Change In Binding Affinity Research Articles

A theoretical study on binding and stabilization of galactose and novel galactose analogues to the human α-galactosidase A variant causing Fabry disease

α-galactosidase A (α-Gal A) catalyzes the hydrolysis of terminal α-galactosyl moieties from globotriaosylceramide, and mutations in this enzyme lead to the lipid metabolism disorder “Fabry disease”. Mutation in α-Gal A possibly causes the protein misfolding, which reduces catalytic activity and stability of the enzyme. A recent study demonstrated that the binding of galactose on the α-Gal A catalytic site significantly increases its stability. Herein, the effect of mutation on secondary structure, structural energy, and galactose affinity of α-Gal A (wild type and A143T variant) was investigated using molecular dynamics simulations and free energy calculations based on MM/GBSA method. The results showed that A143T mutation caused the formation of unusual H-bonds that induced the change in secondary structure and binding affinities toward galactose. The amino acid residues involved in galactose binding were identified. The molecular binding mechanism obtained from this study could be helpful for optimizations and designs of new galactose analogs as pharmacological chaperones against Fabry disease.

Surface chemistry of graphene tailoring the activity of digestive enzymes by modulating interfacial molecular interactions

As a kind of novel functional material, graphene-related nanomaterials (GRMs) have great potentials in industrial and biomedical applications. Meanwhile, the production and wide application of GRMs will increase the risk of unintended or intentional oral exposure to human beings, attracting safety concerns about their biological fates and toxicological effects. The normal enzymatic activity of digestive enzymes is essential for the proper functioning of the gastrointestinal tract system. However, whether and how orally entered GRMs and their surface groups affect digestive enzymes’ activity are still scarce. In this paper, we systematically studied the effects of graphene oxide (GO), graphene modified with hydroxyl groups (OH-G), carboxyl groups (COOH-G), and amino groups (NH2-G) on enzymatic activity of three typical digestive enzymes (pepsin, trypsin, and α-pancreatic amylase). The results showed that the activity of trypsin and α-pancreatic amylase could be greatly changed after GRMs incubation in a surface chemistry dependent manner, while the activity of pepsin was not affected. To elucidate the mechanisms at the molecular level, the interactions between trypsin and GRMs were studied by spectrometry, thermophoresis, and computational simulation approaches, and the key roles of surface chemistry of GRMs in tailoring the activity of trypsin were finally figured out. GO allosterically inhibited trypsin’s activity in the non-competitive manner because of the conformation transition induced by the intensive interactions. COOH-G could effectively hamper enzymatic activity of trypsin in the competitive manner by blocking the active catalytic pocket. As for NH2-G and OH-G, they had little impact on the activity of trypsin due to the weak binding affinity or limited conformational change. Our findings not only indicate surface chemistry plays an important role in tailoring the effects of GRMs on the activity of digestive enzymes but also provide new insights for understanding the oral safety of nanomaterials from daily products and the environment.

Resistor: An algorithm for predicting resistance mutations via Pareto optimization over multistate protein design and mutational signatures.

Resistance to pharmacological treatments is a major public health challenge. Here, we introduce Resistor-a structure- and sequence-based algorithm that prospectively predicts resistance mutations for drug design. Resistor computes the Pareto frontier of four resistance-causing criteria: the change in binding affinity (ΔKa) of the (1) drug and (2) endogenous ligand upon a protein's mutation; (3) the probability a mutation will occur based on empirically derived mutational signatures; and (4) the cardinality of mutations comprising a hotspot. For validation, we applied Resistor to EGFR and BRAF kinase inhibitors treating lung adenocarcinoma and melanoma. Resistor correctly identified eight clinically significant EGFR resistance mutations, including the erlotinib and gefitinib "gatekeeper" T790M mutation and five known osimertinib resistance mutations. Furthermore, Resistor predictions are consistent with BRAF inhibitor sensitivity data from both retrospective and prospective experiments using KinCon biosensors. Resistor is available in the open-source protein design software OSPREY.

