EasyMetagenome: A user-friendly and flexible pipeline for shotgun metagenomic analysis in microbiome research.

Shotgun metagenomics has become a pivotal technology in microbiome research, enabling in-depth analysis of microbial communities at both the high-resolution taxonomic and functional levels. This approach provides valuable insights of microbial diversity, interactions, and their roles in health and disease. However, the complexity of data processing and the need for reproducibility pose significant challenges to researchers.

Effect of TetR Family Transcriptional Regulator PccD on Phytosterol Metabolism of .

Androstenedione (AD) is an important intermediate for the production of steroidal drugs. The process of transforming phytosterols into AD by is mainly the degradation process of the phytosterol side chain, and the excessive accumulation of propionyl-CoA produced by will produce toxic effects, which seriously restricts the transformation performance of strains. In this study, sp.

MetalPrognosis: A Biological Language Model-Based Approach for Disease-Associated Mutations in Metal-Binding Site Prediction.

Protein-metal ion interactions play a central role in the onset of numerous diseases. When amino acid changes lead to missense mutations in metal-binding sites, the disrupted interaction with metal ions can compromise protein function, potentially causing severe human ailments. Identifying these disease-associated mutation sites within metal-binding regions is paramount for understanding protein function and fostering innovative drug development.

DIG-Mol: A Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction.

Molecular property prediction is a key component of AI-driven drug discovery and molecular characterization learning. Despite recent advances, existing methods still face challenges such as limited ability to generalize, and inadequate representation of learning from unlabeled data, especially for tasks specific to molecular structures. To address these limitations, we introduce DIG-Mol, a novel self-supervised graph neural network framework for molecular property prediction.

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years.

Advancing microRNA target site prediction with transformer and base-pairing patterns.

MicroRNAs (miRNAs) are short non-coding RNAs involved in various cellular processes, playing a crucial role in gene regulation. Identifying miRNA targets remains a central challenge and is pivotal for elucidating the complex gene regulatory networks. Traditional computational approaches have predominantly focused on identifying miRNA targets through perfect Watson-Crick base pairings within the seed region, referred to as canonical sites.

Variable calling of m6A and associated features in databases: a guide for end-users.

N6-methyladenosine (m$^{6}$A) is a widely-studied methylation to messenger RNAs, which has been linked to diverse cellular processes and human diseases. Numerous databases that collate m$^{6}$A profiles of distinct cell types have been created to facilitate quick and easy mining of m$^{6}$A signatures associated with cell-specific phenotypes. However, these databases contain inherent complexities that have not been explicitly reported, which may lead to inaccurate identification and interpretation of m$^{6}$A-associated biology by end-users who are unaware of them.

Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.

Motivation: The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular processes. To advance our comprehension of gene regulatory networks, it is essential to develop computational tools for accurately identifying the subcellular localizations of mRNAs.

Osteoblast-specific down-regulation of NLRP3 inflammasome by aptamer-functionalized liposome nanoparticles improves bone quality in postmenopausal osteoporosis rats.

NLRP3 inflammasome is critical in the development and progression of many metabolic diseases driven by chronic inflammation, but its effect on the pathology of postmenopausal osteoporosis (PMOP) remains poorly understood. We here firstly examined the levels of NLRP3 inflammasome in PMOP patients by ELISA. Then we investigated the possible mechanisms underlying the effect of NLRP3 inflammasome on PMOP by RNA sequencing of osteoblasts treated with NLRP3 siRNA and qPCR.

Characterizing Secretion System Effector Proteins With Structure-Aware Graph Neural Networks and Pre-Trained Language Models.

The Type III Secretion Systems (T3SSs) play a pivotal role in host-pathogen interactions by mediating the secretion of type III secretion system effectors (T3SEs) into host cells. These T3SEs mimic host cell protein functions, influencing interactions between Gram-negative bacterial pathogens and their hosts. Identifying T3SEs is essential in biomedical research for comprehending bacterial pathogenesis and its implications on human cells.

