Detection of C′,Cα correlations in proteins using a newtime- and sensitivity-optimal experiment

Sensitivity- and time-optimal experiment, called COCAINE (CO-CA In- and aNtiphase spectra with sensitivity Enhancement), is proposed to correlate chemical shifts of 13C′ and 13Cα spins in proteins. A comparison of the sensitivity and duration of the experiment with the corresponding theoretical unitary bounds shows that the COCAINE experiment achieves maximum possible transfer efficiency in the shortest possible time, and in this sense the sequence is optimal. Compared to the standard HSQC, the COCAINE experiment delivers a 2.7-fold gain in sensitivity. This newly proposed experiment can be used for assignment of backbone resonances in large deuterated proteins effectively bridging 13C′ and 13Cα resonances in adjacent amino acids. Due to the spin-state selection employed, the COCAINE experiment can also be used for efficient measurements of one-bond couplings (e.g. scalar and residual dipolar couplings) in any two-spin system (e.g. the N/H in the backbone of protein

Deep Residual CNN for Multi-Class Chest Infection Diagnosis

The advent of deep learning has significantly propelled the capabilities of automated medical image diagnosis, providing valuable tools and resources in the realm of healthcare and medical diagnostics. This research delves into the development and evaluation of a Deep Residual Convolutional Neural Network (CNN) for the multi-class diagnosis of chest infections, utilizing chest X-ray images. The implemented model, trained and validated on a dataset amalgamated from diverse sources, demonstrated a robust overall accuracy of 93%. However, nuanced disparities in performance across different classes, particularly Fibrosis, underscored the complexity and challenges inherent in automated medical image diagnosis. The insights derived pave the way for future research, focusing on enhancing the model's proficiency in classifying conditions that present more subtle and nuanced visual features in the images, as well as optimizing and refining the model architecture and training process. This paper provides a comprehensive exploration into the development, implementation, and evaluation of the model, offering insights and directions for future research and development in the field

Fungal Secretome Database: Integrated platform for annotation of fungal secretomes

<p>Abstract</p> <p>Background</p> <p>Fungi secrete various proteins that have diverse functions. Prediction of secretory proteins using only one program is unsatisfactory. To enhance prediction accuracy, we constructed Fungal Secretome Database (FSD).</p> <p>Description</p> <p>A three-layer hierarchical identification rule based on nine prediction programs was used to identify putative secretory proteins in 158 fungal/oomycete genomes (208,883 proteins, 15.21% of the total proteome). The presence of putative effectors containing known host targeting signals such as RXLX [EDQ] and RXLR was investigated, presenting the degree of bias along with the species. The FSD's user-friendly interface provides summaries of prediction results and diverse web-based analysis functions through Favorite, a personalized repository.</p> <p>Conclusions</p> <p>The FSD can serve as an integrated platform supporting researches on secretory proteins in the fungal kingdom. All data and functions described in this study can be accessed on the FSD web site at <url>http://fsd.snu.ac.kr/</url>.</p

A quantum mechanical NMR simulation algorithm for protein-scale spin systems

Nuclear magnetic resonance spectroscopy is one of the few remaining areas of physical chemistry for which polynomially scaling simulation methods have not so far been available. Here, we report such a method and illustrate its performance by simulating common 2D and 3D liquid state NMR experiments (including accurate description of spin relaxation processes) on isotopically enriched human ubiquitin - a protein containing over a thousand nuclear spins forming an irregular polycyclic three-dimensional coupling lattice. The algorithm uses careful tailoring of the density operator space to only include nuclear spin states that are populated to a significant extent. The reduced state space is generated by analyzing spin connectivity and decoherence properties: rapidly relaxing states as well as correlations between topologically remote spins are dropped from the basis set. In the examples provided, the resulting reduction in the quantum mechanical simulation time is by many orders of magnitude.Comment: Submitted for publicatio

IMGD: an integrated platform supporting comparative genomics and phylogenetics of insect mitochondrial genomes

<p>Abstract</p> <p>Background</p> <p>Sequences and organization of the mitochondrial genome have been used as markers to investigate evolutionary history and relationships in many taxonomic groups. The rapidly increasing mitochondrial genome sequences from diverse insects provide ample opportunities to explore various global evolutionary questions in the superclass Hexapoda. To adequately support such questions, it is imperative to establish an informatics platform that facilitates the retrieval and utilization of available mitochondrial genome sequence data.</p> <p>Results</p> <p>The Insect Mitochondrial Genome Database (IMGD) is a new integrated platform that archives the mitochondrial genome sequences from 25,747 hexapod species, including 112 completely sequenced and 20 nearly completed genomes and 113,985 partially sequenced mitochondrial genomes. The Species-driven User Interface (SUI) of IMGD supports data retrieval and diverse analyses at multi-taxon levels. The Phyloviewer implemented in IMGD provides three methods for drawing phylogenetic trees and displays the resulting trees on the web. The SNP database incorporated to IMGD presents the distribution of SNPs and INDELs in the mitochondrial genomes of multiple isolates within eight species. A newly developed comparative SNU Genome Browser supports the graphical presentation and interactive interface for the identified SNPs/INDELs.</p> <p>Conclusion</p> <p>The IMGD provides a solid foundation for the comparative mitochondrial genomics and phylogenetics of insects. All data and functions described here are available at the web site <url>http://www.imgd.org/</url>.</p

