Using Phaser and ensembles to improve the performance of SIMBAD.
Ontology highlight
ABSTRACT:
SUBMITTER: Simpkin AJ
PROVIDER: S-EPMC6939438 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Phaser is a program that implements likelihood-based methods to solve macromolecular crystal structures, currently by molecular replacement or single-wavelength anomalous diffraction (SAD). SAD phasing is based on a likelihood target derived from the joint probability distribution of observed and calculated pairs of Friedel-related structure factors. This target combines information from the total structure factor (primarily non-anomalous scattering) and the difference between the Friedel mates (anomalous scattering). Phasing starts from a substructure, which is usually but not necessarily a set of anomalous scatterers. The substructure can also be a protein model, such as one obtained by molecular replacement. Additional atoms are found using a log-likelihood gradient map, which shows the sites where the addition of scattering from a particular atom type would improve the likelihood score. An automated completion algorithm adds new sites, choosing optionally among different atom types, adds anisotropic B-factor parameters if appropriate and deletes atoms that refine to low occupancy. Log-likelihood gradient maps can also identify which atoms in a refined protein structure are anomalous scatterers, such as metal or halide ions. These maps are more sensitive than conventional model-phased anomalous difference Fouriers and the iterative completion algorithm is able to find a significantly larger number of convincing sites.
| S-EPMC3069749 | biostudies-literature
Project description:Micro/nanomachines have attracted extensive attention in the biomedical and environmental fields for realizing functionalities at small scales. However, they have been rarely investigated as active nanocatalysts. Heterogeneous nanocatalysts have exceptional reusability and recyclability, and integration with magnetic materials enables their recovery with minimum loss. Herein, we propose a model active nanocatalyst using magnetic nanomotor ensembles (MNEs) that can degrade contaminants in an aqueous solution with high catalytic performance. MNEs composed of a magnetite core coated with gold nanoparticles as the nanocatalyst can rotate under the action of a programmable external field and carry out rapid reduction of 4-nitrophenol (4-NP). The hydrogen bubbles generated in the catalytic reaction provide random perturbations for the MNEs to travel in the reaction solution, resulting in uniform processing. The reduction can be further boosted by irradiation with near-infrared (NIR) light. Magnetic field induces the rotation of the MNEs and provides microstirring in the catalysis. Light enhances the catalytic activity via the photothermal effect. These MNEs are also capable of moving to the targeted region through the application of a programmable magnetic field and then process the contaminant in the targeted region. We expect that such magnetic MNEs may help better in applying active heterogeneous nanocatalysts with magnetic field and light-enhanced performance in industrial applications due to their advantages of low material cost and short reaction time.
| S-EPMC7366293 | biostudies-literature
Project description:BACKGROUND:Current outcomes prediction tools are largely based on and limited by regression methods. Utilization of machine learning (ML) methods that can handle multiple diverse inputs could strengthen predictive abilities and improve patient outcomes. Inpatient length of stay (LOS) is one such outcome that serves as a surrogate for patient disease severity and resource utilization. OBJECTIVE:To develop a novel method to systematically rank, select, and combine ML algorithms to build a model that predicts LOS following craniotomy for brain tumor. METHODS:A training dataset of 41?222 patients who underwent craniotomy for brain tumor was created from the National Inpatient Sample. Twenty-nine ML algorithms were trained on 26 preoperative variables to predict LOS. Trained algorithms were ranked by calculating the root mean square logarithmic error (RMSLE) and top performing algorithms combined to form an ensemble. The ensemble was externally validated using a dataset of 4592 patients from the National Surgical Quality Improvement Program. Additional analyses identified variables that most strongly influence the ensemble model predictions. RESULTS:The ensemble model predicted LOS with RMSLE of .555 (95% confidence interval, .553-.557) on internal validation and .631 on external validation. Nonelective surgery, preoperative pneumonia, sodium abnormality, or weight loss, and non-White race were the strongest predictors of increased LOS. CONCLUSION:An ML ensemble model predicts LOS with good performance on internal and external validation, and yields clinical insights that may potentially improve patient outcomes. This systematic ML method can be applied to a broad range of clinical problems to improve patient care.
| S-EPMC7137462 | biostudies-literature
Project description:With the advantage of the reusability property of the virtualization technology, users can reuse various types and versions of existing operating systems and drivers in a virtual machine, so as to customize their application environment. In order to prevent users' virtualization environments being impacted by driver faults in virtual machine, Chariot examines the correctness of driver's write operations by the method of combining a driver's write operation capture and a driver's private access control table. However, this method needs to keep the write permission of shadow page table as read-only, so as to capture isolated driver's write operations through page faults, which adversely affect the performance of the driver. Based on delaying setting frequently used shadow pages' write permissions to read-only, this paper proposes an algorithm using shadow page cache to improve the performance of isolated drivers and carefully study the relationship between the performance of drivers and the size of shadow page cache. Experimental results show that, through the shadow page cache, the performance of isolated drivers can be greatly improved without impacting Chariot's reliability too much.
| S-EPMC4359836 | biostudies-other
Project description:Background: Obesity is a worldwide public health problem with increasing prevalence and affects 80% of diabetes mellitus type 2 cases. Zebrafish (Danio rerio) are an established model organism for studying obesity and diabetes including diabetic microvascular complications. We aimed to determine whether physical activity is an appropriate tool to examine training effects in zebrafish and to analyse metabolic and transcriptional processes in trained zebrafish. Methods: A 2- and 8-weeks experimental training phase protocol with adult zebrafish in a swim tunnel system was established. We examined zebrafish basic characteristics before and after training such as body weight, body length and maximum speed and considered overfeeding as an additional parameter in the 8-weeks training protocol. Ultimately, the effects of training and overfeeding on blood glucose, muscle core metabolism and liver gene expression using RNA-Seq were investigated. Results: Zebrafish maximum speed was correlated with body length and was significantly increased after 2 weeks of training. Maximum swim speed further increased after 8 weeks of training in both the normalfed and the overfed groups, but training was found not to be sufficient in preventing weight gain in overfed fish. Metabolome and transcriptome profiling in trained fish exhibited increased blood glucose levels in the short-term and upregulated energy supply pathways in the long-term. Conclusion: Swim training is a valuable tool to study effects of physical activity in zebrafish, which is accompanied by metabolic and transcriptional adaptations.
