HIV OctaScanner: A Machine Learning Approach to Unveil Proteolytic Cleavage Dynamics in HIV-1 Protease Substrates.

The rise of resistance to antiretroviral drugs due to mutations in human immunodeficiency virus-1 (HIV-1) protease is a major obstacle to effective treatment. These mutations alter the drug-binding pocket of the protease and reduce the drug efficacy by disrupting interactions with inhibitors. Traditional methods, such as biochemical assays and structural biology, are crucial for studying enzyme function but are time-consuming and labor-intensive.

Deciphering progressive lesion areas in breast cancer spatial transcriptomics via TGR-NMF.

Identifying spatial domains is critical for understanding breast cancer tissue heterogeneity and providing insights into tumor progression. However, dropout events introduces computational challenges and the lack of transparency in methods such as graph neural networks limits their interpretability. This study aimed to decipher disease progression-related spatial domains in breast cancer spatial transcriptomics by developing the three graph regularized non-negative matrix factorization (TGR-NMF).

Integrative approaches to m6A and m5C RNA modifications in autism spectrum disorder revealing potential causal variants.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that currently affects approximately 1-2% of the global population. Genome-wide studies have identified several loci associated with ASD; however, pinpointing causal variants remains elusive. Therefore, functional studies are essential to discover potential therapeutics for ASD.

Supervised Screening of EGFR Inhibitors Validated through Computational Structural Biology Approaches.

One of the prominent challenges in breast cancer (BC) treatment is human epidermal growth factor receptor (EGFR) overexpression, which facilitates tumor proliferation and presents a viable target for anticancer therapies. This study integrates multiomics data to pinpoint promising therapeutic compounds and employs a machine learning (ML)-based similarity search to identify effective alternatives. We used BC cell line data from the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases and single-cell RNA sequencing (scRNA-seq) information that established afatinib as an efficacious candidate demonstrating superior IC values.

Identification of Novel Drug Molecules Against NS3-Like Helicase Enzyme of Alongshan Virus.

Alongshan virus (ALSV) is a novel tick-borne virus associated with human diseases. The ALSV is a segmented flavivirus from the family Flaviviridae. It is currently considered as tick-borne arbovirus.

Hyperthermophilic xylanase and thermophilicity analysis by molecular dynamic simulation with quantum mechanics.

Thermophilic xylanases catalyzing the cleavage of β-1,4-glycosidic bonds in xylan have applications in food, feed, biorefinery, and pulp industries. In this study, a hyperthermophilic endo-xylanase was obtained by further enhancement of thermal tolerance of a thermophilic GH11 xylanase originated from metagenome of bagasse pile based on rational design. Introducing N13F and Q34L to the previously reported X11P enzyme shifted the optimal working temperature to 85 °C and led to 20.

Dynamic modeling of antibody repertoire reshaping in response to viral infections.

For decades, research has largely focused on the generation of high-affinity, antigen-specific antibodies during viral infections. This emphasis has made it challenging for immunologists to systematically evaluate the mechanisms initiating humoral immunity in specific immune responses. In this study, we employ ordinary differential equations (ODE) to investigate the dynamic reshaping of the entire antibody repertoire in response to viral infections.

Exploring the Dynamic Interplay of Deleterious Variants on the RAF1-RAP1A Binding in Cancer: Conformational Analysis, Binding Free Energy, and Essential Dynamics.

The RAF1-RAP1A interaction activates the MAPK/ERK pathway which is very crucial in the carcinogenesis process. This protein complex influences tumor formation, proliferation, and metastasis. Understanding aberrant interactions driven by clinical mutations is vital for targeted therapies.

Antimicrobial Properties and Therapeutic Potential of Bioactive Compounds in : A Review.

(; Ranunculaceae), commonly referred to as black cumin, is one of the most widely used medicinal plants worldwide, with its seeds having numerous applications in the pharmaceutical and food industries. With the emergence of antibiotic resistance in pathogens as an important health challenge, the need for alternative microbe-inhibitory agents is on the rise, whereby black cumin has gained considerable attention from researchers for its strong antimicrobial characteristics owing to its high content in a wide range of bioactive compounds, including thymoquinone, nigellimine, nigellidine, quercetin, and O-cymene. Particularly, thymoquinone increases the levels of antioxidant enzymes that counter oxidative stress in the liver.

