Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive

Elucidating the role of Rhodiola rosea L. in sepsis-induced acute lung injury via network pharmacology: emphasis on inflammatory response, oxidative stress, and the PI3K-AKT pathway

CONTEXT

Sepsis-induced acute lung injury (ALI) is associated with high morbidity and mortality. Rhodiola rosea L. (Crassulaceae) (RR) and its extracts have shown anti-inflammatory, antioxidant, immunomodulatory, and lung-protective effects.

OBJECTIVE

This study elucidates the molecular mechanisms of RR against sepsis-induced ALI.

MATERIALS AND METHODS

The pivotal targets of RR against sepsis-induced ALI and underlying mechanisms were revealed by network pharmacology and molecular docking. Human umbilical vein endothelial cells (HUVECs) were stimulated by 1 μg/mL lipopolysaccharide for 0.5 h and treated with 6.3, 12.5, 25, 50, 100, and 200 μg/mL RR for 24 h. Then, the lipopolysaccharide-stimulated HUVECs were subjected to cell counting kit-8 (CCK-8), enzyme-linked immunosorbent, apoptosis, and Western blot analyses. C57BL/6 mice were divided into sham, model, low-dose (40 mg/kg), mid-dose (80 mg/kg), and high-dose (160 mg/kg) RR groups. The mouse model was constructed through caecal ligation and puncture, and histological, apoptosis, and Western blot analyses were performed for further validation.

RESULTS

We identified six hub targets (MPO, HRAS, PPARG, FGF2, JUN, and IL6), and the PI3K-AKT pathway was the core pathway. CCK-8 assays showed that RR promoted the viability of the lipopolysaccharide-stimulated HUVECs [median effective dose (ED50) = 18.98 μg/mL]. Furthermore, RR inhibited inflammation, oxidative stress, cell apoptosis, and PI3K-AKT activation in lipopolysaccharide-stimulated HUVECs and ALI mice, which was consistent with the network pharmacology results.

DISCUSSION AND CONCLUSION

This study provides foundational knowledge of the effective components, potential targets, and molecular mechanisms of RR against ALI, which could be critical for developing targeted therapeutic strategies for sepsis-induced ALI.

Molecular dynamic simulation reveals spider antimicrobial peptide Latarcin-1 and human eosinophil cationic protein as peptide inhibitors of SARS-CoV-2 variants

COVID-19 has rapidly proliferated around 180 countries, and new cases are reported frequently. No peptide medication has been developed that can reliably block SARS-CoV-2 infection. The investigation focuses on the crucial host receptors angiotensin-converting enzyme 2 (ACE2) , which can bind receptor-binding domain (RBD) on the SARS-CoV-2 spike protein (S). To investigate the inhibitory effects of human Eosinophil Cationic Protein (hECP) and Latarcin-1 (L1)on SARS-CoV-2 infection, we have selected them as research subjects. Further, we ran extensive molecular dynamics simulations to bring the docked peptide-ACE2 complex into its equilibrium state. The outcomes were then evaluated with g_MMPBSA and interaction analysis. We have also considered the Delta and Omicron variants to examine these peptides' inhibitory effects. The experimental findings revealed an enhanced capability of L1 and hECP as SARS-CoV-2 inhibitors, occupying hot spots and numerous key residues in ACE2. These include ASP30, ASP38, GLU35 and GLU75, which significantly inhibit the binding of RBD and ACE2 and are effective against two common variants in a similar manner. In addition, this study can serve as a springboard for future research on SARS-CoV-2 inhibitors.Communicated by Ramaswamy H. Sarma.

Allosteric inhibition of dengue virus RNA-dependent RNA polymerase by Litsea cubeba phytochemicals: a computational study

RNA-dependent RNA polymerase (RdRp) is considered a potential drug target for dengue virus (DENV) inhibition and has attracted attention in antiviral drug discovery. Here, we screened 121 natural compounds from Litsea cubeba against DENV RdRp using various approaches of computer-based drug discovery. Notably, we identified four potential compounds (Ushinsunine, Cassameridine, (+)-Epiexcelsin, (-)-Phanostenine) with good binding scores and allosteric interactions with the target protein. Moreover, molecular dynamics simulation studies were done to check the conformational stability of the complexes under given conditions. Additionally, we performed post-simulation analysis to find the stability of potential drugs in the target protein. The findings suggest Litsea cubeba-derived phytomolecules as a therapeutic solution to control DENV infection.Communicated by Ramaswamy H. Sarma.

In-silico study of protein-protein interactions in wheat blast using docking and molecular dynamics simulation approach

Despite advancements in agricultural research and the introduction of modern biotechnological and farming techniques, food security remains a significant issue. Although the efforts of farmers to meet the demands of a growing population, many plant diseases caused by pathogens, through their effects on cell division and tissue growth, lead to the annual loss of countless food crops. The recently emerged wheat blast fungus Magnaporthe oryzae pathotype Triticum (MoT) poses a significant danger to worldwide wheat cultivation. The fungus is a highly varied lineage of the M. oryzae, responsible for causing rice blast disease. In spite of being a significant challenge to successful wheat production in South America since 1985, the underlying biology of the wheat blast pathogen is still not fully understood. The initial outbreak of the wheat blast in South Asia had a severe impact on wheat production, resulting in a complete loss of yield in affected fields. For the purpose of enhancing disease management, it's vital to acquire a comprehensive comprehension of the infection biology of the fungus and its interaction with wheat plants on molecular levels. Host-pathogen protein interactions (HPIs) have the potential to reveal the pathogens' mechanism for overcoming the host organism. The current study delves into the interactions between the host plant wheat and MoT through protein-protein interactions, molecular docking, and 100 ns molecular dynamic simulations. This research uncovers the structural and functional basis of these proteins, leading to improved plant health and production.Communicated by Ramaswamy H. Sarma.

Molecular mechanisms of quetiapine bidirectional regulation of bipolar depression and mania based on network pharmacology and molecular docking: Evidence from computational biology

BACKGROUND

Quetiapine monotherapy is recommended as the first-line option for acute mania and acute bipolar depression. However, the mechanism of action of quetiapine is unclear. Network pharmacology and molecular docking were employed to determine the molecular mechanisms of quetiapine bidirectional regulation of bipolar depression and mania.

METHODS

Putative target genes for quetiapine were collected from the GeneCard, SwissTargetPrediction, and DrugBank databases. Targets for bipolar depression and bipolar mania were identified from the DisGeNET and GeneCards databases. A protein-protein interaction (PPI) network was generated using the String database and imported into Cytoscape. DAVID and the Bioinformatics platform were employed to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the top 15 core targets. The drug-pathway-target-disease network was constructed using Cytoscape. Finally, molecular docking was performed to evaluate the interactions between quetiapine and potential targets.

RESULTS

Targets for quetiapine actions against bipolar depression (126 targets) and bipolar mania (81 targets) were identified. Based on PPI and KEGG pathway analyses, quetiapine may affect bipolar depression by targeting the MAPK and PI3K/AKT insulin signaling pathways via BDNF, INS, EGFR, IGF1, and NGF, and it may affect bipolar mania by targeting the neuroactive ligand-receptor interaction signaling pathway via HTR1A, HTR1B, HTR2A, DRD2, and GRIN2B. Molecular docking revealed good binding affinity between quetiapine and potential targets.

LIMITATIONS

Pharmacological experiments should be conducted to verify and further explore these results.

CONCLUSIONS

Our findings suggest that quetiapine affects bipolar depression and bipolar mania through distinct biological core targets, and thus through different mechanisms. Furthermore, our results provide a theoretical basis for the clinical use of quetiapine and possible directions for new drug development.

Exploring the mechanism of Erteng-Sanjie capsule in treating gastric and colorectal cancers via network pharmacology and in-vivo validation

ETHNOPHARMACOLOGICAL RELEVANCE

The Erteng-Sanjie capsule (ETSJC) has therapeutic effects against gastric cancer (GC) and colorectal cancer (CRC). However, its underlying pharmacological mechanism remains unclear.

AIM OF THE STUDY

To explore the pharmacological mechanism of ETSJC against GC and CRC via network pharmacology and in-vivo validation.

MATERIALS AND METHODS

Data on the ingredients of ETSJC were obtained from the TCMSP and HERB databases. Further, details on the related targets of the active ingredients were collected from the HERB and SwissTargetPrediction databases. The targets in GC and CRC, which were screened from the OMIM, GeneCards, and TTD databases, were uploaded to STRING for a separate protein-protein interaction network analysis. The common targets shared by ETSJC, GC, and CRC were then screened. Cytoscape and STRING were used to construct the networks of herbs-compounds-targets and PPI. Metascape was utilized to analyze the enrichment of the GO and KEGG pathways. Molecular docking was used to validate the potential binding mode between the core ingredients and targets. Finally, the predicted results were verified with animal experiment.

RESULTS

Eight core ingredients (resveratrol, quercetin, luteolin, baicalein, delphinidin, kaempferol, pinocembrin, and naringenin) and six core targets (TP53, SRC, PIK3R1, AKT1, MAPK3, and STAT3) were filtered via network analysis. The molecular mechanism mainly involved the positive regulation of various processes such as cell migration, protein phosphorylation, and the PI3K-Akt signaling pathway. Molecular docking revealed that the core ingredients could be significantly combined with all core targets. The animal experiment revealed that ETSJC could suppress proliferation and promote apoptosis of both GC and CRC tumor cells by regulating the PI3K/Akt signaling pathway.

