Protein Data Bank Research Articles

Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines.

Machine learning (ML) identification of covalently ligandable sites may accelerate targeted covalent inhibitor design and help expand the druggable proteome space. Here, we report the rigorous development and validation of the tree-based models and convolutional neural networks (CNNs) trained on a newly curated database (LigCys3D) of over 1000 liganded cysteines in nearly 800 proteins represented by over 10,000 three-dimensional structures in the protein data bank. The unseen tests yielded 94 and 93% area under the receiver operating characteristic curves for the tree models and CNNs, respectively. Based on the AlphaFold2 predicted structures, the ML models recapitulated the newly liganded cysteines in the PDB with over 90% recall values. To assist the community of covalent drug discoveries, we report the predicted ligandable cysteines in 392 human kinases and their locations in the sequence-aligned kinase structure, including the PH and SH2 domains. Furthermore, we disseminate a searchable online database LigCys3D (https://ligcys.computchem.org/) and a web prediction server DeepCys (https://deepcys.computchem.org/), both of which will be continuously updated and improved by including newly published experimental data. The present work represents an important step toward the ML-led integration of big genome data and structure models to annotate the human proteome space for the next-generation covalent drug discoveries.

In silico analysis on binding action of beta-lactam drugs against TEM and SHV class A beta-lactamases from Klebsiella pneumoniae

One of the leading Gram-negative bacteria that causes nosocomial illnesses such as pneumonia, urinary tract infections, meningitis, etc. is Klebsiella pneumoniae. Conventionally, K. pneumoniae infections are treated with beta-lactam (β-lactam) based antibiotics like penicillin; however, these treatments are becoming less and less successful as the bacterium generates various kinds of beta-lactamases (β-lactamases) to inactivate the medicines. In the present study, whole genome sequencing was used to obtain class A β-lactamase from an isolate that showed antibiotic resistance using the disk diffusion method. Class A β-lactamase, TEM and SHV obtained from the isolate were used for docking. We downloaded the structure of two enzymes (amino acids) (TEM and SHV background) from Protein DataBank (PDB) with PDB IDs: 1n9b and 2zd8. The structures of the β-lactams antibiotics (ceftazidime, cefepime, Amoxicillin clavulanic acid, and meropenem) were drawn using Chemsketch. The interactions of the inhibitors with several β-lactams antibiotics were studied after docking using Autodock software. The docking results showed that of all the five drugs docked with the enzymes (inhibitors), cefepime excelled in terms of ability to bind well against both the TEM and SHV enzymes. This was shown with the binding affinity against 1n9b and 2zd8 being − 8.23996162 and − 8.5358305 respectively, as such making it the best β-lactam antibiotic against TEM and SHV of all the five drugs.

In silico prediction of heme binding in proteins

The process of heme binding to a protein is prevalent in almost all forms of life to control many important biological properties, such as O2-binding, electron transfer, gas sensing or to build catalytic power. In these cases, heme typically binds tightly (irreversibly) to a protein in a discrete heme binding pocket, with one or two heme ligands provided most commonly to the heme iron by His, Cys or Tyr residues. Heme binding can also be used as a regulatory mechanism, for example in transcriptional regulation or ion channel control. When used as a regulator, heme binds more weakly, with different heme ligations and without the need for a discrete heme pocket. This makes the characterization of heme regulatory proteins difficult, and new approaches are needed to predict and understand the heme-protein interactions. We apply a modified version of the ProFunc bioinformatics tool to identify heme-binding sites in a test set of heme-dependent regulatory proteins taken from the Protein Data Bank and AlphaFold models. The potential heme binding sites identified can be easily visualized in PyMol and, if necessary, optimized with RosettaDOCK. We demonstrate that the methodology can be used to identify heme-binding sites in proteins, including in cases where there is no crystal structure available, but the methodology is more accurate when the quality of the structural information is high. The ProFunc tool, with the modification used in this work, is publicly available at https://www.ebi.ac.uk/thornton-srv/databases/profunc and can be readily adopted for the examination of new heme binding targets.

