Enzyme Acid Resistance Engineering: Why Do Enzymes "Fear Acid"?

In industrial biocatalysis, pH is one of the key environmental parameters affecting enzyme activity and stability. Many industrial processes—such as food fermentation, feed processing, orally administered enzyme preparations in gastric fluid, and biological treatment of acidic wastewater—require enzymes to maintain high activity and long-term stability under low-pH conditions. However, natural enzymes typically exhibit optimal activity at neutral or weakly alkaline pH; once exposed to acidic environments, their conformations change and activity declines rapidly.

Enzyme acid resistance engineering is a branch of protein engineering developed precisely to address this challenge. Through site-directed mutagenesis, directed evolution, or computational design, it enables enzymes to maintain proper folding conformation and catalytic function under low-pH conditions. This article systematically reviews the molecular mechanisms, mainstream methods, and frontier advances in enzyme acid resistance engineering.

I. Core Reasons for Enzyme Inactivation in Acidic Environments

Enzyme inactivation in acidic environments typically arises from molecular events at several levels:

Protonation of active-site residues. Catalytic residues in the enzyme active center (such as aspartate, glutamate, and histidine) have specific pKa values. When the pH deviates from the protonation state required for these residues to perform catalysis, the charge relay network or acid-base catalytic mechanism is disrupted. Asp/Glu residues serving as catalytic bases lose their negative charge upon protonation below their pKa, rendering them unable to abstract protons from substrates; residues serving as catalytic acids need to maintain their protonated state; histidine, with a pKa near neutrality, becomes excessively protonated at pH < 5, making it the most common cause of activity loss under acidic conditions.

Decreased conformational stability. Under low-pH conditions, the electrostatic interaction networks on the protein surface and interior are disrupted. After protonation of negatively charged residues, the salt bridges that maintain conformational stability are broken, and the tight packing of the hydrophobic core may also be disturbed, triggering partial unfolding of the enzyme molecule. Additionally, the overall positive charge density of the protein increases significantly at low pH, and the enhanced intramolecular electrostatic repulsion directly disrupts secondary and tertiary structural packing.

Increased aggregation propensity. Conformational changes induced by acidic environments often expose hydrophobic regions within the protein, promoting nonspecific intermolecular aggregation and forming insoluble precipitates or soluble aggregates that render the enzyme inactive.

Cofactor loss. For metalloenzymes that depend on metal ions (such as many hydrolases and oxidases), low pH may protonate the metal-coordinating residues, weakening the binding affinity for metal ions and causing cofactor loss, with consequent loss of enzyme activity.

Understanding these inactivation mechanisms provides the molecular basis for acid resistance engineering—the essence of engineering is to combat these acid-induced structural damages through sequence optimization.

II. Mainstream Methods for Enzyme Acid Resistance Engineering

Three mainstream strategies for acid‑tolerance engineering

2.1 Directed Evolution: Structure-Independent "Darwinian" Screening

When high-resolution structural information is lacking, directed evolution is the preferred strategy for improving acid resistance. Its core workflow involves constructing random mutant libraries through error-prone PCR or DNA shuffling, followed by enrichment of mutants capable of maintaining activity under acidic selection pressure.

In the acid resistance engineering of the thermophilic pullulanase PulA, researchers obtained mutant N9 through directed evolution, which exhibited optimal activity at 60°°C and acidic pH. In the engineering of Aspergillus niger xylanase, researchers constructed a primary mutant library through error-prone PCR, screened 16 clones with combined acid and thermal stability, and further combined these beneficial mutations using DNA shuffling technology, ultimately obtaining engineered enzymes with significantly improved acid resistance and thermostability.

The advantage of directed evolution lies in its independence from structural information, but its bottleneck is equally clear: the limited coverage of random mutant libraries, with screening throughput often constraining efficiency.

2.2 Rational Design: Structure-Driven Precision Engineering

When the three-dimensional structure of the enzyme is known, rational design enables more precise identification of key residues affecting acid resistance.

pKa modulation-based engineering strategy. By calculating the pKa values of key residues in the active center, residues that are prone to protonation (or deprotonation) under acidic conditions—and thus lose catalytic function—are identified and replaced with amino acids whose pKa values are better suited to acidic environments, thereby maintaining the charge state required for catalysis at low pH. In a 2025 study published in Food Chemistry, a domestic research team integrated multiple rational design strategies, including pKa modulation and disulfide bond introduction, successfully developing a zearalenone lactone hydrolase mutant with both excellent thermostability and acid resistance. The R76Q/T217C-G242C combinatorial mutant exhibited over 75% relative activity at pH 5.0, representing a more than 25-fold improvement over the wild type.

