Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students

FAIR data for optical tweezers experiments

The single-molecule biophysics community has delivered significant impacts to our understanding of fundamental biological processes, yet the field is also siloed and has fragmented data structures, which impede data sharing and limit the ability to conduct comprehensive meta-analyses. To advance the field of optical tweezers in single-molecule biophysics, it is important that the field adopts open and collaborative data sharing that facilitate meta-analyses that combine diverse resources and supports more advanced analyses, akin to those seen in projects such as the Protein Data Bank and the 1000 Genomes Project. Here, we assess the state of data findability, accessibility, interoperability, and reusability (the FAIR principles) within the single-molecule optical tweezers field. By combining a qualitative review with quantitative tools from bibliometrics, our analysis suggests that the field has significant room for improvement in terms of FAIR adherence. Finally, we discuss the potential of compulsory data deposition and a minimal set of metadata standards to ensure reproducibility and interoperability between systems. While implementing these measures may not be straightforward, they are key steps that will enhance the integration of optical tweezers biophysics with the broader biomedical literature.

UANanoDock: A Web-Based UnitedAtom Multiscale Nanodocking Tool for Predicting Protein Adsorption onto Nanoparticles

UANanoDock is a web-based application with a graphical user interface designed for modeling protein-nanomaterial interactions, accessible via the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.com/compsafenano/uananodock/). The application's foundation lies in the UnitedAtom multiscale model, previously reported for predicting the adsorption energies of biopolymers and small molecules onto nanoparticles (NPs). UANanoDock offers insights into optimal protein orientations when bound to spherical NP surfaces, considering factors such as material type, NP radius, surface potential, and amino acid (AA) ionization states at specific pH levels. The tool's computational time is determined solely by the protein's AA count, regardless of NP size. With its efficiency (e.g., approximately 60 s processing time for a 1331 AA protein) and versatility (accommodating any protein with a standard AA sequence in PDB format), UANanoDock serves as a prescreening tool for identifying proteins likely to adsorb onto NP surfaces. An illustration of UANanoDock's utility is provided, demonstrating its application in the rational design of immunoassays by determining the preferred orientation of the immunoglobulin G (IgG) antibody adsorbed on Ag NPs.

Dapagliflozin in Chronic Kidney Disease: Insights from Network Pharmacology and Molecular Docking Simulation

Chronic kidney disease (CKD) involves inflammation, oxidative stress, and fibrosis, leading to renal dysfunction. Dapagliflozin, an SGLT2 inhibitor, shows renoprotective effects beyond glucose control, but its precise molecular mechanisms remain unclear. This study utilizes network pharmacology and molecular docking to elucidate its multi-target effects in CKD. Dapagliflozin's SMILES structure was analyzed for ADMET properties. Potential targets were identified via SwissTargetPrediction, GeneCards, and SEA, and common CKD-related targets were determined. A protein-protein interaction (PPI) network was constructed, and key pathways were identified using GO and KEGG enrichment analyses. Molecular docking was conducted to validate dapagliflozin's binding affinities with hub proteins. A total of 208 common targets were identified, including EGFR, GSK3β, and IL-6. GO and KEGG analyses highlighted key pathways, such as PI3K-Akt, MAPK, and AGE-RAGE, involved in inflammation, oxidative stress, and metabolic regulation. Molecular docking confirmed strong binding affinities with EGFR (-8.42 kcal/mol), GSK3β (-7.70 kcal/mol), and IL-6 (-6.83 kcal/mol). Dapagliflozin exhibits multi-target therapeutic potential in CKD by modulating inflammation, oxidative stress, and metabolic pathways. This integrative approach enhances the understanding of its mechanisms, supporting future experimental validation and clinical application in CKD management.

What have Data Standards ever done for us?

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies for both the field of molecular interaction and that of mass spectrometry for more than 20 years. This review explores some of the ways that the proteomics community has benefitted from the development of community standards and takes a look at some of the tools and resources that have been improved or developed as a result of the work of the HUPO-PSI.

Liuwei Anshen Capsule alleviates cognitive impairment induced by sleep deprivation by reducing neuroapoptosis and inflammation

ETHNOPHARMACOLOGICAL RELEVANCE

Cognitive dysfunction is a common complication of chronic insomnia. Liuwei Anshen Capsule (LAC), a traditional Chinese patent medicine clinically prescribed for insomnia, has been proved to possess good efficacy in reducing insomnia complications including dementia and anxiety in clinic. However, the active substances in LAC and their mechanisms in treating cognitive deficit associated with sleep disorders remain unclear.

AIM OF THE STUDY

This study aims to explore the potential material basis and therapeutic mechanisms of LAC on cognitive impairment caused by sleep deprivation (SD) through an integrative approach involving serum pharmacochemistry, network pharmacology and experimental validation.

METHODS

The active ingredients of LAC in vitro and in vivo were screened and identified by liquid chromatography-mass spectrometry (LC-MS) technology. The potential targets and signaling pathways of LAC against cognitive impairment were predicted based on network pharmacology and molecular docking. Subsequently, MWM and NOR were employed to evaluate the efficacy of LAC on cognitive impairment in SD rats, and the mechanism was further validated from pathological and molecular biology perspectives.

RESULTS

Totally 85 active ingredients in LAC were accurately identified and 8 components absorbed into blood were found by LC-MS. Network pharmacology and molecular docking analysis predicted potential targets involving caspase-3, MAPK3, MAPK1, and Bcl-2. LAC (192, 384, and 768 mg/kg, i.g.) could improve spatial learning and memory of SD rats in a dose-dependent manner, restrain hippocampal neuronal apoptosis and microglia activation, and diminish TNF-α, IL-1β, and IL-6 expression levels, which were achieved by regulating apoptosis-related proteins (caspase-3, Bax, and Bcl-2) and MAPK (p-ERK and p-P38) signaling pathway.

CONCLUSION

The findings provide evidence that LAC alleviates cognitive abnormality and pathological alterations in sleep-deprived rats by regulating the expression of apoptosis related proteins and MAPK signaling pathway, indicating its potential therapy for the cognitive complaints caused by insomnia or other neurological diseases.

