NRProF: Neural response based protein function prediction algorithm

A large amount of proteomic data is being generated due to the advancements in high-throughput genome sequencing. But the rate of functional annotation of these sequences falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOfigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. The lack of annotation coverage of the existing methods advocates novel methods to improve protein function prediction. Here we present a automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. The main idea of this algorithm is to define a distance metric that corresponds to the similarity of the subsequences and reflects how the human brain can distinguish different sequences. Given query protein, we predict the most similar target protein using a two layered neural response algorithm and thereby assigned the GO term of the target protein to the query. Our method predicted and ranked the actual leaf GO term among the top 5 probable GO terms with 87.66% accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The NRProF program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/. © 2011 IEEE.published_or_final_versionThe 2011 IEEE International Conference on Systems Biology (ISB), Zhuhai, China, 2-4 September 2011. In Conference Proceedings, 2011, p. 33-4

A novel neural response algorithm for protein function prediction

BACKGROUND: Large amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOFigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction. RESULTS: We designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. Firstly, we predict the most similar target protein for a given query protein and thereby assign its GO term to the query sequence. When assessed on test set, our method ranked the actual leaf GO term among the top 5 probable GO terms with accuracy of 86.93%. CONCLUSIONS: The proposed algorithm is the first instance of neural response algorithm being used in the biological domain. The use of HMM profiles along with the secondary structure information to define the neural response gives our method an edge over other available methods on annotation accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/.published_or_final_versio

10 GHz Low Loss Liquid Metal SIW Phase Shifter for Phased Array Antenna

This paper presents a proof of concept demonstrator for a pair of novel phase shifters based on substrate integrated waveguide (SIW) technology. Gallium-based liquid metal (LM) is used to reconfigure each phase shifter. The paper presents LM phase shifters that, for the first time, have a phase shifting range of 360⁰. The phase shifters have a small electrical size, and they are intended for use within phased array antenna applications. The paper also presents a design procedure for the phase shifters. The procedure has been used to design two phase shifters operating at 10 GHz. The design process can be readily scaled for operation at other frequencies. The proposed phase shifters are reciprocal and bidirectional and they have very low insertion loss. A series of reconfigurable LM vias are used to achieve the phase shift. Each of LM via is activated once a drill hole is filled with LM and it is deactivated once LM is removed. Using this method; it is possible to achieve a phase shift step ranging from 1° to 100° using a single LM via. Moreover, the overall phase shift can be extended to 360° by employing several LM vias in series inside the SIW. The proposed phase shifters have an insertion loss lower than 3 dB and provide a total phase shifting range of approximately 360° in eight steps of approximately 45° each. This enables the proposed two phase shifters to have an extraordinary Figure of Merit (FoM) of 131.3 ⁰/dB and 122.4 ⁰/dB

ProF: neural response based protein function prediction algorithm

Poster Presentation: P-H001A large amount of proteomic data is being generated due to advancements in high-throughput genome sequencing methods. But the rate of the experimental functional characterization falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOfigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. The lack of annotation specificity and high complexity of the existing methods advocate the needs to improve automated protein function prediction method. Here we present a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. The main idea of this algorithm is to define a distance metric that corresponds to the similarity of the subsequences and reflects how the human brain can distinguish between different sequences. We predicted the most similar target protein for a given query protein using the two layered neural response algorithm and thereby assigned the GO term associated with the target sequence to the query sequence. Our method predicted and ranked the actual leaf GO term among the top 5 probable GO terms with 87.66% accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method.postprintThe 2011 Hong Kong Inter-University Biochemistry Postgraduate Symposium, Hong Kong, 11 June 2011

Genetic enrichment of cardiomyocytes derived from mouse embryonic stem cells

Pluripotent embryonic stem cells (ESC) have the ability to differentiate into a variety of cell lineages in vitro, including cardiomyocytes. Successful applications of ESC-derived cardiomyocytes in cell therapy and tissue engineering were limited by difficulties in selecting the desired cells from the heterogeneous cell population. We describe a simple method to generate relatively pure cardiomyocytes from mouse ESCs. A construct comprising mouse cardiac α-myosin heavy chain (MHC) promoter driving the neomycin resistance gene and SV40 promoter driving the hygromycin resistant gene designated pMHCneo/ SV40-hygro, was stably transfected into mouse ESCs. The transgenic ESC line, designated MN6 retained the undifferentiated state and the potential of cardiogenic differentiation. After G418 selection, more than 99% of cells expressed α-sarcomeric actin. Immunocytological and ultrastructural analysis demonstrated that, the selected cardiomyocytes were highly differentiated. Our results represent a simple genetic manipulation used to product essentially pure cardiomyocytes from differentiating ESCs. It may facilitate the development of cell therapy in heart diseases.Key words: Embryonic stem cells, α-myosin heavy chain promoter, cardiomyocytes, differentiation, genetic enrichment

Immuno-targeting the multifunctional CD38 using nanobody

published_or_final_versio

Electroacupuncture pretreatment attenuates cerebral ischemic injury through α7 nicotinic acetylcholine receptor-mediated inhibition of high-mobility group box 1 release in rats

<p>Abstract</p> <p>Background</p> <p>We have previously reported that electroacupuncture (EA) pretreatment induced tolerance against cerebral ischemic injury, but the mechanisms underlying this effect of EA are unknown. In this study, we assessed the effect of EA pretreatment on the expression of α7 nicotinic acetylcholine receptors (α7nAChR), using the ischemia-reperfusion model of focal cerebral ischemia in rats. Further, we investigated the role of high mobility group box 1 (HMGB1) in neuroprotection mediated by the α7nAChR and EA.</p> <p>Methods</p> <p>Rats were treated with EA at the acupoint "Baihui (GV 20)" 24 h before focal cerebral ischemia which was induced for 120 min by middle cerebral artery occlusion. Neurobehavioral scores, infarction volumes, neuronal apoptosis, and HMGB1 levels were evaluated after reperfusion. The α7nAChR agonist PHA-543613 and the antagonist α-bungarotoxin (α-BGT) were used to investigate the role of the α7nAChR in mediating neuroprotective effects. The roles of the α7nAChR and HMGB1 release in neuroprotection were further tested in neuronal cultures exposed to oxygen and glucose deprivation (OGD).</p> <p>Results</p> <p>Our results showed that the expression of α7nAChR was significantly decreased after reperfusion. EA pretreatment prevented the reduction in neuronal expression of α7nAChR after reperfusion in the ischemic penumbra. Pretreatment with PHA-543613 afforded neuroprotective effects against ischemic damage. Moreover, EA pretreatment reduced infarct volume, improved neurological outcome, inhibited neuronal apoptosis and HMGB1 release following reperfusion, and the beneficial effects were attenuated by α-BGT. The HMGB1 levels in plasma and the penumbral brain tissue were correlated with the number of apoptotic neurons in the ischemic penumbra. Furthermore, OGD in cultured neurons triggered HMGB1 release into the culture medium, and this effect was efficiently suppressed by PHA-543,613. Pretreatment with α-BGT reversed the inhibitory effect of PHA-543,613 on HMGB1 release.</p> <p>Conclusion</p> <p>These data demonstrate that EA pretreatment strongly protects the brain against transient cerebral ischemic injury, and inhibits HMGB1 release through α7nAChR activation in rats. These findings suggest the novel potential for stroke interventions harnessing the anti-inflammatory effects of α7nAChR activation, through acupuncture or pharmacological strategies.</p