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Lookup NU author(s): Zhen Ou, Dr Katherine JamesORCiD, Professor Anil Wipat
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Selecting representative subsets from large protein sequence datasets is a common challenge in enzyme discovery and related tasks under limited screening capacity. In practice, candidate panels are often constructed using clustering-based redundancy reduction or manual selection guided by phylogenetic or similarity-network analyses, which do not directly optimise subset diversity and require threshold tuning or expert interpretation. Here, we present a bi-level diversity-optimisation framework for representative protein panel selection implemented using a local search heuristic that iteratively updates panel composition to improve diversity. The method formulates panel design as a combinatorial optimisation problem over pairwise distance matrices, combining a MaxMin objective to enforce minimum separation between selected sequences with a MaxSum objective to increase global dispersion. This formulation enables the direct construction of fixed-cardinality panels while remaining independent of the similarity representation used to compute pairwise distances. Benchmarking across four Pfam families shows that the bi-level formulation consistently reduces redundancy among selected sequences, lowering maximum pairwise identity by 43-46% relative to the previous MaxSum-based formulation, while maintaining comparable or improved EC-label coverage. The framework can incorporate sequence- or structure-based similarity measures, providing a flexible strategy for constructing diverse representative panels across homologous protein families.
Author(s): Ou Z, James K, Charnock S, Wipat A
Publication type: Article
Publication status: Submitted
Journal: bioRxiv
Year: 2026
Publisher: Cold Spring Harbor Laboratory
URL: https://doi.org/10.64898/2026.04.17.719243
DOI: 10.64898/2026.04.17.719243
Notes: This article is a preprint and has not been certified by peer review.
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