Exploring potential targets and mechanisms of renal tissue damage caused by N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine quinone (6-PPDQ) through network toxicology and animal experiments: A case of chronic kidney disease

6-PPDQ is a new type of environmental contaminant contained in tire rubber. No studies have been reported on the potential targets and mechanisms of action of 6-PPDQ on renal tissue damage. In the present study, we used CKD as an example to explore the potential targets and biological mechanisms of renal injury caused by 6-PPDQ using Network toxicology and animal experiments. A total of 1361 6-PPDQ-related target genes were obtained from the CTD database. 17,296 CKD-related target genes were obtained through the GeneCards database. After intersecting the two, a total of 908 intersecting genes were obtained. Next, we constructed a PPI protein interaction network. Using different algorithms in Cytoscape software and "Logistic regression analysis", five key target genes were finally identified as NOTCH1, TP53, TNF, IL1B and IL6. We constructed a diagnostic model using five key target genes, and the ROC curves, calibration curves and DCA curves proved that the model has good diagnostic value. Molecular docking demonstrated high affinity between 6-PPDQ and five key target gene proteins. In animal experiments, repeated intraperitoneal injections of 6-PPDQ using different concentration gradients for 28 days revealed that the expression levels of five key target genes in renal tissues increased progressively with the increase of the concentration, and the damage to renal tissues was also aggravated. ssGSEA and animal experiments revealed a key role for activation of the MAPK signaling pathway. Finally, we also identified a significant correlation between five key target genes and the level of infiltration of multiple immune cells. In conclusion, these findings suggest that 6-PPDQ can cause damage to renal tissue and that the level of damage progressively increases with increasing concentration. Among them, NOTCH1, TP53, TNF, IL1B and IL6 may be its potential targets of action. Activation of the MAPK signaling pathway is a potential mechanism of action.

Analyzing the potential targets and mechanisms of chronic kidney disease induced by common synthetic Endocrine Disrupting Compounds (EDCs) in Chinese surface water environment using network toxicology and molecular docking techniques

Long-term exposure to NP and OP, as common synthetic endocrine-disrupting chemicals (EDCs) in surface water environments in China, is closely associated with the development of chronic kidney disease (CKD). However, their potential targets and toxicological mechanisms for inducing CKD remain unknown. This study utilizes network toxicology and molecular docking techniques to explore the potential toxic targets and molecular mechanisms of CKD induction by NP and OP. We identify 49 core targets of NP and OP action in CKD using the Comparative Toxicogenomics Database (CTD) and GeneCards databases. Using the STRING database and Cytoscape software, we identify five hub genes: MAPK3, TNF, BCL2, ESR1, and FOS. We construct a nomogram model based on the CKD dataset GSE66494, utilizing these five hub genes. Calibration and ROC curves demonstrate that the model has good diagnostic value for CKD, and the DCA curve indicates that the model has high clinical utility. Single-gene GSEA enrichment analysis identifies five hub genes that influence the development of CKD through multiple biological pathways, revealing that several immune-regulatory signaling pathways are activated. The CIBERSORT algorithm identifies eight types of immune cell infiltration levels that change significantly during CKD development, and correlation analyses suggest that the five hub genes are strongly associated with multiple immune cell infiltrations. The molecular docking results suggested that ESR1, MAPK3, and TNF had the lowest binding energies and high binding affinities with NP and OP. The results of molecular dynamics simulations similarly confirmed the stability of the interactions between ESR1, MAPK3 and TNF proteins with NP and OP. The results of this study provide a theoretical basis for understanding the potential toxicity targets and mechanisms of NP- and OP-induced CKD and promote the application of network toxicology and molecular docking techniques in the study of environmental pollutants.

Computational insights into irinotecan's interaction with UBE2I in ovarian and endometrial cancers

Endometrial and Ovarian cancers are two highly prevalent and fatal reproductive diseases with poor prognoses among women. Elevated estrogen levels in Ovarian Cancer (OC) stimulate the endometrium, causing Endometrial Cancer (EC). Although numerous studies have reported the crucial genes and pathways in this cancer, the pathogenesis of this disease remains unclear. In this study, used bioinformatics tools to analyse GSE63678, GSE115810, GSE36389, GSE26712, GSE36668, GSE27651, GSE6008, GSE69429, GSE69428, GSE18521, GSE185209, GSE54388 gene expression microarray datasets for both the cancers. We analyzed the differential gene expression, functional association, and structural studies. The analysis identified crucial differentially expressed genes (DEGs) in both cancers associated with DNA damage, DNA integrity, and cell-cycle checkpoint signaling pathways. CLDN7, UBE2I, WT1, JAM2, FOXL2, F11R, JAM3, ZFPM2, MEF2C, and PIAS1 are the top 10 hub genes commonly identified in both cancer types. Only CLDN7 and F11R are upregulated, whereas the remaining hub genes are downregulated in both cancers, suggesting a common framework for contributing to tumorigenesis. Molecular docking and dynamics were performed on the UBE2I protein with Irinotecan Hydrochloride, which could serve as the new approach for treating and managing both cancers. The study reveals the common molecular pathways, pointing out the role of cell cycle and DNA damage and integrity checkpoint signaling in the pathogenesis of both cancer types. This study explored the UBE2I gene as a potential biomarker in OC and EC. Further, this study concludes that the irinotecan hydrochloride drug has higher therapeutic effects on UBE2I protein through docking and dynamics studies.

Exploring the potential pharmacological mechanism of aripiprazole against hyperprolactinemia based on network pharmacology and molecular docking

The current primary therapeutic approach for schizophrenia is antipsychotic medication, and antipsychotic-induced hyperprolactinemia occurs in 40-80% of patients with schizophrenia. Aripiprazole, an atypical antipsychotic belonging to the quinolinone derivative class, can reduce the likelihood of developing hyperprolactinemia, but the pharmacological mechanisms of this reduction are unknown. This study aimed to explore the molecular mechanism of action of aripiprazole in treating hyperprolactinemia based on network pharmacology and molecular docking techniques. This study identified a total of 151 potential targets for aripiprazole from the DrugBank, TCMSP, BATMAN-TCM, TargetNet, and SwissTargetPrediction databases. Additionally, 71 hyperprolactinemia targets were obtained from the PharmGKB, DrugBank, TTD, GeneCards, OMIM, and DisGENET databases. Utilizing Venny 2.1.0 software, an intersection of 27 genes was identified between aripiprazole and hyperprolactinemia. To construct a common target protein-protein interaction (PPI) network, the common targets obtained from both sources were input into the STRING database. The resulting PPI network was then imported into Cytoscape 3.7.2 software, which identified eight core targets associated with aripiprazole's treatment of hyperprolactinemia. Subsequently, a PPI network was established for these targets. Enrichment analysis of the key targets was conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes in the DAVID database. Additionally, molecular docking verification of the interaction between aripiprazole and the core targets was performed using AutoDock Vina software. Aripiprazole's intervention in hyperprolactinemia primarily targets the following core proteins: Solute Carrier Family 6 Member 3 (SLC6A3), monoamine oxidase (MAO-B), Dopamine D2 receptor (DRD2), 5-hydroxytryptamine (serotonin) receptor 2A (HTR2A), 5-hydroxytryptamine (serotonin) receptor 2C (HTR2C), cytochrome P450 2D6 (CYP2D6), Dopamine D1 receptor (DRD1), Dopamine D4 receptor (DRD4). These targets are predominantly involved in biological processes such as the adenylate cyclase-activating adrenergic receptor signaling pathway, G-protein coupled receptor signaling pathway coupled to cyclic nucleotide second messenger, phospholipase C-activating G-protein coupled receptor signaling pathway, chemical synaptic transmission, and response to xenobiotic stimulus. Primary enrichment occurs in signaling pathways such as the neuroactive ligand-receptor interaction and serotonergic synapse pathways. Molecular docking results demonstrate a favorable affinity between aripiprazole and the core target proteins MAO-B, DRD2, SLC6A3, HTR2C, HTR2A, CYP2D6, DRD4, and DRD1. Network pharmacology predicted potential targets and signaling pathways for aripiprazole's intervention in hyperprolactinemia, offering theoretical support and a reference basis for optimizing clinical strategies and drug development involving aripiprazole.