Analysing the Anticancer Properties of Pterostilbene Through Absorption, Distribution, Metabolism, and Excretion (ADME) and Molecular Docking Studies.

Aim The aim of this study is to examine the possible therapeutic effect of pterostilbene (PTS), a chemical present in grapes and blueberries, in the treatment of liver cancer by analysing its interactions with important proteins linked to the wingless/integrated (Wnt) signaling system. Objective Using computational techniques like molecular docking and absorption, distribution, metabolism, and excretion (ADME) studies, this research focuses on examining the pharmacokinetics and molecular interactions of PTS with proteins such as vimentin (Vim), glycogen synthase kinase 3 beta (GSK3-β), epithelial cadherin (E-cadherin), interleukin-6 (IL-6), interleukin-1 beta (IL-1β), c-Jun N-terminal kinase (JNK), and Wnt, all of which are connected to the Wnt signaling pathway in liver cancer. Methods The study includes the synthesis of proteins and ligands, ADME investigations for PTS, and AutoDock Vina molecular docking simulations to evaluate binding affinities and interactions. PTS is obtained from PubChem, while protein structures are obtained from the Protein Data Bank. Results Strong binding affinities between PTS and essential proteins in the Wnt signaling cascade are shown by molecular docking, which also highlights noteworthy hydrogen bonds, hydrophobic interactions, and electrostatic contacts. According to an ADME study, PTS has advantageous pharmacokinetic properties, such as moderate solubility, membrane permeability, and a minimal chance of drug interactions. Conclusion The extensive study highlights PTS's potential as a viable treatment option for liver cancer. The study promotes its investigation in cutting-edge liver cancer therapy approaches and urges more investigation into the molecular mechanisms, underpinning its anticancer properties. This paper sheds important light on the role of natural chemicals in cancer therapy and emphasizes the need for computational methods in drug discovery.

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/.

Exploring the Function of Predicted T‐Cell Epitopes in Enhancing Influenza Virus Vaccine Efficacy

AbstractThis study investigates the potential utility of predicted T‐cell epitopes derived from the hemagglutinin (HA) sequences of the H1N1 and H5N1 serotypes of the influenza virus. The aim is to contribute to the improvement of a valuable influenza virus vaccine. The influenza virus, a member of the Orthomyxoviridae family, causes the contagious illness commonly known as the “flu.” It contains RNA and exhibits symptoms ranging from mild to severe, including fever, runny nose, sore throat, muscle aches, headaches, coughing, fatigue, and weakness. Each year, the flu epidemic results in a significant number of deaths and cases of severe illness worldwide. The study focuses on the HA protein, which is very useful for entry of virus and host cell strap, and the neuraminidase (NA) enzyme, which facilitates the free of the virus from the host cell. By utilizing computational tools such as Propred 1 and Propred, binding predictions for MHC class I and II are generated and conserved sequences are analyzed. The 3D structures of MHC class I and II human molecules are obtained from the Protein Data Bank (PDB) using the PATCH‐DOCK server. Through patch docking of the analyzed conserved sequences with MHC I and II human molecules, conserved epitopes that hold potential for vaccine development are identified. This research highlights the importance of utilizing predicted T‐cell epitopes in the design of effective influenza virus vaccines. By leveraging computational methods and structural analysis, this approach provides insights into the development of vaccines that target specific influenza virus serotypes. The identified conserved epitopes have the potential to enhance vaccine efficacy and contribute to the ongoing efforts to combat influenza virus infections.

