deep-AMPpred: A Deep Learning Method for Identifying Antimicrobial Peptides and Their Functional Activities.

Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As the problem of pathogen resistance caused by the misuse of antibiotics intensifies, the identification of AMPs as alternatives to antibiotics has become a hot topic. Accurately identifying AMPs using computational methods has been a key issue in the field of bioinformatics in recent years.

RiceSNP-ABST: a deep learning approach to identify abiotic stress-associated single nucleotide polymorphisms in rice.

Given the adverse effects faced by rice due to abiotic stresses, the precise and rapid identification of single nucleotide polymorphisms (SNPs) associated with abiotic stress traits (ABST-SNPs) in rice is crucial for developing resistant rice varieties. The scarcity of high-quality data related to abiotic stress in rice has hindered the development of computational models and constrained research efforts aimed at rice improvement and breeding. Genome-wide association studies provide a better statistical power to consider ABST-SNPs in rice.

RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.

Rice consistently faces significant threats from biotic stresses, such as fungi, bacteria, pests, and viruses. Consequently, accurately and rapidly identifying previously unknown single-nucleotide polymorphisms (SNPs) in the rice genome is a critical challenge for rice research and the development of resistant varieties. However, the limited availability of high-quality rice genotype data has hindered this research.

Impairment of the trans-Golgi-Lysosomal Pathway Accelerates Dopaminergic Neuronal Senescence in LRRK2 Rats.

Leucine-rich repeat kinase 2 (LRRK2)-R1628P mutation has been shown to be one of the common risk factors for Parkinson's disease (PD) in Asian populations, but the mechanism by which R1628P mutations cause neuronal dysfunction remains unknown. We used LRRK2 knock-in rats (human LRRK2-R1628P corresponds to rat LRRK2-R1627P) to investigate the R1627P mutation on function of dopaminergic neurons (DANs) and their susceptibility to the environmental toxin Lipopolysaccharide (LPS) during aging. LRRK2 rats showed no significant loss of DANs, dopamine and its metabolites, or motor dysfunction; however, spontaneous exploration and olfactory discrimination reduced, and dendritic spines of DANs showed degeneration.

BES-Designer: A Web Tool to Design Guide RNAs for Base Editing to Simplify Library.

CRISPR/Cas base editors offer precise conversion of single nucleotides without inducing double-strand breaks. This technology finds extensive applications in gene therapy, gene function analysis, and other domains. However, a crucial challenge lies in selecting the appropriate guide RNAs (gRNAs) for base editing.

Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response.

Tumor heterogeneity presents a significant challenge in predicting drug responses, especially as missense mutations within the same gene can lead to varied outcomes such as drug resistance, enhanced sensitivity, or therapeutic ineffectiveness. These complex relationships highlight the need for advanced analytical approaches in oncology. Due to their powerful ability to handle heterogeneous data, graph convolutional networks (GCNs) represent a promising approach for predicting drug responses.

RAB12-LRRK2 complex suppresses primary ciliogenesis and regulates centrosome homeostasis in astrocytes.

The leucine-rich repeat kinase 2 (LRRK2) phosphorylates a subset of RAB GTPases, and their phosphorylation levels are elevated by Parkinson's disease (PD)-linked mutations of LRRK2. However, the precise function of the LRRK2-regulated RAB GTPase in the brain remains to be elucidated. Here, we identify RAB12 as a robust LRRK2 substrate in the mouse brain through phosphoproteomics profiling and solve the structure of RAB12-LRRK2 protein complex through Cryo-EM analysis.

Comprehensive Assessment of BERT-Based Methods for Predicting Antimicrobial Peptides.

In recent years, the prediction of antimicrobial peptides (AMPs) has gained prominence due to their high antibacterial activity and reduced susceptibility to drug resistance, making them potential antibiotic substitutes. To advance the field of AMP recognition, an increasing number of natural language processing methods are being applied. These methods exhibit diversity in terms of pretraining models, pretraining data sets, word vector embeddings, feature encoding methods, and downstream classification models.

Targeting Specific Kinase Substrates Rescues Increased Colitis Severity Induced by the Crohn's Disease-Linked LRRK2-N2081D Variant.

LRRK2 contains a kinase domain where both the N2081D Crohn's disease (CD) risk and the G2019S Parkinson's disease (PD)-pathogenic variants are located. The mechanisms by which the N2081D variant increase CD risk, and how these adjacent mutations result in distinct diseases, remain unclear. To investigate the pathophysiology of the CD-linked LRRK2 N2081D variant, we generated a knock-in (KI) mouse model and compared its effects to those of the LRRK2-G2019S mutation.

CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations.

Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database.

TeaTFactor: a prediction tool for tea plant transcription factors based on BERT.

