AI-Driven Hotspot Prediction and Rational Substitution Engineering for Enhanced Enantioselectivity in R/S Transaminases
- 1Department of Computational Biology and AI, Kcat Enzymatic Pvt. Ltd., #16, Ramakrishnappa Road, Cox Town, Bangalore 560005, Karnataka, India
- 2Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
Abstract
Pyridoxal 5′-phosphate (PLP)-dependent amine transaminases (ATAs) are key biocatalysts for the asymmetric synthesis of chiral amines, with broad applications in pharmaceutical manufacturing. While (S)-selective ATAs are well characterized, (R)-selective counterparts—critical for industrial processes such as sitagliptin synthesis—remain comparatively underexplored. Here, we present a comparative structural and computational analysis of fold type I and fold type IV ATAs to elucidate the determinants of enantioselectivity. Structural superposition and AlphaFold2-assisted modeling reveal a mirrored “handshake-like” inversion of substrate-binding pockets that underpins enantiocomplementarity between R- and S-selective enzymes. To complement structural insights, we explored a structure-informed machine learning framework incorporating residue-level interaction descriptors and physicochemical mutation features. Using an enzyme-level GroupKFold cross-validation strategy, the XGBoost classifier achieved a mean ROC–AUC of 0.72, demonstrating moderate predictive performance for identifying stereocontrolling hotspot residues from integrated structural interaction descriptors and physicochemical mutation features. Feature attribution suggests that stereocontrolling hotspots are primarily located within flexible loop regions and pocket-defining residues rather than conserved catalytic motifs. Collectively, this study presents an integrated comparative framework that combines structural superposition, molecular docking, molecular dynamics simulations, curated engineering datasets, and structure-informed machine learning to systematically compare fold I and fold IV transaminases. By integrating newly generated computational analyses with curated literature evidence, this work identifies transferable structural determinants and engineering hotspots that can guide future transaminase engineering.
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BibTeX
@article{ramalingam2026aidriven,
title = {{AI-Driven Hotspot Prediction and Rational Substitution Engineering for Enhanced Enantioselectivity in R/S Transaminases}},
author = {Pravin Kumar Ramalingam and Ramanathan Karuppasamy},
journal = {Catalysts},
year = {2026},
month = oct,
doi = {10.3390/catal16100925},
url = {https://www.mdpi.com/2073-4344/16/10/925},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - AI-Driven Hotspot Prediction and Rational Substitution Engineering for Enhanced Enantioselectivity in R/S Transaminases
AU - Pravin Kumar Ramalingam
AU - Ramanathan Karuppasamy
PY - 2026
DA - 2026/10/20
JO - Catalysts
DO - 10.3390/catal16100925
UR - https://www.mdpi.com/2073-4344/16/10/925
AB - Pyridoxal 5′-phosphate (PLP)-dependent amine transaminases (ATAs) are key biocatalysts for the asymmetric synthesis of chiral amines, with broad applications in pharmaceutical manufacturing. While (S)-selective ATAs are well characterized, (R)-selective counterparts—critical for industrial processes such as sitagliptin synthesis—remain comparatively underexplored. Here, we present a comparative structural and computational analysis of fold type I and fold type IV ATAs to elucidate the determinants of enantioselectivity. Structural superposition and AlphaFold2-assisted modeling reveal a mirrored “handshake-like” inversion of substrate-binding pockets that underpins enantiocomplementarity between R- and S-selective enzymes. To complement structural insights, we explored a structure-informed machine learning framework incorporating residue-level interaction descriptors and physicochemical mutation features. Using an enzyme-level GroupKFold cross-validation strategy, the XGBoost classifier achieved a mean ROC–AUC of 0.72, demonstrating moderate predictive performance for identifying stereocontrolling hotspot residues from integrated structural interaction descriptors and physicochemical mutation features. Feature attribution suggests that stereocontrolling hotspots are primarily located within flexible loop regions and pocket-defining residues rather than conserved catalytic motifs. Collectively, this study presents an integrated comparative framework that combines structural superposition, molecular docking, molecular dynamics simulations, curated engineering datasets, and structure-informed machine learning to systematically compare fold I and fold IV transaminases. By integrating newly generated computational analyses with curated literature evidence, this work identifies transferable structural determinants and engineering hotspots that can guide future transaminase engineering.
SN - 2073-4344
ER -