraction, which bind to nuclear estrogen receptor-II (nER-II) have been isolated and purified. These are the p32, p55, and p60 of which p32 is the major nER-II binding protein. Indirect evidence reveals that p32 binds to the nuclear export signal (NES) on the nER-II. nER-II is a snRNA binding protein while p32 does not bind to the RNA. nER-II along with p32 and p55 form an effective Mg(++)ATPase complex, the activation of which appears to be the immediate reason behind the RNP exit from the nuclei following estradiol exposure. The three nER-II binding proteins bind to the nuclear pore complex; nER-II does not possess this property.     

Synthesis,characterization and biological evaluation of C5′-N-cyclopropylcarboxamido-C6-amino-C2-alkynylated purine nucleoside analogues

In an effort to develop potent antibacterial and anticancer agents, a series of C5′-N-cyclopropylcarboxamido-C6-amino-C2-alkynylated purine nucleoside analogues 11a-g were synthesized through a Sonogashira cross-coupling reaction. The nine-step synthesis is easy to perform, and employs commercially available reagents. 2-Iodo-5′-N-cyclopropylcarboxamidoadenosine (9) was used as the starting intermediate for the synthesis of title derivatives 11a-g. Synthetic intermediates (2–9) and final products (11a-g) were appropriately characterized by IR, ¹H NMR, ¹³C NMR and mass spectroscopy. The synthesized purine nucleoside analogues (11a-g) were evaluated for their in vitro antibacterial activity against two gram-positive and two gram-negative bacteria. They were then tested for cytotoxicity against MDA-MB-231 and Caco-2 cancer cell lines to determine their anti-cancer activity. Among the tested compounds, compounds 11c and 11g showed most potent antibacterial activity against S.aureus and P.aeruginosa bacterial strains. Compounds 11b and 11e displayed considerable IC₅₀^s of 7.9 and 6.8 µg/mL, respectively, vs MDA-MB-231 cell lines of 7.5 and 8.3 µg/mL, respectively, against the Caco-2 cell lines.     

A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search

Most of the prediction methods for secretory proteins require the presence of a correct N-terminal end of the preprotein for correct classification. As large scale genome sequencing projects sometimes assign the 5'-end of genes incorrectly, many proteins are encoded without the correct N-terminus leading to incorrect prediction. In this study, a systematic attempt has been made to predict secretory proteins irrespective of presence or absence of N-terminal signal peptides (also known as classical and non-classical secreted proteins respectively), using machine-learning techniques; artificial neural network (ANN) and support vector machine (SVM). We trained and tested our methods on a dataset of 3321 secretory and 3654 non-secretory mammalian proteins using five-fold cross-validation technique. First, ANN-based modules have been developed for predicting secretory proteins using 33 physico-chemical properties, amino acid composition and dipeptide composition and achieved accuracies of 73.1%, 76.1% and 77.1%, respectively. Similarly, SVM-based modules using 33 physico-chemical properties, amino acid, and dipeptide composition have been able to achieve accuracies of 77.4%, 79.4% and 79.9%, respectively. In addition, BLAST and PSI-BLAST modules designed for predicting secretory proteins based on similarity search achieved 23.4% and 26.9% accuracy, respectively. Finally, we developed a hybrid-approach by integrating amino acid and dipeptide composition based SVM modules and PSI-BLAST module that increased the accuracy to 83.2%, which is significantly better than individual modules. We also achieved high sensitivity of 60.4% with low value of 5% false positive predictions using hybrid module. A web server SRTpred has been developed based on above study for predicting classical and non-classical secreted proteins from whole sequence of mammalian proteins, which is available from http://www.imtech.res.in/raghava/srtpred/.     

Evaluation of the Inheritance of the Complex Vertebral Malformation Syndrome by Breeding Studies

To investigate the congenital complex vertebral malformation syndrome (CVM) in Holstein calves, two breeding studies were performed including 262 and 363 cows, respectively. Cows were selected from the Danish Cattle Database based on pedigree and insemination records. Selected cows were progeny of sires with an established heterozygous CVM genotype and pregnant after insemination with semen from another sire with heterozygous CVM genotype. Following calving the breeders should state, if the calf was normal and was requested to submit dead calves for necropsy. In both studies, significantly fewer CVM affected calves than expected were obtained; a finding probably reflecting extensive intrauterine mortality in CVM affected foetuses. The findings illustrate increased intrauterine mortality as a major potential bias in observational studies of inherited disorders.     