Hydrogen/Deuterium Exchange Mass Spectrometry: Fundamentals, Limitations, and Opportunities

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) probes dynamic motions of proteins by monitoring the kinetics of backbone amide deuteration. Dynamic regions exhibit rapid HDX, while rigid segments are more protected. Current data readouts focus on qualitative comparative observations (such as "residues X to Y become more protected after protein exposure to ligand Z"). At present, it is not possible to decode HDX protection patterns in an atomistic fashion. In other words, the exact range of protein motions under a given set of conditions cannot be uncovered, leaving space for speculative interpretations. Amide back exchange is an under-appreciated problem, as the widely used (m-m0)/(m100-m0) correction method can distort HDX kinetic profiles. Future data analysis strategies require a better fundamental understanding of HDX events, going beyond the classical Linderstrøm-Lang model. Combined with experiments that offer enhanced spatial resolution and suppressed back exchange, it should become possible to uncover the exact range of motions exhibited by a protein under a given set of conditions. Such advances would provide a greatly improved understanding of protein behavior in health and disease.

Assessment of sedative activity of fraxin: In vivo approach along with receptor binding affinity and molecular interaction with GABAergic system

Insomnia is a sleep disorder in which you have trouble falling and/or staying asleep. This research aims to evaluate the sedative effects of fraxin (FX) on sleeping mice induced by thiopental sodium (TS). In addition, a molecular docking study was conducted to investigate the molecular processes underlying these effects. The study used adult male Swiss albino mice and administered FX (10 and 20 mg/kg, i.p.) and diazepam (DZP) (2 mg/kg) either separately or in combination within the different groups to examine their modulatory effects. After a period of 30 min, the mice that had been treated were administered (TS: 20 mg/kg, i.p.) to induce sleep. The onset of sleep for the mice and the length of their sleep were manually recorded. Additionally, a computational analysis was conducted to predict the role of gamma-aminobutyric acid (GABA) receptors in the sleep process and evaluate their pharmacokinetics and toxicity. The outcomes indicated that FX extended the length of sleep and reduced the time it took to fall asleep. When the combined treatment of FX and DZP showed synergistic sedative action. Also, FX had a binding affinity of -7.2 kcal/mol, while DZP showed -8.4 kcal/mol. The pharmacokinetic investigation of FX demonstrated favorable drug-likeness and strong pharmacokinetic characteristics. Ultimately, FX demonstrated a strong sedative impact in the mouse model, likely via interacting with the GABAA receptor pathways.

Advancing microbial production through artificial intelligence-aided biology

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

Network pharmacology prediction to discover the potential pharmacological action mechanism of Rhizoma Dioscoreae for liver regeneration

Improving liver regeneration (LR) remains a medical issue, and there is currently a lack of safe and effective drugs for LR. Rhizoma Dioscoreae (SanYak, SY) is a traditional Chinese medicine. However, the underlying action mechanism of SY treatment for LR is yet to be fully elucidated. To explore the mechanism by which SY affects LR, we have conducted a series of methods for network pharmacological analysis, molecular docking, and in vivo experimental validation in mice. Overall, 9 compounds and 30 predicted target genes of SY were found to be associated with the therapeutic effects of LR. Compared with the model group, hematoxylin and eosin staining revealed that the mice with preoperative drug intervention possessed fewer postoperative hepatocyte bubbles and relatively regular morphology. Furthermore, the serum alanine transaminase and aspartate aminotransferase levels were reduced, immunohistochemistry revealed elevated proliferating cell nuclear antigen positivity rate, and Western blotting demonstrated that the phospho-protein kinase B (AKT)/AKT ratio was downregulated and that vascular endothelial growth factor A (VEGFA) expression levels were upregulated. This study explored dioscin, the main active ingredient of SY, and its potential therapeutic effects on LR. It repairs damaged liver following surgery and promotes liver cell proliferation. The action mechanism comprises reducing AKT phosphorylation levels and upregulating VEGFA expression levels. Thus, this study provides a new direction for further research on the mechanism of SY promoting LR.

Innovative Strategies in X-ray Crystallography for Exploring Structural Dynamics and Reaction Mechanisms in Metabolic Disorders

Enzymes are crucial in metabolic processes, and their dysfunction can lead to severe metabolic disorders. Structural biology, particularly X-ray crystallography, has advanced our understanding of these diseases by providing 3D structures of pathological enzymes. However, traditional X-ray crystallography faces limitations, such as difficulties in obtaining suitable protein crystals and studying protein dynamics. X-ray free-electron lasers (XFELs) have revolutionized this field with their bright and brief X-ray pulses, providing high-resolution structures of radiation-sensitive and hard-to-crystallize proteins. XFELs also enable the study of protein dynamics through room temperature structures and time-resolved serial femtosecond crystallography, offering comprehensive insights into the molecular mechanisms of metabolic diseases. Understanding these dynamics is vital for developing effective therapies. This review highlights the contributions of protein dynamics studies using XFELs and synchrotrons to metabolic disorder research and their application in designing better therapies. It also discusses G protein-coupled receptors (GPCRs), which, though not enzymes, play key roles in regulating physiological systems and are implicated in many metabolic disorders.

