ArticleComputational and structural biotechnology journal2026
MetaMP Ecosystem for Unified, Auditable, and Benchmark-Ready Data for Reliable Membrane Protein Annotation.
Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Experimentally resolved membrane-protein structures have increased substantially, yet annotations remain fragmented across resources differing in scope, curation criteria, and identifier conventions, complicating cross-database comparison and downstream analysis. We present MetaMP, a membrane-protein reconciliation and benchmarking platform that harmonizes metadata from MPstruc, RCSB PDB, OPM, and UniProt into a unified, searchable resource integrating 4,089 unique structures. MetaMP provides provenance-aware discrepancy analysis, quality-control workflows, and 2 assistive modules as reproducible baselines: (a) a broad structural-group classifier trained on OPM-derived membrane-orientation descriptors and (b) a transmembrane-segment benchmarking layer integrating sequence-based and structure-derived topology sources. Cross-source comparison identified 121 broad-group conflicts between MPstruc and OPM (2.96% of 4,089 harmonized entries). These contested cases were expert-reviewed to form a 121-record discrepancy benchmark. Under strict label matching, OPM agreed with expert annotations for 96 of 121 records (79.34%), while the MetaMP assistive classifier agreed for 25 of 121 (20.66%) and MPstruc for 17 of 121 (14.05%). Under benchmark-aware evaluation applying a biologically motivated label-collapsing rule, agreement reached 88.43% for MetaMP, 80.99% for OPM, and 78.51% for MPstruc. In a 24-participant task-oriented user study, structured training was associated with faster task completion, and the adapted SUS-style usability score averaged 72.81, placing the system in the above-average to good range. MetaMP is not intended to replace primary databases, but to make disagreement among them explicit, traceable, and biologically interpretable, providing a reproducible framework for annotation harmonization, expert-guided curation, and membrane-protein benchmarking. Source code and deployment materials are available at https://github.com/Ebenco36/MetaMP-Server.
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