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This handbook and ready reference presents a combination of statistical, information-theoretic, and data analysis methods to meet the challenge of designing empirical models involving molecular descriptors within bioinformatics.

Phylogenetic inference is mainly based on sequence analysis and requires reliable alignments. This can be challenging, especially when sequences are highly divergent. In this context, the use of three-dimensional protein structures is a promising alternative. In a recent study, we introduced an original topological data analysis method based on persistent homology to estimate the evolutionary distances from structures. The method was successfully tested on 518 protein families representing 22,940 predicted structures. However, as anticipated, the reliability of the estimated evolutionary distances was impacted by the quality of the predicted structures and the presence of indels in the proteins. This paper introduces a new topological descriptor, called bio-topological marker (BTM), which provides a more faithful description of the structures, a topological analysis for estimating evolutionary distances from BTMs, and a new weight-filtering method adapted to protein structures. These new developments significantly improve the estimation of evolutionary distances and phylogenies inferred from structures.

RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5′- and 3′-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.

Controlling the phase-separated structure of polymer alloys is a promising method for tailoring the properties of polymers. However, controlling the morphology of phase-separated structures is challenging. Recently, phase-separated structures have been fabricated via 3D printing; however, only a few methods that enable on-demand control of phase separation have been reported. In this study, laser-scanning stereolithography, a vat photopolymerization method, is used to form a phase-separated structure via polymerization-induced microphase separation by varying the scanning speed and using macro-reversible addition/fragmentation chain transfer (macro-RAFT) agents with different average molar masses, along with multiarmed macro-RAFT agents; such structures were used to fabricate 3D-printed parts. Various phase-separated morphologies including sea-island and reverse sea-island were achieved by controlling the laser scanning speed and RAFT type. Heterogeneous structures with different material properties were also achieved by simply changing the laser scanning speed. As the deformation due to shrinkage in the process of cleaning 3D-printed parts depends on the laser scanning speed, shape correction was introduced to suppress the effect of shrinkage and obtain the desired shape.

Background Computational RNA 3D structure prediction and modeling are rising as complementary approaches to high-resolution experimental techniques for structure determination. They often apply to substitute or complement them. Recently, researchers’ interests have directed towards in silico methods to fit, remodel and refine RNA tertiary structure models. Their power lies in a problem-specific exploration of RNA conformational space and efficient optimization procedures. The aim is to improve the accuracy of models obtained either computationally or experimentally. Results Here, we present RNAfitme, a versatile webserver tool for remodeling of nucleobase- and nucleoside residue conformations in the fixed-backbone RNA 3D structures. Our approach makes use of dedicated libraries that define RNA conformational space. They have been built upon torsional angle characteristics of PDB-deposited RNA structures. RNAfitme can be applied to reconstruct full-atom model of RNA from its backbone; remodel user-selected nucleobase/nucleoside residues in a given RNA structure; predict RNA 3D structure based on the sequence and the template of a homologous molecule of the same size; refine RNA 3D model by reducing steric clashes indicated during structure quality assessment. RNAfitme is a publicly available tool with an intuitive interface. It is freely accessible at http://rnafitme.cs.put.poznan.pl/ Conclusions RNAfitme has been applied in various RNA 3D remodeling scenarios for several types of input data. Computational experiments proved its efficiency, accuracy, and usefulness in the processing of RNAs of any size. Fidelity of RNAfitme predictions has been thoroughly tested for RNA 3D structures determined experimentally and modeled in silico.

Glycogen and starch are the major readily accessible energy storage compounds in nearly all living organisms. Glycogen is a very large branched glucose homopolymer containing about 90% α‐1,4‐glucosidic linkages and 10% α‐1,6 linkages. Its synthesis and degradation constitute central pathways in the metabolism of living cells regulating a global carbon/energy buffer compartment. Glycogen biosynthesis involves the action of several enzymes among which glycogen synthase catalyzes the synthesis of the α‐1,4‐glucose backbone. We now report the first crystal structure of glycogen synthase in the presence and absence of adenosine diphosphate. The overall fold and the active site architecture of the protein are remarkably similar to those of glycogen phosphorylase, indicating a common catalytic mechanism and comparable substrate‐binding properties. In contrast to glycogen phosphorylase, glycogen synthase has a much wider catalytic cleft, which is predicted to undergo an important interdomain ‘closure’ movement during the catalytic cycle. The structures also provide useful hints to shed light on the allosteric regulation mechanisms of yeast/mammalian glycogen synthases.

We present a numerical method specifically designed for simulating three-dimensional fluid–structure interaction (FSI) problems based on the reference map technique (RMT). The RMT is a fully Eulerian FSI numerical method that allows fluids and large-deformation elastic solids to be represented on a single fixed computational grid. This eliminates the need for meshing complex geometries typical in other FSI approaches and greatly simplifies the coupling between fluid and solids. We develop a three-dimensional implementation of the RMT, parallelized using the distributed memory paradigm, to simulate incompressible FSI with neo-Hookean solids. As part of our method, we develop a field extrapolation scheme that works efficiently in parallel. Through representative examples, we demonstrate the method’s suitability in investigating many-body and active systems, as well as its accuracy and convergence. The examples include settling of a mixture of heavy and buoyant soft ellipsoids, lid-driven cavity flow containing a soft sphere, and swimmers actuated via active stress.

Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record.