Subspace exploration Bounds in Projected Frequency Evaluation

The recent breakthrough in the field of protein structure prediction shows the relevance of using knowledge-based based scoring functions in combination with a low-resolution 3D representation of protein macromolecules. The choice of not using all atoms is barely supported by any data in the literature, and is mostly motivated by empirical and practical reasons, such as the computational cost of assessing the numerous folds of the protein conformational space. AGK2 cell line Here, we present a comprehensive study, carried on a large and balanced benchmark of predicted protein structures, to see how different types of structural representations rank in either accuracy or calculation speed, and which ones offer the best compromise between these two criteria. We tested ten representations, including low-resolution, high-resolution, and coarse-grained approaches. We also investigated the generalization of the findings to other formalisms than the widely-used "potential of mean force" (PMF) method. Thus, we observed that representing protein structures by their β carbons-combined or not with Cα-provides the best speed-accuracy trade-off, when using a "total information gain" scoring function. For statistical PMFs, using MARTINI backbone and side-chains beads is the best option. Finally, we also demonstrated the necessity of training the reference state on all atom types, and of including the Cα atoms of glycine residues, in a Cβ-based representation.Paecilomyces penicillatus is one of the pathogens of morels, which greatly affects the yield and quality of Morchella spp.. In the present study, we de novo assembled the genome sequence of the fungus P. penicillatus SAAS_ppe1. We analyzed the transcriptional profile of P. penicillatus SAAS_ppe1 infection of Morchella importuna at different stages (3 days and 6 days after infection) and the response of M. importuna using the transcriptome. The assembled genome sequence of P. penicillatus SAAS_ppe1 was 39.78 Mb in length (11 scaffolds; scaffold N50, 6.50 Mb), in which 99.7% of the expected genes were detected. A total of 7.48% and 19.83% clean transcriptional reads from the infected sites were mapped to the P. penicillatus genome at the early and late stages of infection, respectively. There were 3,943 genes differently expressed in P. penicillatus at different stages of infection, of which 24 genes had increased expression with the infection and infection stage, including diphthamide biosynthesis, aldehyde reductase, and NAD (P)H-hydrate epimerase (P less then 0.05). Several genes had variable expression trends at different stages of infection, indicating P. penicillatus had diverse regulation patterns to infect M. importuna. GO function, involving cellular components, and KEGG pathways, involving glycerolipid metabolism, and plant-pathogen interaction were significantly enriched during infection by P. penicillatus. The expression of ten genes in M. importuna increased during the infection and infection stage, and these may regulate the response of M. importuna to P. penicillatus infection. This is the first comprehensive study on P. penicillatus infection mechanism and M. importuna response mechanism, which will lay a foundation for understanding the fungus-fungus interactions, gene functions, and variety breeding of pathogenic and edible fungi.A recent advance in the disorder prediction field is the development of the quality assessment (QA) scores. QA scores complement the propensities produced by the disorder predictors by identifying regions where these predictions are more likely to be correct. We develop, empirically test and release a new QA tool, QUARTERplus, that addresses several key drawbacks of the current QA method, QUARTER. QUARTERplus is the first solution that utilizes QA scores and the associated input disorder predictions to produce very accurate disorder predictions with the help of a modern deep learning meta-model. The deep neural network utilizes the QA scores to identify and fix the regions where the original/input disorder predictions are poor. More importantly, the accurate QUATERplus's predictions are accompanied by easy to interpret residue-level QA scores that reliably quantify their residue-level predictive quality. We provide these interpretable QA scores for QUARTERplus and 10 other popular disorder predictors. Empirical tests on a large and independent (low similarity) test dataset show that QUARTERplus predictions secure AUC = 0.93 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.