Modular detergents tailor the purification and structural analysis of membrane proteins including G-protein coupled receptors

Detergents enable the purification of membrane proteins and are indispensable reagents instructural biology. Even though a large variety of detergents have been developed in the lastcentury, the challenge remains to identify guidelines that allowfine-tuning of detergents forindividual applications in membrane protein research. Addressing this challenge, here weintroduce the family of oligoglycerol detergents (OGDs). Native mass spectrometry (MS)reveals that the modular OGD architecture offers the ability to control protein purificationand to preserve interactions with native membrane lipids during purification. In addition to abroad range of bacterial membrane proteins, OGDs also enable the purification and analysisof a functional G-protein coupled receptor (GPCR). Moreover, given the modular design ofthese detergents, we anticipatefine-tuning of their properties for specific applications instructural biology. Seen from a broader perspective, this represents a significant advance forthe investigation of membrane proteins and their interactions with lipids

LETTER Communicated by Benjamin Schrauwen Regularized Variational Bayesian Learning of Echo State Networks with Delay&Sum Readout

In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational Bayesian ESN training scheme. The variational approach allows for a seamless combination of sparse Bayesian learning ideas and a variational Bayesian space-alternating generalized expectationmaximization (VB-SAGE) algorithm for estimating parameters of superimposed signals. While the former method realizes automatic regularization of ESNs, which also determines which echo states and input signals are relevant for "explaining" the desired signal, the latter method provides a basis for joint estimation of D&S readout parameters. The proposed training algorithm can naturally be extended to ESNs with fixed filter neurons. It also generalizes the recently proposed expectationmaximization-based D&S readout adaptation method. The proposed algorithm was tested on synthetic data prediction tasks as well as on dynamic handwritten character recognition. Neural Computation 24, 967-995 (2012

Modular detergents tailor the purification and structural analysis of membrane proteins including G-protein coupled receptors

Detergents enable the purification of membrane proteins and are indispensable reagents in structural biology. Even though a large variety of detergents have been developed in the last century, the challenge remains to identify guidelines that allow fine-tuning of detergents for individual applications in membrane protein research. Addressing this challenge, here we introduce the family of oligoglycerol detergents (OGDs). Native mass spectrometry (MS) reveals that the modular OGD architecture offers the ability to control protein purification and to preserve interactions with native membrane lipids during purification. In addition to a broad range of bacterial membrane proteins, OGDs also enable the purification and analysis of a functional G-protein coupled receptor (GPCR). Moreover, given the modular design of these detergents, we anticipate fine-tuning of their properties for specific applications in structural biology. Seen from a broader perspective, this represents a significant advance for the investigation of membrane proteins and their interactions with lipids

NaViA: A Program for the Visual Analysis of Complex Mass Spectra

MOTIVATION: Native mass spectrometry is now a well-established method for the investigation of protein complexes, specifically their subunit stoichiometry and ligand binding properties. Recent advances allowing the analysis of complex mixtures lead to an increasing diversity and complexity in the spectra obtained. These spectra can be time consuming to tackle through manual assignment and challenging for automated approaches. RESULTS: Native Mass Spectrometry Visual Analyser (NaViA) is a web-based tool to augment the manual process of peak assignment. In addition to matching masses to the stoichiometry of its component subunits it allows raw data processing, assignment and annotation and permits mass spectra to be shared with their respective interpretation. AVAILABILITY: NaViA is open-source and can be accessed online under https://navia.ms. The source code and documentation can be accessed at https://github.com/d-que/navia, under the BSD 2-Clause license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

NaViA : a program for the visual analysis of complex mass spectra

Motivation: Native mass spectrometry is now a well-established method for the investigation of protein complexes, specifically their subunit stoichiometry and ligand binding properties. Recent advances allowing the analysis of complex mixtures lead to an increasing diversity and complexity in the spectra obtained. These spectra can be time-consuming to tackle through manual assignment and challenging for automated approaches. Results: Native Mass Spectrometry Visual Analyser is a web-based tool to augment the manual process of peak assignment. In addition to matching masses to the stoichiometry of its component subunits, it allows raw data processing, assignment and annotation and permits mass spectra to be shared with their respective interpretation. Availability and implementation: NaViA is open-source and can be accessed online under https://navia.ms. The source code and documentation can be accessed at https://github.com/d-que/navia, under the BSD 2-Clause licence. Supplementary information: Supplementary data are available at Bioinformatics online

Investigating the regulation of membrane protein assemblies by lipids and other small molecules using native mass spectrometry

In this thesis I describe the application of native mass spectrometry to investigate membrane protein interactions with lipids and other small molecules. This work is a combination of method development, resulting in novel ways to study membrane protein-lipid complexes by native mass spectrometry, and application of established techniques to important biological targets. In chapter 2, I demonstrate for the first time the ability of surface-induced dissociation to distinguish between interfacial and non-interfacial phospholipid binding. Additionally, I show that combining this method with the modern high sensitivity instrumentation and statistical analysis enables the observation of subtle differences in oligomeric stabilities of membrane proteins in the presence of different species of interfacial lipids. Chapter 3 describes application of native mass spectrometry to the investigation of a selected set of novel and established detergents. Controlled delipidation of membrane proteins is readily achieved by selecting a specific detergent from this set. Furthermore, a protocol for selective removal of either phospholipids or lipopolysaccharide with the aid of these detergents is presented. These findings contribute both towards the practical applications to membrane protein research and also towards a better fundamental understanding of detergents in general. Chapter 4 describes the use of native mass spectrometry to probe the effects of lipid binding on a horse Na+/H+ transporter, NHE9. I show, that in the absence of specific lipid interactions NHE9 is unable to maintain its dimeric form. These results complement the recently obtained high-resolution structure of this protein. Finally, chapter 5 illustrates investigation of the human MAPEG protein family. I use collision-induced dissociation experiments to demonstrate the preference of one member of this family, LTC4S, for particular phospholipid classes. I also identify appropriate native mass spectrometry conditions for another member of this family, MGST2, and link its instability to the removal of structurally important lipids by membrane mimetics.</p

Application of the Evidence Procedure to Linear Problems in Signal Processing

The presented work addresses application of the evidence procedure to the field of signal processing where ill-posed estimation problems are frequently encountered. We base our analysis on the Relevance Vector Machines (RVM) technique originally proposed by M. Tipping. It effectively locally maximizes the evidence integral for linear kernel-based models. We extend the RVM technique by considering correlated additive Gaussian observation noise and complex-valued signals. We also show that grouping model parameters wvec , such that a single hyperparameter αk controls the kth cluster can be very effective in practice. In particular, it allows to cluster parameters wvec 's according to their potential relevance which in turns leads to highly improved generalization performance of the therewith parametrized models. The developed scheme is then illustratively applied to the problem of nonlinear system identification based on a discrete-time Volterra model. Similar ideas are used to analyze wireless channels from the channel measurement data. Results for synthetic as well as real-world data are presented