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February 21, 2026Biophysical Journal0 citations

BPS2026 – Probing the effect of pore loop mutations on the gateway transitions of caseinolytic peptidase P using machine learning and molecular dynamics

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HDHayden DennisonADAshan DayanandaTRTharushi Rajaguru

Key Points

  • The study aims to investigate how pore loop mutations affect the gateway transitions of caseinolytic peptidase P.
  • Utilized molecular dynamics simulations to explore structural changes during pore transitions.
  • Employed machine learning models to classify the impact of different point mutations.
  • Computed SHAPley additive explanations (SHAP values) to analyze the influence of structural features.
  • Identified that pore loop mutations significantly perturb the solvent-accessible surface area.
  • Found that the classification models relied heavily on the presence of intra-protomer salt bridges.
  • Revealed that mutations affect peptide bond interactions essential for ClpP's function.

Abstract

One component of the larger protein quality control network in many bacteria species is caseinolytic peptidase P (ClpP), a tetradecamer that sequesters fourteen serine catalytic triads within a barrel-shaped quaternary structure. The indiscriminate nature of the catalytic triads to cleave peptide bonds necessitates the regulation of peptide entry into ClpP via two axial pores that are surrounded by pore loops which can adopt either a “closed” or “open” conformation. In a typical scenario, motor proteins such as ClpX dock to grooves between the pores and equator of an assembled ClpP tetradecamer, serving as both a substrate recognition and peptide threading unit to ensure that only unfolded, damaged, or defective proteins are degraded within the lumen of ClpP. Small molecules such as acyldepsipeptide (ADEP) can bind to the same grooves motor proteins, triggering the dramatic conformational change in ClpP and converting the pore loops to the “open” configuration. In this state, ClpP nonselectively degrades any peptide fragments that diffuse into its central chamber, eventually causing cell death. To investigate the structural features that evolve during the pore opening-closing transitions, we utilize molecular dynamics simulations and classification machine learning models. Seven different point mutations that affect the salt bridges, hydrogen bonds, and hydrophobic interactions that underlie the pore loop transitions are compared by computing SHAPley additive explanations (SHAP values), which reveal the impact of each structural feature in the context of every other feature. We find that the solvent-accessible surface area of the pore wall is highly perturbed by the mutations, indicated by the reliance of the classification models on that feature for accurate predictions, along with the presence or absence of intra-protomer salt bridges and native contacts.

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Cite This Study

Dennison et al. (2026) studied this question.

synapsesocial.com/papers/69990df65b97ab4c14ac2bddhttps://doi.org/10.1016/j.bpj.2025.11.1113
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