Understanding how protein structures respond dynamically to pH changes has remained a challenging frontier in cell biology. Proteins, the molecular workhorses of cells, often undergo conformational shifts that modulate their activity in response to the acidic or basic environment. Discerning which proteins are sensitive to these pH variations is paramount, as it may unlock new pathways for therapeutic intervention. Currently, experimental approaches to identify pH-sensitive proteins are labor-intensive and time-consuming, often requiring painstaking analyses of individual proteins in isolation.

In a groundbreaking advancement, researchers at the University of Notre Dame have introduced a powerful computational pipeline capable of scanning hundreds of proteins within days, rather than years. This novel method accelerates the identification of pH-sensitive domains within proteins, revolutionizing the initial screening phase of biomolecular research. By leveraging existing structural data and experimental insights, the team created an algorithmic process that predicts specific residues within proteins that could mediate pH-dependent allosteric regulation.

Dr. Katharine White, Clare Boothe Luce Assistant Professor in the Department of Chemistry and Biochemistry at Notre Dame, emphasized the transformative nature of this technology. “Prior to this development, scientists were searching for a needle in a haystack when identifying pH-responsive proteins,” she remarked. The computational pipeline effectively refines that haystack into a manageable collection of candidate proteins, setting the stage for focused experimentation and drug design.

Historically, only a handful of cytoplasmic proteins — approximately seventy — have been validated as pH-sensitive via experimental studies despite the hypothesis that many more possess this characteristic. Moreover, detailed mechanistic insights exist for fewer than a third of these known proteins. The challenge stems from the complexity of measuring pH-dependent conformational changes, which often involve subtle shifts in ionizable amino acid networks that are difficult to capture through traditional experimental modalities.

The new study, recently published in the journal Science Signaling, represents an important leap forward. With funding support from the National Science Foundation and the National Institutes of Health, White and her team formed a modular pipeline adept at integrating conformational data from protein crystal structures, pKa predictions of ionizable groups, and bioinformatic annotations. The pipeline can systematically identify so-called “ionizable networks,” clusters of amino acids whose protonation states modulate protein structure and function in response to pH changes.

A particularly salient application of this method was the analysis of the Src homology 2 (SH2) domain, a conserved protein module central to signal transduction pathways regulating cell growth, differentiation, and immune responses. The SH2 domain is recurrently mutated in various cancers, making it a prime target for understanding pH-mediated regulatory mechanisms. White’s team experimentally validated the in silico prediction that the SH2 domain exhibits marked pH sensitivity, confirming both its biological relevance and the accuracy of the computational model.

Further insights emerged concerning c-Src, a non-receptor tyrosine kinase with pivotal roles in oncogenic signaling. The study elucidated the precise molecular locale where pH influences c-Src activity, underscoring how acid-base chemistry interfaces with protein allosteric regulation. Such mechanistic clarity holds promise for the development of precision therapeutics that exploit the protonation states of key residues to modulate enzyme function selectively.

Papa Kobina Van Dyck, lead author and recent doctoral graduate in biophysics at Notre Dame, reflected on the magnitude of the achievement: “We condensed what would have taken decades of biochemical experimentation into a matter of weeks using computational methods.” This acceleration dramatically enhances the pace at which research can move from hypothesis to experimental validation and, eventually, clinical application.

Beyond cancer and neurodegeneration, the implications of mapping pH-sensitive protein networks extend to a broad spectrum of medical conditions characterized by dysregulated pH dynamics, including diabetes, autoimmune diseases, and traumatic brain injury. The Notre Dame pipeline therefore represents a versatile tool not only for fundamental biological discovery but also for translational efforts aimed at drug discovery and personalized medicine.

In summary, this pioneering work exemplifies how integrative computational biology can circumvent traditional experimental bottlenecks, offering new vistas for exploring the complex molecular choreography dictated by pH fluctuations in cells. By illuminating the ionizable networks that govern protein allostery, the study provides a foundation for innovative therapies targeting diseases that span oncology, neurology, and beyond.

For readers seeking to delve deeper into this transformative research, the full article titled “Ionizable networks mediate pH-dependent allostery in the SH2 domain–containing signaling proteins SHP2 and SRC” is accessible through Science Signaling. The comprehensive study meticulously outlines the computational methodologies and experimental validations that underpin this advancement, heralding a new era in cellular physiology and disease biology.

Subject of Research: pH-dependent regulation of protein structure and function in cellular signaling pathways.

Article Title: Ionizable networks mediate pH-dependent allostery in the SH2 domain–containing signaling proteins SHP2 and SRC

News Publication Date: 11-Nov-2025

Web References:

• Original article: https://www.science.org/doi/10.1126/scisignal.adt3018
• University of Notre Dame overview: https://research.nd.edu/news-and-events/news/new-computational-process-could-help-condense-decades-of-disease-biology-research-into-days/

Image Credits: Photo by Peter Ringenberg/University of Notre Dame

Keywords: Cellular processes, Life sciences, Diseases and disorders, Breast cancer, Signaling pathways

At the heart of the study lies the ambitious goal of crafting enzymes capable of catalyzing Kemp elimination reactions—a model reaction of great interest due to its relevance in studying enzyme mechanisms and design strategies. Unlike traditional directed evolution approaches, this work harnesses modular assembly and sophisticated design algorithms to generate entirely new enzymatic backbones, finely tuned active sites, and enhanced catalytic capabilities without reliance on natural enzyme templates.

The initial backbone generation involved an ingenious modular strategy leveraging multiple homologous protein structures. Through precise alignment of five distinct imidazole glycerol-phosphate synthase (IGPS) protein backbones, segments were dissected and recombined at structurally conserved junctions. Computational sequence design refined these chimeric constructs, guided by position-specific scoring matrices to ensure stability and compatibility across fragments, thus generating thousands of candidate backbones tailored for subsequent catalytic modification.

