How to build the virtual cell with artificial intelligence: Priorities and opportunities.

Cells are essential to understanding health and disease, yet traditional models fall short of modeling and simulating their function and behavior. Advances in AI and omics offer groundbreaking opportunities to create an AI virtual cell (AIVC), a multi-scale, multi-modal large-neural-network-based model that can represent and simulate the behavior of molecules, cells, and tissues across diverse states. This Perspective provides a vision on their design and how collaborative efforts to build AIVCs will transform biological research by allowing high-fidelity simulations, accelerating discoveries, and guiding experimental studies, offering new opportunities for understanding cellular functions and fostering interdisciplinary collaborations in open science.

QuickBind: A Light-Weight And Interpretable Molecular Docking Model.

Predicting a ligand's bound pose to a target protein is a key component of early-stage computational drug discovery. Recent developments in machine learning methods have focused on improving pose quality at the cost of model runtime. For high-throughput virtual screening applications, this exposes a capability gap that can be filled by moderately accurate but fast pose prediction.

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells.

Fighting sepsis-induced liver damage with biosynthesized silver nanoparticles.

Sepsis is a potentially fatal disease that arises from an infection and is characterized by an uncontrolled immune system reaction. Global healthcare systems bear a heavy financial burden from treating sepsis. This study aimed to provide information on the effective properties of silver nanoparticles derived from pomegranate peel extract (P-AgNP) against sepsis-induced hepatic injury.

Harnessing the power of Neobacillus niacini AUMC-B524 for silver oxide nanoparticle synthesis: optimization, characterization, and bioactivity exploration.

Background: Biotechnology provides a cost-effective way to produce nanomaterials such as silver oxide nanoparticles (AgONPs), which have emerged as versatile entities with diverse applications. This study investigated the ability of endophytic bacteria to biosynthesize AgONPs.

Results: A novel endophytic bacterial strain, Neobacillus niacini AUMC-B524, was isolated from Lycium shawii Roem.

The Arabic Version of Compulsive Exercise Test among Saudi Population; Translation and Validation.

Compulsive exercise is a condition characterized by uncontrollable exercise behaviour that may lead to severe and harmful physical and psychological consequences. Indeed, compulsive exercise is among the early symptoms of eating disorders that may affect different age groups. Globally and among Arab countries, compulsive exercise is common, while the screening methods used to assess compulsive exercise are limited.

Mapping variant effects on anti-tumor hallmarks of primary human T cells with base-editing screens.

Single-nucleotide variants (SNVs) in key T cell genes can drive clinical pathologies and could be repurposed to improve cellular cancer immunotherapies. Here, we perform massively parallel base-editing screens to generate thousands of variants at gene loci annotated with known or potential clinical relevance. We discover a broad landscape of putative gain-of-function (GOF) and loss-of-function (LOF) mutations, including in PIK3CD and the gene encoding its regulatory subunit, PIK3R1, LCK, SOS1, AKT1 and RHOA.

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space.

Massively parallel base editing screens to map variant effects on anti-tumor hallmarks of primary human T cells.

Base editing enables generation of single nucleotide variants, but large-scale screening in primary human T cells is limited due to low editing efficiency, among other challenges . Here, we developed a high-throughput approach for high-efficiency and massively parallel adenine and cytosine base-editor screening in primary human T cells. We performed multiple large-scale screens editing 102 genes with central functions in T cells and full-length tiling mutagenesis of selected genes, and read out variant effects on hallmarks of T cell anti-tumor immunity, including activation, proliferation, and cytokine production.

Growing ecosystem of deep learning methods for modeling protein-protein interactions.

Numerous cellular functions rely on protein-protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions.

Nephroprotective Efficacy of against a Glycerol-Induced Acute Kidney Injury Model.

Nephroprotection or renal rescue is to revive and restore kidney function after damage, with no need for further dialysis. During acute kidney injury (AKI), sudden and recent reductions in kidney functions occur. Causes are multiple, and prompt intervention can be critical to diminish or prevent morbidity.

Association of Oral and General Health with Nutritional Status of Older Adults Attending Two Medical Centers in Riyadh, Saudi Arabia: A Cross-Sectional Study.

