ConTP reshapes transporter functional space to resolve substrate specificity beyond evolutionary proximity

Membrane transporter annotation has long relied on a homology-centric paradigm that treats evolutionary proximity as a proxy for substrate specificity. Yet transporter functional space is intrinsically long-tailed, partially multi-label, and often decoupled from phylogeny, creating systematic blind spots in substrate-level inference. We reformulate substrate annotation as a chemically coherent multi-label problem and construct benchmarks spanning 70 fine-grained substrate types and 1352 Transporter Classification (TC) families. Here we introduce ConTP, an evolution-informed contrastive framework that realigns pretrained protein language model embeddings around substrate semantics rather than sequence similarity, enabling taxon-agnostic, prototype-based inference. In this aligned manifold, cross-family convergence, exemplified by sodium transport across distinct TC superfamilies, and authentic multi-substrate specificity in NRAMP transporters are faithfully recovered. Furthermore, projection of generated sequences exposes substrate-fidelity violations in contemporary design models. Together, these findings support a geometry-aware view of transporter specificity beyond raw evolutionary similarity.

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Publication Details

Journal
Communications Biology
Published
2026-09-28
DOI
https://doi.org/10.1038/s42003-026-11044-8
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
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article

ConTP reshapes transporter functional space to resolve substrate specificity beyond evolutionary proximity

Xiaopeng Xu, Mark A. Tester, Yunchuan Wang, Wenjia He et al.
Communications Biology
Machine Learning in Bioinformatics
article

ConTP reshapes transporter functional space to resolve substrate specificity beyond evolutionary proximity

Xiaopeng Xu, Mark A. Tester, Yunchuan Wang, Wenjia He, Xin D. Gao, Chenjie Feng, Min Zhou, Junbo Yin
article en

Abstract

Membrane transporter annotation has long relied on a homology-centric paradigm that treats evolutionary proximity as a proxy for substrate specificity. Yet transporter functional space is intrinsically long-tailed, partially multi-label, and often decoupled from phylogeny, creating systematic blind spots in substrate-level inference. We reformulate substrate annotation as a chemically coherent multi-label problem and construct benchmarks spanning 70 fine-grained substrate types and 1352 Transporter Classification (TC) families. Here we introduce ConTP, an evolution-informed contrastive framework that realigns pretrained protein language model embeddings around substrate semantics rather than sequence similarity, enabling taxon-agnostic, prototype-based inference. In this aligned manifold, cross-family convergence, exemplified by sodium transport across distinct TC superfamilies, and authentic multi-substrate specificity in NRAMP transporters are faithfully recovered. Furthermore, projection of generated sequences exposes substrate-fidelity violations in contemporary design models. Together, these findings support a geometry-aware view of transporter specificity beyond raw evolutionary similarity.

Communications Biology
Ningxia Medical University (CN), King Abdullah University of Science and Technology (SA)
Openalex Percentile: Top 19%
Machine Learning in Bioinformatics
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ConTP reshapes transporter functional space to resolve substrate specificity beyond evolutionary proximity — Xiaopeng Xu, Mark A. Tester, et al. · Communications Biology (2026) | TGRS Research Map | TGRS