Model for the structure of bacteriorhodopsin based on high-resolution electron cryo-microscopy.

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from this is the last candidate. next esc will revert to uncompleted text. he publisher. Overview Dataset shift is a challenging situation where the joint distribution of inputs and outputs differs between the training and test stages. Covariate shift is a simpler particular case of dataset shift where only the input distribution changes (covariate denotes input), while the conditional distribution of the outputs given the inputs p(y|x) remains unchanged. Dataset shift is present in most practical applications for reasons ranging from the bias introduced by experimental design, to the mere irreproducibility of the testing conditions at training time. For example, in an image classification task, training data might have been recorded under controlled laboratory conditions, whereas the test data may show different lighting conditions. In other applications, the process that generates data is in itself adaptive. Some of our authors consider the problem of spam email filtering: successful " spammers " will try to build spam in a form that differs from the spam the automatic filter has been built on. Dataset shift seems to have raised relatively little interest in the machine learning community until very recently. Indeed, many machine learning algorithms are based on the assumption that the training data is drawn from exactly the same distribution as the test data on which the model will later be evaluated. Semi-supervised learning and active learning, two problems that seem very similar to covariate shift have received much more attention. How do they differ from covariate shift? Semi-supervised learning is designed to take advantage of unlabeled data present at training time, but is not conceived to be robust against changes in the input distribution. In fact, one can easily construct examples of covariate shift for which common SSL strategies such as the " cluster assumption " will lead to disaster. In active learning the algorithm is asked to select from the available unlabeled inputs those for which obtaining the label will be most beneficial for learning. This is very relevant in contexts where labeling data is very costly, but active learning strategies 2 Contents are not specifically design for dealing with covariate shift. This book attempts to give an overview of the different recent efforts that are being …

Adaptive computation and machine learning

We discuss the view that the folding of many, perhaps most, integral membrane proteins can be considered as a two-stage process. In stage I, hydrophobic alpha-helices are established across the lipid bilayer. In stage II, they interact to form functional transmembrane structures. This model is suggested by the nature of transmembrane segments in known structures, refolding experiments, the assembly of integral membrane protein from fragments, and the existence of very small integral membrane protein subunits. It may extend to proteins with a variety of functions, including the formation of transmembrane aqueous channels. The model is discussed in the context of the forces involved in membrane protein folding and the interpretation of sequence data.

Membrane protein folding and oligomerization: the two-stage model.

http://dosequis.colorado.edu/Courses/InsaneMembrane/papers/BR,%20HR%20and%20PRC/7electron%20crystalographic%20refinement%20of%20bacteriorhodopsin.pdf

Electron-crystallographic refinement of the structure of bacteriorhodopsin.

Cloning and sequence analysis of DNA complementary to porcine cerebral messenger RNA encoding the muscarinic acetylcholine receptor predict the complete amino-acid sequence of this protein. Expression of the complementary DNA produced functional muscarinic receptor in Xenopus oocytes. The muscarinic receptor is homologous with the beta-adrenergic receptor and rhodopsin in both amino-acid sequence and suggested transmembrane topography.

Cloning, sequencing and expression of complementary DNA encoding the muscarinic acetylcholine receptor

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1016/0014-5793%2879%2980338-5

The structural basis of the functioning of bacteriorhodopsin: an overview.

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1016/0014-5793%2877%2981046-6

Recent findings in the structure—functional characteristics of bacteriorhodopsin

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1016/0014-5793%2878%2980366-4

Products of limited proteolysis of bacteriorhodopsin generate a membrane potential

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1016/0014-5793%2885%2980548-2

The antigenic structure and topography of bacteriorhodopsin in purple membranes as determined by interaction with monoclonal antibodies

https://febs.onlinelibrary.wiley.com/doi/pdf/10.1016/0014-5793%2886%2980043-6

A.V. Kiselev

Papers

The structural basis of the functioning of bacteriorhodopsin: an overview.

Recent findings in the structure—functional characteristics of bacteriorhodopsin

Products of limited proteolysis of bacteriorhodopsin generate a membrane potential

The antigenic structure and topography of bacteriorhodopsin in purple membranes as determined by interaction with monoclonal antibodies

The water‐exposed C‐terminal sequence of bacteriorhodopsin does not affect H+ pumping