Background: The comparative modeling approach to protein structure prediction inherently relies on a template structure. Before building a model such a template protein has to be found and aligned with the query sequence. Any error made on this stage may dramatically affects the quality of result. There is a need, therefore, to develop accurate and sensitive alignment protocols.
Results: BioShell threading software is a versatile tool for aligning protein structures, protein sequences or sequence profiles and query sequences to a template structures. The software is also capable of sub-optimal alignment generation. It can be executed as an application from the UNIX command line, or as a set of Java classes called from a script or a Java application. The implemented Monte Carlo search engine greatly facilitates the development and benchmarking of new alignment scoring schemes even when the functions exhibit non-deterministic polynomial-time complexity.
Conclusions: Numerical experiments indicate that the new threading application offers template detection abilities and provides much better alignments than other methods. The package along with documentation and examples is available at: http://bioshell.pl/threading3d.
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http://dx.doi.org/10.1186/1471-2105-15-22 | DOI Listing |
Phys Chem Chem Phys
January 2025
Ural Federal University, Ekaterinburg, Russia.
This work is devoted to the study of the static magnetization of immobilized multi-core particles (MCPs) and their ensembles. These objects model aggregates of superparamagnetic nanoparticles that are taken up by biological cells and subsequently used, for example, as magnetoactive agents for cell imaging. In this study, we derive an analytical formula that allows us to predict the static magnetization of MCPs consisting of immobilized granules, in which the magnetic moment rotates freely the Néel mechanism.
View Article and Find Full Text PDFComput Stat
September 2024
Department of Statistics, Purdue University, West Lafayette, IN 47907.
State estimation for large-scale non-Gaussian dynamic systems remains an unresolved issue, given nonscalability of the existing particle filter algorithms. To address this issue, this paper extends the Langevinized ensemble Kalman filter (LEnKF) algorithm to non-Gaussian dynamic systems by introducing a latent Gaussian measurement variable to the dynamic system. The extended LEnKF algorithm can converge to the right filtering distribution as the number of stages become large, while inheriting the scalability of the LEnKF algorithm with respect to the sample size and state dimension.
View Article and Find Full Text PDFSingle-omics approaches often provide a limited view of complex biological systems, whereas multiomics integration offers a more comprehensive understanding by combining diverse data views. However, integrating heterogeneous data types and interpreting the intricate relationships between biological features-both within and across different data views-remains a bottleneck. To address these challenges, we introduce COSIME (Cooperative Multi-view Integration and Scalable Interpretable Model Explainer).
View Article and Find Full Text PDFFront Vet Sci
January 2025
Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada.
Introduction: Antimicrobial resistance (AMR) is a growing threat to the efficacy of antimicrobials in humans and animals, including those used to control bovine respiratory disease (BRD) in high-risk calves entering western Canadian feedlots. Successful mitigation strategies require an improved understanding of the epidemiology of AMR. Specifically, the relative contributions of antimicrobial use (AMU) and contagious transmission to AMR emergence in animal populations are unknown.
View Article and Find Full Text PDFACS Phys Chem Au
January 2025
Department of Chemistry, McGill University, Montréal, Québec H3A 0B8, Canada.
Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter (e.
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