Carbonic anhydrase under the computational lens: a review of advances, challenges, and future directions
Clinical Snapshot
PICO Framework
| P — Population | Not applicable - this is a narrative review of computational methods |
| I — Intervention | Not applicable - review of computational approaches |
| C — Comparator | Not applicable - narrative review |
| O — Outcomes | Not applicable - methodological review |
Bottom Line
This narrative review examines computational methods used to study carbonic anhydrases, including quantum mechanics, molecular dynamics, and machine learning approaches. While it provides valuable insights for researchers developing CA modulators for conditions like cancer and Alzheimer's disease, it is not a systematic review and cannot be appraised using clinical evidence frameworks. The review lacks systematic methodology, quality assessment, and clinical outcome measures. For clinicians, this work primarily serves as background on the computational tools being used to develop potential CA-targeted therapies rather than providing evidence to guide current practice. The methodological focus limits its immediate clinical applicability, though it may inform future drug development efforts.
Key Findings
Effect Size: Not applicable - narrative review
Primary Outcome: Review of computational methods for studying carbonic anhydrases
Nnt Or Sensitivity: Not applicable - methodological review
Clinical Application
Limited direct clinical application - primarily methodological May inform Australian research into CA-targeted therapeutics, but no direct clinical practice implications Researchers studying carbonic anhydrase inhibitors and activators
Abstract
CONTEXT: Carbonic anhydrases (CAs) are zinc metalloenzymes responsible for the catalysis of the reversible hydration of carbon dioxide and play pivotal roles in many physiological and pathological processes. The complexity of CA isoforms with distinct expression patterns and catalytic characteristics has aroused fierce interest in the development of isoform-selective inhibitors and activators for therapeutic purposes and diagnostics. In this review, a systematic overview of computational and theoretical methods utilized to determine the structure, mechanism, inhibition, and activation of CAs was given. It reviews the evolution and utilization of computational methods in deciphering catalytic and proton transfer mechanisms as well as active-site interactions into atomic resolution and also features recent developments in drug design and screening strategies engineered for CA isoforms, particularly those involved in cancers and Alzheimer's disease. This study can be a reference for multidisciplinary researchers and inspire the future rational design of CA modulators. METHODS: This review summarizes the literature that employed QM and QM/MM by using like B3LYP, BE0-D3BJ, and MD simulations, GROMACS and Desmond packages, and various force fields: OPLS-AA, AMBER14, and CHARMM36. Molecular docking was performed using such as Glide, AutoDock Vina, and GOLD. Machine learning and AI techniques including SVM, LightGBM, SHAP, and neural networks were also reviewed. This review addresses the limitations in these models as well as prospects with newly emerging instruments such as polarizable force fields and real-time DFT as well as explainable AI frameworks, too. Incorporating data-driven insights with physics-based simulations, the next era of work is primed to provide predictive, scalable platforms for rational design of next-generation CA modulators.
References
- 1.Ghiasi, M., & Noohi, B. (2026). Carbonic anhydrase under the computational lens: a review of advances, challenges, and future directions. Journal of Molecular Modeling. https://doi.org/10.1021/acs.jpcb.4c06393
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