Electrostatic network optimization-based engineering strategy. Studies have shown that electrostatic interactions between charged residues are key factors in enzyme pH adaptability. By introducing additional acidic residues (Asp/Glu) on the protein surface and reducing exposed positively charged residues, the overall positive charge density of the molecule at low pH is lowered, charge repulsion is reduced, and a stable surface electrostatic network is reconstructed. In a study on serratiopeptidase, researchers combined constant-pH molecular dynamics simulations with site-directed mutagenesis to introduce the N412D single-point mutation on the enzyme surface, significantly enhancing the enzyme's functional stability under both acidic and alkaline stress.

Flexible region modification-based strategy. Molecular dynamics simulations can identify "flexible hotspots" in the enzyme molecule that undergo conformational changes first under acidic conditions. By introducing proline or hydrophobic residues in these regions to restrict their conformational freedom, acid-induced unfolding can be reduced. A domestic university team systematically modified four highly flexible regions of the acid-resistant GH11 xylanase from Aspergillus niger AG11, significantly improving both thermostability and stability under acidic conditions—the flexible region rigidification strategy often simultaneously enhances resistance to both thermal and acid denaturation.

2.3 Computational and AI-Driven Design

With advances in computational power and AI models, computational-assisted design is becoming a core driver of acid resistance engineering.

Deep learning for predicting enzyme optimal pH. A 2025 study published in ACS Synthetic Biology developed CatOpt—a deep learning-based enzyme optimal pH predictor. The model quantitatively describes how pH affects enzyme catalytic activity and guides mutant design to improve acid resistance. In this study, a single-point mutation guided by CatOpt shifted the optimal pH of Pyrococcus horikoshii diacetylchitobiose deacetylase toward acidity by 0.7 pH units, increasing relative activity at pH 4.5 from below 10% to 17%; after combinatorial optimization of multiple mutations, the optimal pH could be further shifted by 1.2 units.

Surface charge design-based strategy. For maltogenic amylase from lactic acid bacteria, researchers designed mutation sites from two dimensions—catalytic center pKa values and surface charge—successfully improving acid resistance through single-point and combinatorial mutations. The study indicated that stabilizing the negative charge of key catalytic residues (such as the nucleophile) can significantly enhance enzyme activity at low pH.

Multi-strategy synergistic systematic design. Single modification approaches often yield limited effects; current frontier research increasingly favors multi-dimensional synergistic optimization. A domestic research team integrated three methods—active-center pKa calculation, surface charge optimization, and flexible region modification—to successfully develop an engineered enzyme with both thermostability and acid resistance. This "three-pronged" strategy represents the systematic approach to current acid resistance engineering—no longer relying on a single modification method, but achieving synergistic optimization across multiple molecular levels.

III. Strategy Selection Recommendations for Acid Resistance Engineering

Strategy selection for practical acid‑tolerance engineering

In practice, method selection should be tailored to the specific enzyme. When structural information is lacking and broad exploration of sequence space is needed, directed evolution is the preferred choice—while it does not require structural information and screening throughput may be a bottleneck, it can discover unexpected beneficial mutations. When high-resolution structures are available and engineering goals are clear, rational design (including pKa modulation, charge optimization, and flexible region modification) is more efficient, offering high precision with low experimental burden. When seeking maximum efficiency and prediction accuracy, AI-assisted design (such as CatOpt and other deep learning tools) provides data-driven mutation prediction, substantially shortening R&D cycles. For projects pursuing systematic, multi-dimensional optimization, the integrated "three-pronged" strategy combining pKa calculation, surface charge optimization, and flexible region modification provides the most comprehensive solution—coordinating optimization across multiple molecular levels and covering the main factors affecting acid resistance.

IV. Summary and Outlook

AI-Powered Acid Resistance Engineering

From mining natural acid-resistant enzymes, to random screening via directed evolution, to AI-driven precision prediction and protein design, enzyme acid resistance engineering is evolving from a "craft" into a "data-driven science."

Currently, acid resistance engineering still faces several core challenges: first, the stability-activity trade-off—many mutations that improve acid resistance often come with reduced catalytic activity, and finding the optimal balance between the two is key to engineering applications; second, limited cross-family generalizability—the mechanisms of acid inactivation vary significantly across enzyme families, and universal engineering strategies are still lacking; third, prediction accuracy of rational design still has room for improvement, particularly for predicting long-range electrostatic effects far from the active center.

MatwingsVenus™

However, these challenges are precisely the areas where AI technology can provide advantages. With the continued iteration of protein language models, deep learning pH predictors, and other AI tools, as well as the maturation of industrial-grade AI platforms such as MatwingsVenus™ (Xiaowu™) protein agent, enzyme acid resistance engineering will move from "single-strategy trial-and-error" toward "multi-dimensional global optimization," providing increasingly powerful technical foundations for food processing, the feed industry, orally administered enzyme preparations, and acidic wastewater treatment.