Protein structure Quality Assessment (QA) is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA. In this work, we developed a new Hidden Markov Model (HMM) to assess the compatibility of protein sequence and structure for capturing their complex relationship. More specifically, the emission of the HMM consists of protein local structures in angular space, secondary structures, and sequence profiles. This model has two capabilities: (1) encoding local structure of each position by jointly considering sequence and structure information, and (2) assigning a global score to estimate the overall quality of a predicted structure, as well as local scores to assess the quality of specific regions of a structure, which provides useful guidance for targeted structure refinement. We compared the HMM model to state-of-art single structure quality assessment methods OPUSCA, DFIRE, GOAP, and RW in protein structure selection. Computational results showed our new score HMM.Z can achieve better overall selection performance on the benchmark datasets.

Mass spectrometry is one of the widely utilized important methods to study protein functions and components. The challenge of mono-isotope pattern recognition from large scale protein mass spectral data needs computational algorithms and tools to speed up the analysis and improve the analytic results. We utilized naïve Bayes network as the classifier with the assumption that the selected features are independent to predict mono-isotope pattern from mass spectrometry. Mono-isotopes detected from validated theoretical spectra were used as prior information in the Bayes method. Three main features extracted from the dataset were employed as independent variables in our model. The application of the proposed algorithm to publicMo dataset demonstrates that our naïve Bayes classifier is advantageous over existing methods in both accuracy and sensitivity.

We present a theoretically-exact and stable computed tomography (CT) reconstruction algorithm that is capable of handling interrupted illumination and therefore of using all measured data at arbitrary pitch. This algorithm is based on a differentiated backprojection (DBP) on M-lines. First, we discuss the problem of interrupted illumination and how it affects the DBP. Then we show that it is possible to take advantage of some properties of the DBP to compensate for the effects of interrupted illumination in a mathematically exact way. From there, we have developed an efficient algorithm which we have successfully implemented. We show encouraging preliminary results using both computer-simulated data and real data. Our results show that our method is capable of achieving a substantial reduction of image noise when decreasing the helix pitch compared with the maximum pitch case. We conclude that the proposed algorithm defines for the first time a theoretically-exact and stable reconstruction method that is capable of beneficially using all measured data at arbitrary pitch.

A consistency condition is developed for computed tomography (CT) projection data acquired from a straight-line X-ray source trajectory. The condition states that integrals of normalized projection data along detector lines parallel to the X-ray path must be equal. The projection data is required to be untruncated only along the detector lines parallel to the X-ray path, a less restrictive requirement compared to Fourier conditions that necessitate completely untruncated data. The condition is implemented numerically on simple image functions, a discretization error bound is estimated, and detection of motion inconsistencies is demonstrated. The results show that the consistency condition may be used to quantitatively compare the quality of projection data sets obtained from different scans of the same image object.

There has been a renewed interest in algorithm development for image reconstruction from highly incomplete data in computed tomography (CT). Such algorithms may lead to reduced imaging dose and time, and to the design of innovative configurations tailored to specific imaging tasks. In recent years, a carbon-nanotube (CNT)-based field-emission x-ray source has been developed, which offers easy electronic control of radiation and thus can be an ideal candidate for gated imaging. We have recently proposed algorithms for image reconstruction from fan- and cone-beam data collected at highly sparse angular views through minimization of the total-variation (TV) of the image subject to the condition that the estimated data are consistent with the measured data. In this work, we investigate and demonstrate the application of the TV-minimization algorithm to reconstructing images from mouse data acquired with a CNT-based CT scanner at a number of views much lower than what is used in conventional CT imaging. The results demonstrate that the TV-minimization algorithm can yield images with quality comparable to those obtained from a large number of views by use of the conventional algorithms. The significance of the work may lie in that the substantial reduction of projection views promised by the TV-minimization algorithm can be exploited for reducing imaging dose and time or for improving temporal resolution in tasks such as dynamic imaging.

A direct filtered-backprojection (FBP) reconstruction algorithm is presented for circular cone-beam computed tomography (CB-CT) that allows the filter operation to be applied efficiently with shift-variant band-pass characteristics on the kernel function. Our algorithm is derived from the ramp-filter based FBP method of Feldkamp et al. and obtained by decomposing the ramp filtering into a convolution involving the Hilbert kernel (global operation) and a subsequent differentiation operation (local operation). The differentiation is implemented as a finite difference of two (Hilbert filtered) data samples and carried out as part of the backprojection step. The spacing between the two samples, which defines the low-pass characteristics of the filter operation, can thus be selected individually for each point in the image volume. We here define the sample spacing to follow the magnification of the divergent-beam geometry and thus obtain a novel, depth-dependent filtering algorithm for circular CB-CT. We evaluate this resulting algorithm using computer-simulated CB data and demonstrate that our algorithm yields results where spatial resolution and image noise are distributed much more uniformly over the field-of-view, compared to Feldkamp's approach.

We investigate the feasibility of dual-energy method for image contrast enhancement in small animal studies using a low kV X-ray radiographic system. A robust method for X-ray spectrum estimation from transmission measurements, based on expectation-maximization (EM) method, is applied to an X-ray specimen radiographic system for dual energy imaging of a mouse. From transmission measurements of two known attenuators at two different X-ray tube voltages, the X-ray energy spectra are reconstructed using the EM-based method. From the spectra information thus obtained, the transmission data for bone and soft tissue in terms of various thicknesses are generated. Two polynomial functions of transmission data are then sought for to fit the inverted thicknesses of bone and soft-tissue. Scatters in cone-beam projection data acquired at two X-ray energies were corrected. From the scatter-corrected data, a bone thickness map is separated from a soft-tissue thickness map by use of the polynomial functions.

The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations, the BPF algorithm involves a back-projection with a spatially varying weighting factor, which can result in the non-uniform noise levels in reconstructed images and increased computation time. In this work, we propose a BPF algorithm to eliminate the spatially varying weighting factor by using a rebinned geometry for a general scanning trajectory. This proposed BPF algorithm has an improved noise property, while retaining the advantages of the original BPF algorithm such as minimum data requirement.

The spike protein of the severe acute respiratory syndrome coronavirus (SARS-CoV) mediates cell fusion by binding to target cell surface receptors. This paper reports a simple method for dissecting the viral protein and for searching for foldable fragments in a random but systematic manner. The method involves digestion by DNase I to generate a pool of short DNA segments, followed by an additional step of reassembly of these segments to produce a library of DNA fragments with random ends but controllable lengths. To rapidly screen for discrete folded polypeptide fragments, the reassembled gene fragments were further cloned into a vector as N-terminal fusions to a folding reporter gene which was a variant of green fluorescent protein. Two foldable fragments were identified for the SARS-CoV spike protein, which coincide with various anti-SARS peptides derived from the hepated repeat (HR) region 2 of the spike protein. The method should be applicable to other viral proteins to isolate antigen or vaccine candidates, thus providing an alternative to the full-length proteins (subunits) or linear short peptides.