CONCLUSIONS

Multiple targets (TP53, SRC, AKT1, and STAT3) were important in GC and CRC. ETSJC could act on these targets and engage in different pathways against GC and CRC. Simultaneously, inhibiting the PI3K/Akt signaling pathway was a promising therapeutic mechanism for treating GC and CRC.

Molecular interactions of the Omicron, Kappa, and Delta SARS-CoV-2 spike proteins with quantum dots of graphene oxide

CONTEXT

The Omicron, Kappa, and Delta variants are different strains of the SARS-CoV-2 virus. Graphene oxide quantum dots (GOQDs) represent a burgeoning class of oxygen-enriched, zero-dimensional materials characterized by their sub-20-nm dimensions. Exhibiting pronounced quantum confinement and edge effects, GOQDs manifest exceptional physical-chemical attributes. This study delves into the potential of graphene oxide quantum dots, elucidating their inherent properties pertinent to the surface structures of SARS-CoV-2, employing an integrated computational approach for the repositioning of inhibitory agents.

METHODS

Following rigorous adjustment tests, a spectrum of divergent bonding conformations emerged, with particular emphasis placed on identifying the conformation exhibiting optimal adjustment scores and interactions. The investigation employed molecular docking simulations integrating affinity energy evaluations, electrostatic potential clouds, molecular dynamics encompassing average square root calculations, and the computation of Gibbs-free energy. These values quantify the strength of interaction between GOQDs and SARS-CoV-2 spike protein variants. The receptor structures were optimized using the CHARM-GUI server employing force field AMBERFF14SB. The algorithm embedded in CHARMM offers an efficient interpolation scheme and automatic step size selection, enhancing the efficiency of the optimization process. The 3D structures of the ligands are constructed and optimized with density functional theory (DFT) method based on the most stable conformer of each binder. Autodock Vina Software (ADV) was utilized, where essential parameters were specified. Electrostatic potential maps (MEPs) provide a visual depiction of molecules' charge distributions and related properties. After this, molecular dynamics simulations employing the CHARM36 force field in Gromacs 2022.2 were conducted to investigate GOs' interactions with surface macromolecules of SARS-CoV-2 in an explicit aqueous environment. Furthermore, our investigation suggests that lower values indicate stronger binding. Notably, GO-E consistently showed the most negative values across interactions with different variants, suggesting a higher affinity compared to other GOQDs (GO-A to GO-D).

Licochalcone A alleviates ferroptosis in doxorubicin-induced cardiotoxicity via the PI3K/AKT/MDM2/p53 pathway

Licochalcone A (Lico A), a flavonoid found in licorice, possesses multiple pharmacological activities in modulating oxidative stress, glycemia, inflammation, and lipid metabolism. This study aimed to explore the potential mechanism of Lico A in mitigating ferroptosis associated with doxorubicin-induced cardiotoxicity (DIC). Initially, network pharmacology analysis was applied to identify the active components present in licorice and their targeted genes associated with DIC. Subsequently, to assess the role of Lico A in a DIC mouse model, electrocardiograms, myocardial injury markers, and myocardial histopathological changes were measured. Additionally, cell viability, reactive oxygen species (ROS), ferrous iron, glutathione/glutathione disulfide (GSH/GSSG), and malondialdehyde (MDA) were measured in the cell model as hallmarks of ferroptosis. Finally, the PI3K/AKT/MDM2/p53 signaling pathway and ferroptosis-related proteins were measured in vitro and in vivo. Bioinformatics results revealed that 8 major compounds of licorice, including Lico A, primarily regulated targets such as p53 and the PI3K/AKT signaling pathways in DIC. In the mouse model of DIC, Lico A significantly ameliorated serum biomarkers, histopathology, and electrocardiogram abnormalities. Pretreatment with Lico A enhanced the viability of H9C2 cells treated with doxorubicin. Furthermore, Lico A administration resulted in decreased levels of ROS, ferrous iron, and MDA and increased levels of GSH/GSSG. At the protein level, Lico A increased the phosphorylation of PI3K/AKT/MDM2, reduced p53 accumulation, and induced the upregulation of SLC7A11 and GPX4 expression. However, selective inhibition of PI3K/AKT and plasmid-based overexpression of p53 significantly abolished the anti-ferroptosis functions of Lico A. In conclusion, Lico A attenuates DIC by suppressing p53-mediated ferroptosis through activating PI3K/AKT/MDM2 signaling.

Systems-wide analysis of A. fumigatus using kinetic modeling of metabolic pathways to identify putative drug targets

Aspergillosis is a major causative factor for morbidity in those with impaired immune systems, often caused by Aspergillus fumigatus. The diagnosis and treatment are difficult due to the diversity of individuals and risk factors and still pose a challenge for medical professionals. To understand the pathogenicity of any organism, it is critical to identify the significant metabolic pathways that are involved. Our work focused on developing kinetic models of critical pathways crucial for the survival of A. fumigatus using COPASI. While focusing on the folate biosynthesis, ergosterol biosynthesis and glycolytic pathway; sensitivity, time-course and steady-state analysis were performed to find the proteins/enzymes that are essential in the pathway and can be considered as potential drug targets. For further analysis of the interaction of drug targets identified, a protein-protein interaction (PPI) network was built, and hub nodes were identified using the Cytohubba package from Cytoscape. Based on the findings, dihydropteroate-synthase, dihydrofolate-reductase, 4-amino-4-deoxychorismate synthase, HMG-CoA-reductase, PG-isomerase and hexokinase could act as potential drug targets. Further, molecular docking and MM-GBSA analysis were performed with ligands chosen from DrugBank, and PubChem, and validated by experimental evidence and existing literature based on results from kinetic modeling and PPI network analysis. Based on docking scores and MM-GBSA results, molecular simulations were carried out for 1AJ2-dapsone, 1DIS-sulfamethazine, 1T02-lovastatin and 70YL-3-bromopyruvic acid complexes, which validated our findings. Our study provides a deeper insight into the mechanisms of A. fumigatus's metabolism to reveal dapsone, sulfamethazine, lovastatin and 3-bromopyruvic acid as potential drugs for the treatment of Aspergillosis.

Comprehensive computational study in the identification of novel potential cholesterol lowering agents targeting proprotein convertase subtilisin/kexin type 9

The enzymatic target proprotein convertase subtilisin/kexin type 9 (PCSK9) is critically involved in the regulation of the lipoprotein metabolism leading to the degradation of low-density lipoprotein receptors (LDLRs) upon binding. Drugs that lower LDL cholesterol (LDL-C) through the inhibition of PCSK9 are useful in the management of hypercholesterolemia which greatly reduces the associated risk of atherosclerotic cardiovascular disease (CVD). In 2015, anti-PCSK9 monoclonal antibodies (mAbs), alirocumab and evolocumab were approved but owing to their high costs their prior authorization practices were impeded, reducing their long-term adherence. This has drawn considerable attention for the development of small-molecule PCSK9 inhibitors. In this research work, novel and diverse molecules with affinity towards PCSK9 thereby having ability to lower cholesterol. A hierarchical multistep docking was implemented to identify small molecules from chemical libraries with a score cutoff -8.00 kcal/mol, thereby weeding all the non-potential molecules. A set of seven representative molecules Z1139749023, Z1142698190, Z2242867634, Z2242893449, Z2242894417, Z2242909019, and Z2242914794 have been identified from a comprehensive computational study which included assessment of pharmacokinetics and toxicity profiles and binding interactions along with in-depth analysis of structural dynamics and integrity using prolong molecular dynamics (MD) simulation (in-duplicate). Furthermore the binding affinity of these PCSK9 inhibitory candidates molecules was ascertained over 1000 trajectory frames using MM-GBSA calculations. The molecules reported herein are propitious candidates for further development through necessary experimental considerations.Communicated by Ramaswamy H. Sarma.

Targeting decaprenylphosphoryl-β-D-ribose 2'-epimerase for Innovative Drug Development Against Mycobacterium Tuberculosis Drug-Resistant Strains

Tuberculosis (TB) remains a global health challenge with the emergence of drug-resistant Mycobacterium tuberculosis variants, necessitating innovative drug molecules. One potential target is the cell wall synthesis enzyme decaprenylphosphoryl-β-D-ribose 2'-epimerase (DprE1), crucial for virulence and survival. This study employed virtual screening of 111 Protein Data Bank (PDB) database molecules known for their inhibitory biological activity against DprE1 with known IC50 values. Six compounds, PubChem ID: 390820, 86287492, 155294899, 155522922, 162651615, and 162665075, exhibited promising attributes as drug candidates and validated against clinical trial inhibitors BTZ043, TBA-7371, PBTZ169, and OPC-167832. Concurrently, this research focused on DprE1 mutation effects using molecular dynamic simulations. Among the 10 mutations tested, C387N significantly influenced protein behavior, leading to structural alterations observed through root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent-accessible surface area (SASA) analysis. Ligand 2 (ID: 390820) emerged as a promising candidate through ligand-based pharmacophore analysis, displaying enhanced binding compared with reference inhibitors. Molecular dynamic simulations highlighted ligand 2's interaction with the C387N mutation, reducing fluctuations, augmenting hydrogen bonding, and influencing solvent accessibility. These collective findings emphasize ligand 2's efficacy, particularly against severe mutations, in enhancing protein-ligand complex stability. Integrated computational and pharmacophore methodologies offer valuable insights into drug candidates and their interactions within intricate protein environments. This research lays a strategic foundation for targeted interventions against drug-resistant TB, highlighting ligand 2's potential for advanced drug development strategies.

MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions

Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided in this regard in recent years. Yet, two challenges remain: (i) balancing the extraction of profound, local cohesive characteristics while warding off gradient disappearance and (ii) globally representing and understanding the interactions between the drug and target local attributes, which is vital for delivering molecular level insights indispensable to drug development. In response to these challenges, we propose a DL network structure, MolLoG, primarily comprising two modules: local feature encoders (LFE) and global interactive learning (GIL). Within the LFE module, graph convolution networks and leap blocks capture the local features of drug and protein molecules, respectively. The GIL module enables the efficient amalgamation of feature information, facilitating the global learning of feature structural semantics and procuring multihead attention weights for abstract features stemming from two modalities, providing biologically pertinent explanations for black-box results. Finally, predictive outcomes are achieved by decoding the unified representation via a multilayer perceptron. Our experimental analysis reveals that MolLoG outperforms several cutting-edge baselines across four data sets, delivering superior overall performance and providing satisfactory results when elucidating various facets of drug-target interaction predictions.

Clinical genomics expands the link between erroneous cell division, primary microcephaly and intellectual disability

Primary microcephaly is a rare neurogenic and genetically heterogeneous disorder characterized by significant brain size reduction that results in numerous neurodevelopmental disorders (NDD) problems, including mild to severe intellectual disability (ID), global developmental delay (GDD), seizures and other congenital malformations. This disorder can arise from a mutation in genes involved in various biological pathways, including those within the brain. We characterized a recessive neurological disorder observed in nine young adults from five independent consanguineous Pakistani families. The disorder is characterized by microcephaly, ID, developmental delay (DD), early-onset epilepsy, recurrent infection, hearing loss, growth retardation, skeletal and limb defects. Through exome sequencing, we identified novel homozygous variants in five genes that were previously associated with brain diseases, namely CENPJ (NM_018451.5: c.1856A > G; p.Lys619Arg), STIL (NM_001048166.1: c.1235C > A; p.(Pro412Gln), CDK5RAP2 (NM_018249.6 c.3935 T > G; p.Leu1312Trp), RBBP8 (NM_203291.2 c.1843C > T; p.Gln615*) and CEP135 (NM_025009.5 c.1469A > G; p.Glu490Gly). These variants were validated by Sanger sequencing across all family members, and in silico structural analysis. Protein 3D homology modeling of wild-type and mutated proteins revealed substantial changes in the structure, suggesting a potential impact on function. Importantly, all identified genes play crucial roles in maintaining genomic integrity during cell division, with CENPJ, STIL, CDK5RAP2, and CEP135 being involved in centrosomal function. Collectively, our findings underscore the link between erroneous cell division, particularly centrosomal function, primary microcephaly and ID.

Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining

BACKGROUND

Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising source of therapeutic agents due to their structural diversity and functional versatility. However, their biosynthetic capacity and ecological functions remain largely underexplored.

RESULTS

Here, we aim to explore the biosynthetic profile of RiPPs and their potential roles in the interactions between microbes and viruses in the ocean, which encompasses a vast diversity of unique biomes that are rich in interactions and remains chemically underexplored. We first developed TrRiPP to identify RiPPs from ocean metagenomes, a deep learning method that detects RiPP precursors in a hallmark gene-independent manner to overcome the limitations of classic methods in processing highly fragmented metagenomic data. Applying this method to metagenomes from the global ocean microbiome, we uncover a diverse array of previously uncharacterized putative RiPP families with great novelty and diversity. Through correlation analysis based on metatranscriptomic data, we observed a high prevalence of antiphage defense-related and phage-related protein families that were co-expressed with RiPP families. Based on this putative association between RiPPs and phage infection, we constructed an Ocean Virus Database (OVD) and established a RiPP-involving host-phage interaction network through host prediction and co-expression analysis, revealing complex connectivities linking RiPP-encoding prokaryotes, RiPP families, viral protein families, and phages. These findings highlight the potential of RiPP families involved in prokaryote-phage interactions and coevolution, providing insights into their ecological functions in the ocean microbiome.

CONCLUSIONS

This study provides a systematic investigation of the biosynthetic potential of RiPPs from the ocean microbiome at a global scale, shedding light on the essential insights into the ecological functions of RiPPs in prokaryote-phage interactions through the integration of deep learning approaches, metatranscriptomic data, and host-phage connectivity. This study serves as a valuable example of exploring the ecological functions of bacterial secondary metabolites, particularly their associations with unexplored microbial interactions. Video Abstract.

CytoSIP: an annotated structural atlas for interactions involving cytokines or cytokine receptors

Therapeutic agents targeting cytokine-cytokine receptor (CK-CKR) interactions lead to the disruption in cellular signaling and are effective in treating many diseases including tumors. However, a lack of universal and quick access to annotated structural surface regions on CK/CKR has limited the progress of a structure-driven approach in developing targeted macromolecular drugs and precision medicine therapeutics. Herein we develop CytoSIP (Single nucleotide polymorphisms (SNPs), Interface, and Phenotype), a rich internet application based on a database of atomic interactions around hotspots in experimentally determined CK/CKR structural complexes. CytoSIP contains: (1) SNPs on CK/CKR; (2) interactions involving CK/CKR domains, including CK/CKR interfaces, oligomeric interfaces, epitopes, or other drug targeting surfaces; and (3) diseases and phenotypes associated with CK/CKR or SNPs. The database framework introduces a unique tri-level SIP data model to bridge genetic variants (atomic level) to disease phenotypes (organism level) using protein structure (complexes) as an underlying framework (molecule level). Customized screening tools are implemented to retrieve relevant CK/CKR subset, which reduces the time and resources needed to interrogate large datasets involving CK/CKR surface hotspots and associated pathologies. CytoSIP portal is publicly accessible at https://CytoSIP.biocloud.top , facilitating the panoramic investigation of the context-dependent crosstalk between CK/CKR and the development of targeted therapeutic agents.

Network pharmacology and experimental validation to investigate the mechanism of Nao-Ling-Su capsule in the treatment of ischemia/reperfusion-induced acute kidney injury

ETHNOPHARMACOLOGICAL RELEVANCE

Nao-Ling-Su Capsule (NLSC) is a traditional prescription, which is composed of fifteen herbs such as epimedium, Polygala tenuifolia, and Schisandra chinensis. It has the effect of strengthening the brain, calming nerves, and protecting the kidney, which has been used clinically for many years to strengthen the brain and kidney. However, the effect of NLSC in the treatment of acute kidney injury (AKI) is still unclear.

AIM OF THE STUDY

The present study aims to elucidate the pharmacological actions of NLSC in the treatment of AKI.

MATERIALS AND METHODS

Molecular targets for NLSC and AKI were obtained from various databases, and then we built networks of interactions between proteins (PPI) by employing string databases. Additionally, we employed the DAVID database to conduct gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Molecular docking was conducted to analyze the interaction between core components and their corresponding core targets. Next, the C57BL male mice model of ischemia/reperfusion damage (IRI) was developed, and the nephridial protective effect of NLSC was evaluated. The accuracy of the expected targets was confirmed using real-time quantitative polymerase chain reaction (RT-qPCR). The renal protective effect of NLSC was assessed using an immortalized human kidney tubular (HK-2) cell culture produced by oxygen-glucose deprivation (OGD).

RESULTS

Network pharmacology analysis identified 199 common targets from NLSC and AKI. STAT3, HSP90AA1, TP53, MAPK3, JUN, JAK2, and VEGFA could serve as potential drug targets and were associated with JAK2/STAT3 signaling pathway, PI3K-Akt signaling pathway, etc. The molecular docking analysis confirmed significant docking activity between the main bioactive components and core targets, including STAT3 and KIM-1. Moreover, the AKI mice model was successfully established and NLSC pretreatment could improve renal function and alleviate renal damage. NLSC could alleviate renal inflammation and tubular cell apoptosis, and decrease the expression of STAT3 and KIM-1 in AKI mice. In vitro, both NLSC and drug-containing serum may protect HK-2 cells by inhibiting STAT3 signaling, especially STAT3-mediated apoptosis and KIM-1 expression.

CONCLUSION

NLSC could alleviate renal inflammation and apoptosis, exerting its beneficial effects by targeting the STAT3/KIM-1 pathway. NLSC is a promising candidate for AKI treatment and provides a new idea and method for the treatment of AKI.