309 Effects of SARS-CoV-2 Variants on CD8+ T cell Epitope Diversity: Estimating Clinical Severity in the United States

OBJECTIVES/GOALS: Our goal was to distinguish SARS-CoV-2 CD8+ T cell epitopes of spike, membrane, and nucleocapsid products in 27 of the most frequent HLA-Aand -B alleles.We hypothesize that differences mediated by variation in SARS-CoV-2 and host HLA genetics affect the differential clinical severity and presentation of acute infection and PASC. METHODS/STUDY POPULATION: Genomic sequences of SARS-CoV-2 variants were blasted against the original Wuhan strain using Ensembl’s SARS-CoV-2 browser. We examined 16 COVID variants: 2 Alpha (B.1 and B.1.1.7), 5 Delta (AY.100, AY.25, AY.3, AY.3.1, and AY.44), and 9 Omicron (BA.1, BA.1.1, BA.2, BA.4, BA.5, BQ.1, BQ.1.1, XBB.1, and XBB.1.5), sequenced from the Louisiana patient population. cDNA sequences were translated using the Expasy tool. To predict MHC-I epitope binding, we used the Immune Epitope Database and Analysis Resource, via TepiTool utilizing the IEDB recommended default prediction and the 27 most frequent HLA-A and -B alleles. In silico peptide docking was conducted on FoldX, utilizing HLA-B*15:01 structures (n= 7) from the Protein Data Bank. RESULTS/ANTICIPATED RESULTS: CD8+ epitope conservation was estimated at 87.6-96.5% in S, 92.5-99.6% in M, and 94.6-99% for N. As the virus mutated, an increasing proportion of S epitopes experienced reduced predicted binding affinity: 70% of Omicron BQ.1- XBB.1.5 S epitopes experienced decreased predicted binding, as compared to ~ 3% and ~15% in Delta AY.100-AY.44 and Omicron BA.1-BA.5 respectively. Additionally, we identified several novel candidate haplotypes that may be susceptible to severe disease, notably HLA-A*32:01, -A*26:01, -B*58:01, and -B*53:01, and relatively protected from disease, such as -A*01:01, -A*02:01,-A*31:01, -B*15:01, -B*40:01, -B*44:03, and -B*57:01. In silico analysis of COVID peptides and HLA-B*15:01, a common allotype in the United States, largely matched predicted binding patterns. DISCUSSION/SIGNIFICANCE: To elicit long term COVID-19 immunity and prevent PASC, it is important to understand the relationship between T-cells, viral variants, and HLA genetics. This project is one of the first to explore the interaction between CD8+ epitope diversity and viral genetics for the majority of the United States population.

Mathematical topology and geometry-based classification of tauopathies

Neurodegenerative diseases, like Alzheimer’s, are associated with the presence of neurofibrillary lesions formed by tau protein filaments in the cerebral cortex. While it is known that different morphologies of tau filaments characterize different neurodegenerative diseases, there are few metrics of global and local structure complexity that enable to quantify their structural diversity rigorously. In this manuscript, we employ for the first time mathematical topology and geometry to classify neurodegenerative diseases by using cryo-electron microscopy structures of tau filaments that are available in the Protein Data Bank. By employing mathematical topology metrics (Gauss linking integral, writhe and second Vassiliev measure) we achieve a consistent, but more refined classification of tauopathies, than what was previously observed through visual inspection. Our results reveal a hierarchy of classification from global to local topology and geometry characteristics. In particular, we find that tauopathies can be classified with respect to the handedness of their global conformations and the handedness of the relative orientations of their repeats. Progressive supranuclear palsy is identified as an outlier, with a more complex structure than the rest, reflected by a small, but observable knotoid structure (a diagrammatic structure representing non-trivial topology). This topological characteristic can be attributed to a pattern in the beginning of the R3 repeat that is present in all tauopathies but at different extent. Moreover, by comparing single filament to paired filament structures within tauopathies we find a consistent change in the side-chain orientations with respect to the alpha carbon atoms at the area of interaction.

