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Phenylalanine modification in plasma-driven biocatalysis revealed by solvent accessibility and reactive dynamics in combination with protein mass spectrometry

This study combines solvent accessibility analysis, reactive molecular dynamics simulations, and mass spectrometry to identify and validate phenylalanine modifications in enzymes caused by plasma-generated species, thereby establishing a predictive framework to guide plasma-driven biocatalysis.

Original authors: Hanna-Friederike Poggemann, Sabrina Klopsch, Simon Homann, Tim Dirks, Sina Schäkermann, Julia E. Bandow, Timo Jacob, Christoph Jung

Published 2026-06-23

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CC BY 4.0

Original authors: Hanna-Friederike Poggemann, Sabrina Klopsch, Simon Homann, Tim Dirks, Sina Schäkermann, Julia E. Bandow, Timo Jacob, Christoph Jung

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: A High-Tech "Plasma" Problem

Imagine you are trying to bake a cake (a chemical reaction) using a special, delicate oven (an enzyme). To make the cake rise, you need a specific ingredient: hydrogen peroxide ( $H_2O_2$ ).

Usually, getting this ingredient is tricky. If you add too little, the cake doesn't rise. If you add too much, it burns the oven and ruins the cake.

The scientists in this paper are testing a new way to deliver this ingredient: Non-thermal Plasma. Think of plasma as a "magic mist" or a "storm of tiny, energetic particles" that can be generated by electricity. This mist naturally creates the hydrogen peroxide needed for the reaction.

The Problem: While this plasma mist is great at making the ingredient, it also contains other "wild" particles (like hydroxyl radicals and oxygen atoms) that are so energetic they might accidentally smash the delicate oven (the enzyme) instead of just helping it bake.

The Goal: Predicting the Damage

The researchers wanted to know: Which parts of the enzyme are most likely to get hit and damaged by this plasma mist?

If they can predict the "weak spots," they can either protect them or understand why the enzyme stops working.

The Method: A Digital "Wind Tunnel" and a "Flashlight"

To figure this out without destroying thousands of real enzymes in a lab, the team used two main tools:

1. The "Flashlight" (SASA Analysis)
Imagine shining a flashlight around a statue (the enzyme) in a dark room. The light hits the parts of the statue sticking out the most.

The Science: They used a computer method called SASA (Solvent Accessible Surface Area). This maps out every tiny spot on the enzyme's surface that is exposed to the outside world.
The Simulation: They then simulated dropping different "plasma particles" (like tiny magnets) onto these exposed spots to see which ones stick the strongest.
The Finding: They found that the plasma particles loved to stick to specific amino acids (the building blocks of the enzyme), particularly Phenylalanine (a ring-shaped building block), Lysine, and Arginine. It's like the plasma mist has a magnetic attraction to these specific spots.

2. The "Wind Tunnel" (Molecular Dynamics)
Once they found the "sticky spots" with the flashlight, they wanted to see what would actually happen if a particle hit them.

The Simulation: They ran high-speed computer movies (Molecular Dynamics) where they threw these plasma particles at the enzyme.
The Twist: They ran these movies in two ways:
- In a vacuum (empty space): The particles hit the enzyme and stuck immediately.
- In water (real life): The enzyme is surrounded by water molecules. The simulation showed that the plasma particles often got distracted by the water first, like a fly getting stuck in a spiderweb before it can reach the flower. The water acts as a shield, protecting the enzyme from some of the damage.

The Experiment: The "Plasma Shower"

To prove their computer predictions were right, they did a real-world experiment:

They took a specific enzyme called CviUPO.
They gave it a 5-minute "shower" of plasma.
They chopped the enzyme into tiny pieces and analyzed them with a Mass Spectrometer (a machine that weighs molecules to see if their shape has changed).

The Results: The Computer Was Right!

The computer predicted that Phenylalanine would be a major target for the plasma.

The Reality: The mass spectrometer confirmed this! The Phenylalanine spots on the enzyme were indeed modified (oxidized) after the plasma shower.
The Surprise: The computer also predicted that Methionine (a sulfur-containing building block) would be safe because it was "buried" deep inside the enzyme, hidden from the outside. However, the experiment showed that Methionine did get damaged. The researchers realized that even though it was buried, the plasma was strong enough to reach it, or perhaps the enzyme wiggled enough to expose it.

The Takeaway

This paper is like a "weather forecast" for enzymes.

The Forecast: The computer model (SASA) successfully predicted that the "storm" (plasma) would hit the exposed "Phenylalanine" spots hardest.
The Lesson: By understanding exactly where the plasma hits, scientists can learn how to make enzymes more durable. They might be able to "reinforce" the weak spots or change the way the plasma is delivered so it doesn't destroy the enzyme.

In short: They built a digital map to predict where a chemical storm would hit a protein, tested it in the real world, and found that their map was surprisingly accurate, especially regarding the Phenylalanine "weak spots."

