Emerging Computational Advances in Simulating PCSK9 Variant Dynamics for Therapeutic Design Against Hypercholesterolemia
7 affiliations
- 1Facultad de Medicina Veterinaria y Agronomía, Instituto de Ciencias Naturales, Universidad de Las Américas, Santiago 7500975, Chile
- 2Centro de Investigación en Ciencias Biológicas y Químicas, Universidad de Las Américas, Santiago 7500975, Chile
- 3Programa de Magister en Ciencias Biológicas y Químicas, Facultad de Medicina Veterinaria y Agronomía, Instituto de Ciencias Naturales, Universidad de las Américas, Santiago 7500000, Chile
- 4Departamento de Ciencias de la Ingeniería, Facultad de Ingeniería, Universidad Andrés Bello, Santiago 8370146, Chile
- 5Institute for Advanced Biomedical Research, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA
- 6Center for Bioinformatics and Integrative Biology (CBIB), Facultad de Ciencias de La Vida, Universidad Andres Bello, Santiago 8370146, Chile
- 7ANID–Millenium Science Initiative Program–Millenium Nucleus in Data Science for Plant Resilience (PhytoLearning), Santiago 8370186, Chile
Abstract
Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a premier model for precision medicine, having moved from genetic discovery to approved therapeutics within two decades. Yet despite the clinical success of monoclonal antibodies, small interfering RNA and the newly approved oral macrocyclic-peptide inhibitor class, targeting PCSK9 remains a demanding biophysical challenge. Clinical translation has concentrated almost entirely on the rigid, well-resolved subtilisin-like catalytic domain that interfaces with the low-density lipoprotein receptor (LDLR). By contrast, the C-terminal cysteine- and histidine-rich domain (CHRD), which governs allosteric regulation, protein trafficking and post-translational modification, is highly disordered and largely elusive in experimental structures, leaving a critical regulatory network therapeutically unexploited. This review examines how advanced computational biology is bridging that structural gap, organizing the computational landscape of PCSK9 around the biophysical mechanisms of gain- and loss-of-function variants. We detail how explicit-solvent molecular dynamics, free-energy calculations and cryptic-pocket detection expose the functional plasticity and allosteric networks that static crystallography cannot capture, and we evaluate the convergence of these physics-based approaches with artificial intelligence, including deep-learning structure prediction and generative design of de novo nanobody and peptide binders. Therapeutic development and the structural literature currently point in diverging directions: drug discovery remains focused on the crowded LDLR-binding interface, whereas computational methods are beginning to identify candidate sites beyond it. We argue that the next advance in PCSK9 therapeutics will come from integrating generative artificial intelligence with rigorous enhanced sampling in a unified pipeline, deliberately shifting focus toward the disordered regulatory domains that have evaded classical screening.
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BibTeX
@article{opazo2026emerging,
title = {{Emerging Computational Advances in Simulating PCSK9 Variant Dynamics for Therapeutic Design Against Hypercholesterolemia}},
author = {Ma. Cecilia Opazo and Andrés Gallegos and Antar Muñoz and Roger Zapata and Juan Carlos Rivera and Romina V. Sepúlveda and Daniel Aguayo},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27219676},
url = {https://www.mdpi.com/1422-0067/27/21/9676},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Emerging Computational Advances in Simulating PCSK9 Variant Dynamics for Therapeutic Design Against Hypercholesterolemia
AU - Ma. Cecilia Opazo
AU - Andrés Gallegos
AU - Antar Muñoz
AU - Roger Zapata
AU - Juan Carlos Rivera
AU - Romina V. Sepúlveda
AU - Daniel Aguayo
PY - 2026
DA - 2026/10/29
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27219676
UR - https://www.mdpi.com/1422-0067/27/21/9676
AB - Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a premier model for precision medicine, having moved from genetic discovery to approved therapeutics within two decades. Yet despite the clinical success of monoclonal antibodies, small interfering RNA and the newly approved oral macrocyclic-peptide inhibitor class, targeting PCSK9 remains a demanding biophysical challenge. Clinical translation has concentrated almost entirely on the rigid, well-resolved subtilisin-like catalytic domain that interfaces with the low-density lipoprotein receptor (LDLR). By contrast, the C-terminal cysteine- and histidine-rich domain (CHRD), which governs allosteric regulation, protein trafficking and post-translational modification, is highly disordered and largely elusive in experimental structures, leaving a critical regulatory network therapeutically unexploited. This review examines how advanced computational biology is bridging that structural gap, organizing the computational landscape of PCSK9 around the biophysical mechanisms of gain- and loss-of-function variants. We detail how explicit-solvent molecular dynamics, free-energy calculations and cryptic-pocket detection expose the functional plasticity and allosteric networks that static crystallography cannot capture, and we evaluate the convergence of these physics-based approaches with artificial intelligence, including deep-learning structure prediction and generative design of de novo nanobody and peptide binders. Therapeutic development and the structural literature currently point in diverging directions: drug discovery remains focused on the crowded LDLR-binding interface, whereas computational methods are beginning to identify candidate sites beyond it. We argue that the next advance in PCSK9 therapeutics will come from integrating generative artificial intelligence with rigorous enhanced sampling in a unified pipeline, deliberately shifting focus toward the disordered regulatory domains that have evaded classical screening.
SN - 1422-0067
ER -