Synthesis, Anticancer Evaluation and Molecular Docking Studies of 1-(2-Fluorobenzyl)piperazine Triazoles: A Novel Breast Cancer Cell Inhibitors

New series of 1-(2-fluorobenzyl)piperazine triazoles 7(a-k) have been synthesized and evaluated for anticancer activity. The molecular structures of all the compounds were established by employing 1H NMR, 13C NMR and mass spectral analysis. In vitro anticancer activity was evaluated using MTT assay against MCF7 breast cancer cell line. Compounds 7i and 7j bearing 4-fluorophenyl and 2-fluorophenyl pendant from triazole substituent phenyl ring exhibited the highest anticancer efficacy with IC50 values of 12.09 µg/mL and 15.12 µg/mL, respectively. A molecular docking study conducted on human HER2 complexed with hercepatin fab was acquired from Protein Data Bank (PDB ID: 1N8Z). Molecular docking studies demonstrated Leu443, Gly442 and Leu27 as key residues interacting with active compounds.

Insight into antioxidant-like activity and computational exploration of identified bioactive compounds in Talinum triangulare (Jacq.) aqueous extract as potential cholinesterase inhibitors

BackgroundRecent reports have highlighted the significance of plant bioactive components in drug development targeting neurodegenerative disorders such as Alzheimer’s disease (AD). Thus, the current study assessed antioxidant activity and enzyme inhibitory activity of the aqueous extract of Talinum triangulare leave (AETt) as well as molecular docking/simulation of the identified phytonutrients against human cholinesterase activities.MethodsIn vitro assays were carried out to assess the 2,2- azinobis (3-ethyl-benzothiazoline-6-sulfonic acid) (ABTS) cation radicals and cholinesterase inhibitory activities of AETt using standard protocols. High performance liquid chromatography coupled with diode-array detection (HPLC–DAD) was employed to identify compounds in AETt. Also, for computational analysis, identified bioactive compounds from AETt were docked using Schrodinger's GLIDE against human cholinesterase obtained from the protein data bank (https://www.rcsb.org/).ResultsThe results revealed that AETt exhibited a significant concentration-dependent inhibition against ABTS cation radicals (IC50 = 308.26 ± 4.36 µg/ml) with butylated hydroxytoluene (BHT) as the reference. Similarly, AETt demonstrated a significant inhibition against acetylcholinesterase (AChE, IC50 = 326.49 ± 2.01 µg/ml) and butyrylcholinesterase (BChE, IC50 = 219.86 ± 4.13 µg/ml) activities with galanthamine as the control. Molecular docking and simulation analyses revealed rutin and quercetin as potential hits from AETt, having showed strong binding energies for both the AChE and BChE. In addition, these findings were substantiated by analyses, including radius of gyration, root mean square fluctuation, root mean square deviation, as well as mode similarity and principal component analyses.ConclusionOverall, this study offers valuable insights into the interactions and dynamics of protein–ligand complexes, offering a basis for further drug development targeting these proteins in AD.

Synthesis, Cytotoxic Activity Evaluation and Molecular Docking Studies of Some Benzimidazole Derivatives

In this study, the synthesis of 2-(2-acetyl-1H-benzimidazol-1-yl)-1-arylethanone (3a-3d) and 1-methyl-3-phenyl-benzo[4,5]imidazo[1,2-a]pyrazine derivatives (4a-4d) and to investigate their cytotoxic activity were aimed. APCI, IR, 1HNMR, and 13CNMR spectra were utilized to determine the structure of the synthesized compounds. The cytotoxic activity of selected compounds were detected in A549 (human lung carcinoma) and NIH3T3 (mouse embryonic fibroblasts) cell lines. Compounds 4c and 4d were found to be selectively cytotoxic against A549 and NIH3T3 cell lines. Molecular docking studies were performed using the data retrieved from the Protein Data Bank server (PDBID: 4QTX).

KCD: A prediction web server of knowledge-based circular dichroism.