Potential mechanism of Chai Gui Zexie Decoction for NSCLC treatment assessed using network pharmacology, bioinformatics, and molecular docking: An observational study

To explore the potential mechanism of Chai Gui Zexie Decoction for non-small cell lung cancer (NSCLC) treatment using network pharmacology, bioinformatics, and molecular docking. The active ingredients of Chai Gui Zexie Decoction and the associated predicted targets were screened using the TCMSP database. NSCLC-related targets were obtained from GeneCards and OMIM. Potential action targets, which are intersecting drug-predicted targets and disease targets, were obtained from Venny 2.1. The protein-protein interaction network was constructed by importing potential action targets into the STRING database, and the core action targets and core ingredients were obtained via topological analysis. The core action targets were entered into the Metascape database, and Gene Ontology annotation analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed. Differentially expressed genes were screened using the Gene Expression Omnibus, and the key targets were obtained by validating the core action targets. The key targets were input into The Tumor IMmune Estimation Resource for immune cell infiltration analysis. Finally, the molecular docking of key targets and core ingredients was performed. We obtained 60 active ingredients, 251 drug prediction targets, and 2133 NSCLC-related targets. Meanwhile, 147 potential action targets were obtained, and 47 core action targets and 40 core ingredients were obtained via topological analysis. We detected 175 pathways related to NSCLC pharmaceutical therapy. In total, 1249 Gene Ontology items were evaluated. Additionally, 3102 differential genes were screened, and tumor protein P53, Jun proto-oncogene, interleukin-6, and mitogen-activated protein kinase 3 were identified as the key targets. The expression of these key targets in NSCLC was correlated with macrophage, CD4+ T, CD8+ T, dendritic cell, and neutrophil infiltration. The molecular docking results revealed that the core ingredients have a potent affinity for the key targets. Chai Gui Zexie Decoction might exert its therapeutic effect on NSCLC through multiple ingredients, targets, and signaling pathways.

Phytoconstituents of Artemisia Annua as potential inhibitors of SARS CoV2 main protease: an in silico study

BACKGROUND

In November 2019, the world faced a pandemic called SARS-CoV-2, which became a major threat to humans and continues to be. To overcome this, many plants were explored to find a cure.

METHODS

Therefore, this research was planned to screen out the active constituents from Artemisia annua that can work against the viral main protease Mpro as this non-structural protein is responsible for the cleavage of replicating enzymes of the virus. Twenty-five biocompounds belonging to different classes namely alpha-pinene, beta-pinene, carvone, myrtenol, quinic acid, caffeic acid, quercetin, rutin, apigenin, chrysoplenetin, arteannunin b, artemisinin, scopoletin, scoparone, artemisinic acid, deoxyartemisnin, artemetin, casticin, sitogluside, beta-sitosterol, dihydroartemisinin, scopolin, artemether, artemotil, artesunate were selected. Virtual screening of these ligands was carried out against drug target Mpro by CB dock.

RESULTS

Quercetin, rutin, casticin, chrysoplenetin, apigenin, artemetin, artesunate, sopolin and sito-gluside were found as hit compounds. Further, ADMET screening was conducted which represented Chrysoplenetin as a lead compound. Azithromycin was used as a standard drug. The interactions were studied by PyMol and visualized in LigPlot. Furthermore, the RMSD graph shows fluctuations at various points at the start of simulation in Top1 (Azithromycin) complex system due to structural changes in the helix-coil-helix and beta-turn-beta changes at specific points resulting in increased RMSD with a time frame of 50 ns. But this change remains stable after the extension of simulation time intervals till 100 ns. On other side, the Top2 complex system remains highly stable throughout the time scale. No such structural dynamics were observed bu the ligand attached to the active site residues binds strongly.

CONCLUSION

This study facilitates researchers to develop and discover more effective and specific therapeutic agents against SARS-CoV-2 and other viral infections. Finally, chrysoplenetin was identified as a more potent drug candidate to act against the viral main protease, which in the future can be helpful.

GSTP alleviates acute lung injury by S-glutathionylation of KEAP1 and subsequent activation of NRF2 pathway

Oxidative stress plays an important role in the pathogenesis of acute lung injury (ALI). As a typical post-translational modification triggered by oxidative stress, protein S-glutathionylation (PSSG) is regulated by redox signaling pathways and plays diverse roles in oxidative stress conditions. In this study, we found that GSTP downregulation exacerbated LPS-induced injury in human lung epithelial cells and in mice ALI models, confirming the protective effect of GSTP against ALI both in vitro and in vivo. Additionally, a positive correlation was observed between total PSSG level and GSTP expression level in cells and mice lung tissues. Further results demonstrated that GSTP inhibited KEAP1-NRF2 interaction by promoting PSSG process of KEAP1. By the integration of protein mass spectrometry, molecular docking, and site-mutation validation assays, we identified C434 in KEAP1 as the key PSSG site catalyzed by GSTP, which promoted the dissociation of KEAP1-NRF2 complex and activated the subsequent anti-oxidant genes. In vivo experiments with AAV-GSTP mice confirmed that GSTP inhibited LPS-induced lung inflammation by promoting PSSG of KEAP1 and activating the NRF2 downstream antioxidant pathways. Collectively, this study revealed the novel regulatory mechanism of GSTP in the anti-inflammatory function of lungs by modulating PSSG of KEAP1 and the subsequent KEAP1/NRF2 pathway. Targeting at manipulation of GSTP level or activity might be a promising therapeutic strategy for oxidative stress-induced ALI progression.

Unraveling the mechanisms of Sofosbuvir resistance in HCV NS3/4A protease: Structural and molecular simulation-based insights

Current management of HCV infection is based on Direct-Acting Antiviral Drugs (DAAs). However, resistance-associated mutations, especially in the NS3 and NS5B regions are gradually decreasing the efficacy of DAAs. Among the most effective HCV NS3/4A protease drugs, Sofosbuvir also develops resistance due to mutations in the NS3 and NS5B regions. Four mutations at positions A156Y, L36P, Q41H, and Q80K are classified as high-level resistance mutations. The resistance mechanism of HCV NS3/4A protease toward Sofosbuvir caused by these mutations is still unclear, as there is less information available regarding the structural and functional effects of the mutations against Sofosbuvir. In this work, we combined molecular dynamics simulation, molecular mechanics/Generalized-Born surface area calculation, principal component analysis, and free energy landscape analysis to explore the resistance mechanism of HCV NS3/4A protease due to these mutations, as well as compare interaction changes in wild-type. Subsequently, we identified that the mutant form of HCV NS3/4A protease affects the activity of Sofosbuvir. In this study, the resistance mechanism of Sofosbuvir at the atomic level is proposed. The proposed drug-resistance mechanism will provide valuable guidance for the design of HCV drugs.

Diverse models of cavity engineering in enzyme modification: Creation, filling, and reshaping

Most enzyme modification strategies focus on designing the active sites or their surrounding structures. Interestingly, a large portion of the enzymes (60%) feature active sites located within spacious cavities. Despite recent discoveries, cavity-mediated enzyme engineering remains crucial for enhancing enzyme properties and unraveling folding-unfolding mechanisms. Cavity engineering influences enzyme stability, catalytic activity, specificity, substrate recognition, and docking. This article provides a comprehensive review of various cavity engineering models for enzyme modification, including cavity creation, filling, and reshaping. Additionally, it also discusses feasible tools for geometric analysis, functional assessment, and modification of cavities, and explores potential future research directions in this field. Furthermore, a promising universal modification strategy for cavity engineering that leverages state-of-the-art technologies and methodologies to tailor cavities according to the specific requirements of industrial production conditions is proposed.

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes

Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts. ALDELE incorporates both structural and sequence representations of proteins, alongside representations of ligands by subgraphs and overall physicochemical properties. Comprehensive evaluation demonstrated that ALDELE can predict the catalytic activities of enzymes, and particularly, it identifies residue-based hotspots to guide enzyme engineering and generates substrate heat maps to explore the substrate scope for a given biocatalyst. Moreover, our models notably match empirical data, reinforcing the practicality and reliability of our approach through the alignment with confirmed mutation sites. ALDELE offers a facile and comprehensive solution by integrating different toolkits tailored for different purposes at affordable computational cost and therefore would be valuable to speed up the discovery of new functional enzymes for their exploitation by the industry.

High-throughput prioritization of target proteins for development of new antileishmanial compounds

Leishmaniasis, a vector-borne disease, is caused by the infection of Leishmania spp., obligate intracellular protozoan parasites. Presently, human vaccines are unavailable, and the primary treatment relies heavily on systemic drugs, often presenting with suboptimal formulations and substantial toxicity, making new drugs a high priority for LMIC countries burdened by the disease, but a low priority in the agenda of most pharmaceutical companies due to unattractive profit margins. New ways to accelerate the discovery of new, or the repositioning of existing drugs, are needed. To address this challenge, our study aimed to identify potential protein targets shared among clinically-relevant Leishmania species. We employed a subtractive proteomics and comparative genomics approach, integrating high-throughput multi-omics data to classify these targets based on different druggability metrics. This effort resulted in the ranking of 6502 ortholog groups of protein targets across 14 pathogenic Leishmania species. Among the top 20 highly ranked groups, metabolic processes known to be attractive drug targets, including the ubiquitination pathway, aminoacyl-tRNA synthetases, and purine synthesis, were rediscovered. Additionally, we unveiled novel promising targets such as the nicotinate phosphoribosyltransferase enzyme and dihydrolipoamide succinyltransferases. These groups exhibited appealing druggability features, including less than 40% sequence identity to the human host proteome, predicted essentiality, structural classification as highly druggable or druggable, and expression levels above the 50th percentile in the amastigote form. The resources presented in this work also represent a comprehensive collection of integrated data regarding trypanosomatid biology.

