Exploring the Activation Process of the Glycine Receptor.

Glycine receptors (GlyR) conduct inhibitory glycinergic neurotransmission in the spinal cord and the brainstem. They play an important role in muscle tone, motor coordination, respiration, and pain perception. However, the mechanism underlying GlyR activation remains unclear. There are five potential glycine binding sites in α1 GlyR, and different binding patterns may cause distinct activation or desensitization behaviors. In this study, we investigated the coupling of protein conformational changes and glycine binding events to elucidate the influence of binding patterns on the activation and desensitization processes of α1 GlyRs. Subsequently, we explored the energetic distinctions between the apical and lateral pathways during α1 GlyR conduction to identify the pivotal factors in the ion conduction pathway preference. Moreover, we predicted the mutational effects of the key residues and verified our predictions using electrophysiological experiments. For the mutants that can be activated by glycine, the predictions of the mutational directions were all correct. The strength of the mutational effects was assessed using Pearson's correlation coefficient, yielding a value of -0.77 between the calculated highest energy barriers and experimental maximum current amplitudes. These findings contribute to our understanding of GlyR activation, identify the key residues of GlyRs, and provide guidance for mechanistic studies on other pLGICs.

Assessing the Catalytic Role of Native Glucagon Amyloid Fibrils.

Glucagon stands out as a pivotal peptide hormone, instrumental in controlling blood glucose levels and lipid metabolism. While the formation of glucagon amyloid fibrils has been documented, their biological functions remain enigmatic. Recently, we demonstrated experimentally that glucagon amyloid fibrils can act as catalysts in several biological reactions including esterolysis, lipid hydrolysis, and dephosphorylation. Herein, we present a multiscale quantum mechanics/molecular mechanics (QM/MM) simulation of the acylation step in the esterolysis of para-nitrophenyl acetate (p-NPA), catalyzed by native glucagon amyloid fibrils, serving as a model system to elucidate their catalytic function. This step entails a concerted mechanism, involving proton transfer from serine to histidine, followed by the nucleophilic attack of the serine oxy anion on the carbonyl carbon of p-NPA. We computed the binding energy and free-energy profiles of this reaction using the protein-dipole Langevin-dipole (PDLD) within the linear response approximation (LRA) framework (PDLD/S-LRA-2000) and the empirical valence bond (EVB) methods. This included simulations of the reaction in an aqueous environment and in the fibril, enabling us to estimate the catalytic effect of the fibril. Our EVB calculations obtained a barrier of 23.4 kcal mol-1 for the enzyme-catalyzed reaction compared to the experimental value of 21.9 kcal mol-1 (and a calculated catalytic effect of 3.2 kcal mol-1 compared to the observed effect of 4.7 kcal mol-1). This close agreement together with the barrier reduction when transitioning from the reference solution reaction to the amyloid fibril provides supporting evidence to the catalytic role of glucagon amyloid fibrils. Moreover, employing the PDLD/S-LRA-2000 approach further reinforced exclusively the enzyme's catalytic role. The results presented in this study contribute significantly to our understanding of the catalytic role of glucagon amyloid fibrils, marking, to the best of our knowledge, the first-principles mechanistic investigation of fibrils using QM/MM methods. Therefore, our findings offer fruitful insights for future research into the mechanisms of related amyloid catalysis.

Exploring the Light-Emitting Agents in Renilla Luciferases by an Effective QM/MM Approach.

