Functional discrimination of membrane proteins using machine learning techniques

<p>Abstract</p> <p>Background</p> <p>Discriminating membrane proteins based on their functions is an important task in genome annotation. In this work, we have analyzed the characteristic features of amino acid residues in membrane proteins that perform major functions, such as channels/pores, electrochemical potential-driven transporters and primary active transporters.</p> <p>Results</p> <p>We observed that the residues Asp, Asn and Tyr are dominant in channels/pores whereas the composition of hydrophobic residues, Phe, Gly, Ile, Leu and Val is high in electrochemical potential-driven transporters. The composition of all the amino acids in primary active transporters lies in between other two classes of proteins. We have utilized different machine learning algorithms, such as, Bayes rule, Logistic function, Neural network, Support vector machine, Decision tree etc. for discriminating these classes of proteins. We observed that most of the algorithms have discriminated them with similar accuracy. The neural network method discriminated the channels/pores, electrochemical potential-driven transporters and active transporters with the 5-fold cross validation accuracy of 64% in a data set of 1718 membrane proteins. The application of amino acid occurrence improved the overall accuracy to 68%. In addition, we have discriminated transporters from other α-helical and β-barrel membrane proteins with the accuracy of 85% using k-nearest neighbor method. The classification of transporters and all other proteins (globular and membrane) showed the accuracy of 82%.</p> <p>Conclusion</p> <p>The performance of discrimination with amino acid occurrence is better than that with amino acid composition. We suggest that this method could be effectively used to discriminate transporters from all other globular and membrane proteins, and classify them into channels/pores, electrochemical and active transporters.</p

GAP waveguides

The coming years will show new applications of wireless communications at higher frequencies (30 GHz and above). Modern wireless technologies like massive MIMO and gigabit transmission will become a reality. The industrial winners will be the companies that can provide the hardware at the lowest cost. This requires new waveguide and mmWave packaging technologies that are more cost-effective than normal rectangular waveguide technology and are more power efficient (lower losses) than PCB-based microstrip and coplanar waveguides. The gap waveguide has this potential. The present chapter gives the historical background of gap waveguide technology until its invention in 2008 and how it has evolved since then to include many different kinds of gap waveguide types. Several useful waveguide components have been developed for integration in complete RF front ends. Passive gap waveguide parts and components like filters, couplers, and transitions have been realized very successfully, and active microwave electronics have been packaged. The chapter contains also an overview of the gap waveguide antennas that have been developed during the last years