We present a web server that predicts the far-UV circular dichroism (CD) spectra of proteins by utilizing their three-dimensional (3D) structures from the Protein Data Bank (PDB). The main algorithm is based on the classical theory of optical activity together with a set of atomic complex polarizabilities, which are obtained from the analysis of a series of synchrotron radiation CD spectra and their related 3D structures from the PDB. The results of our knowledge-based CD method (KCD) are in good agreement with measured spectra that could include the effect of D-amino acids. Our method also delivers some of the most accurate predictions, in comparison with the calculated spectra from well-established models. Specifically, using a metric of closeness based on normalized absolute deviations between experimental and calculated spectra, the mean values for a series of 57 test proteins give the following figures for such models: 0.26 KCD, 0.27 PDBMD2CD, 0.30 SESCA, and 0.47 DichroCalc. From another point of view, it is worth mentioning the remarkable capabilities of the recent approaches based on artificial intelligence, which can precisely predict the native structure of proteins. The structure of proteins, however, is flexible and can be modified by a diversity of environmental factors such as interactions with other molecules, mechanical stresses, variations of temperature, pH, or ionic strength. Experimental CD spectra together with reliable predictions can be utilized to assess eventual secondary structural changes. A similar kind of evaluation can be done for the case of an incomplete protein structure that has been reconstructed by using different approaches. The KCD method can be freely accessed from: https://kcd.cinvestav.mx/.

RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction

With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser availability and lower structural diversity of the experimentally resolved RNA structures in comparison to protein structures. These challenges are often poorly addressed by the existing literature, many of which report inflated performance due to using training and testing sets with significant structural overlap. Further, the most recent Critical Assessment of Structure Prediction (CASP15) has shown that deep learning models for RNA structure are currently outperformed by traditional methods.In this paper we present RNA3DB, a dataset of structured RNAs, derived from the Protein Data Bank (PDB), that is designed for training and benchmarking deep learning models. The RNA3DB method arranges the RNA 3D chains into distinct groups (Components) that are non-redundant both with regard to sequence as well as structure, providing a robust way of dividing training, validation, and testing sets. Any split of these structurally-dissimilar Components are guaranteed to produce test and validations sets that are distinct by sequence and structure from those in the training set. We provide the RNA3DB dataset, a particular train/test split of the RNA3DB Components (in an approximate 70/30 ratio) that will be updated periodically. We also provide the RNA3DB methodology along with the source-code, with the goal of creating a reproducible and customizable tool for producing structurally-dissimilar dataset splits for structural RNAs.

FLEXR GUI: a graphical user interface for multi-conformer modeling of proteins.

Proteins are well known 'shapeshifters' which change conformation to function. In crystallography, multiple conformational states are often present within the crystal and the resulting electron-density map. Yet, explicitly incorporating alternative states into models to disentangle multi-conformer ensembles is challenging. We previously reported the tool FLEXR, which, within a few minutes, automatically separates conformational signal from noise and builds the corresponding, often missing, structural features into a multi-conformer model. To make the method widely accessible for routine multi-conformer building as part of the computational toolkit for macromolecular crystallography, we present a graphical user interface (GUI) for FLEXR, designed as a plugin for Coot 1. The GUI implementation seamlessly connects FLEXR models with the existing suite of validation and modeling tools available in Coot. We envision that FLEXR will aid crystallographers by increasing access to a multi-conformer modeling method that will ultimately lead to a better representation of protein conformational heterogeneity in the Protein Data Bank. In turn, deeper insights into the protein conformational landscape may inform biology or provide new opportunities for ligand design. The code is open source and freely available on GitHub at https://github.com/TheFischerLab/FLEXR-GUI.

