DOI: 10.6084/m9.figshare.28738289
title: Novel Insights into the Structural and Functional Analysis of ORF10 Protein Binding Pockets: Identification and Characterization of Two Distinct Druggable Sites.

description: Novel Claim & Introduction:The ORF10 protein contains two distinct binding pockets, P_0 and P_1, with differential physicochemical properties that suggest distinct functional roles. Pocket P_0, characterized by its larger size, higher hydrophobicity, and better druggability score, is likely the primary site for hydrophobic ligand binding or interactions with host proteins such as ZYG11B. In contrast, Pocket P_1, with its higher number of hydrogen bond acceptors and balanced hydrophobicity, may serve as a secondary site for polar or mixed-type ligands. These findings provide a structural basis for understanding ORF10's potential interactions with host proteins and highlight P_0 as a promising target for drug discovery.This dataset presents the structural analysis of the ORF10 protein from SARS-CoV-2, including its theoretical model, secondary structure predictions, and visualization outputs. The study aims to explore the structural features and potential functional regions of ORF10, which is an accessory protein encoded by the SARS-CoV-2 genome. Below is a detailed breakdown of the contents:1. Theoretical Model of ORF10 ProteinThe 3D structure of the ORF10 protein was modeled using the SWISS leveraging AlphaFold v2 as the primary template. The resulting model consists of 38 amino acids, with the sequence: MGYINVFAFPFTIYSLLLCRMNSRNYIAQVDVVNFNLT.<br>Key details about the model include:Template Information :The model was built based on the AlphaFold v2 prediction for the UniProt ID A0A0U3GM54.Model Quality Metrics :Global Model Quality Estimate (GMQE) : 0.56, indicating moderate reliability.Sequence Identity : 72.22%, reflecting a reasonable alignment between the query and template sequences.Structure Features :The model predicts a globular structure with specific secondary structural elements, such as helices, strands, and coils, as identified through computational tools.2. Secondary Structure PredictionsSecondary structure predictions were performed to identify regions of α-helices, β-strands, and coils within the ORF10 protein. These predictions provide insights into the protein's folding patterns and potential functional domains:Helices :Regions predicted to form α-helices are likely to be stable and contribute to the protein's tertiary structure.Strands :β-strand regions may participate in forming β-sheets or other extended structures.Coils :Coil regions lack regular secondary structure but may play roles in flexibility or ligand binding.Confidence Levels :Confidence scores for these predictions are included, highlighting regions with high certainty in their structural assignments.3. Binding Pocket AnalysisBinding pocket identification was conducted using DoGSiteScorerTwo distinct pockets were identified:Pocket P_0 :Volume : 164.67 Å³Surface Area : 463.76 Å²Depth : 8.93 ÅDruggability Score : 0.29Hydrophobicity Ratio : 0.73Key Residues : Likely involves residues around positions 20–30, contributing to hydrophobic interactions.Pocket P_1 :Volume : 122.82 Å³Surface Area : 327.04 Å²Depth : 8.06 ÅDruggability Score : 0.24Hydrophobicity Ratio : 0.50Key Residues :Likely involves residues around positions 10–20, balancing hydrophobic and polar interactions.These pockets suggest potential sites for ligand binding or interaction with host proteins, particularly given their druggability scores and residue compositions.4. VisualizationsTo aid in understanding the structural and functional characteristics of ORF10, I generated several visualizations:3D Animation Frames :A series of 360 frames (TahirHB_Extract_frame_*.png) representing a 360-degree rotation of the ORF10 protein structure. These frames facilitate the exploration of the protein's surface topology and highlight potential binding sites.Ramachandran Plot :Analyzed to assess the stereochemical quality of the model, ensuring that most residues fall within allowed or generously allowed regions.Amino Acid Properties :Categorized residues based on their chemical properties (e.g., hydrophobic, polar, aromatic) to understand their contributions to the protein's overall structure and function.Secondary Structure Diagram :Depicts the distribution of helices, strands, and coils along the sequence, providing a visual summary of the protein's secondary structure.<br>5. Methodological DetailsThe following methods were employed in this study:Homology Modeling : SWISS-MODEL was used to generate the 3D structure based on AlphaFold v2 predictions.Binding Pocket Prediction :DoGSiteScorer was utilized to identify and characterize potential binding pockets.Visualization Tools :PyMOL was used to generate 3D animations and analyze structural features.Secondary Structure Prediction :Tools like DeepTMHMM and PsiPred were employed to predict secondary structures and confidence levels.<br>6. ImplicationsThe structural and functional analysis of ORF10 provides valuable insights into its potential roles in viral pathogenesis and interactions with host proteins.<br>Key findings include:Potential Druggable Sites : The identified pockets offer targets for small-molecule inhibitors or therapeutic interventions.Functional Regions : The secondary structure predictions highlight regions critical for stability and ligand binding.Biological Relevance : Understanding ORF10's structure can inform studies on its interactions with host proteins like ZYG11B and CUL2ZER1, potentially elucidating its role in immune evasion or viral replication.<br>7. Data Files IncludedThe deposit includes the following files:PDB File : The theoretical 3D model of ORF10 in PDB format.Animation Frames : 360 PNG images (TahirHB_Extract_frame_*.png) representing a 360-degree rotation of the protein.Analysis Reports : Detailed reports from tools like DoGSiteScorer, Ramachandran plots, and secondary structure predictions.Metadata : Descriptions of the modeling process, template usage, and quality metrics.8. AcknowledgmentsThis work leverages publicly available resources, including:SWISS-MODEL for homology modeling.AlphaFold v2 for template generation.DoGSiteScorer for binding pocket prediction.PyMOL for visualization and analysis.<br>9. LicensingThe SWISS-MODEL protein model is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). Users must cite the corresponding references mentioned in the header section of the PDB file when publishing results derived from this model.<br>ConclusionThis dataset offers a comprehensive structural and functional analysis of the ORF10 protein, providing a foundation for further experimental validation and drug discovery efforts targeting SARS-CoV-2. The inclusion of theoretical models, binding pocket predictions, and visualizations makes this resource valuable for researchers studying viral proteins and their interactions.Published on Figshare, DOI: 10.6084/m9.figshare.28738289https://orcid.org/0009-0003-6042-1616This dataset presents the structural analysis of the ORF10 protein from SARS-CoV-2, including its theoretical model, secondary structure predictions, and visualization outputs. The study was conducted by TahirHB, an independent researcher. For more information about the author, please visit their ORCID profile: https://orcid.org/0009-0003-6042-1616 .