Broken Kramers' degeneracy in altermagnetic MnTe

Altermagnetism is a newly identified fundamental class of magnetism with vanishing net magnetization and time-reversal symmetry broken electronic structure. Probing the unusual electronic structure with nonrelativistic spin splitting would be a direct experimental verification of altermagnetic phase. By combining high-quality film growth and $in~situ$ angle-resolved photoemission spectroscopy, we report the electronic structure of an altermagnetic candidate, $\alpha$-MnTe. Temperature dependent study reveals the lifting of Kramers{\textquoteright} degeneracy accompanied by a magnetic phase transition at $T_N=267\text{ K}$ with spin splitting of up to $370\text{ meV}$, providing direct spectroscopic evidence for altermagnetism in MnTe

A Screen of FDA-Approved Drugs Identifies Inhibitors of Protein Tyrosine Phosphatase 4A3 (PTP4A3 or PRL-3)

Protein tyrosine phosphatase 4A3 (PTP4A3 or PRL-3) is highly expressed in a variety of cancers, where it promotes tumor cell migration and metastasis leading to poor prognosis. Despite its clinical significance, small molecule inhibitors of PRL-3 are lacking. Here, we screened 1443 FDA-approved drugs for their ability to inhibit the activity of the PRL phosphatase family. We identified five specific inhibitors for PRL-3 as well as one selective inhibitor of PRL-2. Additionally, we found nine drugs that broadly and significantly suppressed PRL activity. Two of these broad-spectrum PRL inhibitors, Salirasib and Candesartan, blocked PRL-3-induced migration in human embryonic kidney cells with no impact on cell viability. Both drugs prevented migration of human colorectal cancer cells in a PRL-3 dependent manner and were selective towards PRLs over other phosphatases. In silico modeling revealed that Salirasib binds a putative allosteric site near the WPD loop of PRL-3, while Candesartan binds a potentially novel targetable site adjacent to the CX5R motif. Inhibitor binding at either of these sites is predicted to trap PRL-3 in a closed conformation, preventing substrate binding and inhibiting function

A litmus test for classifying recognition mechanisms of transiently binding proteins

Partner recognition in protein binding is critical for all biological functions, and yet, delineating its mechanism is challenging, especially when recognition happens within microseconds. We present a theoretical and experimental framework based on straight-forward nuclear magnetic resonance relaxation dispersion measurements to investigate protein binding mechanisms on sub-millisecond timescales, which are beyond the reach of standard rapid-mixing experiments. This framework predicts that conformational selection prevails on ubiquitin’s paradigmatic interaction with an SH3 (Src-homology 3) domain. By contrast, the SH3 domain recognizes ubiquitin in a two-state binding process. Subsequent molecular dynamics simulations and Markov state modeling reveal that the ubiquitin conformation selected for binding exhibits a characteristically extended C-terminus. Our framework is robust and expandable for implementation in other binding scenarios with the potential to show that conformational selection might be the design principle of the hubs in protein interaction networks

Spontaneous breaking of mirror symmetry beyond critical doping in Pb-Bi2212

Identifying ordered phases and their underlying symmetries is the first and most important step toward understanding the mechanism of high-temperature superconductivity; critical behaviors of ordered phases are expected to be correlated with superconductivity. Efforts to find such ordered phases have been focused on symmetry breaking in the pseudogap region while the Fermi liquid-like metal region beyond the so-called critical doping $p_{c}$ has been regarded as a trivial disordered state. Here, we used rotational anisotropy second harmonic generation and uncovered a broken mirror symmetry in the Fermi liquid-like phase in (Bi,Pb)$_{2}$Sr$_{2}$CaCu$_{2}$O$_{8+\delta}$ with $p = 0.205 > p_{c}$. By tracking the temperature evolution of the symmetry-breaking response, we verify an order parameter-like behavior with the onset temperature $T_{up}$ at which the strange metal to Fermi liquid-like-metal crossover takes place. Complementary angle-resolved photoemission study showed that the quasiparticle coherence between $\mathrm{CuO_{2}}$ bilayers is enhanced in proportion to the symmetry-breaking response as a function of temperature, indicating that the change in metallicity and symmetry breaking are linked. These observations contradict the conventional quantum disordered scenario for over-critical-doped cuprates and provide new insight into the nature of the quantum critical point in cuprates.Comment: 8 pages, 4 figure

Deep learning-based statistical noise reduction for multidimensional spectral data

In spectroscopic experiments, data acquisition in multi-dimensional phase space may require long acquisition time, owing to the large phase space volume to be covered. In such case, the limited time available for data acquisition can be a serious constraint for experiments in which multidimensional spectral data are acquired. Here, taking angle-resolved photoemission spectroscopy (ARPES) as an example, we demonstrate a denoising method that utilizes deep learning as an intelligent way to overcome the constraint. With readily available ARPES data and random generation of training data set, we successfully trained the denoising neural network without overfitting. The denoising neural network can remove the noise in the data while preserving its intrinsic information. We show that the denoising neural network allows us to perform similar level of second-derivative and line shape analysis on data taken with two orders of magnitude less acquisition time. The importance of our method lies in its applicability to any multidimensional spectral data that are susceptible to statistical noise.Comment: 8 pages, 8 figure