Identification of Repurposed FDA Drugs by Targeting Sclerostin via the Wnt Pathway for Alveolar Bone Formation

Objective Natural wingless-related integration site (Wnt) pathway antagonist sclerostin (SOST) has attracted much attention because unusual bone illnesses characterized by the increased bone mass result from its absence of action. The Wnt ligand is prevented from attaching to the Frizzled family receptor when SOST is present. In the active destruction complex, -catenin is phosphorylated. -Catenin molecules do not enter the nucleus and are broken down by a proteasome. As a result, Wnt-responsive genes are not activated, which lowers bone formation and raises bone resorption. A humanized monoclonal antibody called romosozumab binds to and inhibits SOST with significant cardiac side effects. As a result, the current study's objective is to find and screen Food and Drug Administration (FDA) medications that target SOST. Materials and Methods SOST's structure was retrieved from Protein Data Bank (PDB) (ID: 6l6r). Pharmacophore modeling and molecular operating environment-based virtual testing of FDA-approved medicines. Using the Desmond program, docking and molecular dynamics simulations were performed. Results Our findings revealed medications with FDA approval (ZINC000253387843) Amphotericin B. The stability and receptor–ligand interactions are pretty substantial, as demonstrated by the findings of docking and Molecular dynamics simulations, which have a docking score of −7.3 k/mol and root mean square deviation stability at 40 nanoseconds, respectively. Conclusion The suggested pharmacological therapy shows promise since it uses the Wnt pathway to target the primary bone formation mechanism. However, additional prospective studies are required to apply the available data to clinical practice.

Unveiling success determinants for AMB-assisted phase expansion of fusion proteins in ARP/wARP

Fusion proteins (FPs) are frequently utilized as a biotechnological tool in the determination of macromolecular structures using X-ray methods. Here, we explore the use of different protein tags in various FP, to obtain initial phases by using them in a partial molecular replacement (MR) and constructing the remaining FP structure with ARP/wARP. Usually, the tag is removed prior to crystallization, however leaving the tag on may facilitate crystal formation, and structural determination by expanding phases from known to unknown segments of the complex. In this study, the Protein Data Bank was mined for an up-to-date list of FPs with the most used protein tags, Maltose Binding Protein (MBP), Green Fluorescent Protein (GFP), Thioredoxin (TRX), Glutathione transferase (GST) and the Small Ubiquitin-like Modifier Protein (SUMO). Partial MR using the protein tag, followed by automatic model building, was tested on a subset of 116 FP. The efficiency of this method was analyzed and factors that influence the coordinate construction of a substantial portions of the fused protein were identified. Using MBP, GFP, and SUMO as phase generators it was possible to build at least 75 % of the protein of interest in 36 of the 116 cases tested. Our results reveal that tag selection has a significant impact; tags with greater structural stability, such as GFP, increase the success rate. Further statistical analysis identifies that resolution, Wilson B factor, solvent percentage, completeness, multiplicity, protein tag percentage in the FP (considering amino acids), and the linker length play pivotal roles using our approach. In cases where a structural homologous is absent, this method merits inclusion in the toolkit of protein crystallographers.

Abstract 6001: Repurposing dihydroergotamine mesylate as a potential MCL-1 inhibitor and its antitumor effect in pancreatic cancer cells