Bioluminescence is a fascinating natural phenomenon, wherein organisms produce light through specific biochemical reactions. Among these organisms, Renilla luciferase (RLuc) derived from the sea pansy Renilla reniformis is notable for its blue light emission and has potential applications in bioluminescent tagging. Our study focuses on RLuc8, a variant of RLuc with eight amino acid substitutions. Recent studies have shown that the luminescent emitter coelenteramide can adopt multiple protonation states, which may be influenced by nearby residues at the enzyme's active site, demonstrating a complex interplay between protein structure and bioluminescence. Herein, using the quantum mechanical consistent force field method and the semimacroscopic protein dipole-Langevin dipole method with linear response approximation, we show that the phenolate state of coelenteramide in RLuc8 is the primary light-emitting species in agreement with experimental results. Our calculations also suggest that the proton transfer (PT) from neutral coelenteramide to Asp162 plays a crucial role in the bioluminescence process. Additionally, we reproduced the observed emission maximum for the amide anion in RLuc8-D120A and the pyrazine anion in the presence of a Na+ counterion in RLuc8-D162A, suggesting that these are the primary emitters. Furthermore, our calculations on the neutral emitter in the engineered AncFT-D160A enzyme, structurally akin to RLuc8-D162A but with a considerably blue-shifted emission peak, aligned with the observed data, possibly explaining the variance in emission peaks. Overall, this study demonstrates an effective approach to investigate chromophores' bimolecular states while incorporating the PT process in emission spectra calculations, contributing valuable insights for future studies of PT in photoproteins.

Exploring the activation mechanism of metabotropic glutamate receptor 2.

Homomeric dimerization of metabotropic glutamate receptors (mGlus) is essential for the modulation of their functions and represents a promising avenue for the development of novel therapeutic approaches to address central nervous system diseases. Yet, the scarcity of detailed molecular and energetic data on mGlu2 impedes our in-depth comprehension of their activation process. Here, we employ computational simulation methods to elucidate the activation process and key events associated with the mGlu2, including a detailed analysis of its conformational transitions, the binding of agonists, Gi protein coupling, and the guanosine diphosphate (GDP) release. Our results demonstrate that the activation of mGlu2 is a stepwise process and several energy barriers need to be overcome. Moreover, we also identify the rate-determining step of the mGlu2's transition from the agonist-bound state to its active state. From the perspective of free-energy analysis, we find that the conformational dynamics of mGlu2's subunit follow coupled rather than discrete, independent actions. Asymmetric dimerization is critical for receptor activation. Our calculation results are consistent with the observation of cross-linking and fluorescent-labeled blot experiments, thus illustrating the reliability of our calculations. Besides, we also identify potential key residues in the Gi protein binding position on mGlu2, mGlu2 dimer's TM6-TM6 interface, and Gi α5 helix by the change of energy barriers after mutation. The implications of our findings could lead to a more comprehensive grasp of class C G protein-coupled receptor activation.

Fine-tuning activation specificity of G-protein-coupled receptors via automated path searching.

Physics-based simulation methods can grant atomistic insights into the molecular origin of the function of biomolecules. However, the potential of such approaches has been hindered by their low efficiency, including in the design of selective agonists where simulations of myriad protein-ligand combinations are necessary. Here, we describe an automated input-free path searching protocol that offers (within 14 d using Graphics Processing Unit servers) a minimum free energy path (MFEP) defined in high-dimension configurational space for activating sphingosine-1-phosphate receptors (S1PRs) by arbitrary ligands. The free energy distributions along the MFEP for four distinct ligands and three S1PRs reached a remarkable agreement with Bioluminescence Resonance Energy Transfer (BRET) measurements of G-protein dissociation. In particular, the revealed transition state structures pointed out toward two S1PR3 residues F263/I284, that dictate the preference of existing agonists CBP307 and BAF312 on S1PR1/5. Swapping these residues between S1PR1 and S1PR3 reversed their response to the two agonists in BRET assays. These results inspired us to design improved agonists with both strong polar head and bulky hydrophobic tail for higher selectivity on S1PR1. Through merely three in silico iterations, our tool predicted a unique compound scaffold. BRET assays confirmed that both chiral forms activate S1PR1 at nanomolar concentration, 1 to 2 orders of magnitude less than those for S1PR3/5. Collectively, these results signify the promise of our approach in fine agonist design for G-protein-coupled receptors.

Predicting Mutational Effects on Ca2+-Activated Chloride Conduction of TMEM16A Based on a Simulation Study.