Employing Machine Learning-Based QSAR for Targeting Zika Virus NS3 Protease: Molecular Insights and Inhibitor Discovery

Zika virus infection is a mosquito-borne viral disease that has become a global health concern recently. Zika virus belongs to the Flavivirus genus and is primarily transmitted by Aedes mosquitoes. Prevention of Zika virus infection involves avoiding mosquito bites by using repellent, wearing protective clothing, and staying in screened areas, especially for pregnant women. Treatment focuses on managing symptoms with rest, fluids, and acetaminophen, with close monitoring for pregnant women. Currently, there is no specific antiviral treatment or vaccine for the Zika virus, highlighting the importance of prevention strategies to control its spread. Therefore, in this study, the Zika virus non-structural protein NS3 was targeted to inhibit Zika infection by identifying the novel inhibitor through an in silico approach. Here, 2864 natural compounds were screened using a machine learning-based QSAR model, and later docking was performed to select the potential target. Subsequently, Tanimoto similarity and clustering were performed to obtain the potential target. The three most potential compounds were obtained: (a) 5297, (b) 432449, and (c) 85137543. The protein-ligand complex's stability and flexibility were then investigated by dynamic modelling. The 300 ns simulation showed that 5297 exhibited the steadiest deviation and constant creation of hydrogen bonds. Compared to the other compounds, 5297 demonstrated a superior binding free energy (ΔG = -20.81 kcal/mol) with the protein when the MM/GBSA technique was used. The study determined that 5297 showed significant therapeutic potential and justifies further experimental investigation as a possible inhibitor of the NS2B-NS3 protease target implicated in Zika virus infection.

Mechanism investigation of anti-NAFLD of Shugan Yipi Granule based on network pharmacology analysis and experimental verification

As a classical traditional Chinese patent medicine, Shugan Yipi Granule is widely used in China to treat non-alcoholic fatty liver disease (NAFLD) recently. Our previous study confirmed that Shugan Yipi Granule are effective in NAFLD. However, its underlying mechanism is still unknown. This study aims to investigate the mechanism of Shugan Yipi Granule on NAFLD based on network pharmacology prediction, liquid chromatography-mass spectrometry (LC-MS) analysis and in vitro verification. We obtained the active ingredients and targets of Shugan Yipi Granule and NAFLD from 6 traditional Chinese medicine databases, and the crucial components and targets screened by protein-protein interaction (PPI) network were used for molecular docking. Plasma metabolomics of NAFLD patients treated with Shugan Yipi Granule for one month was analyzed using LC-MS methods and MetaboAnalyst 4.0 to obtain significant differential metabolites and pathways. Finally, free fatty acid (FFA) induced HepG2 cells were treated with different concentrations of quercetin and kaempferol, then oil red o (ORO) and triglyceride (TG) level were tested to verify the lipid deposition of the cell. Network pharmacology analysis showed that the main active ingredients of Shugan Yipi Granule include quercetin, kaempferol and other 58 ones, as well as 188 potential targets. PI3K/Akt signaling pathway was found to be the most relevant pathway for the treatment of NAFLD. Non-targeted metabolomics showed that quercetin and kaempferol were significantly up-regulated differential metabolites and were involved in metabolic pathways such as thyroid hormone signaling. In vitro results showed that quercetin, kaempferol were effective in reducing lipid deposition and TG content by inhibiting cellular fatty acid uptake. Ultimately, with the network pharmacology and serum metabolomics analysis, quercetin and kaempferol were found to be the important active ingredients and significantly up-regulated differential metabolites of Shugan Yipi Granule against NAFLD, which we inferred that they may regulate NAFLD through PI3K/Akt signaling pathway and thyroid hormone metabolism pathway. The in vitro experiment verification results showed that quercetin and kaempferol attenuated the lipid accumulation and TG content by inhibiting the fatty acid uptake in the FFA-induced HepG2 cell. Current study provides the necessary experimental basis for subsequent in-depth mechanism research.

AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles

Like the black knight in the classic Monty Python movie, grand scientific challenges such as protein folding are hard to finish off. Notably, AlphaFold is revolutionizing structural biology by bringing highly accurate structure prediction to the masses and opening up innumerable new avenues of research. Despite this enormous success, calling structure prediction, much less protein folding and related problems, "solved" is dangerous, as doing so could stymie further progress. Imagine what the world would be like if we had declared flight solved after the first commercial airlines opened and stopped investing in further research and development. Likewise, there are still important limitations to structure prediction that we would benefit from addressing. Moreover, we are limited in our understanding of the enormous diversity of different structures a single protein can adopt (called a conformational ensemble) and the dynamics by which a protein explores this space. What is clear is that conformational ensembles are critical to protein function, and understanding this aspect of protein dynamics will advance our ability to design new proteins and drugs.

Serial X-ray liquidography: multi-dimensional assay framework for exploring biomolecular structural dynamics with microgram quantities

Understanding protein structure and kinetics under physiological conditions is crucial for elucidating complex biological processes. While time-resolved (TR) techniques have advanced to track molecular actions, their practical application in biological reactions is often confined to reversible photoreactions within limited experimental parameters due to inefficient sample utilization and inflexibility of experimental setups. Here, we introduce serial X-ray liquidography (SXL), a technique that combines time-resolved X-ray liquidography with a fixed target of serially arranged microchambers. SXL breaks through the previously mentioned barriers, enabling microgram-scale TR studies of both irreversible and reversible reactions of even a non-photoactive protein. We demonstrate its versatility in studying a wide range of biological reactions, highlighting its potential as a flexible and multi-dimensional assay framework for kinetic and structural characterization. Leveraging X-ray free-electron lasers and micro-focused X-ray pulses promises further enhancements in both temporal resolution and minimizing sample quantity. SXL offers unprecedented insights into the structural and kinetic landscapes of molecular actions, paving the way for a deeper understanding of complex biological processes.

Impact of Missense Mutations on Spike Protein Stability and Binding Affinity in the Omicron Variant

The global effort to combat the COVID-19 pandemic faces ongoing uncertainty with the emergence of Variants of Concern featuring numerous mutations on the Spike (S) protein. In particular, the Omicron Variant is distinguished by 32 mutations, including 10 within its receptor-binding domain (RBD). These mutations significantly impact viral infectivity and the efficacy of vaccines and antibodies currently in use for therapeutic purposes. In our study, we employed structure-based computational saturation mutagenesis approaches to predict the effects of Omicron missense mutations on RBD stability and binding affinity, comparing them to the original Wuhan-Hu-1 strain. Our results predict that mutations such as G431W and P507W induce the most substantial destabilizations in the Wuhan-Hu-1-S/Omicron-S RBD. Notably, we postulate that mutations in the Omicron-S exhibit a higher percentage of enhancing binding affinity compared to Wuhan-S. We found that the mutations at residue positions G447, Y449, F456, F486, and S496 led to significant changes in binding affinity. In summary, our findings may shed light on the widespread prevalence of Omicron mutations in human populations. The Omicron mutations that potentially enhance their affinity for human receptors may facilitate increased viral binding and internalization in infected cells, thereby enhancing infectivity. This informs the development of new neutralizing antibodies capable of targeting Omicron's immune-evading mutations, potentially aiding in the ongoing battle against the COVID-19 pandemic.