MERITS: a web-based integrated PE/PPE protein database.

Motivation: PE/PPE proteins, highly abundant in the genome, play a vital role in virulence and immune modulation. Understanding their functions is key to comprehending the internal mechanisms of . However, a lack of dedicated resources has limited research into PE/PPE proteins.

Asymmetric Carbene-Alkyne Metathesis-Mediated Cascade: Synthesis of Benzoxazine Polychiral Polyheterocycles and Discovery of a Novel Pain Blocker.

This study introduces a novel approach for synthesizing Benzoxazine-centered Polychiral Polyheterocycles (BPCPHCs) via an innovative asymmetric carbene-alkyne metathesis-triggered cascade. Overcoming challenges associated with intricate stereochemistry and multiple chiral centers, the catalytic asymmetric Carbene Alkyne Metathesis-mediated Cascade (CAMC) is employed using dirhodium catalyst/Brønsted acid co-catalysis, ensuring precise stereo control as validated by X-ray crystallography. Systematic substrate scope evaluation establishes exceptional diastereo- and enantioselectivities, creating a unique library of BPCPHCs.

Nav1.7 Modulator Bearing a 3-Hydroxyindole Backbone Holds the Potential to Reverse Neuropathic Pain.

Chronic pain is a growing global health problem affecting at least 10% of the world's population. However, current chronic pain treatments are inadequate. Voltage-gated sodium channels (Navs) play a pivotal role in regulating neuronal excitability and pain signal transmission and thus are main targets for nonopioid painkiller development, especially those preferentially expressed in dorsal root ganglial (DRG) neurons, such as Nav1.

Degradation of Chloroquine by Ammonia-Oxidizing Bacteria: Performance, Mechanisms, and Associated Impact on NO Production.

Since the mass production and extensive use of chloroquine (CLQ) would lead to its inevitable discharge, wastewater treatment plants (WWTPs) might play a key role in the management of CLQ. Despite the reported functional versatility of ammonia-oxidizing bacteria (AOB) that mediate the first step for biological nitrogen removal at WWTP (i.e.

TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion.

Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs.

PLANNER: a multi-scale deep language model for the origins of replication site prediction.

Origins of replication sites (ORIs) are crucial genomic regions where DNA replication initiation takes place, playing pivotal roles in fundamental biological processes like cell division, gene expression regulation, and DNA integrity. Accurate identification of ORIs is essential for comprehending cell replication, gene expression, and mutation-related diseases. However, experimental approaches for ORI identification are often expensive and time-consuming, leading to the growing popularity of computational methods.

PmxPred: A data-driven approach for the identification of active polymyxin analogues against gram-negative bacteria.

The multidrug-resistant Gram-negative bacteria has evolved into a worldwide threat to human health; over recent decades, polymyxins have re-emerged in clinical practice due to their high activity against multidrug-resistant bacteria. Nevertheless, the nephrotoxicity and neurotoxicity of polymyxins seriously hinder their practical use in the clinic. Based on the quantitative structure-activity relationship (QSAR), analogue design is an efficient strategy for discovering biologically active compounds with fewer adverse effects.

ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction.

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data.

iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.

Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic.

Digerati - A multipath parallel hybrid deep learning framework for the identification of mycobacterial PE/PPE proteins.

The genome of Mycobacterium tuberculosis contains a relatively high percentage (10%) of genes that are poorly characterised because of their highly repetitive nature and high GC content. Some of these genes encode proteins of the PE/PPE family, which are thought to be involved in host-pathogen interactions, virulence, and disease pathogenicity. Members of this family are genetically divergent and challenging to both identify and classify using conventional computational tools.

TIMER is a Siamese neural network-based framework for identifying both general and species-specific bacterial promoters.

Background: Promoters are DNA regions that initiate the transcription of specific genes near the transcription start sites. In bacteria, promoters are recognized by RNA polymerases and associated sigma factors. Effective promoter recognition is essential for synthesizing the gene-encoded products by bacteria to grow and adapt to different environmental conditions.