Integrative Omics-Guided Druggability Analysis of VCX2 in Hepatocellular Carcinoma

A 1.9 MB supplementary document detailing a computational pipeline for identifying novel drug targets in hepatocellular carcinoma. The study, authored by Luis Daniel Goyzueta-Mamani and last updated in May 2026, integrates single-cell RNA sequencing, molecular dynamics simulations, and virtual screening of a Peruvian natural products database. It identifies the cancer/testis antigen VCX2 as a promising target and luteolin-5-O-glucoside as a top-scoring ligand.

Use Cases

• Benchmarking computational pipelines for target prioritization based on the described omics-guided methodology.
• Studying protein-ligand interactions for shallow, polar binding sites based on the VCX2 model and docking results.
• Identifying lead compounds from natural product libraries based on the virtual screening of the PeruNPDB.
• Analyzing the role of cancer/testis antigens in chromosomal instability based on the findings for VCX2.

Strengths

• Provides a detailed, hypothesis-generating framework integrating multiple computational biology techniques.
• Includes specific docking and binding affinity scores (e.g., Glide score: -3.949 ± 0.85 kcal/mol) for the top ligand.
• Released under a permissive CC-BY-4.0 license, allowing for reuse and adaptation.

Limitations

• The core data is a 1.9 MB DOCX file; the underlying computational datasets (e.g., gene expression matrices, protein structures) are not directly included.
• Row count and column-level documentation for any associated data are unknown, limiting suitability assessment.
• The study's findings are computational and require experimental validation.

Provenance

Source

figshare

Collection Method

Computational pipeline integrating scRNA-seq, DGE analysis, PPI mapping, AlphaFold modeling, molecular dynamics, and virtual screening.

Time Range

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Freshness

Last updated 2026-05-18 05:39:21

Geography

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