Reviewing the Structure-Function Paradigm in Polyglutamine Disorders: A Synergistic Perspective on Theoretical and Experimental Approaches

Polyglutamine (polyQ) disorders are a group of neurodegenerative diseases characterized by the excessive expansion of CAG (cytosine, adenine, guanine) repeats within host proteins. The quest to unravel the complex diseases mechanism has led researchers to adopt both theoretical and experimental methods, each offering unique insights into the underlying pathogenesis. This review emphasizes the significance of combining multiple approaches in the study of polyQ disorders, focusing on the structure-function correlations and the relevance of polyQ-related protein dynamics in neurodegeneration. By integrating computational/theoretical predictions with experimental observations, one can establish robust structure-function correlations, aiding in the identification of key molecular targets for therapeutic interventions. PolyQ proteins' dynamics, influenced by their length and interactions with other molecular partners, play a pivotal role in the polyQ-related pathogenic cascade. Moreover, conformational dynamics of polyQ proteins can trigger aggregation, leading to toxic assembles that hinder proper cellular homeostasis. Understanding these intricacies offers new avenues for therapeutic strategies by fine-tuning polyQ kinetics, in order to prevent and control disease progression. Last but not least, this review highlights the importance of integrating multidisciplinary efforts to advancing research in this field, bringing us closer to the ultimate goal of finding effective treatments against polyQ disorders.

Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery

G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrödinger Dock with an area under the curve of 0.72, accuracy of 0.68 and true positive rate of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and root mean squared error of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with three (F545-1970, K297-0698, S948-0241) out of eight candidates. Furthermore, we also successfully obtained six active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.

Dual-wield NTPases: A novel protein family mined from AlphaFold DB

AlphaFold protein structure database (AlphaFold DB) archives a vast number of predicted models. We conducted systematic data mining against AlphaFold DB and discovered an uncharacterized P-loop NTPase family. The structure of the protein family was surprisingly novel, showing an atypical topology for P-loop NTPases, noticeable twofold symmetry, and two pairs of independent putative active sites. Our findings show that structural data mining is a powerful approach to identifying undiscovered protein families.

Network pharmacology analysis and experimental validation to explore the mechanism of kaempferol in the treatment of osteoporosis

Osteoporosis (OP) is a prevalent global disease characterized by bone mass loss and microstructural destruction, resulting in increased bone fragility and fracture susceptibility. Our study aims to investigate the potential of kaempferol in preventing and treating OP through a combination of network pharmacology and molecular experiments. Kaempferol and OP-related targets were retrieved from the public database. A protein-protein interaction (PPI) network of common targets was constructed using the STRING database and visualized with Cytoscape 3.9.1 software. Enrichment analyses for GO and KEGG of potential therapeutic targets were conducted using the Hiplot platform. Molecular docking was performed using Molecular operating environment (MOE) software, and cell experiments were conducted to validate the mechanism of kaempferol in treating OP. Network pharmacology analysis identified 54 overlapping targets between kaempferol and OP, with 10 core targets identified. The primarily enriched pathways included atherosclerosis-related signaling pathways, the AGE/RAGE signaling pathway, and the TNF signaling pathway. Molecular docking results indicated stable binding of kaempferol and two target proteins, AKT1 and MMP9. In vitro cell experiments demonstrated significant upregulation of AKT1 expression in MC3T3-E1 cells (p < 0.001) with kaempferol treatment, along with downregulation of MMP9 expression (p < 0.05) compared to the control group. This study predicted the core targets and pathways of kaempferol in OP treatment using network pharmacology, and validated these findings through in vitro experiments, suggesting a promising avenue for future clinical treatment of OP.

Prediction of potential targets and toxicological insights of Astragalus in liver cancer based on network pharmacology: Integrating systems biology, drug interaction networks, and toxicological perspectives

This study investigates Astragalus's efficacy as a novel therapeutic option for primary liver cancer (PLC), capitalizing on its anti-inflammatory and antiviral effects. We utilized network pharmacology to unveil Astragalus's potential targets against PLC, revealing significant gene expression alterations in treated samples-20 genes were up-regulated, and 20 were down-regulated compared to controls. Our analysis extended to single-cell resolution, where we processed scRNA-seq data to discern 15 unique cell clusters within the immune, malignant, and stromal compartments through advanced algorithms like UMAP and tSNE. To delve deeper into the functional implications of these gene expression changes, we conducted comprehensive gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, alongside Gene Set Variation Analysis, to elucidate the biological processes and pathways involved. Further, we constructed protein-protein interaction networks to visualize the intricate molecular interplay, highlighting the down-regulation of MT1E in PLC cells, a finding corroborated by quantitative polymerase chain reaction. Molecular docking studies affirmed the potent interaction between Astragalus's active compounds and MT proteins, underscoring a targeted therapeutic mechanism. Our investigation also encompassed a detailed cellular landscape analysis, identifying nine cell subgroups related to MT1 expression and specifying five cell subsets through the SingleR package. Advanced trajectory and cell-cell interaction analyses offered deeper insights into the dynamics of MT1-associated cellular subpopulations. This comprehensive methodology not only underpins Astragalus's promising role in PLC treatment but also advances our understanding of its molecular and cellular mechanisms, paving the way for targeted therapeutic strategies.

In vivo biological validation of in silico analysis: A novel approach for predicting the effects of TLR4 exon 3 polymorphisms on brucellosis

The role of the Toll-like receptor 4 (TLR4) is of recognising intracellular and extracellular pathogens and of activating the immune response. This process can be compromised by single nucleotide polymorphisms (SNPs) which might affect the activity of several TLRs. The aim of this study is of ascertaining whether SNPs in the TLR4 of Bubalus bubalis infected by Brucella abortus, compromise the protein functionality. For this purpose, a computational analysis was performed. Next, computational predictions were confirmed by performing genotyping analysis. Finally, NMR-based metabolomics analysis was performed to identify potential biomarkers for brucellosis. The results indicate two SNPs (c. 672 A > C and c. 902 G > C) as risk factor for brucellosis in Bubalus bubalis, and three metabolites (lactate, 3-hydroxybutyrate and acetate) as biological markers for predicting the risk of developing the disease. These metabolites, together with TLR4 structural modifications in the MD2 interaction domain, are a clear signature of the immune system alteration during diverse Gram-negative bacterial infections. This suggests the possibility to extend this study to other pathogens, including Mycobacterium tuberculosis. In conclusion, this study combines multidisciplinary approaches to evaluate the biological and structural effects of SNPs on protein function.