The dysfunction and defects of ion channels are associated with many human diseases, especially for loss-of-function mutations in ion channels such as cystic fibrosis transmembrane conductance regulator mutations in cystic fibrosis. Understanding ion channels is of great current importance for both medical and fundamental purposes. Such an understanding should include the ability to predict mutational effects and describe functional and mechanistic effects. In this work, we introduce an approach to predict mutational effects based on kinetic information (including reaction barriers and transition state locations) obtained by studying the working mechanism of target proteins. Specifically, we take the Ca2+-activated chloride channel TMEM16A as an example and utilize the computational biology model to predict the mutational effects of key residues. Encouragingly, we verified our predictions through electrophysiological experiments, demonstrating a 94% prediction accuracy regarding mutational directions. The mutational strength assessed by Pearson's correlation coefficient is -0.80 between our calculations and the experimental results. These findings suggest that the proposed methodology is reliable and can provide valuable guidance for revealing functional mechanisms and identifying key residues of the TMEM16A channel. The proposed approach can be extended to a broad scope of biophysical systems.

Mechanistic study of the transmission pattern of the SARS-CoV-2 omicron variant.

The omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) characterized by 30 mutations in its spike protein, has rapidly spread worldwide since November 2021, significantly exacerbating the ongoing COVID-19 pandemic. In order to investigate the relationship between these mutations and the variant's high transmissibility, we conducted a systematic analysis of the mutational effect on spike-angiotensin-converting enzyme-2 (ACE2) interactions and explored the structural/energy correlation of key mutations, utilizing a reliable coarse-grained model. Our study extended beyond the receptor-binding domain (RBD) of spike trimer through comprehensive modeling of the full-length spike trimer rather than just the RBD. Our free-energy calculation revealed that the enhanced binding affinity between the spike protein and the ACE2 receptor is correlated with the increased structural stability of the isolated spike protein, thus explaining the omicron variant's heightened transmissibility. The conclusion was supported by our experimental analyses involving the expression and purification of the full-length spike trimer. Furthermore, the energy decomposition analysis established those electrostatic interactions make major contributions to this effect. We categorized the mutations into four groups and established an analytical framework that can be employed in studying future mutations. Additionally, our calculations rationalized the reduced affinity of the omicron variant towards most available therapeutic neutralizing antibodies, when compared with the wild type. By providing concrete experimental data and offering a solid explanation, this study contributes to a better understanding of the relationship between theories and observations and lays the foundation for future investigations.

Energetic and structural insights behind calcium induced conformational transition in calmodulin.

Calmodulin (CaM) is a key signaling protein that triggers several cellular and physiological processes inside the cell. Upon binding with calcium ion, CaM undergoes large scale conformational transition from a closed state to an open state that facilitates its interaction with various target protein and regulates their activity. This work explores the origin of the energetic and structural variation of the wild type and mutated CaM and explores the molecular origin for the structural differences between them. We first calculated the sequential calcium binding energy to CaM using the PDLD/S-LRA/ß approach. This study  shows a very good correlation with experimental calcium binding energies. Next we calculated the calcium binding energies to the wild type CaM and several mutated CaM systems which were reported experimentally. On the structural aspect, it has been reported experimentally that certain mutation (Q41L-K75I) in calcium bound CaM leads to complete conformational transition from an open to a closed state. By using equilibrium molecular dynamics simulation, free energy calculation and contact frequency map analysis, we have shown that the formation of a cluster of long-range hydrophobic contacts, initiated by the Q41L-K75I CaM variant is the driving force behind its closing motion. This study unravels the energetics and structural aspects behind calcium ion induced conformational changes in wild type CaM and its variant.

A Chemical Strategy for the Degradation of the Main Protease of SARS-CoV-2 in Cells.

SARS-CoV-2 is the virus that causes the global pandemic of COVID-19. The main protease (Mpro) of SARS-CoV-2 is essential for viral infection and is one of the major therapeutic targets for COVID-19. Here, we report the design, synthesis, and biological characterization of a novel heterobifunctional small molecule that could effectively induce the degradation of SARS-CoV-2 Mpro and its drug-resistant mutants in HEK 293T cells, thus demonstrating a new alternative strategy for intervening with proteins important for this novel coronavirus.

Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences.

The availability of natural protein sequences synergized with generative AI provides new paradigms to engineer enzymes. Although active enzyme variants with numerous mutations have been designed using generative models, their performance often falls short of their wild type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase (RLuc) homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate to improve either luciferase activity or stability of designed single mutants is ~50%. This finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in RLuc toward emitting blue light that holds advantages in terms of water penetration compared to other light spectra. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences.

