Computational identification of residues that modulate voltage sensitivity of voltage-gated potassium channels

BACKGROUND: Studies of the structure-function relationship in proteins for which no 3D structure is available are often based on inspection of multiple sequence alignments. Many functionally important residues of proteins can be identified because they are conserved during evolution. However, residues that vary can also be critically important if their variation is responsible for diversity of protein function and improved phenotypes. If too few sequences are studied, the support for hypotheses on the role of a given residue will be weak, but analysis of large multiple alignments is too complex for simple inspection. When a large body of sequence and functional data are available for a protein family, mature data mining tools, such as machine learning, can be applied to extract information more easily, sensitively and reliably. We have undertaken such an analysis of voltage-gated potassium channels, a transmembrane protein family whose members play indispensable roles in electrically excitable cells. RESULTS: We applied different learning algorithms, combined in various implementations, to obtain a model that predicts the half activation voltage of a voltage-gated potassium channel based on its amino acid sequence. The best result was obtained with a k-nearest neighbor classifier combined with a wrapper algorithm for feature selection, producing a mean absolute error of prediction of 7.0 mV. The predictor was validated by permutation test and evaluation of independent experimental data. Feature selection identified a number of residues that are predicted to be involved in the voltage sensitive conformation changes; these residues are good target candidates for mutagenesis analysis. CONCLUSION: Machine learning analysis can identify new testable hypotheses about the structure/function relationship in the voltage-gated potassium channel family. This approach should be applicable to any protein family if the number of training examples and the sequence diversity of the training set that are necessary for robust prediction are empirically validated. The predictor and datasets can be found at the VKCDB web site [1]

GePEToS : A Geant4 Monte Carlo simulation package for Positron Emission Tomography

GePEToS is a simulation framework developed over the last few years for assessing the instrumental performance of future PET scanners. It is based on Geant4, written in Object-Oriented C++ and runs on Linux platforms. The validity of GePEToS has been tested on the well-known Siemens ECAT EXACT HR+ camera. The results of two application examples are presented : the design optimization of a liquid Xe micro-PET camera dedicated to small animal imaging as well as the evaluation of the effect of a strong axial magnetic field on the image resolution of a Concorde P4 micro-PET camera.Comment: 5 pages, 12 figures, submitted to IEEE Transactions on Nuclear Scienc

Experimental study of a liquid Xenon PET prototype module

A detector using liquid Xenon in the scintillation mode is studied for Positron Emission Tomography (PET). The specific design aims at taking full advantage of the liquid Xenon properties. It does feature a promising insensitive to any parallax effect. This work reports on the performances of the first LXe prototype module, equipped with a position sensitive PMT operating in the VUV range (178 nm).Comment: Proc. of the 7th International Workshops on Radiation Imaging Detectors (IWORID-7), Grenoble, France 4-7 July 200

Linac modeling for external beam radiotherapy quality assurance using a dedicated 2D pixelated detector

International audienceQuality assurance is a key issue in intensity modulated radiotherapy. Errors can occur in the dose delivery process induces significant differences between the planned treatment and the delivered one. In this context, the Medical Application Physics group of the LPSC is developing TraDeRa (Transparent Detector for Radiotherapy), a 2D pixelated matrix of ionization chambers located upstream to the patient. The signal map obtained with TraDeRa has to be processed to provide medical observables to quantify the quality of the treatment delivery. This relies on accurate Monte Carlo simulations benchmarked with measurements performed under a linear accelerator (Linac).The work described in this paper lies in the optimization of the Linac head simulation and the development of an innovative Monte Carlo/measurements comparison method to perform an accurate enough model of the X-ray production device. An optimized parametrization of the particles transport allowed an increase of the simulation efficiency by a factor 3. The characteristics of an electron beam of a reference Linac were matched with the simulation results by using dose deposition of the created X-ray beam in a water tank. Two parameters are particularly critical: the nominal energy of the electrons and the radial distribution of impact on the target. The innovative method was able to provide within minutes those two parameters for any Linac, achieving, for example, a 10 keV precision on the energy determination for a 6 MV operating Linac

A Digitally Calibrated 12 bits 25 MS/s Pipelined ADC with a 3 input multiplexer for CALICE Integrated Readout

The necessity of full integrated electronics readout for the next ILC ECAL presents many challenges for low power mixed signal design. The analog to digital converter is a critical stage for the system going from the very front-end stages to digital memories. We present here a high speed converter configuration designed to multiplex 3 analog channels through one analog to digital converter. It is a first step for a multiplexed 64 channel design. A CMOS 0.35μm process is used. The dynamic range is 2V over a 3.3V power supply, and the total power dissipation at 25 MHz is approximately 40mW. An analog power management is included to allow a fast switching into a standby mode that reduces the DC power dissipation by a ratio of three orders of magnitude (1/1000)

Improving the accuracy of protein secondary structure prediction using structural alignment

BACKGROUND: The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high. RESULTS: We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25%) onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based) secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics) indicate that this new method can achieve a Q3 score approaching 88%. CONCLUSION: By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at . For high throughput or batch sequence analyses, the PROTEUS programs, databases (and server) can be downloaded and run locally

NIKEL: Electronics and data acquisition for kilopixels kinetic inductance camera

A prototype of digital frequency multiplexing electronics allowing the real time monitoring of microwave kinetic inductance detector (MKIDs) arrays for mm-wave astronomy has been developed. Thanks to the frequency multiplexing, it can monitor simultaneously 400 pixels over a 500 MHz bandwidth and requires only two coaxial cables for instrumenting such a large array. The chosen solution and the performances achieved are presented in this paper.Comment: 21 pages, 14 figure

Design of High Dynamic Range Digital to Analog Converters for the Calibration of the CALICE Si-W Ecal readout electronics

The ILC ECAL front-end chip will integrate many functions of the readout electronics including a DAC dedicated to calibration. We present two versions of DAC with respectively 12 and 14 bits, designed in a CMOS 0.35μm process. Both are based on segmented arrays of switched capacitors controlled by a Dynamic Element Matching (DEM) algorithm. A full differential architecture is used, and the amplifiers can be turned into a standby mode reducing the power dissipation. The 12 bit DAC features an INL lower than 0.3 LSB at 5MHz, and dissipates less than 7mW. The 14 bit DAC is an improved version of the 12 bit design