---
title: "GO-Term Enrichment of Proteome-Scale Docking Profiles to Reduce Candidate Target Space"
id: "biorxiv-14-go-term-enrichment-of-proteome-scale-docking-profiles-as-a-biological-search"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-14-go-term-enrichment-of-proteome-scale-docking-profiles-as-a-biological-search"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.15.751870v1?rss=1"
published_at: "2026-09-22T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# GO-Term Enrichment of Proteome-Scale Docking Profiles to Reduce Candidate Target Space
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-14-go-term-enrichment-of-proteome-scale-docking-profiles-as-a-biological-search
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.15.751870v1?rss=1)
- **Published At:** 2026-09-22T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors developed a workflow that converts ranked **proteome-scale docking** results into stable **Gene Ontology (GO) Biological Process** enrichment signatures to reduce and prioritize candidate protein targets for small molecules. - Docking outputs were kept at the PDB-chain level, mapped to unique human genes, and analyzed with **PANTHER** overrepresentation against the structural gene universe screened. - GO enrichment was computed repeatedly across top-ranked protein lists (from 25 to 505 in steps of 10); a stable compound-level GO profile was selected when Jaccard similarity ≥ 0.80 across three consecutive transitions. - Benchmarking used the **Yamanishi** drug-target interaction dataset with 682 mapped compounds split into development and held-out sets (552/130) and evaluated at canonical, mechanistic, and fine-mechanistic biological label resolutions. - In the corrected full benchmark (642 compounds with usable canonical GO profiles), within-class GO-profile similarity exceeded between-class similarity (delta = 0.0088; permutation p = 0.00222); canonical class explained 1.34% of GO-profile variation by PERMANOVA (p < 1e-4). - Fine-mechanistic labels produced stronger organization across the full dataset (delta = 0.0245; PERMANOVA R2 = 0.0976; both p < 1e-4). - Held-out validation showed modest, metric-dependent results: mechanistic labels reached significance by PERMANOVA (R2 = 0.0626, p = 0.0437); frozen fine-mechanistic analysis showed greater within-class similarity and significant nearest-neighbor recovery but not significant PERMANOVA. - A principal held-out search-space experiment evaluated 107 compounds against 753,492 candidate protein rows and 424 gold-standard targets: a direct GO gate retained 1.26% of candidates while retaining 11.32% of known targets (8.96-fold enrichment); ontology-propagated GO associations retained 5.10% of candidates and 21.46% of known targets (4.21-fold enrichment). - When matching candidate-space sizes, GO-associated prioritization retained higher fractions of known targets than direct GO gating at 5%, 10%, and 20% candidate sizes. - Additive protein-level GO reranking was mixed: among 605 evaluable compounds, 21.7% improved best known-target rank, but mean reciprocal rank decreased from 0.0276 to 0.0189. - Authors conclude **GO enrichment** is useful as an intermediate **biological search-space reduction and prioritization layer**, but not as a universal direct target-scoring replacement.
## Clinical Analysis & Structured Key Points
GO-Term Enrichment of Proteome-Scale Docking Profiles as a Biological Search-Space Reduction Layer for Protein Target Discovery | bioRxiv Skip to main content New Results GO-Term Enrichment of Proteome-Scale Docking Profiles as a Biological Search-Space Reduction Layer for Protein Target Discovery Chase M Harms , Judith Klein-Seetharaman doi: https://doi.org/10.64898/2026.09.15.751870 Chase M Harms Arizona State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: chase.harms{at}gmail.com Judith Klein-Seetharaman Arizona State University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Info/History Metrics Preview PDF Abstract Identifying protein targets from phenotype-first or mechanism-uncertain compounds remains difficult because proteome-scale docking can generate thousands of structurally plausible interactions per compound. We developed a workflow that converts ranked proteome-scale docking profiles into stable Gene Ontology (GO) Biological Process enrichment signatures and evaluates whether those signatures can reduce the candidate target space while preferentially retaining known drug-target relationships. Docking targets were retained at the PDB-chain level, mapped to unique human gene identities, and analyzed with PANTHER overrepresentation against the screened structural gene universe. GO enrichment was evaluated from the top 25 through 505 ranked proteins in increments of 10, and a stable compound-level GO profile was selected using a Jaccard stability threshold of 0.80 across three consecutive transitions. Benchmarking used Yamanishi drug-target interactions, with 682 mapped compounds assigned to a prespecified development/held-out split (552/130) and evaluated at canonical, mechanistic, and fine-mechanistic biological resolutions. In the full corrected benchmark, 642 compounds with usable canonical GO profiles showed greater within-class than between-class similarity (0.1019 versus 0.0931; delta = 0.0088; 100,000-permutation p = 0.00222), and canonical class explained 1.34% of multivariate GO-profile variation by PERMANOVA (p < 1e-4). Fine-mechanistic labels showed stronger organization in the full dataset (delta = 0.0245; PERMANOVA R-squared = 0.0976; both p < 1e-4). Held-out validation was more modest and metric-dependent: mechanistic labels were significant by PERMANOVA (R-squared = 0.0626, p = 0.0437), whereas the frozen fine-mechanistic analysis showed greater within-class similarity (0.1350 versus 0.1110; p = 0.038) and significant nearest-neighbor recovery (p = 0.0495), but not significant PERMANOVA (p = 0.119). The principal held-out search-space experiment evaluated 107 compounds, 753,492 candidate protein rows, and 424 represented gold-standard targets. A direct GO gate retained 1.26% of candidates while retaining 11.32% of known targets (8.96-fold enrichment); ontology-propagated GO associations retained 5.10% of candidates and 21.46% of known targets (4.21-fold enrichment). At matched candidate-space sizes, GO-associated prioritization retained 27.59% versus 22.41% of known targets at approximately 5% of candidates, 39.39% versus 36.08% at 10%, and 58.73% versus 56.13% at 20%. By contrast, additive protein-level GO reranking was heterogeneous: among 605 evaluable compounds, 21.7% improved their best known-target rank, but mean reciprocal rank decreased from 0.0276 to 0.0189. These results support GO enrichment as an intermediate biological search-space reduction and prioritization layer rather than a universal direct target-scoring function. Keywords: proteome-scale docking; Gene Ontology; target discovery; targetome; PANTHER; target fishing; biological filtering; search-space reduction; Yamanishi benchmark Competing Interest Statement The authors have declared no competing interest. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license . Back to top Previous Next Posted September 22, 2026. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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