A mathematical model simulating the adaptive immune response in various vaccines and vaccination strategies.

Vaccination has been widely recognized as an effective measure for preventing infectious diseases. To facilitate quantitative research into the activation of adaptive immune responses in the human body by vaccines, it is important to develop an appropriate mathematical model, which can provide valuable guidance for vaccine development. In this study, we constructed a novel mathematical model to simulate the dynamics of antibody levels following vaccination, based on principles from immunology.

AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.

The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, while pursuing high thermodynamic stability and rigidity of proteins, inevitably sacrifices biological functions closely related to protein flexibility.

Unveiling the Therapeutic Potential of Paclitaxel Combinations Against Breast Carcinoma and Identification of In Vivo Biomarkers.

Breast cancer (BC) is one of the leading causes of high mortality rates in women worldwide. Although advancements have been made in the design of therapeutic strategies and drug discovery, drug resistance remains one of the key challenges. One of the ways to overcome drug resistance is finding potential drug combinations since the efficacy of combined drugs is higher than their individual efficacies if the combination is a synergistic pair.

Increasing trends of non-tuberculous mycobacteria clinical isolates in Guangzhou, China.

Non-tuberculous mycobacteria (NTM) are one of major public health concern. The current study aimed to find the prevalence trends of NTM in Guangzhou, China from January 2018 to December 2023. A total of 26,716 positive mycobacterial cultures were collected.

Correction: Suleman et al. Immunoinformatics and Immunogenetics-Based Design of Immunogenic Peptides Vaccine against the Emerging Tick-Borne Encephalitis Virus (TBEV) and Its Validation through In Silico Cloning and Immune Simulation. 2021, , 1210.

The authors would like to make the following correction to this published paper [...

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis.

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of mycoplasmas. Improved management of mycoplasma infections is essential due to the frequent ineffectiveness of current antibiotic treatments in completely eliminating these pathogens from the host.

DDSBC: A Stacking Ensemble Classifier-Based Approach for Breast Cancer Drug-Pair Cell Synergy Prediction.

Breast cancer (BC) ranks as a leading cause of mortality among women worldwide, with incidence rates continuing to rise. The quest for effective treatments has led to the adoption of drug combination therapy, aiming to enhance drug efficacy. However, identifying synergistic drug combinations remains a daunting challenge due to the myriad of potential drug pairs.

CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction.

Functional peptides play crucial roles in various biological processes and hold significant potential in many fields such as drug discovery and biotechnology. Accurately predicting the functions of peptides is essential for understanding their diverse effects and designing peptide-based therapeutics. Here, we propose CELA-MFP, a deep learning framework that incorporates feature Contrastive Enhancement and Label Adaptation for predicting Multi-Functional therapeutic Peptides.

Sources, transmission and hospital-associated outbreaks of nontuberculous mycobacteria: a review.

Nontuberculous mycobacteria (NTM) are widespread environmental organisms found in both natural and man-made settings, such as building plumbing, water distribution networks and hospital water systems. Their ubiquitous presence increases the risk of transmission, leading to a wide range of human infections, particularly in immunocompromised individuals. NTM primarily spreads through environmental exposures, such as inhaling aerosolized particles, ingesting contaminated food and introducing it into wounds.

An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model.

Identification of interactions between chemical compounds and proteins is crucial for various applications, including drug discovery, target identification, network pharmacology, and elucidation of protein functions. Deep neural network-based approaches are becoming increasingly popular in efficiently identifying compound-protein interactions with high-throughput capabilities, narrowing down the scope of candidates for traditional labor-intensive, time-consuming and expensive experimental techniques. In this study, we proposed an end-to-end approach termed SPVec-SGCN-CPI, which utilized simplified graph convolutional network (SGCN) model with low-dimensional and continuous features generated from our previously developed model SPVec and graph topology information to predict compound-protein interactions.

In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches.

Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is responsible for pancreatic cancer and is an attractive drug target.

Abrogation of ORF8-IRF3 binding interface with Carbon nanotube derivatives to rescue the host immune system against SARS-CoV-2 by using molecular screening and simulation approaches.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to over six million deaths worldwide. In human immune system, the type 1 interferon (IFN) pathway plays a crucial role in fighting viral infections. However, the ORF8 protein of the virus evade the immune system by interacting with IRF3, hindering its nuclear translocation and consequently downregulate the type I IFN signaling pathway.