Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.10.11.758284

Mechanism-guided Discovery of Fluoroacetate Dehalogenase (FAcD) Variants for Enzymatic PFAS Defluorination

Abstract

Per- and polyfluoroalkyl substances (PFAS) are persistent anthropogenic "forever chemicals" whose strong C-F bonds (~460-533 kJ mol-1) resist defluorination, and the recast EU Drinking Water Directive sets parametric values of 0.10 ug L-1 for the sum of 20 specified PFAS and 0.50 ug L-1 for total PFAS. Here, we present DeFluorX, a mechanism-first pipeline that uses Boltz-2 to co-fold 2,150 bacterial fluoroacetate dehalogenase (FAcD) variants with 26 PFAS and fluoroacetate, generating uniformly high model confidence (rho = +0.08) but poorly tracked catalytic competence. Thus, the complexes were ranked and tiered by SN2-defluorination geometry based on the known mechanism of control of FAcDs RPA1163 with fluoroacetate: Odelta...C-alpha distance and Odelta-C-alpha-F angle; relay; carboxylate clamp; F-stabiliser; F-cradle; and penalties for scissile bond strength, backside occlusion, beta-fluorination and bond multiplicity. The tiering identified eight complexes (med angle 173.2 degrees, distance 2.91 A) with a high probability of degradation, including Alphaproteobacterial enzymes, with mono-, di- and trifluoroacetate attacking at 172-178 degrees versus 163 degrees for the control. We performed WaterMap, restrained 1 us Desmond MD, MM-GBSA and scans of the three WaterMap-aware best MD frames per complex using B3LYP/6-31G** QM/MM. All 12 frames exhibited product-end C-F cleavage with barriers ranging from 1.1 to 11.8 kcal mol-1, and the departing fluoride carried -0.43 to -0.63 e. The Laboratory validation is ongoing.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ahmad, S., Forouzandeh, A., Togni, B., Randloev, J. B., Ogden, M., Nielsen, T. K.. 2026-10-16. Mechanism-guided Discovery of Fluoroacetate Dehalogenase (FAcD) Variants for Enzymatic PFAS Defluorination. https://doi.org/10.64898/2026.10.11.758284

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Carrier-Limited Release Risk in Electrospun Nanofibers for Regenerative Endodontics: Constraint Discovery from 25 Formulations

Regenerative endodontics needs an intracanal carrier that keeps disinfecting across a dressing interval that clinicians schedule at one to four weeks, without harming the stem cells that rebuild the pulp. We ask what one completed 25-formulation electrospinning campaign of poly(vinylpyrrolidone) (PVP) fibers carrying metronidazole, ciprofloxacin, minocycline and Ca(OH)2 can decide, and use machine learning for constraint discovery and decision support rather than as a new predictor. After an audit against the source thesis, logistic regression predicts fine-fiber formation at 96% leave-one-out accuracy, but a one-threshold rule does as well, the gain over the 80% majority baseline is not significant (exact McNemar p = 0.125), and accuracy falls to 84% when mixture loading is encoded per component. Diameter models interpolate within formulation families (ridge MAE 193 nm, 95% CI 96 to 317 nm) and fail across them (R^2 < 0). Because the campaign never measured release, we assemble a same-drug, cross-carrier evidence table: 17 bounded metronidazole and ciprofloxacin observations from seven studies, with two-sided censoring. Water-soluble carriers (PVP, PVA) completed release within 80 h (PVP within 8 h); no water-insoluble monolithic carrier reached 90% within its observation window, releasing 1.5 to 50%; both within-study comparisons separate at matched loading; and blend, core-shell and thick-mat architectures released over 3 to 28 days. The PVP mats therefore carry a carrier-limited release risk, not a proven ceiling, and one dissolution experiment can confirm or overturn it. A censoring-aware screen keeps three necessarily and one possibly nondominated formulation, and fixed-budget replays show that uncertainty sampling finds failed formulations sooner, except the one it is confidently wrong about.

bioinformatics↗

CellMethMarker provides an integrated database of human cell- and tissue-type-specific DNA methylation markers

Cell- and tissue-type-specific DNA methylation markers provide epigenetic signatures of cellular identity and are widely used to infer cellular origin from bulk and single-cell methylomes. Existing resources mainly emphasize disease-associated biomarkers, methylome atlases, genomic annotations or RNA-based cell markers, leaving an unmet need for an integrated region-level resource dedicated to such markers. Here we present CellMethMarker (http://gu-zhanglab.hfcas.ac.cn/CellMethMarker), a curated database integrating 347,514 de novo markers derived from uniformly processed whole-genome and reduced-representation bisulfite sequencing datasets, 2,203,507 literature-derived marker records and harmonized metadata for 2,810 samples. CellMethMarker enables cell- and tissue-centered marker retrieval, coordinate-based search, genome-browser visualization, interactive methylation analysis and data download. In an independent single-cell methylome atlas, these markers recover the expected cell-type-associated methylation patterns, and a platform-matched reference built from them lowers the mean deconvolution error from 0.365 to 0.144. CellMethMarker therefore links region-level DNA methylation variation to cellular identity and supports methylome-based cell-type annotation and deconvolution.

bioinformatics↗

IG-28 enables identification and quantification of high-confidence isoforms from large bulk and single-cell full-length cDNA datasets

The technology to sequence full-length transcriptomes from bulk and single-cell samples has rapidly advanced. As a result, full-length cDNA datasets now reach billions of reads in size which is beyond the capability of most established isoform identification tools. Here, we introduce IG-28 which addresses this challenge with its capability to process and analyze large fl-cDNA datasets fast and using minimal computational resources. In contrast to its predecessor Mandalorion, IG-28 is not limited to bulk fl-cDNA but is capable of parsing, processing, and analyzing single-cell fl-cDNA datasets. It does so in a lightweight, flexible manner, integrating seamlessly with established scRNA-seq analysis workflows. We benchmarked IG-28 against a variety of established and new isoform identification tools using several bulk and a complex single-cell fl-cDNA dataset and found that IG-28 consistently outperformed the other tools by identifying high-confidence public isoforms.

bioinformatics↗