To complement this, the research expanded its scope by mining sequence databases extensively, identifying thousands of IGPS homologues. These sequences underwent clustering and rigorous structural prediction via the latest AlphaFold2 implementations. The high-confidence models then entered a cycle of stability design focused on preserving structural integrity while allowing active site flexibility—a crucial balance for efficient catalysis.

Engineering the catalytic site itself was meticulously executed through theozyme modeling. By incorporating precise geometric constraints derived from quantum chemical calculations, the team employed Rosetta’s Matcher algorithm to embed catalytic residues within the designed scaffolds, optimizing their positioning to mimic transition states effectively. This level of geometrical rigor ensured that the catalytic apparatus would be primed for the Kemp elimination’s mechanistic demands.

Once the initial active sites were integrated, the designed enzymes underwent an exhaustive round of sequence optimization focused on the microenvironment surrounding the ligand and catalytic residues. Utilizing Rosetta’s sequence design within spatial proximity to the active site and leveraging mutational scanning data, the design process strategically narrowed the sequence landscape. A ‘fuzzy’-logic objective function balanced energy considerations, van der Waals interactions, solvation effects, and geometric fidelity, filtering millions of designs to identify promising candidates.

Active-site and core stabilization efforts pushed the designs closer to practical viability. Enumerating low-energetic mutations in the active site and scanning the broader protein structure for positions amenable to beneficial substitutions permitted iterative rounds of refinement. Employing FuncLib calculations allowed for the judicious combination of stabilizing mutations while avoiding deleterious effects, ultimately enhancing the robustness of the enzyme across diverse conditions.

Validation extended into computational dynamical analyses, where the preorganization of the active site was tested through rigid-body minimization simulations absent of ligands. Designs failing to maintain catalytic residue alignment beyond acceptable root-mean-square deviations were discarded, ensuring that functionally viable geometries were preserved intrinsically. Additionally, the congruence between model predictions from different computational approaches was verified to further guarantee reliability.

The iterative optimization of active-site constellations was key to achieving high catalytic efficiency. Targeted mutagenesis simulated via FuncLib—eschewing homologous sequence constraints due to the de novo nature of designs—facilitated exploration of sequence diversity while maintaining stability. The best-performing designs were advanced for experimental validation, narrowing the gap between in silico predictions and laboratory realization.

Protein expression protocols were developed with precision to ensure that the computationally designed enzymes could be produced reliably and at scale. Harnessing bacterial expression systems and affinity purification techniques, the team prepared high-purity samples essential for detailed biochemical characterization and crystallographic analysis, confirming the fidelity of designs from sequence to structure.

Activity assays monitored enzymatic function using spectrophotometric methods detecting product formation, enabling kinetic parameter determination under varying substrate concentrations. The data fitting to Michaelis-Menten kinetics provided insights into catalytic turnover rates and substrate affinity, highlighting the practical effectiveness of the computationally designed enzymes compared to natural counterparts.

Thermal stability assessments using nano differential scanning fluorimetry revealed the robustness of the new enzymes, a critical factor for potential industrial or therapeutic applications. The temperature ramping experiments showcased the engineered proteins’ ability to maintain structural integrity under stress, consistent with the stability enhancements incorporated during design.

Crystallographic studies offered definitive structural validation, with multiple enzyme variants crystallized and their structures solved to resolutions near or below 2.1 Å. These analyses verified the accuracy of the computational models and provided atomic-level insights into active-site architecture, substrate positioning, and dynamic features instrumental for catalysis.

To complement static structural data, extensive molecular dynamics simulations spanning multiple microseconds illuminated the enzymes’ dynamic behaviors in bound and unbound states. Employing enhanced sampling techniques and state-of-the-art force fields, these simulations elucidated substrate binding modes, active-site flexibility, and solvent interactions, painting a comprehensive picture of the catalytic process in motion.

Electrostatic Valence Bond (EVB) simulations further probed the reaction mechanism at a quantum-mechanical/molecular-mechanical interface, distinguishing between reactive substrate conformers and capturing transient states of the Kemp elimination process. These simulations offered quantitative free-energy profiles that correlated closely with experimental activity, underpinning the rationale behind the designed enzymes’ functionality.

Collectively, this multidisciplinary approach—spanning computational modeling, structural biology, biophysical characterization, and dynamic simulations—presents a paradigm shift in enzyme design. The researchers’ ability to computationally generate highly efficient Kemp eliminases from scratch portends transformative impacts on enzyme engineering, enabling custom biocatalysts for diverse chemical transformations without exhaustive laboratory evolution.

By harnessing sophisticated algorithms, thorough validation pipelines, and rigorous biochemical assays, the study sets a new standard for the scope and precision of computer-aided enzyme design. This breakthrough holds promise not only for academic exploration but also for practical applications in sustainable manufacturing, drug development, and synthetic biology, where tailored catalysts can accelerate innovation and reduce environmental impact.

The confluence of modular backbone assembly, advanced design algorithms, and comprehensive dynamic simulations represents a masterclass in modern enzymology, painting a hopeful future where enzyme engineering is limited only by imagination and computational power. This work invites further refinement and expansion, including exploration of other challenging reactions and incorporation of allosteric regulation, ushering in an era of bespoke enzymes crafted entirely by computation.

Subject of Research: Computational design and engineering of high-efficiency Kemp elimination enzymes.

Article Title: Complete computational design of high-efficiency Kemp elimination enzymes.

Article References:
Listov, D., Vos, E., Hoffka, G. et al. Complete computational design of high-efficiency Kemp elimination enzymes. Nature (2025). https://doi.org/10.1038/s41586-025-09136-2

Image Credits: AI Generated