PPI3D: a web server for searching, analyzing and modeling protein-protein, protein-peptide and protein-nucleic acid interactions

Structure-resolved protein interactions with other proteins, peptides and nucleic acids are key for understanding molecular mechanisms. The PPI3D web server enables researchers to query preprocessed and clustered structural data, analyze the results and make homology-based inferences for protein interactions. PPI3D offers three interaction exploration modes: (i) all interactions for proteins homologous to the query, (ii) interactions between two proteins or their homologs and (iii) interactions within a specific PDB entry. The server allows interactive analysis of the identified interactions in both summarized and detailed manner. This includes protein annotations, structures, the interface residues and the corresponding contact surface areas. In addition, users can make inferences about residues at the interaction interface for the query protein(s) from the sequence alignments and homology models. The weekly updated PPI3D database includes all the interaction interfaces and binding sites from PDB, clustered based on both protein sequence and structural similarity, yielding non-redundant datasets without loss of alternative interaction modes. Consequently, the PPI3D users avoid being flooded with redundant information, a typical situation for intensely studied proteins. Furthermore, PPI3D provides a possibility to download user-defined sets of interaction interfaces and analyze them locally. The PPI3D web server is available at https://bioinformatics.lt/ppi3d.

Xplor-NIH: Better parameters and protocols for NMR protein structure determination

The present work describes an update to the protein covalent geometry and atomic radii parameters in the Xplor-NIH biomolecular structure determination package. In combination with an improved treatment of selected non-bonded interactions between atoms three bonds apart, such as those involving methyl hydrogens, and a previously developed term that affects the system's gyration volume, the new parameters are tested using structure calculations on 30 proteins with restraints derived from nuclear magnetic resonance data. Using modern structure validation criteria, including several formally adopted by the Protein Data Bank, and a clear measure of structural accuracy, the results show superior performance relative to previous Xplor-NIH implementations. Additionally, the Xplor-NIH structures compare favorably against originally determined NMR models.

ProNet DB: a proteome-wise database for protein surface property representations and RNA-binding profiles

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures poses a significant challenge for computational biology in leveraging structural information and accurate representation of protein surface properties. Recently, AlphaFold2 released the comprehensive proteomes of various species, and protein surface property representation plays a crucial role in protein-molecule interaction predictions, including those involving proteins, nucleic acids and compounds. Here, we proposed the first extensive database, namely ProNet DB, that integrates multiple protein surface representations and RNA-binding landscape for 326 175 protein structures. This collection encompasses the 16 model organism proteomes from the AlphaFold Protein Structure Database and experimentally validated structures from the Protein Data Bank. For each protein, ProNet DB provides access to the original protein structures along with the detailed surface property representations encompassing hydrophobicity, charge distribution and hydrogen bonding potential as well as interactive features such as the interacting face and RNA-binding sites and preferences. To facilitate an intuitive interpretation of these properties and the RNA-binding landscape, ProNet DB incorporates visualization tools like Mol* and an Online 3D Viewer, allowing for the direct observation and analysis of these representations on protein surfaces. The availability of pre-computed features enables instantaneous access for users, significantly advancing computational biology research in areas such as molecular mechanism elucidation, geometry-based drug discovery and the development of novel therapeutic approaches. Database URL:  https://proj.cse.cuhk.edu.hk/aihlab/pronet/.

Investigating Potential Cancer Therapeutics: Insight into Histone Deacetylases (HDACs) Inhibitions

Histone deacetylases (HDACs) are enzymes that remove acetyl groups from ɛ-amino of histone, and their involvement in the development and progression of cancer disorders makes them an interesting therapeutic target. This study seeks to discover new inhibitors that selectively inhibit HDAC enzymes which are linked to deadly disorders like T-cell lymphoma, childhood neuroblastoma, and colon cancer. MOE was used to dock libraries of ZINC database molecules within the catalytic active pocket of target HDACs. The top three hits were submitted to MD simulations ranked on binding affinities and well-occupied interaction mechanisms determined from molecular docking studies. Inside the catalytic active site of HDACs, the two stable inhibitors LIG1 and LIG2 affect the protein flexibility, as evidenced by RMSD, RMSF, Rg, and PCA. MD simulations of HDACs complexes revealed an alteration from extended to bent motional changes within loop regions. The structural deviation following superimposition shows flexibility via a visual inspection of movable loops at different timeframes. According to PCA, the activity of HDACs inhibitors induces structural dynamics that might potentially be utilized to define the nature of protein inhibition. The findings suggest that this study offers solid proof to investigate LIG1 and LIG2 as potential HDAC inhibitors.

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition

Protein sequence design can provide valuable insights into biopharmaceuticals and disease treatments. Currently, most protein sequence design methods based on deep learning focus on network architecture optimization, while ignoring protein-specific physicochemical features. Inspired by the successful application of structure templates and pre-trained models in the protein structure prediction, we explored whether the representation of structural sequence profile can be used for protein sequence design. In this work, we propose SPDesign, a method for protein sequence design based on structural sequence profile using ultrafast shape recognition. Given an input backbone structure, SPDesign utilizes ultrafast shape recognition vectors to accelerate the search for similar protein structures in our in-house PAcluster80 structure database and then extracts the sequence profile through structure alignment. Combined with structural pre-trained knowledge and geometric features, they are further fed into an enhanced graph neural network for sequence prediction. The results show that SPDesign significantly outperforms the state-of-the-art methods, such as ProteinMPNN, Pifold and LM-Design, leading to 21.89%, 15.54% and 11.4% accuracy gains in sequence recovery rate on CATH 4.2 benchmark, respectively. Encouraging results also have been achieved on orphan and de novo (designed) benchmarks with few homologous sequences. Furthermore, analysis conducted by the PDBench tool suggests that SPDesign performs well in subdivided structures. More interestingly, we found that SPDesign can well reconstruct the sequences of some proteins that have similar structures but different sequences. Finally, the structural modeling verification experiment indicates that the sequences designed by SPDesign can fold into the native structures more accurately.

5-chloro-3-(2-(2,4-dinitrophenyl) hydrazono)indolin-2-one: synthesis, characterization, biochemical and computational screening against SARS-CoV-2

Chemical prototypes with broad-spectrum antiviral activity are important toward developing new therapies that can act on both existing and emerging viruses. Binding of the SARS-CoV-2 spike protein to the host angiotensin-converting enzyme 2 (ACE2) receptor is required for cellular entry of SARS-CoV-2. Toward identifying new chemical leads that can disrupt this interaction, including in the presence of SARS-CoV-2 adaptive mutations found in variants like omicron that can circumvent vaccine, immune, and therapeutic antibody responses, we synthesized 5-chloro-3-(2-(2,4-dinitrophenyl)hydrazono)indolin-2-one (H2L) from the condensation reaction of 5-chloroisatin and 2,4-dinitrophenylhydrazine in good yield. H2L was characterised by elemental and spectral (IR, electronic, Mass) analyses. The NMR spectrum of H2L indicated a keto-enol tautomerism, with the keto form being more abundant in solution. H2L was found to selectively interfere with binding of the SARS-CoV-2 spike receptor-binding domain (RBD) to the host angiotensin-converting enzyme 2 receptor with a 50% inhibitory concentration (IC50) of 0.26 μM, compared to an unrelated PD-1/PD-L1 ligand-receptor-binding pair with an IC50 of 2.06 μM in vitro (Selectivity index = 7.9). Molecular docking studies revealed that the synthesized ligand preferentially binds within the ACE2 receptor-binding site in a region distinct from where spike mutations in SARS-CoV-2 variants occur. Consistent with these models, H2L was able to disrupt ACE2 interactions with the RBDs from beta, delta, lambda, and omicron variants with similar activities. These studies indicate that H2L-derived compounds are potential inhibitors of multiple SARS-CoV-2 variants, including those capable of circumventing vaccine and immune responses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11696-023-03274-5.

Structural basis for Retriever-SNX17 assembly and endosomal sorting

During endosomal recycling, Sorting Nexin 17 (SNX17) facilitates the transport of numerous membrane cargo proteins by tethering them to the Retriever complex. Despite its importance, the mechanisms underlying this interaction have remained elusive. Here, we report the structure of the Retriever-SNX17 complex determined using cryogenic electron microscopy (cryo-EM). Our structure reveals that the C-terminal tail of SNX17 engages with a highly conserved interface between the VPS35L and VPS26C subunits of Retriever. Through comprehensive biochemical, cellular, and proteomic analyses, we demonstrate that disrupting this interface impairs the Retriever-SNX17 interaction, subsequently affecting the recycling of SNX17-dependent cargos and altering the composition of the plasma membrane proteome. Intriguingly, we find that the SNX17-binding pocket on Retriever can be utilized by other ligands that share a consensus acidic C-terminal tail motif. By showing how SNX17 is linked to Retriever, our findings uncover a fundamental mechanism underlying endosomal trafficking of critical cargo proteins and reveal a mechanism by which Retriever can engage with other regulatory factors.