The availability of natural protein sequences synergized with generative artificial intelligence (AI) provides new paradigms to create enzymes. Although active enzyme variants with numerous mutations have been produced using generative models, their performance often falls short compared to their wild-type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate of designed single mutants is ~50% to improve either luciferase activity or stability. These finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in Renilla luciferase towards emitting blue light that holds advantages in terms of water penetration compared to other light spectrum. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering.

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. By applying generative models, we could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, activity, and stability, rationalizing the laboratory evolution of de novo enzymes, decoding protein sequence semantics, and its applications in enzyme engineering. Notably, the prediction of enzyme activity and stability using natural enzyme sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

MEDIATE - Molecular DockIng at homE: Turning collaborative simulations into therapeutic solutions.

INTRODUCTION: Collaborative computing has attracted great interest in the possibility of joining the efforts of researchers worldwide. Its relevance has further increased during the pandemic crisis since it allows for the strengthening of scientific collaborations while avoiding physical interactions. Thus, the E4C consortium presents the MEDIATE initiative which invited researchers to contribute via their virtual screening simulations that will be combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds will be tested, and the biological results will be shared with the scientific community. AREAS COVERED: In this paper, the MEDIATE initiative is described. This shares compounds' libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative's capacity to identify active compounds. EXPERT OPINION: Structure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files.

Analyzing the Reaction of Orotidine 5'-Phosphate Decarboxylase as a Way to Examine Some Key Catalytic Proposals.

This study analyzes the origin of enzyme catalysis by focusing on the reaction of orotidine 5'-phosphate decarboxylase (ODCase). This reaction involves an enormous catalytic effect of 23 kcal/mol that has been attributed to reactant state destabilization associated with the use of binding energy through the so-called Circe effect. However, our early studies and subsequent key experiments have shown that the presumed effect of the binding energy (namely, the strain exerted by a bond to a phosphate group) does not contribute to the catalysis. In this study, we perform quantitative empirical valence bond calculations that reproduce the catalytic effect of ODCase and the effect of removing the phosphate side chain. The calculations demonstrate that the effect of the phosphate is due to a change in reorganization energy and should not be described as an induced fit effect. Similarly, we show that the overall catalytic effect is due to electrostatic transition state stabilization, which again reflects the smaller reorganization energy in the enzyme than in water. We also elaborate on the problems with the induced fit proposal, including the fact that it does not serve to tell us what the actual origin of the action of the catalytic effect is. In addition to the above points, we use this paper to discuss misconceptions about the meaning of the preorganization effect, as well as other misunderstandings of what is being done in consistent calculations of enzyme catalysis.

Elucidation of the α-Ketoamide Inhibition Mechanism: Revealing the Critical Role of the Electrostatic Reorganization Effect of Asp17 in the Active Site of the 20S Proteasome.

The 20S proteasome is an attractive drug target for the development of anticancer agents because it plays an important role in cellular protein degradation. It has a threonine residue that can act as a nucleophile to attack inhibitors with an electrophilic warhead, forming a covalent adduct. Fundamental understanding of the reaction mechanism between covalent inhibitors and the proteasome may assist the design and refinement of compounds with the desired activity. In this study, we investigated the covalent inhibition mechanism of an α-keto phenylamide inhibitor of the proteasome. We calculated the noncovalent binding free energy using the PDLD/S-LRA/ß method and the reaction free energy through the empirical valence bond method (EVB). Several possible reaction pathways were explored. Subsequently, we validated the calculated activation and reaction free energies of the most plausible pathways by performing kinetic experiments. Furthermore, the effects of different ionization states of Asp17 on the activation energy at each step were also discussed. The results revealed that the ionization states of Asp17 remarkably affect the activation energies and there is an electrostatic reorganization of Asp17 during the course of the reaction. Our results demonstrate the critical electrostatic effect of Asp17 in the active site of the 20S proteasome.

Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering.

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

Exploring the Role of Chemical Reactions in the Selectivity of Tyrosine Kinase Inhibitors.

