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93 and are statistically more accurate than the results of twelve state-of-the-art disorder predictors. We also demonstrate that the new QA scores produced by QUARTERplus are highly correlated with the actual predictive quality and that they can be effectively used to identify regions of correct disorder predictions. This feature empowers the users to easily identify which parts of the predictions generated by the modern disorder predictors are more trustworthy. QUARTERplus is available as a convenient webserver at http//biomine.cs.vcu.edu/servers/QUARTERplus/.Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed. In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies. The integration approaches are divided into three categories early, intermediate, and late data integration. For each category, we describe the underlying conceptual principles and main characteristics, as well as provide examples of currently available tools and how they have been applied to analyze single-cell multi-omics data. Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines to single-cell multi-omics.Protein design usually involves sequence search process and evaluation criteria. Commonly used methods primarily implement the Monte Carlo or simulated annealing algorithm with a single-energy function to obtain ideal solutions, which is often highly time-consuming and limited by the accuracy of the energy function. In this report, we introduce a multiobjective algorithm named Hydra for protein design, which employs two different energy functions to optimize solutions simultaneously and makes use of the latent quantitative relationship between different amino acid types to facilitate the search process. The framework uses two kinds of prior information to transform the original disordered discrete sequence space into a relatively ordered space, and decoy sequences are searched in this ordered space through a multiobjective swarm intelligence algorithm. This algorithm features high accuracy and a high-speed search process. Our method was tested on 40 targets covering different fold classes, which were computationally verified to be well folded, and it experimentally solved the 1UBQ fold by NMR in excellent agreement with the native structure with a backbone RMSD deviation of 1.074 Å. KU-55933 cost The Hydra software package can be downloaded from http//www.csbio.sjtu.edu.cn/bioinf/HYDRA/ for academic use.Plants employ sophisticated mechanisms to control developmental processes and to cope with environmental changes at transcriptional and post-transcriptional levels. MicroRNAs (miRNAs) and long noncoding RNAs (lncRNAs), two classes of endogenous noncoding RNAs, are key regulators of gene expression in plants. Recent studies have identified the interplay between miRNAs and lncRNAs as a novel regulatory layer of gene expression in plants. On one hand, miRNAs target lncRNAs for the production of phased small interfering RNAs (phasiRNAs). On the other hand, lncRNAs serve as origin of miRNAs or regulate the accumulation or activity of miRNAs at transcription and post-transcriptional levels. Theses lncRNA-miRNA interplays are crucial for plant development, physiology and responses to biotic and abiotic stresses. In this review, we summarize recent advances in the biological roles, interaction mechanisms and computational predication methods of the interplay between miRNAs and lncRNAs in plants.We study the models submitted to round 12 of the Critical Assessment of protein Structure Prediction (CASP) experiment to assess how well the binding properties are conserved when the X-ray structures of the target proteins are replaced by their models. To explore small molecule binding we generate distributions of molecular probes - which are fragment-sized organic molecules of varying size, shape, and polarity - around the protein, and count the number of interactions between each residue and the probes, resulting in a vector of interactions we call a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model of the protein, is determined by calculating the correlation coefficient between the two vectors. The resulting correlation coefficients are shown to correlate with global measures of accuracy established in CASP, and the relationship yields an accuracy threshold that has to be reached for meaningful binding surface conservation. The clusters formed by the probe molecules reliably predict binding hot spots and ligand binding sites in both X-ray structures and reasonably accurate models of the target, but ensembles of models may be needed for assessing the availability of proper binding pockets. We explored ligand docking to the few targets that had bound ligands in the X-ray structure. More targets were available to assess the ability of the models to reproduce protein-protein interactions by docking both the X-ray structures and models to their interaction partners in complexes. It was shown that this application is more difficult than finding small ligand binding sites, and the success rates heavily depend on the local structure in the potential interface. In particular, predicted conformations of flexible loops are frequently incorrect in otherwise highly accurate models, and may prevent predicting correct protein-protein interactions.Because of high stability and slow unfolding rates of G-quadruplexes (G4), cells have evolved specialized helicases