CRD: A de novo design algorithm for the prediction of cognate protein receptors for small molecule ligands

While predicting a ligand that binds to a protein is feasible with current methods, the opposite, i.e., the prediction of a receptor for a ligand remains challenging. We present an approach for predicting receptors of a given ligand that uses de novo design and structural bioinformatics. We have developed the algorithm CRD, comprising multiple modules combining fragment-based sub-site finding, a machine learning function to estimate the size of the site, a genetic algorithm that encodes knowledge on protein structures and a physics-based fitness scoring scheme. CRD includes a pseudo-receptor design component followed by a mapping component to identify proteins that might contain these sites. CRD recovers the sites and receptors of several natural ligands. It designs similar sites for similar ligands, yet to some extent can distinguish between closely related ligands. CRD correctly predicts receptor classes for several drugs and might become a valuable tool for drug discovery.

DVA: predicting the functional impact of single nucleotide missense variants

BACKGROUND

In the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants.

RESULTS

We present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein-protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods.

CONCLUSIONS

DVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.

Design, synthesis, and biochemical and computational screening of novel oxindole derivatives as inhibitors of Aurora A kinase and SARS-CoV-2 spike/host ACE2 interaction

Isatin (indol-2,3-dione), a secondary metabolite of tryptophan, has been used as the core structure to design several compounds that have been tested and identified as potent inhibitors of apoptosis, potential antitumor agents, anticonvulsants, and antiviral agents. In this work, several analogs of isatin hybrids have been synthesized and characterized, and their activities were established as inhibitors of both Aurora A kinase and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike/host angiotensin-converting enzyme II (ACE2) interactions. Amongst the synthesized isatin hybrids, compounds 6a, 6f, 6g, and 6m exhibited Aurora A kinase inhibitory activities (with IC50 values < 5 μ M), with GScore values of -7.9, -7.6, -8.2 and -7.7 kcal/mol, respectively. Compounds 6g and 6i showed activities in blocking SARS-CoV-2 spike/ACE2 binding (with IC50 values in the range < 30 μ M), with GScore values of -6.4 and -6.6 kcal/mol, respectively. Compounds 6f, 6g, and 6i were both capable of inhibiting spike/ACE2 binding and blocking Aurora A kinase. Pharmacophore profiling indicated that compound 6g tightly fits Aurora A kinase and SARS-CoV-2 pharmacophores, while 6d fits SARS-CoV-2 and 6l fits Aurora A kinase pharmacophore. This work is a proof of concept that some existing cancer drugs may possess antiviral properties. Molecular modeling showed that the active compound for each protein adopted different binding modes, hence interacting with a different set of amino acid residues in the binding site. The weaker activities against spike/ACE2 could be explained by the small sizes of the ligands that fail to address the important interactions for binding to the ACE2 receptor site.

Modulation of Krüppel-like factors (KLFs) interaction with their binding partners in cancers through acetylation and phosphorylation

Post-translational modifications (PTMs) of transcription factors regulate transcriptional activity and play a key role in essentially all biological processes and generate indispensable insight towards biological function including activity state, subcellular localization, protein solubility, protein folding, substrate trafficking, and protein-protein interactions. Amino acids modified chemically via PTMs, function as molecular switches and affect the protein function and characterization and increase the proteome complexity. Krüppel-like transcription factors (KLFs) control essential cellular processes including proliferation, differentiation, migration, programmed cell death and various cancer-relevant processes. We investigated the interactions of KLF group-2 members with their binding partners to assess the role of acetylation and phosphorylation in KLFs on their binding affinity. It was observed that acetylation and phosphorylation at different positions in KLFs have a variable effect on binding with specific partners. KLF2-EP300, KLF4-SP1, KLF6-ATF3, KLF6-JUN, and KLF7-JUN show stabilization upon acetylation or phosphorylation at variable positions. On the other hand, KLF4-CBP, KLF4-EP300, KLF5-CBP, KLF5-WWP1, KLF6-SP1, and KLF7-ATF3 show stabilization or destabilization due to acetylation or phosphorylation at variable positions in KLFs. This provides a molecular explanation of the experimentally observed dual role of KLF group-2 members as a suppressor or activator of cancers in a PTM-dependent manner.

PinMyMetal: A hybrid learning system to accurately model metal binding sites in macromolecules

Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural (4-residue predominate), catalytic (3-residue predominate), or regulatory (2-residue predominate) roles. Computational tools for modeling metal ions in protein structures, especially for transient, reversible, and concentration-dependent regulatory sites, remain immature. We present PinMyMetal (PMM), a sophisticated hybrid machine learning system for predicting zinc ion localization and environment in macromolecular structures. Compared to other predictors, PMM excels in predicting regulatory sites (median deviation of 0.34 Å), demonstrating superior accuracy in locating catalytic sites (median deviation of 0.27 Å) and structural sites (median deviation of 0.14 Å). PMM assigns a certainty score to each predicted site based on local structural and physicochemical features independent of homolog presence. Interactive validation through our server, CheckMyMetal, expands PMM's scope, enabling it to pinpoint and validates diverse functional zinc sites from different structure sources (predicted structures, cryo-EM and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top. While currently trained on zinc, the PMM workflow can easily adapt to other metals through expanded training data.

Genome Sequence Analysis of Native Xenorhabdus Strains Isolated from Entomopathogenic Nematodes in Argentina

Entomopathogenic nematodes from the genus Steinernema (Nematoda: Steinernematidae) are capable of causing the rapid killing of insect hosts, facilitated by their association with symbiotic Gram-negative bacteria in the genus Xenorhabdus (Enterobacterales: Morganellaceae), positioning them as interesting candidate tools for the control of insect pests. In spite of this, only a limited number of species from this bacterial genus have been identified from their nematode hosts and their insecticidal properties documented. This study aimed to perform the genome sequence analysis of fourteen Xenorhabdus strains that were isolated from Steinernema nematodes in Argentina. All of the strains were found to be able of killing 7th instar larvae of Galleria mellonella (L.) (Lepidoptera: Pyralidae). Their sequenced genomes harbour 110 putative insecticidal proteins including Tc, Txp, Mcf, Pra/Prb and App homologs, plus other virulence factors such as putative nematocidal proteins, chitinases and secondary metabolite gene clusters for the synthesis of different bioactive compounds. Maximum-likelihood phylogenetic analysis plus average nucleotide identity calculations strongly suggested that three strains should be considered novel species. The species name for strains PSL and Reich (same species according to % ANI) is proposed as Xenorhabdus littoralis sp. nov., whereas strain 12 is proposed as Xenorhabdus santafensis sp. nov. In this work, we present a dual insight into the biocidal potential and diversity of the Xenorhabdus genus, demonstrated by different numbers of putative insecticidal genes and biosynthetic gene clusters, along with a fresh exploration of the species within this genus.

Phytocompounds as potential inhibitors of mycobacterial multidrug efflux pump Rv1258c: an in silico approach

The number of infections and deaths caused by multidrug resistant (MDR) tuberculosis is increasing globally. One of the efflux pumps, that makes Mycobacterium tuberculosis resistant to a number of antibiotics and results in unfavourable treatment results is Tap or Rv1258c. In our study, we tried to utilize a rational drug design technique using in silico approach to look for an efficient and secure efflux pump inhibitor (EPI) against Rv1258c. The structure of Rv1258c was built using the homology modeling tool MODELLER 9.24. 210 phytocompounds were used for blind and site-specific ligand docking against the modelled structure of Rv1258c using AutoDock Vina software. The best docked plant compounds were further analysed for druglikeness and toxicity. In addition to having excellent docking scores, two plant compounds-ellagic acid and baicalein-also exhibited highly desirable drug-like qualities. These substances outperform more well-known EPIs like piperine and verapamil in terms of effectiveness. This data shows that these two compounds might be further investigated for their potential as Rv1258c inhibitors.

TM-search: An Efficient and Effective Tool for Protein Structure Database Search

The quickly increasing size of the Protein Data Bank is challenging biologists to develop a more scalable protein structure alignment tool for fast structure database search. Although many protein structure search algorithms and programs have been designed and implemented for this purpose, most require a large amount of computational time. We propose a novel protein structure search approach, TM-search, which is based on the pairwise structure alignment program TM-align and a new iterative clustering algorithm. Benchmark tests demonstrate that TM-search is 27 times faster than a TM-align full database search while still being able to identify ∼90% of all high TM-score hits, which is 2-10 times more than other existing programs such as Foldseek, Dali, and PSI-BLAST.

Mechanism of Ligand Binding to Theophylline RNA Aptamer

Studying RNA-ligand interactions and quantifying their binding thermodynamics and kinetics are of particular relevance in the field of drug discovery. Here, we combined biochemical binding assays and accelerated molecular simulations to investigate ligand binding and dissociation in RNA using the theophylline-binding RNA as a model system. All-atom simulations using a Ligand Gaussian accelerated Molecular Dynamics method (LiGaMD) have captured repetitive binding and dissociation of theophylline and caffeine to RNA. Theophylline's binding free energy and kinetic rate constants align with our experimental data, while caffeine's binding affinity is over 10,000 times weaker, and its kinetics could not be determined. LiGaMD simulations allowed us to identify distinct low-energy conformations and multiple ligand binding pathways to RNA. Simulations revealed a "conformational selection" mechanism for ligand binding to the flexible RNA aptamer, which provides important mechanistic insights into ligand binding to the theophylline-binding model. Our findings suggest that compound docking using a structural ensemble of representative RNA conformations would be necessary for structure-based drug design of flexible RNA.