Abstract Background: Myeloid cell leukemia 1 (MCL-1) is an anti-apoptotic member of the BCL-2 protein family which is involved in intrinsic (mitochondrial) pathway of apoptosis and is deregulated in several cancers. Current MCL-1 specific inhibitors have limitations in terms of efficiency, tolerability, and off-target effects and challenges associated with shallow binding groove of MCL-1. In this study, dihydroergotamine mesylate (DHE), an approved anti-migraine drug, was found as a potential ligand of the MCL-1 BH3-binding pocket and was further evaluated for its potential MCL-1 inhibition and effect on pancreatic cancer cells. Methods: PubChem Database was used for library preparation and virtual screening (OpenBabel and AutoDock Vina). The 55 crystal structures available for MCL-1 in Protein Data Bank (PDB) were clustered using principal component analysis with Bio3D R-package to select representative PDBs for further screening. Consensus voting and k-means clustering was used to shortlist compounds for Molecular dynamics (MD) simulations. MD simulations (100 nanoseconds) by GROMACS were employed to measure stability of ligands in MCL-1 binding pocket. DHE efficacy on cell proliferation was evaluated with MTS and clonogenic assays and on apoptosis using the FITC/Annexin V flow cytometry assay in MiaPaCa-2 and PANC-1 pancreatic cancer cell lines. Effect of DHE treatment on MCL-1 gene and protein expression was analyzed using qRT-PCR and western blotting, respectively. Results: Computational screening identified DHE (PubChem ID: 12308974) as a top hit exhibiting consistent interactions within the MCL-1 BH3-binding pocket. Notably, DHE displayed stable RMSD and formed four hydrogen bonds throughout MD simulations. In vitro results showed that DHE significantly reduced viability of MiaPaCa-2 and PANC-1 pancreatic cancer cells with IC50 values of 31 μM and 47 μM, respectively. DHE significantly (P<0.001) suppressed colony formation by pancreatic cancer cells in a dose-dependent (10μM, 20μM, and 40μM) manner. The percentage of Annexin V-positive and Propidium iodide-negative fraction in MiaPaCa-2 cells treated with DHE also increased in a dose-dependent manner. DHE did not have significant effect on MCL-1 mRNA expression. However, western blotting demonstrated MCL-1 protein levels were significantly reduced in both pancreatic cancer cell lines upon DHE treatment. Conclusion: Our findings showed DHE suppresses the pancreatic cancer cells viability and proliferation, and significantly increased early apoptotic cells. DHE treatment had no effect on MCL-1 at the transcription level but significantly reduced the protein, suggesting posttranslational modifications, and thus future mechanism of action studies is required. The present study reports that the anti-migraine drug DHE might be a novel therapeutic agent for pancreatic cancer and needs to be explored further. Citation Format: Pooja Mittal, Jae Ho Lo, Shivani Soni, Francesca Battaglin, Shradheya RR Gupta, Lesly Torres-Gonzalez, Yan Yang, Sandra Algaze, Priya Jayachandran, Karam Ashouri, Alexandra Wong, Wu Zhang, Jian Yu, Lin Zhang, Joshua Millstein, Indrakant Kumar Singh, Heinz Josef Lenz. Repurposing dihydroergotamine mesylate as a potential MCL-1 inhibitor and its antitumor effect in pancreatic cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6001.

Abstract 2348: A global multiscale map of protein assemblies from integration of protein interactions and images

Abstract Cells regulate growth and function through a hierarchical structure of subcellular protein assemblies, in which alterations can result in cellular dysfunction leading to disease such as cancer. Much of this structure remains uncharted, resulting in recent efforts to map subcellular organization at different physical scales. Here, we report a global architectural map of human cancer cell protein assemblies spanning 10−9 to 10−5 nm, based on integration of near-proteome-wide affinity purification mass spectrometry-based protein interactions and immunofluorescent imaging in U-2 OS osteosarcoma cancer cells. The U-2 OS multi-scale integrated cell map places >5000 proteins into 270 distinct subcellular protein assemblies across biological scales, representing approximately half of expressed proteins. There are known organelles and protein complexes recovered in the map, as well as 152 putative assemblies, such as a putative interferon signaling complex consisting of a serine protease and STAT transcription factors. The map also incorporates 128 previously uncharacterized proteins into protein assemblies, such as C18orf21 association with a canonical RNAse complex. Protein subsystems in the U-2 OS map were evaluated for the potential for downstream integrative structural modeling, where available structural data (e.g. crosslinking, Protein Data Bank structures, AlphaFold predictions) are combined in a Bayesian framework to model the physical structures of protein complexes. Using this framework, we created an integrated structural model of new proteins interacting with the Rag-Ragulator complex, which regulates MAPK and mTOR signaling. In summary, the global proteome architecture map provides a resource for cellular biology discovery and the systematic determination of protein functions and structures. Citation Format: Leah V. Schaffer, Mengzhou Hu, Edward L. Huttlin, Gege Qian, Abantika Pal, Neelesh Soni, Andrew Latham, Laura Pontano Vaites, Trang Le, Yue Qin, Christopher Churas, Dexter Pratt, Ignacia Echeverria, Andrej Sali, J Wade Harper, Steven P. Gygi, Emma Lundberg, Trey Ideker. A global multiscale map of protein assemblies from integration of protein interactions and images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2348.