that disrupt these non-canonical DNA and RNA structures in an ATP-dependent manner. One example is DHX36, a DEAH-box helicase, which participates in gene expression and replication by recognizing and unwinding parallel G4s. Here, we studied the molecular basis for the high affinity and specificity of DHX36 for parallel-type G4s using all-atom molecular dynamics simulations. By computing binding free energies, we found that the two main G4-interacting subdomains of DHX36, DSM and OB, separately exhibit high G4 affinity but they act cooperatively to recognize two distinctive features of parallel G4s the exposed planar face of a guanine tetrad and the unique backbone conformation of a continuous guanine tract, respectively. Our results also show that DSM-mediated interactions are the main contributor to the binding free energy and rely on making extensive van der Waals contacts between the GXXXG motifs and hydrophobic residues of DSM and a flat guanine plane. Accordingly, the sterically more accessible 5'-G-tetrad allows for more favorable van der Waals and hydrophobic interactions which leads to the preferential binding of DSM to the 5'-side. In contrast to DSM, OB binds to G4 mostly through polar interactions by flexibly adapting to the 5'-terminal guanine tract to form a number of strong hydrogen bonds with the backbone phosphate groups. We also identified a third DHX36/G4 interaction site formed by the flexible loop missing in the crystal structure.Protein microarrays are versatile tools for high throughput study of the human proteome, but systematic and non-systematic sources of bias constrain optimal interpretation and the ultimate utility of the data. Published guidelines to limit technical variability whilst maintaining important biological variation favour DNA-based microarrays that often differ fundamentally in their experimental design. Rigorous tools to guide background correction, the quantification of within-sample variation, normalisation, and batch correction specifically for protein microarrays are limited, require extensive investigation and are not centrally accessible. Here, we develop a generic one-stop-shop pre-processing suite for protein microarrays that is compatible with data from the major protein microarray scanners. Our graphical and tabular interfaces facilitate a detailed inspection of data and are coupled with supporting guidelines that enable users to select the most appropriate algorithms to systematically address bias arising in customized experiments. The localization and distribution of background signal intensities determine the optimal correction strategy. A novel function overcomes the limitations in the interpretation of the coefficient of variation when signal intensities are at the lower end of the detection threshold. We demonstrate essential considerations in the experimental design and their impact on a range of algorithms for normalization and minimization of batch effects. Our user-friendly interactive web-based platform eliminates the need for prowess in programming. The open-source R interface includes illustrative examples, generates an auditable record, enables reproducibility, and can incorporate additional custom scripts through its online repository. This versatility will enhance its broad uptake in the infectious disease and vaccine development community.N-glycosylation is a physiologically vital post-translational modification of proteins in eukaryotic organisms. Initial work on Haemonchus contortus - a blood-sucking nematode of ruminants with a broad geographical distribution - has shown that this parasite harbors N-glycans with exclusive chitobiose modifications. Besides, several immunogenic proteins (e.g., amino- and metallo-peptidases) are known to be N-glycosylated in adult worms. However, an informative atlas of N-glycosylation in H. contortus is not yet available. Herein, we report 291 N-glycosylated proteins with a total of 425 modification sites in the parasite. Among them, many peptidase families (e.g., peptidase C1 and M1) including potential vaccine targets were enriched. Notably, the glycan-rich conjugates are distributed primarily in the intestine and gonads of adult worms, and consequently hidden from the host's immune system. Collectively, these data provide a comprehensive atlas of N-glycosylation in a prevalent parasitic nematode while underlining its significance for infection, immunity and prevention.Gene manipulation is a useful approach for understanding functions of genes and is important for investigating basic mechanisms of brain function on the level of single neurons and circuits. Despite the development and the wide range of applications of CRISPR-Cas9 and base editors (BEs), their implementation for an analysis of individual neurons in vivo remained limited. In fact, conventional gene manipulations are generally achieved only on the population level. Here, we combined either CRISPR-Cas9 or BEs with the targeted single-cell electroporation technique as a proof-of-concept test for gene manipulation in single neurons in vivo. Our assay consisted of CRISPR-Cas9- or BEs-induced gene knockout in single Purkinje cells in the cerebellum. Our results demonstrate the feasibility of both gene editing and base editing in single cells in the intact brain, providing a tool through which molecular perturbations of individual neurons can be used for analysis of circuits and, ultimately, behaviors.