Identification of hub genes significantly linked to temporal lobe epilepsy and apoptosis via bioinformatics analysis

Background

Epilepsy stands as an intricate disorder of the central nervous system, subject to the influence of diverse risk factors and a significant genetic predisposition. Within the pathogenesis of temporal lobe epilepsy (TLE), the apoptosis of neurons and glial cells in the brain assumes pivotal importance. The identification of differentially expressed apoptosis-related genes (DEARGs) emerges as a critical imperative, providing essential guidance for informed treatment decisions.

Methods

We obtained datasets related to epilepsy, specifically GSE168375 and GSE186334. Utilizing differential expression analysis, we identified a set of 249 genes exhibiting significant variations. Subsequently, through an intersection with apoptosis-related genes, we pinpointed 16 genes designated as differentially expressed apoptosis-related genes (DEARGs). These DEARGs underwent a comprehensive array of analyses, including enrichment analyses, biomarker selection, disease classification modeling, immune infiltration analysis, prediction of miRNA and transcription factors, and molecular docking analysis.

Results

In the epilepsy datasets examined, we successfully identified 16 differentially expressed apoptosis-related genes (DEARGs). Subsequent validation in the external dataset GSE140393 revealed the diagnostic potential of five biomarkers (CD38, FAIM2, IL1B, PAWR, S100A8) with remarkable accuracy, exhibiting an impressive area under curve (AUC) (The overall AUC of the model constructed by the five key genes was 0.916, and the validation set was 0.722). Furthermore, a statistically significant variance (p < 0.05) was observed in T cell CD4 naive and eosinophil cells across different diagnostic groups. Exploring interaction networks uncovered intricate connections, including gene-miRNA interactions (164 interactions involving 148 miRNAs), gene-transcription factor (TF) interactions (22 interactions with 20 TFs), and gene-drug small molecule interactions (15 interactions involving 15 drugs). Notably, IL1B and S100A8 demonstrated interactions with specific drugs.

Conclusion

In the realm of TLE, we have successfully pinpointed noteworthy differentially expressed apoptosis-related genes (DEARGs), including CD38, FAIM2, IL1B, PAWR, and S100A8. A comprehensive understanding of the implications associated with these identified genes not only opens avenues for advancing our comprehension of the underlying pathophysiology but also bears considerable potential in guiding the development of innovative diagnostic methodologies and therapeutic interventions for the effective management of epilepsy in the future.

Mutational analysis of consanguineous families and their targeted therapy against dwarfism

Dwarfism is a medical term used to describe individuals with a height-vertex measurement that falls below two standard deviations (-2SD) or the third percentile for their gender and age. Normal development of growth is a complicated dynamic procedure that depends upon the coordination of different aspects involving diet, genetics, and biological aspects like hormones in equilibrium. Any severe or acute pathologic procedure may disturb the individual's normal rate of growth. In this research, we examined four (A-D) Pakistani consanguineous families that exhibited syndromic dwarfism, which was inherited in an autosomal recessive pattern. The genomic DNA of each family member was extracted by using phenol-chloroform and Kit methods. Whole Exome Sequencing (WES) of affected family members (IV-11, III-5, IV-4 and III-13) from each group was performed at the Department of Medical Genetics, University of Antwerp, Belgium. After filtering the exome data, the mutations in PPM1F, FGFR3, ERCC2, and PCNT genes were determined by Sanger sequencing of each gene by using specific primers. Afterward, FGFR3 was found to be a suitable drug target among all the mutations to treat achondroplasia also known as disproportionate dwarfism. BioSolveIT softwares were used to discover the lead active inhibitory molecule against FGFR3. This research will not only provide short knowledge to the concerned pediatricians, researchers, and family physicians for the preliminary assessment and management of the disorder but also provide a lead inhibitor for the treatment of disproportionate dwarfism.Communicated by Ramaswamy H. Sarma.

Molecular Dynamics and Machine Learning reveal distinguishing mechanisms of Competitive Ligands to perturb α,β-Tubulin

The mechanisms of action of ligands competing for the Colchicine Binding Site (CBS) of the α,β-Tubulin are non-standard compared to the commonly witnessed ligand-induced inhibition of proteins. This is because their potencies are not solely judged by the binding affinity itself, but also by their capacity to bias the conformational states of the dimer. Regarding the latter requirement, it is observed that ligands competing for the same pocket that binds colchicine exhibit divergence in potential clinical outcomes. Molecular dynamics-based ∼5.2 µs sampling of α,β-Tubulin complexed with four different ligands has revealed that each ligand has its customized way of influencing the complex. Primarily, it is the proportion of twisting and/or bending characteristic of modes of the intrinsic dynamics which is revealed to be 'fundamental' to tune this variation in the mechanism. The milder influence of 'bending' makes a ligand (TUB092), better classifiable under the group of vascular disrupting agents (VDAs), which are phenotypically effective on cytoskeletons; whereas a stronger impact of 'bending' makes the classical ligand Colchicine (COL) a better Anti-Mitotic agent (AMA). Two other ligands BAL27862 (2RR) and Nocodazole (NZO) fall in the intermediate zone as they fail to explicitly induce bending modes. Random Forest Classification method and K-means Clustering is applied to reveal the efficiency of Machine Learning methods in classifying the Tubulin conformations according to their ligand-specific perturbations and to highlight the significance of specific amino acid residues, mostly positioned in the α-β and β-β interfaces involved in the mechanism. These key residues responsible to yield discriminative actions of the ligands are likely to be highly useful in future endeavours to design more precise inhibitors.

New thiazolyl-isoxazole derivatives as potential anti-infective agents: design, synthesis, in vitro and in silico antimicrobial efficacy

Antimicrobial resistance threatens the efficacious prevention and treatment of infectious diseases caused by microorganisms. To combat microbial infections, the need for new drug candidates is essential. In this context, the design, synthesis, antimicrobial screening, and in silico study of a new series of 5-aryl-3-(2-arylthiazol-4-yl)isoxazole (9a-t) have been reported. The structure of new compounds was confirmed by spectrometric methods. Compounds 9a-t were evaluated for in vitro antitubercular and antimicrobial activity. Against M. tuberculosis H37Rv, fourteen compounds showed good to excellent antitubercular activity with MIC 2.01-9.80 µM. Compounds 9a, 9b, and 9r showed four-fold more activity than the reference drug isoniazid. Nine compounds, 9a, 9b, 9d, 9e, 9i, 9q, 9r, 9s, and 9t, showed good antibacterial activity against E. coli with MIC 7.8-15.62 µg/mL. Against A. niger, four compounds showed good activity with MIC 31.25 µg/mL. Against C. albicans, all twenty compounds reported excellent to good activity with MIC 7.8-31.25 µg/mL. Compounds 9c-e, 9g-j, and 9q-t showed comparable activity concerning the reference drug fluconazole. The compounds 9a-t were screened for cytotoxicity against 3t3l1 cell lines and found to be less or non-cytotoxic. The in silico study exposed that these compounds displayed high affinity towards the M. tuberculosis targets PanK, DprE1, DHFR, PknA, KasA, and Pks13, and C. albicans targets NMT, CYP51, and CS. The compound 9r was evaluated for structural dynamics and molecular dynamics simulations. The potent antitubercular and antimicrobial activity of 5-aryl-3-(2-arylthiazol-4-yl)isoxazole (9a-t) derivatives has recommended that these compounds could assist in treating microbial infections.Communicated by Ramaswamy H. Sarma.

A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches

Proteins play an important role in life activities and are the basic units for performing functions. Accurately annotating functions to proteins is crucial for understanding the intricate mechanisms of life and developing effective treatments for complex diseases. Traditional biological experiments struggle to keep pace with the growing number of known proteins. With the development of high-throughput sequencing technology, a wide variety of biological data provides the possibility to accurately predict protein functions by computational methods. Consequently, many computational methods have been proposed. Due to the diversity of application scenarios, it is necessary to conduct a comprehensive evaluation of these computational methods to determine the suitability of each algorithm for specific cases. In this study, we present a comprehensive benchmark, BeProf, to process data and evaluate representative computational methods. We first collect the latest datasets and analyze the data characteristics. Then, we investigate and summarize 17 state-of-the-art computational methods. Finally, we propose a novel comprehensive evaluation metric, design eight application scenarios and evaluate the performance of existing methods on these scenarios. Based on the evaluation, we provide practical recommendations for different scenarios, enabling users to select the most suitable method for their specific needs. All of these servers can be obtained from https://csuligroup.com/BEPROF and https://github.com/CSUBioGroup/BEPROF.