Green processing and characterization of Novel HETERO-ligand Co(II) complexes with cyanopyridine derivatives: Quantitative structure, Computational assessment, and Biological activity relationship

Novel hetero-ligand Co(II) complexes prepared from mixed ligand of ammonium metavanadate and cyanopyridine derivatives as a polydentate ligands by an easy and green ball milling technique to limit the use of hazardous compounds in the preparation process. The isolated solid chelates were demonstrated via various spectroscopic techniques including (13C NMR, 1H NMR, UV-visible, IR, SEM, XRD, and EXD), thermal analyses (TG, DTG), and elemental analyses. Also, the optimized structure for the isolated solid compounds were supported by applying DFT theory along with the calculations of altered energetic parameters for instance EHOMO and ELUMO. Moreover, the optimized target isolated solid compounds were proposed to be used for docking and were ordered using the defaulting methodology of the Ligprep software in Schrödinger's suite. Whereas, the three-dimensional complex structures of E. coli (PDB code: 1 xk6), S. aureus (PDB code: 6 gyw), C. albicans (PDB code: 3 ppc) and Covid 19 virus (PDB code: 7 jpy) were regained from the Protein Data Bank. Lastly, Bleomycin dependent DNA damage, antioxidant, and anti-microbia were performed for all isolated solid compounds to invistigate the impact of complexation on the biological activity of cyanopyridine derivatives.

A study of the molecular mechanism of action of Jiawei Guizhishaoyaozhimu Decoction during rheumatoid arthritis therapy based on basic of network pharmacology and experimental verification.

Rheumatoid arthritis (RA) is a chronic autoimmune disease, which primarily affects the joints. The aim of the present study was to predict the main active ingredients of Jiawei Guizhishaoyaozhimu Decoction (JWGZSYZMD) and potential targets of this treatment during RA therapy by using molecular docking and network pharmacology methods. In addition, another aim was to investigate the therapeutic effects and mechanism of JWGZSYZMD on joint inflammation in rat models of collagen Ⅱ-induced arthritis (CIA). JWGZSYZMD ingredients and targets and genes associated with RA first extracted from traditional Chinese medicine (TCM) Systems Pharmacology Database and Analysis Platform, Bioinformatics Analysis Tool of Molecular Mechanism-TCM and Genecards databases, which were then transferred to the STRING database to set up protein interaction networks. The crystal structures of target proteins were also downloaded from the Protein Data Bank before molecular docking of compounds onto the protein targets was performed using AutoDock Vina software. In addition, a drug compound target visualization network was constructed using Cytoscape 3.7.2 software, which was used to elucidate the main mechanism underlying the anti-RA effect of JWGZSYZMD. A CIA rat model was established and animals were divided into the control, CIA model, JWGZSYZMD treatment (low-, medium- and high-dose) and tripterygium glycoside groups. Compared with the rats in the CIA model group, the joint scores of the rats in the high-dose group of JWGZSYZMD were significantly lower after 21 days of treatment. The expression levels of IL-6, TNF-α, IL-1β and IL-17A in the synovial supernatant of the model rats were lower compared with those in the CIA group. Also, the expression of the aforementioned cytokines in the high-dose JWGZSYZMD group was significantly lower compared with those in the CIA model group. To conclude, using molecular docking combined with network pharmacology, the material basis and molecular mechanism underlying the effects of JWGZSYZMD during RA therapy were studied, which could potentially provide a reference for future clinical applications.