Technical Summary: Phenylalanine Modification in Plasma-Driven Biocatalysis

Problem Statement
Biocatalysis offers an environmentally friendly alternative to conventional chemical catalysis, yet it faces significant challenges regarding the supply of reactive co-substrates, particularly hydrogen peroxide ( $H_2O_2$ ). While essential for catalytic turnover, high concentrations of $H_2O_2$ and other reactive plasma-generated species (PGS) can inactivate enzymes. Non-thermal plasma sources (e.g., dielectric barrier discharge) can deliver these species in controlled concentrations, but the specific interactions between PGS (such as $H_2O_2$ , $OH$, $O$ , $O_2$ , $NO$, and $H$ ) and enzyme structures remain poorly understood at the atomistic level. Understanding these interactions is critical to preventing enzyme deactivation while maintaining catalytic efficiency.

Methodology
The study employs a combined computational and experimental approach to investigate the interactions between PGS and three enzymes: AaeUPO (a model unspecific peroxygenase), CviUPO (the primary model enzyme for this study), and GapA (a glycolytic enzyme inactivated by $H_2O_2$ ).

Computational Screening (SASA & MD):
- Solvent Accessible Surface Area (SASA) Analysis: A custom package built on VMD and LAMMPS was used to map the interaction energies of PGS with the solvent-accessible surface of the enzymes. This involved placing probe molecules at SASA points (1.4 Å from the surface) and performing constrained energy minimizations to identify regions of strong interaction.
- Reactive Molecular Dynamics (rMD): Using the ReaxFF force field (biomolecule parameterization by Monti et al.) within LAMMPS, the study simulated dynamic bond breaking and formation. Simulations were conducted at 300 K, 400 K, and 500 K, both in vacuum and in explicit solvent, to assess the stability of predicted interaction sites and the effect of protein unfolding.
- High Concentration Simulations: To mimic realistic diffusion conditions, simulations were performed with high concentrations of PGS (e.g., 100 $OH$ radicals) randomly distributed around the enzyme.
Experimental Validation:
- Plasma Treatment: Recombinant CviUPO (rCviUPO) was treated with a dielectric barrier discharge (DBD) plasma source for 5 minutes.
- Mass Spectrometry (MS): Treated proteins were reduced, alkylated, and digested with trypsin. LC-MS/MS analysis was performed to detect specific amino acid modifications, focusing on residues predicted to have high interaction energies (e.g., Phenylalanine, Methionine, Cysteine, Arginine, Lysine).

Key Results

Interaction Profiles: The SASA analysis revealed that PGS interact most strongly with charged and polar residues. Specifically, $OH$ radicals showed strong interactions with Lysine (Lys), Phenylalanine (Phe), and Arginine (Arg). $H_2O_2$ interacted strongly with Serine (Ser) and Glutamate (Glu).
Temperature Effects: As temperature increased (simulating unfolding), interaction profiles shifted. At 400 K and 500 K, previously buried residues became accessible, increasing the likelihood of modification at the active site (e.g., near the heme center in CviUPO).
Solvent Role: In solvent simulations, the majority of reactive species reacted with water molecules rather than the protein surface, suggesting that the solvent acts as a protective barrier. However, high-concentration simulations confirmed that over time, species still bind to residues predicted by the SASA screening.
Experimental Confirmation: Mass spectrometry of plasma-treated CviUPO confirmed significant oxidation of Phenylalanine residues (Phe51, Phe67, Phe208) and Methionine residues (Met42, Met220).
- The modification of Phe aligns with the SASA prediction of strong $OH$ interaction with exposed aromatic rings.
- The modification of Met occurred despite low SASA interaction scores, which the authors attribute to Met residues being buried but still accessible via diffusion or specific local environments not fully captured by the static SASA model.
- Modifications to Arg and Lys were detected only at low intensities, while Cys modifications were not observed, likely due to the specific structural context (e.g., Cys19 is covalently bound to the heme).

Significance and Claims
The paper claims that the combination of SASA-based interaction screening and reactive molecular dynamics is a viable tool for predicting potential modification sites on enzymes exposed to plasma. The study demonstrates that:

Predictive Utility: The computational approach successfully identified Phenylalanine as a primary target for plasma-induced oxidation, a finding that was experimentally validated. This suggests the method can direct time-consuming experimental efforts toward specific residues.
Mechanistic Insight: The results highlight that solvent accessibility and the diffusion path of reactive species are critical factors; prolonged diffusion through the liquid phase increases the probability of species reacting with the solvent rather than the enzyme.
Methodological Integration: The work underscores the value of integrating computational screening with mass spectrometry. While the SASA method provided a "rough scan" that successfully prioritized Phe for analysis, the authors note that high interaction energy does not guarantee binding, and experimental validation remains necessary.

The authors conclude that while the SASA/MD approach is effective for initial screening, future work should focus on expanding the range of tested modifications and applying the model to other systems to verify its general validity. The study does not claim to have solved the problem of enzyme inactivation but rather provides a framework for understanding and predicting the specific molecular targets of plasma-induced damage.