Malnutrition could profoundly affect older adults' oral health and quality of life, whereas oral health might, in turn, impact dietary intake and nutritional status. The present study aimed to investigate the association between general and oral health and nutritional status among older adults attending nutrition clinics at two main medical centers in Riyadh, Saudi Arabia. A cross-section study was carried out among adult patients (≥60 years) who attended a geriatric clinic or nutrition clinic at King Khalid University Hospital or King Abdulaziz Medical City, Riyadh.

Effect of Vitamin D3 on Depressive Behaviors of Rats Exposed to Chronic Unpredictable Mild Stress.

Depression is a psychiatric disorder that negatively affects how a person feels, thinks, and acts. Several studies have reported a positive association between vitamin D (VD) deficiency and depression. Therefore, we aimed to examine the effects of intraperitoneal injection of VD3, fluoxetine (antidepressant), and a combination of VD3 + fluoxetine on a rat model of chronic unpredictable mild stress (CUMS).

OpenProteinSet: Training data for structural biology at scale.

Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades. Recent breakthroughs like AlphaFold2 that use transformers to attend directly over large quantities of raw MSAs have reaffirmed their importance. Generation of MSAs is highly computationally intensive, however, and no datasets comparable to those used to train AlphaFold2 have been made available to the research community, hindering progress in machine learning for proteins.

A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules.

Kinases have been the focus of drug discovery programs for three decades leading to over 70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 kinase-compound pairs. Nonetheless, the precise target spectrum for many kinases remains only partly understood. In this study, we describe a computational approach to unlocking qualitative and quantitative kinome-wide binding measurements for structure-based machine learning.

High-throughput deep learning variant effect prediction with Sequence UNET.

Understanding coding mutations is important for many applications in biology and medicine but the vast mutation space makes comprehensive experimental characterisation impossible. Current predictors are often computationally intensive and difficult to scale, including recent deep learning models. We introduce Sequence UNET, a highly scalable deep learning architecture that classifies and predicts variant frequency from sequence alone using multi-scale representations from a fully convolutional compression/expansion architecture.

Structural biology at the scale of proteomes.

AlphaFold2 has already changed structural biology but its true power may lie in how it changes the way we think about cells and organisms. Two studies broadly analyze and assess the performance of AlphaFold2 to outline the extent of its utility and limitations in providing structural models that shed light on biological questions, including mutations, post-translational modifications, and protein-protein complex interactions.

Diagnostic Value of Inflammatory Markers in Elderly Arab Women with Sarcopenia.

Several studies have found a correlation between inflammatory markers and sarcopenia; however, limited research has been conducted on the Arabic population. Therefore, this study aimed to investigate the value of inflammatory parameters in Saudi elderly women with sarcopenia. In this cross-sectional study, 76 elderly Saudi women (>65 years) were stratified according to the presence (n = 26) or absence (n = 50) of sarcopenia, using the operational definition of the Asian Working Group for Sarcopenia (AWGS).

Single-sequence protein structure prediction using a language model and deep learning.

AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins.

The next decade of protein structure.

With recent dramatic advances in various techniques used for protein structure research, we asked researchers to comment on the next exciting questions for the field and about how these techniques will advance our knowledge not only about proteins but also about human health and diseases.

Podocyte specific deletion of PKM2 ameliorates LPS-induced podocyte injury through beta-catenin.

Background: Acute kidney injury (AKI) is associated with a severe decline in kidney function caused by abnormalities within the podocytes' glomerular matrix. Recently, AKI has been linked to alterations in glycolysis and the activity of glycolytic enzymes, including pyruvate kinase M2 (PKM2). However, the contribution of this enzyme to AKI remains largely unexplored.

Value of Adding Bioelectrical Impedance Analysis to Anthropometric Indices in the Diagnosis of Metabolic Syndrome in 10-16 Years Old Schoolgirls.

The use of bioelectrical impedance analysis (BIA) in clinical settings is common. However, the value of BIA-based parameters in diagnosing metabolic syndrome (MetS) in children is under-investigated. Herein, we aimed to study the usefulness of BIA-indices in the diagnoses of MetS in 6-10-year-old girls.

Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.

Deep learning using neural networks relies on a class of machine-learnable models constructed using 'differentiable programs'. These programs can combine mathematical equations specific to a particular domain of natural science with general-purpose, machine-learnable components trained on experimental data. Such programs are having a growing impact on molecular and cellular biology.