Integrated Network Pharmacology Approach to Evaluate Bioactive Phytochemicals of Acalypha indica and Their Mechanistic Actions to Suppress Target Genes of Tuberculosis

Mycobacterium tuberculosis is responsible for tuberculosis (TB) all over the world. Despite tremendous advancements in biomedical research, new treatment approaches, and preventive measures, TB incidence rates continue to ascend. The herbaceous plant Acalypha indica, also known as Indian Nettle, belongs to the Euphorbiaceae family and is known as one of the most important sources of medicines and pharmaceuticals for the medical therapy for a range of ailments. However, the precise molecular mechanism of its therapeutic action is still unknown. In this study, an integrated network pharmacology approach was employed to explore the potential mechanism of A. indica phytochemicals against TB. The active chemical components of A. indica were collected from two independent databases and published sources, whereas SwissTargetPrediction was used to identify the target genes of these phytochemicals. GeneCards and DisGeNET databases were employed to retrieve tuberculosis-related genes and variants. Following the evaluation of overlapped genes, gene enrichment analysis and PPI network analysis were performed using the DAVID and STRING databases, respectively. Later, to identify the potential target(s) for the disease, molecular docking was performed. A. indica revealed 9 active components with 259 potential therapeutic targets; TB attributed 694 intersecting genes from the two data sets; and both TB and A. indica overlapped 44 potential targets. The in-depth analysis based on the degree revealed that AKT1 and EGFR formed the foundation of the PPI network. Moreover, docking analysis followed by molecular dynamics simulations revealed that phytosterol and stigmasterol have higher binding affinities to AKT1 and EGFR to suppress tuberculosis. This study provides a convincing proof that A. indica can be exploited to target TB after experimental endorsement; further, it lays the framework for more experimental research on A. indica's anti-TB activity.

QiDiTangShen granules alleviates diabetic nephropathy podocyte injury: A network pharmacology study and experimental validation in vivo and vitro

Background

QiDiTangShen granules (QDTS), a traditional Chinese medicine (TCM) compound prescription, have remarkable efficacy in diabetic nephropathy (DN) patients, and their pharmacological mechanism needs further exploration.

Methods

According to the active ingredients and targets of the QDTS in the TCMSP database, the network pharmacology of QDTS was investigated. The potential active ingredients were chosen based on the oral bioavailability and the drug similarity index. At the same time, targets for DN-related disease were obtained from GeneCards, OMIM, PharmGKB, TTD, and DrugBank. The TCM-component-target network and the protein-protein interaction (PPI) network were constructed with the Cytoscape and STRING platforms, respectively, and then the core targets of DN were selected with CytoNCA. GO and KEGG enrichment analysis using R software. Molecular docking to identify the core targets of QDTS for DN. In vivo, db/db mice were treated as DN models, and the urine microalbuminuria, the pathological changes in the kidney and the protein expression levels of p-PI3K, p-Akt, JUN, nephrin and synaptopodin were detected by immunohistochemistry, immunofluorescence method and Western blotting. After QDTS was used in vitro, the protein expression of mouse podocyte clone-5 (MPC5) cells was detected by immunohistochemistry, immunofluorescence and Western blot.

Results

Through network pharmacology analysis, 153 potential targets for DN in QDTS were identified, 19 of which were significant. The KEGG enrichment analysis indicated that QDTS might have therapeutic effects on IL-17, TNF, AGE-RAGE, PI3K-Akt, HIF-1, and EGFR through interfering with Akt1 and JUN. The main active ingredients in QDTS are quercetin, β-sitosterol, stigmasterol and kaempferol. Both in vivo and in vitro studies showed that QDTS could decrease the urine microalbuminuria and renal pathology of db/db mice, and alleviate podocyte injuries through the PI3K/Akt signaling pathway.

Conclusion

Through network pharmacology, in vivo and in vitro experiments, QDTS has been shown to improve the urine microalbuminuria and renal pathology in DN, and to reduce podocyte damage via the PI3K/Akt pathway.

LAMP3 is a potent uterine corpus endometrial carcinoma prognostic biomarker associated with immune behavior

BACKGROUND

Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecological malignancies and its incidence and mortality continue apace. Lysosome-associated membrane protein 3 (LAMP3) is the third member of the LAMP family and its overexpression has been described to be involved in the progression of breast, ovarian and cervical cancers, but there has been an absence of research focusing on its role in UCEC.

METHODS

WGCNA, TIMER, LinkedOmics, GSEA, Cytoscape, Kaplan-Meier plotter, GDC, GeneMANIA, cBioPortal, PDB, RNAinter, miRNet were applied in this research.

RESULTS

Our study uncovers that LAMP3 possesses higher expression levels in UCEC compared to normal tissues, and this differential expression profile is tightly aligned with clinical and pathological features, and patients demonstrating high LAMP3 expression tend to have a shorter survival expectancy. The high expression of LAMP3 is modulated by the designated ceRNA network. LAMP3 is engaged in UCEC progression by functioning in a variety of biological roles of relevance to immunity. Furthermore, we predicted several prospering drugs based on drug sensitivity. Finally, we also constructed possible docking patterns of LAMP3 with ABCA3, RAB9A, and SGTB.

CONCLUSIONS

LAMP3 is a formidable biomarker for UCEC and could be a prospective candidate for the diagnosis, treatment and prognostic assessment of UCEC.

The neuroprotective mechanisms of naringenin: Inhibition of apoptosis through the PI3K/AKT pathway after hypoxic-ischemic brain damage

ETHNOPHARMACOLOGICAL RELEVANCE

Naringenin (NGN) is a widely distributed flavonoid with potent antioxidant and neuroprotective properties. Neuroprotective agents play a crucial role in the treatment of hypoxic-ischemic encephalopathy (HIE). It has shown potential therapeutic effects for neurological disorders. However, its efficacy on HIE is yet to be investigated.

AIM OF THE STUDY

This study aims to investigate the potential neuroprotective effect of naringenin and its underlying molecular mechanisms in reducing oxidative stress, apoptosis, and improving brain outcomes following HIE. Additionally, the study aims to identify the potential targets, mechanisms, and functions of naringenin using network pharmacology analysis.

MATERIALS AND METHODS

Neonatal mice were exposed to the hypoxic-ischemic brain damage (HIBD) model to determine brain water content, and brain tissue was subjected to hematoxylin and eosin (HE), immunohistochemistry (IHC), terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL), and Nissl staining to investigate its neuroprotective effects. Furthermore, the neonatal mouse primary neuron oxygen-glucose deprivation (OGD) model to measure reactive oxygen species (ROS) production in vitro. The protein levels were characterized by Western Blot, and mRNA levels were evaluated by a real-time quantitative PCR detecting system (qPCR). Transmission electron microscopy (TEM) and mitochondrial fluorescent staining were used to observe mitochondrial morphology. Neuronal nuclei (NeuN) and microtubule-associated protein 2 (MAP2) were detected by Immunofluorescence (IF). Finally, network pharmacology was employed to determine the common target of naringenin and HIE. The core genes were obtained via protein-protein interaction networks (PPI) analysis and molecular docking was examined, and the mechanism of action was explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Additionally, small interfering RNA (siRNA) was constructed for verification.

RESULTS

Naringenin has a neuroprotective effect in HIBD by modulating Vegfa expression and activating the PI3K/AKT pathway to inhibit apoptosis. Furthermore, molecular docking results suggest that Vegfa is a potential binding target of naringenin, and silencing Vegfa partially reverses the pharmacological effects of NGN.

CONCLUSION

Our findings suggest that naringenin demonstrates potential clinical application for treating HIE as a novel neuroprotective agent.

Data integration strategies for whole-cell modeling

Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.

Proposal of pharmacophore model for HIV reverse transcriptase inhibitors: Combined mutational effect analysis, molecular dynamics, molecular docking and pharmacophore modeling study

OBJECTIVES

Antiretroviral therapy (ART) efficacy is jeopardized by the emergence of drug resistance mutations in HIV, compromising treatment effectiveness. This study aims to propose novel analogs of Effavirenz (EFV) as potential direct inhibitors of HIV reverse transcriptase, employing computer-aided drug design methodologies.

METHODS

Three key approaches were applied: a mutational profile study, molecular dynamics simulations, and pharmacophore development. The impact of mutations on the stability, flexibility, function, and affinity of target proteins, especially those associated with NRTI, was assessed. Molecular dynamics analysis identified G190E as a mutation significantly altering protein properties, potentially leading to therapeutic failure. Comparative analysis revealed that among six first-line antiretroviral drugs, EFV exhibited notably low affinity with viral reverse transcriptase, further reduced by the G190E mutation. Subsequently, a search for EFV-similar inhibitors yielded 12 promising molecules based on their affinity, forming the basis for generating a pharmacophore model.

RESULTS

Mutational analysis pinpointed G190E as a crucial mutation impacting protein properties, potentially undermining therapeutic efficacy. EFV demonstrated diminished affinity with viral reverse transcriptase, exacerbated by the G190E mutation. The search for EFV analogs identified 12 high-affinity molecules, culminating in a pharmacophore model elucidating key structural features crucial for potent inhibition.

CONCLUSION

This study underscores the significance of EFV analogs as potential inhibitors of HIV reverse transcriptase. The findings highlight the impact of mutations on drug efficacy, particularly the detrimental effect of G190E. The generated pharmacophore model serves as a pivotal reference for future drug development efforts targeting HIV, providing essential structural insights for the design of potent inhibitors based on EFV analogs identified in vitro.