Evidence map›Paper›PMID 41489796›Full record

ArticleBioresources and bioprocessing2026

Resurrection and characterization of ancestral xylose transporters enhance the capability of xylose uptake in the mixed sugar co-fermentation of Recombinant Saccharomyces cerevisiae.

Peining Zhang, Zhaoqing He, Huanan Li, Zhengbing Jiang

Abstract read
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Article in Bioresources and bioprocessing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Synthetic and systems biotechnology · 2026
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Peining ZhangState Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, People's Republic of China.
Zhaoqing HeState Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, People's Republic of China.
Huanan LiState Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, People's Republic of China. huananli@hubu.edu.cn.
Zhengbing JiangState Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, People's Republic of China. zhbjiang@hubu.edu.cn.

Funding

Project supported by the Young Scientists Fund of the National Natural Science Foundation of China No. 22308093the China National Key Research and Development Program 2020YFA0908400, 2023YFD1201600
6 · The paper itself

Abstract

Ancestral sequence reconstruction (ASR) offers a revolutionary approach to resurrect functional proteins, yet its potential in transporter engineering remains underexplored. Here, we pioneered the application of ASR to reconstructing ancestral xylose transporters, addressing the persistent challenge of glucose-mediated inhibition of xylose uptake in Saccharomyces cerevisiae during xylose co-fermentation. Through rigorous ASR analysis, we reconstructed ancestral xylose transporters (Xt) and selected two candidates—Xt3 (approximately 140 million years old) and Xt7 (approximately 40 million years old)—based on their phylogenetic positioning, degree of sequence divergence from extant homologs, and predicted structural integrity. Functional characterization demonstrated that both Xt3 and Xt7 significantly enhance xylose uptake efficiency and mitigate glucose-induced repression. In fermentation experiments with mixed sugars (40 g/L xylose and 40 g/L glucose) within 72 h, recombinant S. cerevisiae expressing Xt3 achieved 22.75 g/L xylose consumption, surpassing the benchmark N326FXltr1p (16.22 g/L) by 40.27% and outperforming Xt7 (21.36 g/L) by 6.51%, highlighting Xt3 as the most efficient transporter. Molecular docking suggested a potentially more favorable binding mode for xylose in the ancestral transporters (binding affinity: −3.68 kcal/mol for Xt3 vs. −3.15 kcal/mol for N326FXltr1p). Molecular dynamics simulations further demonstrated that the ancestral transporters formed complexes with xylose that exhibited faster convergence to a stable state and maintained significantly greater conformational stability throughout the simulation compared to the N326FXltr1p complex. These computational insights provide a plausible structural basis for their enhanced performance. This work contributes to the advancement of lignocellulosic biorefinery technology and provides a practical reference for resurrecting other valuable proteins using ASR’.

Indexed as

Ancestral sequence reconstructionGlucose/xylose co-utilizationSaccharomyces cerevisiaeXylose transporter

Identifiers

PMID41489796
PMCPMC12770209

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.