Structural bioinformatics

Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants...

The red abalone (Haliotis rufescens) represents North America's most important aquaculture species. Its hepatopancreas is rich in cellulases and other polysaccharide-degrading enzymes, which provide it the remarkable ability to digest cellulose-rich macroalgae; nevertheless, its cellulolytic systems are poorly explored. This manuscript describes some functional and structural properties of an endogenous trimeric glycosylated endoglucanase from H. rufescens. The purified enzyme showed a molecular mass of 23...

Biological functions of glycans are intimately linked to fine details in branches and linkages, which make structural identification extremely challenging. Here, we present a protocol for automated N-glycan sequencing using multi-stage mass spectrometry (MSn ). We describe steps for release/purification and derivation of glycans and procedures for MSn scanning. We then detail "glycan intelligent precursor selection" to computationally guide MSn experiments. The protocol can be used for both discrete individual glycans and isomeric glycan mixtures...

As medical safety and drug regulation gain heightened attention, the detection of spurious drug-drug interactions (DDI) has become key in healthcare. Although current research using graph neural networks (GNNs) to predict DDI has shown impressive results, it often fails to account for false DDI in the constructed DDI networks. Such inaccuracies caused by data errors, false alarms, or incorrect drug details can skew the network's structure and hinder the accuracy of GNN-based predictions. To tackle this challenge, we propose ANSM, a network-enhancement method specifically designed to identify and attenuate spurious links between drugs for ensuring the accuracy of DDI networks...

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants of unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations with disease-related phenotypes. These studies have had a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, computational methods for variants comparable to biological activity predictions...

With the explosive growth of protein-related data, we are confronted with a critical scientific inquiry: How can we effectively retrieve, compare, and profoundly comprehend these protein structures to maximize the utilization of such data resources? PS-GO, a parametric protein search engine, has been specifically designed and developed to maximize the utilization of the rapidly growing volume of protein-related data. This innovative tool addresses the critical need for effective retrieval, comparison, and deep understanding of protein structures...

Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate the local and first-order structures of scRNA-seq data using graph representations, while scRNA-seq data frequently display complex high-dimensional structures...

In recent years, the wide application of mannan has driven the demand for the exploration of mannanase. As one of the main components of hemicellulose, mannan is an important polysaccharide that ruminants need to degrade and utilize, making rumen a rich source of mannanases. In this study, gene mining of mannanases was performed using bioinformatics, and potential dual-catalytic domain mannanases were heterologously expressed to analyze their properties. The hydrolysis pattern and enzymatic products were identified by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS)...

To understand the genome-wide information of the GRF family genes in broomcorn millet and their expression profile in the vegetative meristems, bioinformatic methods and transcriptome sequencing were used to analyze the characteristics, physical and chemical properties, phylogenetic relationship, chromosome distribution, gene structure, cis-acting elements and expression profile in stem meristem for the GRF family members. The results showed that the GRF gene family of millet contains 21 members, and the PmGRF gene is unevenly distributed on 12 chromosomes...

MOTIVATION: It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information...

MOTIVATION: We describe a new Python implementation of FlowSOM, a clustering method for cytometry data. RESULTS: This implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and includes all the original visualizations, such as the star and pie plot. AVAILABILITY: The FlowSOM Python implementation is freely available on GitHub: https://github...

A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation...

BACKGROUND AND AIMS: Blood disorders, such as sickle cell disease, and other clinical conditions are often accompanied by intravascular hemolytic events along with the development of severe coagulopathies. Hemolysis, in turn, leads to the accumulation of Fe(II/III)-protoporphyrin IX (heme) in the intravascular compartment, which can trigger a variety of proinflammatory and prothrombotic reactions. As such, heme binding to the blood coagulation proteins factor VIII (FVIII), fibrinogen, and activated protein C with functional consequences has been demonstrated earlier...

MDC1 (Mediator of DNA damage Checkpoint protein 1) functions to facilitate the localization of numerous DNA damage response (DDR) components to DNA double-strand break sites. MDC1 is an integral component in preserving genomic stability and appropriate DDR regulation. There haven't been systematic investigations of MDC1 mutations that induce cancer and genomic instability. Variations in nsSNPs have the potential to modify the protein chemistry and their function. Describing functional SNPs in disease-associated genes presents a significant conundrum for investigators, it is possible to assess potential functional SNPs before conducting larger population examinations...

Glycans, present across all domains of life, comprise a wide range of monosaccharides assembled into complex, branching structures. Here, we present an in silico protocol to construct biosynthetic networks from a list of observed glycans using the Python package glycowork. We describe steps for data preparation, network construction, feature analysis, and data export. This protocol is implemented in Python using example data and can be adapted for use with customized datasets. For complete details on the use and execution of this protocol, please refer to Thomès et al...

The landscape of cancer therapy has witnessed a paradigm shift with the emergence of innovative delivery systems, and Guanidinium-based Peptide Dendrimers have emerged as a vanguard in this transformative journey. With their unique molecular architecture and intrinsic biocompatibility, these dendrimers offer a promising avenue for the targeted delivery of therapeutic cargo in cancer treatment. This comprehensive review delves into the intricate world of Guanidinium- based Peptide Dendrimers, unraveling their structural intricacies, mechanisms of action, and advancements that have propelled them from laboratory curiosities to potential clinical champions...

SCOPE: Arginine kinase (AK) is an important enzyme for energy metabolism of invertebrate cells by participating in the maintenance of constant levels of ATP. However, AK is also recognized as a major allergen in insects and crustaceans capable of cross-reactivity with sera of patients sensitized to orthologous proteins. In the perspective of introducing insects or their derivatives in the human diet in Western world, it is of primary importance to evaluate possible risks for allergic consumers...

Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST...

The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance...

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense...