PROTEIN DISULFIDE ANALYSIS AND DESIGN

The significant contribution of naturally occurring disulfide bonds to protein stability has encouraged development of methods to engineer non-native disulfides in proteins. These have yielded mixed results. We summarize applications of the program MODIP for disulfide engineering. The program predicts sites in proteins where disulfides can be stably introduced. The program has also been used as an aid in conformational analysis of naturally occurring disulfides in a-helices, antiparallel and parallel beta-strands. Disulfides in a-helices occur only at N-termini, where the first cysteine residue is the N-cap residue of the helix. The disulfide occurs as a CXXC motif and can possess redox activity. In antiparallel beta-strands, disulfides occur exclusively at non-hydrogen bonded (NHB) registered pairs of antiparallel beta-sheets with only 1 known natural example occurring at a hydrogen bonded (HB) registered pair. Conformational analysis suggests that disulfides between HB residue pairs are under torsional strain. A similar analysis to characterize disulfides in parallel beta-strands was carried out. We observed that only 9 instances of cross-strand disulfides exist in a non-redundant dataset. Stereochemical analysis shows that while tbe chi(square) angles are similar to those of other disulfides, the chi(1) and chi(2) angles show more variation and that one of tbe strands is generally an edge strand.

ON THE ROLE OF THE HILBERT TRANSFORM IN BOOSTING THE PERFORMANCE OF THE ANNIHILATING FILTER

We consider the problem of parameter estimation from real-valued multi-tone signals. Such problems arise frequently in spectral estimation. More recently, they have gained new importance in finite-rate-of-innovation signal sampling and reconstruction. The annihilating filter is a key tool for parameter estimation in these problems. The standard annihilating filter design has to be modified to result in accurate estimation when dealing with real sinusoids, particularly because the real-valued nature of the sinusoids must be factored into the annihilating filter design. We show that the constraint on the annihilating filter can be relaxed by making use of the Hilbert transform. We refer to this approach as the Hilbert annihilating filter approach. We show that accurate parameter estimation is possible by this approach. In the single-tone case, the mean-square error performance increases by 6 dB for signal-to-noise ratio (SNR) greater than 0 dB. We also present experimental results in the multi-tone case, which show that a significant improvement (about 6 dB) is obtained when the parameters are close to 0 or pi. In the mid-frequency range, the improvement is about 2 to 3 dB.

IMAGE DENOISING IN MULTIPLICATIVE NOISE

We address the problem of denoising images corrupted by multiplicative noise. The noise is assumed to follow a Gamma distribution. Compared with additive noise distortion, the effect of multiplicative noise on the visual quality of images is quite severe. We consider the mean-square error (MSE) cost function and derive an expression for an unbiased estimate of the MSE. The resulting multiplicative noise unbiased risk estimator is referred to as MURE. The denoising operation is performed in the wavelet domain by considering the image-domain MURE. The parameters of the denoising function (typically, a shrinkage of wavelet coefficients) are optimized for by minimizing MURE. We show that MURE is accurate and close to the oracle MSE. This makes MURE-based image denoising reliable and on par with oracle-MSE-based estimates. Analogous to the other popular risk estimation approaches developed for additive, Poisson, and chi-squared noise degradations, the proposed approach does not assume any prior on the underlying noise-free image. We report denoising results for various noise levels and show that the quality of denoising obtained is on par with the oracle result and better than that obtained using some state-of-the-art denoisers.