A variety of diseases are associated with tyrosine kinase enzymes that activate many proteins via signal transduction cascades. The similar ATP-binding pockets of these tyrosine kinases make it extremely difficult to design selective covalent inhibitors. The present study explores the contribution of the chemical reaction steps to the selectivity of the commercialized inhibitor acalabrutinib over the Bruton's tyrosine kinase (BTK) and the interleukin-2-inducible T-cell kinase (ITK). Ab initio and empirical valence bond (EVB) simulations of the two kinases indicate that the most favorable reaction path involves a water-assisted mechanism of the 2-butynamide reactive group of acalabrutinib. BTK reacts with acalabrutinib with a substantially lower barrier than ITK, according to our calculated free-energy profile and kinetic simulations. Such a difference is due to the microenvironment of the active site, as further supported by a sequence-based analysis of specificity determinants for several commercialized inhibitors. Our study involves a new approach of simulating directly the IC50 and inactivation efficiency keff, instead of using the standard formulas. This new strategy is particularly important in studies of covalent inhibitors with a very exothermic bonding step. Overall, our results demonstrate the importance of understanding the chemical reaction steps in designing selective covalent inhibitors for tyrosine kinases.

Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes.

Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ([Formula: see text]) inferred from their natural homologous sequences using the maximum entropy model. The [Formula: see text] of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the [Formula: see text] for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the [Formula: see text] landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design.

Fast and Effective Prediction of the Absolute Binding Free Energies of Covalent Inhibitors of SARS-CoV-2 Main Protease and 20S Proteasome.

The COVID-19 pandemic has been a public health emergency with continuously evolving deadly variants around the globe. Among many preventive and therapeutic strategies, the design of covalent inhibitors targeting the main protease (Mpro) of SARS-CoV-2 that causes COVID-19 has been one of the hotly pursued areas. Currently, about 30% of marketed drugs that target enzymes are covalent inhibitors. Such inhibitors have been shown in recent years to have many advantages that counteract past reservation of their potential off-target activities, which can be minimized by modulation of the electrophilic warhead and simultaneous optimization of nearby noncovalent interactions. This process can be greatly accelerated by exploration of binding affinities using computational models, which are not well-established yet due to the requirement of capturing the chemical nature of covalent bond formation. Here, we present a robust computational method for effective prediction of absolute binding free energies (ABFEs) of covalent inhibitors. This is done by integrating the protein dipoles Langevin dipoles method (in the PDLD/S-LRA/ß version) with quantum mechanical calculations of the energetics of the reaction of the warhead and its amino acid target, in water. This approach evaluates the combined effects of the covalent and noncovalent contributions. The applicability of the method is illustrated by predicting the ABFEs of covalent inhibitors of SARS-CoV-2 Mpro and the 20S proteasome. Our results are found to be reliable in predicting ABFEs for cases where the warheads are significantly different. This computational protocol might be a powerful tool for designing effective covalent inhibitors especially for SARS-CoV-2 Mpro and for targeted protein degradation.

Electrostatic influence on IL-1 transport through the GSDMD pore.

A variety of signals, including inflammasome activation, trigger the formation of large transmembrane pores by gasdermin D (GSDMD). There are primarily two functions of the GSDMD pore, to drive lytic cell death, known as pyroptosis, and to permit the release of leaderless interleukin-1 (IL-1) family cytokines, a process that does not require pyroptosis. We are interested in the mechanism by which the GSDMD pore channels IL-1 release from living cells. Recent studies revealed that electrostatic interaction, in addition to cargo size, plays a critical role in GSDMD-dependent protein release. Here, we determined computationally that to enable electrostatic filtering against pro-IL-1ß, acidic lipids in the membrane need to effectively neutralize positive charges in the membrane-facing patches of the GSDMD pore. In addition, we predicted that salt has an attenuating effect on electrostatic filtering and then validated this prediction using a liposome leakage assay. A calibrated electrostatic screening factor is necessary to account for the experimental observations, suggesting that ion distribution within the pore may be different from the bulk solution. Our findings corroborate the electrostatic influence of IL-1 transport exerted by the GSDMD pore and reveal extrinsic factors, including lipid and salt, that affect the electrostatic environment.