Tryptophan mutations in G3BP1 tune the stability of a cellular signaling hub by weakening transient interactions with Caprin1 and USP10

Intrinsically disordered proteins (IDPs) often coordinate transient interactions with multiple proteins to mediate complex signals within large protein networks. Among these, the IDP hub protein G3BP1 can form complexes with cytoplasmic phosphoprotein Caprin1 and ubiquitin peptidase USP10; the resulting control of USP10 activity contributes to a pathogenic virulence system that targets endocytic recycling of the ion channel CFTR. However, while the identities of protein interactors are known for many IDP hub proteins, the relationship between pairwise affinities and the extent of protein recruitment and activity is not well understood. Here, we describe invitro analysis of these G3BP1 affinities and show tryptophan substitutions of specific G3BP1 residues reduce its affinity for both USP10 and Caprin1. We show that these same mutations reduce the stability of complexes between the full-length proteins, suggesting that copurification can serve as a surrogate measure of interaction strength. The crystal structure of G3BP1 TripleW (F15W/F33W/F124W) mutant reveals a clear reorientation of the side chain of W33, creating a steric clash with USP10 and Caprin1. Furthermore, an amino-acid scan of USP10 and Caprin1 peptides reveals similarities and differences in the ability to substitute residues in the core motifs as well as specific substitutions with the potential to create higher affinity peptides. Taken together, these data show that small changes in component binding affinities can have significant effects on the composition of cellular interaction hubs. These specific protein mutations can be harnessed to manipulate complex protein networks, informing future investigations into roles of these networks in cellular processes.

Cry4Aa and Cry4Ba Mosquito-Active Toxins Utilize Different Domains in Binding to a Particular Culex ALP Isoform: A Functional Toxin Receptor Implicating Differential Actions on Target Larvae.

The three-domain Cry4Aa toxin produced from Bacillus thuringiensis subsp. israelensis was previously shown to be much more toxic to Culex mosquito larvae than its closely related toxin—Cry4Ba. The interaction of these two individual toxins with target receptors on susceptible larval midgut cells is likely to be the critical determinant in their differential toxicity. Here, two full-length membrane-bound alkaline phosphatase (mALP) isoforms from Culex quinquefasciatus larvae, Cq-mALP1263and Cq-mALP1264, predicted to be GPI-linked was cloned and functionally expressed in Spodoptera frugiperda (Sf9) cells as 57- and 61-kDa membrane-bound proteins, respectively. Bioinformatics analysis disclosed that both Cq-mALP isoforms share significant sequence similarity to Aedes aegypti-mALP—a Cry4Ba toxin receptor. In cytotoxicity assays, Sf9 cells expressing Cq-mALP1264, but not Cq-mALP1263, showed remarkably greater susceptibility to Cry4Aa than Cry4Ba, while immunolocalization studies revealed that both toxins were capable of binding to each Cq-mALP expressed on the cell membrane surface. Molecular docking of the Cq-mALP1264-modeled structure with individual Cry4 toxins revealed that Cry4Aa could bind to Cq-mALP1264 primarily through particular residues on three surface-exposed loops in the receptor-binding domain—DII, including Thr512, Tyr513 and Lys514 in the β10-β11loop. Dissimilarly, Cry4Ba appeared to utilize only certain residues in its C-terminal domain—DIII to interact with such a Culex counterpart receptor. Ala-substitutions of selected β10-β11loop residues (T512A, Y513A and K514A) revealed that only the K514A mutant displayed a drastic decrease in biotoxicity against C. quinquefasciatus larvae. Further substitution of Lys514 with Asp (K514D) revealed a further decrease in larval toxicity. Furthermore, in silico calculation of the binding affinity change (ΔΔGbind) in Cry4Aa-Cq-mALP1264 interactions upon these single-substitutions revealed that the K514D mutation displayed the largest ΔΔGbind value as compared to three other mutations, signifying an adverse impact of a negative charge at this critical receptor-binding position. Altogether, our present study has disclosed that these two related-Cry4 mosquito-active toxins conceivably exploited different domains in functional binding to the same Culex membrane-bound ALP isoform—Cq-mALP1264 for mediating differential toxicity against Culex target larvae.

Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein-Protein Binding Affinity Changes upon Mutation.