A transcription factor (TF) is a sequence-specific DNA-binding protein, which plays key roles in cell-fate decision by regulating gene expression. Predicting TFs is key for tea plant research community, as they regulate gene expression, influencing plant growth, development, and stress responses. It is a challenging task through wet lab experimental validation, due to their rarity, as well as the high cost and time requirements.

RAB12-LRRK2 Complex Suppresses Primary Ciliogenesis and Regulates Centrosome Homeostasis in Astrocytes.

Leucine-rich repeat kinase 2 (LRRK2) phosphorylates a subset of RAB GTPases, and the phosphorylation levels are elevated by Parkinson's disease (PD)-linked mutations of LRRK2. However, the precise function of the specific RAB GTPase targeted by LRRK2 signaling in the brain remains to be elucidated. Here, we identify RAB12 as a robust LRRK2 substrate in the mouse brains through phosphoproteomics profiling and solve the structure of RAB12-LRRK2 protein complex through Cryo-EM analysis.

Functional validation of AaCaM3 response to high temperature stress in Amorphophallus albus.

Amorphophallus is a perennial monocotyledonous herbaceous plant native to the southwestern region of China, widely used in various fields such as food processing, biomedicine and chemical agriculture. However, Amorphophallus is a typical thermolabile plant, and the continuous high temperature in summer have seriously affected the growth, development and economic yield of Amorphophallus in recent years. Calmodulin (CaM), a Ca sensor ubiquitous in eukaryotes, is the most important multifunctional receptor protein in plant cells, which affects plant stress resistance by participating in the activities of a variety of signaling molecules.

Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning.

Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed.

Integrated proteomics reveals autophagy landscape and an autophagy receptor controlling PKA-RI complex homeostasis in neurons.

Autophagy is a conserved, catabolic process essential for maintaining cellular homeostasis. Malfunctional autophagy contributes to neurodevelopmental and neurodegenerative diseases. However, the exact role and targets of autophagy in human neurons remain elusive.

Silencing Parkinson's risk allele Rit2 sex-specifically compromises motor function and dopamine neuron viability.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease and arises from dopamine (DA) neuron death selectively in the substantia nigra pars compacta (SNc). Rit2 is a reported PD risk allele, and recent single cell transcriptomic studies identified a major RIT2 cluster in PD DA neurons, potentially linking Rit2 expression loss to a PD patient cohort. However, it is still unknown whether Rit2 loss itself impacts DA neuron function and/or viability.

Crop-GPA: an integrated platform of crop gene-phenotype associations.

With the increasing availability of large-scale biology data in crop plants, there is an urgent demand for a versatile platform that fully mines and utilizes the data for modern molecular breeding. We present Crop-GPA ( https://crop-gpa.aielab.

PDDGCN: A Parasitic Disease-Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network.

The precise identification of associations between diseases and drugs is paramount for comprehending the etiology and mechanisms underlying parasitic diseases. Computational approaches are highly effective in discovering and predicting disease-drug associations. However, the majority of these approaches primarily rely on link-based methodologies within distinct biomedical bipartite networks.

Molecular profiling of human substantia nigra identifies diverse neuron types associated with vulnerability in Parkinson's disease.

Parkinson's disease (PD) is characterized pathologically by the loss of dopaminergic (DA) neurons in the substantia nigra (SN). Whether cell types beyond DA neurons in the SN show vulnerability in PD remains unclear. Through transcriptomic profiling of 315,867 high-quality single nuclei in the SN from individuals with and without PD, we identified cell clusters representing various neuron types, glia, endothelial cells, pericytes, fibroblasts, and T cells and investigated cell type-dependent alterations in gene expression in PD.

The experiences of reproductive concerns in cancer survivors: A systematic review and meta-synthesis of qualitative studies.

Aim: The aim of this study was to synthesize qualitative research evidence on cancer survivors' experiences with reproductive concerns (RC).

Methods: We conducted a systematic search of qualitative studies and utilized the meta-aggregation approach. The database searches were extended up to May 14, 2023, encompassing 12 databases, specifically MEDLINE, CINAHL, PubMed, EMBASE, Scopus, Web of Science (Core Collection), AMED, PsycINFO, The Cochrane Library, CNKI, Wan Fang Data, and VIP.

APDDD: Animal parasitic diseases and drugs database.

Animal parasitic diseases not only have an economic impact, but also have serious social and public health impacts. Although antiparasitic drugs can treat these diseases, it seems difficult for users to comprehensively utilize the information, due to incomplete and difficult data collection. Thus, there is an urgent need to establish a comprehensive database, that includes parasitic diseases and related drugs.

Named entity recognition of rice genes and phenotypes based on BiGRU neural networks.

Named Entity Recognition (NER) is a fundamental but crucial task in natural language processing (NLP) and big data analysis, with wide application range. NER for rice genes and phenotypes is a technique to identify genes and phenotypes from a large amount of text. NER for rice genes and phenotypes can facilitate the acquisition of information in the field of crops and provide references for our research on higher quality crops.