Computer-aided biotechnology: from immuno-informatics to reverse vaccinology

Genome sequences from many organisms, including humans, have been completed, and high-throughput analyses have produced burgeoning volumes of 'omics' data. Bioinformatics is crucial for the management and analysis of such data and is increasingly used to accelerate progress in a wide variety of large-scale and object-specific functional analyses. Refined algorithms enable biotechnologists to follow 'computer-aided strategies' based on experiments driven by high-confidence predictions. In order to address compound problems, current efforts in immuno-informatics and reverse vaccinology are aimed at developing and tuning integrative approaches and user-friendly, automated bioinformatics environments. This will herald a move to 'computer-aided biotechnology': smart projects in which time-consuming and expensive large-scale experimental approaches are progressively replaced by prediction-driven investigations.     

Refolding and simultaneous purification by three-phase partitioning of recombinant proteins from inclusion bodies

Many recombinant eukaryotic proteins tend to form insoluble aggregates called inclusion bodies, especially when expressed in Escherichia coli. We report the first application of the technique of three-phase partitioning (TPP) to obtain correctly refolded active proteins from solubilized inclusion bodies. TPP was used for refolding 12 different proteins overexpressed in E. coli. In each case, the protein refolded by TPP gave either higher refolding yield than the earlier reported method or succeeded where earlier efforts have failed. TPP-refolded proteins were characterized and compared to conventionally purified proteins in terms of their spectral characteristics and/or biological activity. The methodology is scaleable and parallelizable and does not require subsequent concentration steps. This approach may serve as a useful complement to existing refolding strategies of diverse proteins from inclusion bodies.     

In vivo and in vitro characterization of novel microparticulates based on hyaluronan and chitosan hydroglutamate

This study examined the application of previously characterized microparticles composed of hyaluronan (HA) and chitosan hydroglutamate (CH) as well as novel microparticles consisting of both polymers (HA/CH) to improve the nasal delivery of a model drug. The rabbit bioavailabilities of gentamicin incorporated in HA, CH, and HA/CH microparticles were increased 23-, 31-, and 42-fold, respectively, compared with the control intranasal solution of gentamicin, indicating that all test microparticles were retained for longer periods on the nasal mucosa of the rabbits as supported by previous in vitro dissolution as well as frog palate mucoadhesion studies, thereby improving drug absorption. The higher bioavailabilities of CH-based formulations (CH and HA/CH) suggest the penetration-enhancing effects of CH may also be partially responsible for the improvement. A model was developed, based on a glass impinger device, to deliver dry powder formulations reproducibly onto the surface of cultured cell monolayers. In vitro permeability and fluorescence microscopy studies on the tight junctions of the 16HBE14o- cell lines further confirmed the ability of CH-based formulations to enhance penetration. Furthermore, the in vitro absorption profile from cell culture studies was consistent with those determined from in vivo studies. The complementary effect from the mucoadhesive nature of HA coupled with the penetration-enhancing effects of CH makes the novel HA/CH formulation a promising nasal delivery system.     

Estradiol dependent anchoring of the goat uterine estrogen receptor activation factor (E-RAF) at the endoplasmic reticulum by a 55 kDa anchor protein (ap55)

The primary intracellular site of localization of the estrogen receptor activation factor (E-RAF) is shown here to be the endoplasmic reticulum where the protein remains anchored through an estrogen dependent mechanism. The retention of E-RAF by the endoplasmic reticulum is facilitated by two proteins: (1) a 55 kDa anchor protein (ap55) which is an integral membrane protein of the endoplasmic reticulum. ap55 is a high affinity estrogen binding protein. A conformational change induced by estrogen binding is thought to favor the anchoring process. (2) The anchoring of E-RAF by ap55 is mediated by yet another protein. This is the 66 kDa transport protein (tp66) which recognizes ap55 on the one hand and E-RAF on the other. The presence of estradiol that saturates the hormone binding sites on ap55 appears to favor the anchoring of tp66-E-RAF complex to ap55. This interaction appears to be weakened by levels of estradiol below 7 nM concentration leading to the dissociation of the tp66-E-RAF complex from ap55. The tp66-E-RAF complex moves towards the nucleus.     

BetaTPred: prediction of beta-TURNS in a protein using statistical algorithms

MOTIVATION: beta-turns play an important role from a structural and functional point of view. beta-turns are the most common type of non-repetitive structures in proteins and comprise on average, 25% of the residues. In the past numerous methods have been developed to predict beta-turns in a protein. Most of these prediction methods are based on statistical approaches. In order to utilize the full potential of these methods, there is a need to develop a web server. RESULTS: This paper describes a web server called BetaTPred, developed for predicting beta-TURNS in a protein from its amino acid sequence. BetaTPred allows the user to predict turns in a protein using existing statistical algorithms. It also allows to predict different types of beta-TURNS e.g. type I, I', II, II', VI, VIII and non-specific. This server assists the users in predicting the consensus beta-TURNS in a protein. AVAILABILITY: The server is accessible from http://imtech.res.in/raghava/betatpred/