Data Sheet 2_Integrative proteome-wide structural analysis and high-throughput docking ide

A computational screening dataset from 2026 by Anderson Pereira Soares, identifying potential antiviral scaffolds against five flaviviruses. It contains results from homology modeling, pocket prediction, and high-throughput docking of a 160-compound library against viral proteins. The dataset is shared under a CC-BY-4.0 license and is 5.3 KB in size.

Use Cases

• Prioritizing lead compounds for experimental validation based on estimated binding energies (Kd) and multitarget profiles.
• Analyzing conserved binding sites across Zika, Yellow Fever, West Nile, Saint Louis Encephalitis, and Usutu viruses.
• Identifying natural product scaffolds or repurposed drugs with dual-site binding potential to viral proteins like NS5 polymerase and E glycoprotein.

Strengths

• Integrates multiple computational methods: homology modeling, pocket prediction (Concavity), electrostatic profiling (APBS), and pharmacokinetic filtering.
• Includes specific estimated binding affinities for lead compounds, such as temoporfin (Kd ≈ 1.2 nM).
• Focuses on a defined library of 160 natural product scaffolds and repurposed antivirals.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (5.3 KB), indicating limited scope, likely containing summary results rather than raw docking data.

Provenance

Source

figshare, author Anderson Pereira Soares

Collection Method

Computational pipeline combining homology modeling, structure-based pocket prediction, electrostatic profiling, pharmacokinetic filtering, and exhaustive molecular docking (AutoDock4/Vina).

Freshness

Last updated 2026-04-30 05:24:54; freshness should be verified.