2021-12-06 | GSE183023 | GEO
Project description:Molecular docking algorithms suggest possible structures for molecular complexes. They are used to model biological function and to discover potential ligands. A present challenge for docking algorithms is the treatment of molecular flexibility. Here, the rigid body program, DOCK, is modified to allow it to rapidly fit multiple conformations of ligands. Conformations of a given molecule are pre-calculated in the same frame of reference, so that each conformer shares a common rigid fragment with all other conformations. The ligand conformers are then docked together, as an ensemble, into a receptor binding site. This takes advantage of the redundancy present in differing conformers of the same molecule. The algorithm was tested using three organic ligand protein systems and two protein-protein systems. Both the bound and unbound conformations of the receptors were used. The ligand ensemble method found conformations that resembled those determined in X-ray crystal structures (RMS values typically less than 1.5 A). To test the method's usefulness for inhibitor discovery, multi-compound and multi-conformer databases were screened for compounds known to bind to dihydrofolate reductase and compounds known to bind to thymidylate synthase. In both cases, known inhibitors and substrates were identified in conformations resembling those observed experimentally. The ligand ensemble method was 100-fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 molecules in one to four CPU days on a workstation.
| S-EPMC2143983 | biostudies-other
Project description:A method is described for generating protein fragments suitable for use as molecular-replacement (MR) template models. The template model for a protein suspected to undergo a conformational change is perturbed along combinations of low-frequency normal modes of the elastic network model. The unperturbed structure is then compared with each perturbed structure in turn and the structurally invariant regions are identified by analysing the difference distance matrix. These fragments are scored with SCEDS, which is a combined measure of the sphericity of the fragments, the continuity of the fragments with respect to the polypeptide chain, the equality in number of atoms in the fragments and the density of C(α) atoms in the triaxial ellipsoid of the fragment extents. The fragment divisions with the highest SCEDS are then used as separate template models for MR. Test cases show that where the protein contains fragments that undergo a change in juxtaposition between template model and target, SCEDS can identify fragments that lead to a lower R factor after ten cycles of all-atom refinement with REFMAC5 than the original template structure. The method has been implemented in the software Phaser.
| S-EPMC3817695 | biostudies-literature
Project description:The objective of this study is to replace a traditional methacrylate-based primer (glycine, N-(2-hydroxy-3-(2-methyl-1-oxo-2-propenyl)propyl)-N-(4-methylphenyl) monosodium salt, NTG-GMA) with a hydrolytically stable ether-based primer (glycine, N-2-hydroxy-3-(4-vinylbenzyloxy)-propyl-N-(4-methylphenyl), monosodium salt, NTG-VBGE). The performance and durability of bonding composites to detin of two primers combined with methacrylate-based or ether-based adhesives were evaluated using shear bond strength (SBS) and micro-tensile bond strength (μTBS) combined with thermal cycling. The hydrolysis resistance of NTG-VBGE against hydrolysis was tested by challenging primed hydroxyapatite crystals with an esterase. The hydrophilicity of the primers and the resin spreading kinetics of adhesives on primed dentin were characterized by water contact angle measurements. The new primer NTG-VBGE was found to be compatible with both methacrylate-based adhesives and ether-based adhesives. The highest μTBS values were found in the test group of NTG-VBGE and ether-based adhesive, which was consistent with the resin spreading kinetics results. The more hydrophobic and hydrolytically stable primer/adhesive achieved improved dentin infiltration and bonding strength, suggesting significant potential for further developing dental restorative materials with extended service life.
| S-EPMC9501844 | biostudies-literature
Project description:Haplotype phasing of genetic variants is important for clinical interpretation of the genome, population genetic analysis and functional genomic analysis of allelic activity. Here we present phASER, an accurate approach for phasing variants that are overlapped by sequencing reads, including those from RNA sequencing (RNA-seq), which often span multiple exons due to splicing. Using diverse RNA-seq data we demonstrate that this provides more accurate phasing of rare variants compared with population-based phasing and allows phasing of variants in the same gene up to hundreds of kilobases away that cannot be obtained from DNA sequencing (DNA-seq) reads. We show that in the context of medical genetic studies this improves the resolution of compound heterozygotes. Additionally, phASER provides measures of haplotypic expression that increase power and accuracy in studies of allelic expression. In summary, phasing using RNA-seq and phASER is accurate and improves studies where rare variant haplotypes or allelic expression is needed.
| S-EPMC5025529 | biostudies-literature
Project description:Heterogeneous ensembles are an effective approach in scenarios where the ideal data type and/or individual predictor are unclear for a given problem. These ensembles have shown promise for protein function prediction (PFP), but their ability to improve PFP at a large scale is unclear. The overall goal of this study is to critically assess this ability of a variety of heterogeneous ensemble methods across a multitude of functional terms, proteins and organisms. Our results show that these methods, especially Stacking using Logistic Regression, indeed produce more accurate predictions for a variety of Gene Ontology terms differing in size and specificity. To enable the application of these methods to other related problems, we have publicly shared the HPC-enabled code underlying this work as LargeGOPred ( https://github.com/GauravPandeyLab/LargeGOPred).
| S-EPMC6221071 | biostudies-literature