Probable digenic inheritance of Diamond-Blackfan anemia

A 26-year-old female proband with a clinical diagnosis and consistent phenotype of Diamond-Blackfan anemia (DBA, OMIM 105650) without an identified genotype was referred to the Undiagnosed Diseases Network. DBA is classically associated with monoallelic variants that have an autosomal-dominant or -recessive mode of inheritance. Intriguingly, her case was solved by a detection of a digenic interaction between non-allelic RPS19 and RPL27 variants. This was confirmed with a machine learning structural model, co-segregation analysis, and RNA sequencing. This is the first report of DBA caused by a digenic effect of two non-allelic variants demonstrated by machine learning structural model. This case suggests that atypical phenotypic presentations of DBA may be caused by digenic inheritance in some individuals. We also conclude that a machine learning structural model can be useful in detecting digenic models of possible interactions between products encoded by alleles of different genes inherited from non-affected carrier parents that can result in DBA with an unrealized 25% recurrence risk.

DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score

Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local quality for 3D cryo-EM density maps, using a deep-learning algorithm based on map-model fit score. DeepQs is a parameter-free method for users and incorporates structural information between map and its related atomic model into well-trained models by deep learning. More specifically, the DeepQs approach leverages the interplay between map and atomic model through predefined map-model fit score, Q-score. DeepQs can get close results to the ground truth map-model fit scores with only cryo-EM map as input. In experiments, DeepQs demonstrates the lowest root mean square error with standard method Fourier shell correlation metric and high correlation with map-model fit score, Q-score, when compared with other local quality estimation methods in high-resolution dataset (<=5 Å). DeepQs can also be applied to evaluate the quality of the post-processed maps. In both cases, DeepQs runs faster by using GPU acceleration. Our program is available at http://www.csbio.sjtu.edu.cn/bioinf/DeepQs for academic use.

The Application of MD Simulation to Lead Identification, Vaccine Design, and Structural Studies in Combat against Leishmaniasis - A Review

Drug discovery, vaccine design, and protein interaction studies are rapidly moving toward the routine use of molecular dynamics simulations (MDS) and related methods. As a result of MDS, it is possible to gain insights into the dynamics and function of identified drug targets, antibody-antigen interactions, potential vaccine candidates, intrinsically disordered proteins, and essential proteins. The MDS appears to be used in all possible ways in combating diseases such as cancer, however, it has not been well documented as to how effectively it is applied to infectious diseases such as Leishmaniasis. As a result, this review aims to survey the application of MDS in combating leishmaniasis. We have systematically collected articles that illustrate the implementation of MDS in drug discovery, vaccine development, and structural studies related to Leishmaniasis. Of all the articles reviewed, we identified that only a limited number of studies focused on the development of vaccines against Leishmaniasis through MDS. Also, the PCA and FEL studies were not carried out in most of the studies. These two were globally accepted utilities to understand the conformational changes and hence it is recommended that this analysis should be taken up in similar approaches in the future.

The interoperability of crystallographic data and databases

Interoperability of crystallographic data with other disciplines is essential for the smooth and rapid progress of structure-based science in the computer age. Within crystallography and closely related subject areas, there is already a high level of conformance to the generally accepted FAIR principles (that data be findable, accessible, interoperable and reusable) through the adoption of common information exchange protocols by databases, publishers, instrument vendors, experimental facilities and software authors. Driven by the success within these domains, the IUCr has worked closely with CODATA (the Committee on Data of the International Science Council) to help develop the latter's commitment to cross-domain integration of discipline-specific data. The IUCr has, in particular, emphasized the need for standards relating to data quality and completeness as an adjunct to the FAIR data landscape. This can ensure definitive reusable data, which in turn can aid interoperability across domains. A microsymposium at the IUCr 2023 Congress provided an up-to-date survey of data interoperability within and outside of crystallography, expounded using a broad range of examples.

Identifying the natural products in the treatment of atherosclerosis by increasing HDL-C level based on bioinformatics analysis, molecular docking, and in vitro experiment

BACKGROUND

Previous studies have demonstrated that high-density lipoprotein cholesterol (HDL-C) plays an anti-atherosclerosis role through reverse cholesterol transport. Several studies have validated the efficacy and safety of natural products in treating atherosclerosis (AS). However, the study of raising HDL-C levels through natural products to treat AS still needs to be explored.

METHODS

The gene sets associated with AS were collected and identified by differential gene analysis and database query. By constructing a protein-protein interaction (PPI) network, the core submodules in the network are screened out. At the same time, by calculating node importance (Nim) in the PPI network of AS disease and combining it with Kyoto Encyclopedia of genes and genomes (KEGG) pathways enrichment analysis, the key target proteins of AS were obtained. Molecular docking is used to screen out small natural drug molecules with potential therapeutic effects. By constructing an in vitro foam cell model, the effects of small molecules on lipid metabolism and key target expression of foam cells were investigated.

RESULTS

By differential gene analysis, 451 differential genes were obtained, and a total of 313 disease genes were obtained from 6 kind of databases, then 758 AS-related genes were obtained. The enrichment analysis of the KEGG pathway showed that the enhancement of HDL-C level against AS was related to Lipid and atherosclerosis, Cholesterol metabolism, Fluid shear stress and atherosclerosis, PPAR signaling pathway, and other pathways. Then we intersected 31 genes in the core module of the PPI network, the top 30 genes in Nims, and 32 genes in the cholesterol metabolism pathway, and finally found 3 genes. After the above analysis and literature collection, we focused on the following three related gene targets: APOA1, LIPC, and CETP. Molecular docking showed that Genistein has a good binding affinity for APOA1, CETP, and LIPC. In vitro, experiments showed that Genistein can up-regulated APOA1, LIPC, and CETP levels.

CONCLUSIONS

Based on our research, Genistein may have the effects of regulating HDL-C and anti-atherosclerosis. Its mechanism of action may be related to the regulation of LIPC, CETP, and APOA1 to improve lipid metabolism.

Local Disordered Region Sampling (LDRS) for ensemble modeling of proteins with experimentally undetermined or low confidence prediction segments

SUMMARY

The Local Disordered Region Sampling (LDRS, pronounced loaders) tool is a new module developed for IDPConformerGenerator, a previously validated approach to model intrinsically disordered proteins (IDPs). The IDPConformerGenerator LDRS module provides a method for generating all-atom conformations of intrinsically disordered protein regions at N- and C-termini of and in loops or linkers between folded regions of an existing protein structure. These disordered elements often lead to missing coordinates in experimental structures or low confidence in predicted structures. Requiring only a pre-existing PDB or mmCIF formatted structural template of the protein with missing coordinates or with predicted confidence scores and its full-length primary sequence, LDRS will automatically generate physically meaningful conformational ensembles of the missing flexible regions to complete the full-length protein. The capabilities of the LDRS tool of IDPConformerGenerator include modeling phosphorylation sites using enhanced Monte Carlo-Side Chain Entropy, transmembrane proteins within an all-atom bilayer, and multi-chain complexes. The modeling capacity of LDRS capitalizes on the modularity, the ability to be used as a library and via command-line, and the computational speed of the IDPConformerGenerator platform.

AVAILABILITY AND IMPLEMENTATION

The LDRS module is part of the IDPConformerGenerator modeling suite, which can be downloaded from GitHub at https://github.com/julie-forman-kay-lab/IDPConformerGenerator. IDPConformerGenerator is written in Python3 and works on Linux, Microsoft Windows, and Mac OS versions that support DSSP. Users can utilize LDRS's Python API for scripting the same way they can use any part of IDPConformerGenerator's API, by importing functions from the "idpconfgen.ldrs_helper" library. Otherwise, LDRS can be used as a command line interface application within IDPConformerGenerator. Full documentation is available within the command-line interface as well as on IDPConformerGenerator's official documentation pages (https://idpconformergenerator.readthedocs.io/en/latest/).

The impact of molecular variants, crystallization conditions and the space group on ligand-protein complexes: a case study on bacterial phosphotriesterase

A bacterial phosphotriesterase was employed as an experimental paradigm to examine the effects of multiple factors, such as the molecular constructs, the ligands used during protein expression and purification, the crystallization conditions and the space group, on the visualization of molecular complexes of ligands with a target enzyme. In this case, the ligands used were organophosphates that are fragments of the nerve agents and insecticides on which the enzyme acts as a bioscavenger. 12 crystal structures of various phosphotriesterase constructs obtained by directed evolution were analyzed, with resolutions of up to 1.38 Å. Both apo forms and holo forms, complexed with the organophosphate ligands, were studied. Crystals obtained from three different crystallization conditions, crystallized in four space groups, with and without N-terminal tags, were utilized to investigate the impact of these factors on visualizing the organophosphate complexes of the enzyme. The study revealed that the tags used for protein expression can lodge in the active site and hinder ligand binding. Furthermore, the space group in which the protein crystallizes can significantly impact the visualization of bound ligands. It was also observed that the crystallization precipitants can compete with, and even preclude, ligand binding, leading to false positives or to the incorrect identification of lead drug candidates. One of the co-crystallization conditions enabled the definition of the spaces that accommodate the substituents attached to the P atom of several products of organophosphate substrates after detachment of the leaving group. The crystal structures of the complexes of phosphotriesterase with the organophosphate products reveal similar short interaction distances of the two partially charged O atoms of the P-O bonds with the exposed β-Zn2+ ion and the buried α-Zn2+ ion. This suggests that both Zn2+ ions have a role in stabilizing the transition state for substrate hydrolysis. Overall, this study provides valuable insights into the challenges and considerations involved in studying the crystal structures of ligand-protein complexes, highlighting the importance of careful experimental design and rigorous data analysis in ensuring the accuracy and reliability of the resulting phosphotriesterase-organophosphate structures.

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

Local Disordered Region Sampling (LDRS) for Ensemble Modeling of Proteins with Experimentally Undetermined or Low Confidence Prediction Segments

The Local Disordered Region Sampling (LDRS, pronounced loaders) tool, developed for the IDPConformerGenerator platform (Teixeira et al. 2022), provides a method for generating all-atom conformations of intrinsically disordered regions (IDRs) at N- and C-termini of and in loops or linkers between folded regions of an existing protein structure. These disordered elements often lead to missing coordinates in experimental structures or low confidence in predicted structures. Requiring only a pre-existing PDB structure of the protein with missing coordinates or with predicted confidence scores and its full-length primary sequence, LDRS will automatically generate physically meaningful conformational ensembles of the missing flexible regions to complete the full-length protein. The capabilities of the LDRS tool of IDPConformerGenerator include modeling phosphorylation sites using enhanced Monte Carlo Side Chain Entropy (MC-SCE) (Bhowmick and Head-Gordon 2015), transmembrane proteins within an all-atom bilayer, and multi-chain complexes. The modeling capacity of LDRS capitalizes on the modularity, ability to be used as a library and via command-line, and computational speed of the IDPConformerGenerator platform.

RCSB Protein Data Bank: Efficient Searching and Simultaneous Access to One Million Computed Structure Models Alongside the PDB Structures Enabled by Architectural Advances

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides open access to experimentally-determined three-dimensional (3D) structures of biomolecules. The RCSB PDB RCSB.org research-focused web portal is used annually by many millions of users around the world. They access biostructure information, run complex queries utilizing various search services (e.g., full-text, structural and chemical attribute, chemical, sequence, and structure similarity searches), and visualize macromolecules in 3D, all at no charge and with no limitations on data usage. Notwithstanding more than 24,000-fold growth of the PDB over the past five decades, experimentally-determined structures are only available for a small subset of the millions of proteins of known sequence. Recently developed machine learning software tools can predict 3D structures of proteins at accuracies comparable to lower-resolution experimental methods. The RCSB PDB now provides access to ∼1,000,000 Computed Structure Models (CSMs) of proteins coming from AlphaFold DB and the ModelArchive alongside ∼200,000 experimentally-determined PDB structures. Both CSMs and PDB structures are available on RCSB.org and via well-established RCSB PDB Data, Search, and 1D-Coordinates application programming interfaces (APIs). Simultaneous delivery of PDB data and CSMs provides users with access to complementary structural information across the human proteome and those of model organisms and selected pathogens. API enhancements are backwards-compatible and programmatic users can "opt in" to access CSMs with minimal effort. Herein, we describe modifications to RCSB PDB cyberinfrastructure required to support sixfold scaling of 3D biostructure data delivery and lay the groundwork for scaling to accommodate hundreds of millions of CSMs.

Aegle marvels (L.) Correa Leaf Essential Oil and Its Phytoconstituents as an Anticancer and Anti-Streptococcus mutans Agent

Aegle mamelons (A. marmelos) or Indian Bael leaves possess anti-cancerous and antibacterial properties and are used in the traditional medicine system for the treatment of oral infections. In the present study, the essential oil of the leaves of A. marmelos was explored for its anticancer, antioxidant, and anti-cariogenic properties. The hydro-distilled oil of A. marmelos leaves was analyzed using gas chromatography coupled with mass spectrometry (GC-MS). Monoterpene limonene (63.71%) was found to have the highest percentage after trans-2-Hydroxy-1,8-cineole and p-Menth-2,8-dien-1-ol. The MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay was used to investigate the anticancer activity of the extracted oil against human oral epidermal carcinoma (KB), and the results showed significantly higher (**** p < 0.0001) anticancer activity (45.89%) in the doxorubicin (47.87%) when compared to the normal control. The antioxidant activity of the essential oil was evaluated using methods of DPPH (2,2-diphenyl-1-picrylhydrazyl) and ABTS (2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)). The results showed a significant (*** p < 0.001) percentage of inhibition of DPPH-induced free radical (70.02 ± 1.6%) and ABTS-induced free radical (70.7 ± 1.32%) at 100 µg/mL with IC50, 72.51 and 67.33 µg/mL, respectively, comparatively lower than standard compound ascorbic acid. The results of the molecular docking study of the significant compound limonene with the receptors tyrosinase and tyrosine kinase 2 supported the in vitro antioxidant potential. The anti-cariogenic activity was evaluated against Streptococcus mutans (S. mutans). Results showed a significant minimum inhibitor concentration of 0.25 mg/mL and the killing time was achieved at 3 to 6 h. The molecular-docking study showed that limonene inhibits the surface receptors of the S. mutans c-terminal domain and CviR protein. The study found that A. marmelos leaves have potential anti-carcinoma, antioxidant, and anti-cariogenic effects on human oral epidermal health, making them a valuable natural therapeutic agent for managing oral cancer and infections.

Comprehensive classification of proteins based on structures that engage lipids by COMPOSEL

Structures can now be predicted for any protein using programs like AlphaFold and Rosetta, which rely on a foundation of experimentally determined structures of architecturally diverse proteins. The accuracy of such artificial intelligence and machine learning (AI/ML) approaches benefits from the specification of restraints which assist in navigating the universe of folds to converge on models most representative of a given protein's physiological structure. This is especially pertinent for membrane proteins, with structures and functions that depend on their presence in lipid bilayers. Structures of proteins in their membrane environments could conceivably be predicted from AI/ML approaches with user-specificized parameters that describe each element of the architecture of a membrane protein accompanied by its lipid environment. We propose the Classification Of Membrane Proteins based On Structures Engaging Lipids (COMPOSEL), which builds on existing nomenclature types for monotopic, bitopic, polytopic and peripheral membrane proteins as well as lipids. Functional and regulatory elements are also defined in the scripts, as shown with membrane fusing synaptotagmins, multidomain PDZD8 and Protrudin proteins that recognize phosphoinositide (PI) lipids, the intrinsically disordered MARCKS protein, caveolins, the β barrel assembly machine (BAM), an adhesion G-protein coupled receptor (aGPCR) and two lipid modifying enzymes - diacylglycerol kinase DGKε and fatty aldehyde dehydrogenase FALDH. This demonstrates how COMPOSEL communicates lipid interactivity as well as signaling mechanisms and binding of metabolites, drug molecules, polypeptides or nucleic acids to describe the operations of any protein. Moreover COMPOSEL can be scaled to express how genomes encode membrane structures and how our organs are infiltrated by pathogens such as SARS-CoV-2.

A Tribute to Phil Bourne-Scientist and Human

This Special Issue of Biomolecules[...].

RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.

Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future

As a discipline, structural biology has been transformed by the three-dimensional electron microscopy (3DEM) "Resolution Revolution" made possible by convergence of robust cryo-preservation of vitrified biological materials, sample handling systems, and measurement stages operating a liquid nitrogen temperature, improvements in electron optics that preserve phase information at the atomic level, direct electron detectors (DEDs), high-speed computing with graphics processing units, and rapid advances in data acquisition and processing software. 3DEM structure information (atomic coordinates and related metadata) are archived in the open-access Protein Data Bank (PDB), which currently holds more than 11,000 3DEM structures of proteins and nucleic acids, and their complexes with one another and small-molecule ligands (~ 6% of the archive). Underlying experimental data (3DEM density maps and related metadata) are stored in the Electron Microscopy Data Bank (EMDB), which currently holds more than 21,000 3DEM density maps. After describing the history of the PDB and the Worldwide Protein Data Bank (wwPDB) partnership, which jointly manages both the PDB and EMDB archives, this review examines the origins of the resolution revolution and analyzes its impact on structural biology viewed through the lens of PDB holdings. Six areas of focus exemplifying the impact of 3DEM across the biosciences are discussed in detail (icosahedral viruses, ribosomes, integral membrane proteins, SARS-CoV-2 spike proteins, cryogenic electron tomography, and integrative structure determination combining 3DEM with complementary biophysical measurement techniques), followed by a review of 3DEM structure validation by the wwPDB that underscores the importance of community engagement.

RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D

Now in its 52nd year of continuous operations, the Protein Data Bank (PDB) is the premiere open-access global archive housing three-dimensional (3D) biomolecular structure data. It is jointly managed by the Worldwide Protein Data Bank (wwPDB) partnership. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is funded by the National Science Foundation, National Institutes of Health, and US Department of Energy and serves as the US data center for the wwPDB. RCSB PDB is also responsible for the security of PDB data in its role as wwPDB-designated Archive Keeper. Every year, RCSB PDB serves tens of thousands of depositors of 3D macromolecular structure data (coming from macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction). The RCSB PDB research-focused web portal (RCSB.org) makes PDB data available at no charge and without usage restrictions to many millions of PDB data consumers around the world. The RCSB PDB training, outreach, and education web portal (PDB101.RCSB.org) serves nearly 700 K educators, students, and members of the public worldwide. This invited Tools Issue contribution describes how RCSB PDB (i) is organized; (ii) works with wwPDB partners to process new depositions; (iii) serves as the wwPDB-designated Archive Keeper; (iv) enables exploration and 3D visualization of PDB data via RCSB.org; and (v) supports training, outreach, and education via PDB101.RCSB.org. New tools and features at RCSB.org are presented using examples drawn from high-resolution structural studies of proteins relevant to treatment of human cancers by targeting immune checkpoints.