Protein-protein interactions (PPIs) are involved in almost all biological processes in the cell. Understanding protein-protein interactions holds the key for the understanding of biological functions, diseases and the development of therapeutics. Recently, artificial intelligence (AI) models have demonstrated great power in PPIs. However, a key issue for all AI-based PPI models is efficient molecular representations and featurization. Here, we propose Hom-complex-based PPI representation, and Hom-complex-based machine learning models for the prediction of PPI binding affinity changes upon mutation, for the first time. In our model, various Hom complexes Hom(G1, G) can be generated for the graph representation G of protein-protein complex by using different graphs G1, which reveal G1-related inner connections within the graph representation G of protein-protein complex. Further, for a specific graph G1, a series of nested Hom complexes are generated to give a multiscale characterization of the PPIs. Its persistent homology and persistent Euler characteristic are used as molecular descriptors and further combined with the machine learning model, in particular, gradient boosting tree (GBT). We systematically test our model on the two most-commonly used data sets, that is, SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great potential of our model for the analysis of PPIs. Our model can be used for the analysis and design of efficient antibodies for SARS-CoV-2.

Model-Based Approach to Selecting Pegfilgrastim Dose for Pharmacokinetic and Pharmacodynamic Similarity Studies in Biosimilar Development.

This study applied modeling and simulation (M&S) approaches to evaluate the sensitivity of pegfilgrastim pharmacokinetics (PKs) and pharmacodynamics (PDs) to changes in dose amount, and linear or nonlinear clearance (CL) over pegfilgrastim subcutaneous dose of 2-6 mg. A previously published model was adapted to better describe pegfilgrastim PK and PD data in healthy subjects and used in simulation. Nonlinear CL accounts for 98% and 77%, respectively, of the total CL of pegfilgrastim at 2 and 6 mg. The sensitivity analyses showed: (i) PK of 2 and 6 mg doses are similarly sensitive to detect differences for a 5% change in dose; (ii) PK of 2 mg dose is more sensitive to changes in receptor binding affinity, a model parameter for nonlinear CL, and a product quality attribute characterized with orthogonal methods as part of demonstrating analytical similarity between products; (iii) PK of approved 6 mg dose is more sensitive to changes in linear CL, which has not been associated with any specific product quality attributes, and (iv) the PDs are not sensitive to changes in linear or nonlinear CL. Taken together, our analyses support that the approved pegfilgrastim dose of 6 mg is appropriate for detecting differences between a biosimilar and the reference products in pegfilgrastim PK and PD similarity studies. The described M&S approaches can be adopted to support dose selection for biosimilars with nonlinear PK and complex PK-PD interplay.

Molecular Basis of Mink ACE2 Binding to SARS-CoV-2 and Its Mink-Derived Variants.

ABSTRACTSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is transmitted between humans and minks, and some mutations in the spike (S) protein, especially in the receptor-binding domain (RBD), have been identified in mink-derived viruses. Here, we examined binding of the mink angiotensin-converting enzyme 2 (ACE2) receptor to mink-derived and important human-originating variants, and we demonstrated that most of the RBD variants increased the binding affinities to mink ACE2 (mkACE2). Cryo-electron microscopy structures of the mkACE2-RBD Y453F (with a Y-to-F change at position 453) and mkACE2-RBD F486L complexes helped identify the key residues that facilitate changes in mkACE2 binding affinity. Additionally, the data indicated that the Y453F and F486L mutations reduced the binding affinities to some human monoclonal antibodies, and human vaccinated sera efficiently prevented infection of human cells by pseudoviruses expressing Y453F, F486L, or N501T RBD. Our findings provide an important molecular mechanism for the rapid adaptation of SARS-CoV-2 in minks and highlight the potential influence of the main mink-originating variants for humans.IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a broad range of hosts. Mink-derived SARS-CoV-2 can transmit back to humans. There is an urgent need to understand the binding mechanism of mink-derived SARS-CoV-2 variants to mink receptor. In this study, we identified all mutations in the receptor-binding domain (RBD) of spike (S) protein from mink-derived SARS-CoV-2, and we demonstrated the enhanced binding affinity of mink angiotensin-converting enzyme 2 (ACE2) to most of the mink-derived RBD variants as well as important human-originating RBD variants. Cryo-electron microscopy structures revealed that the Y453F and F486L mutations enhanced the binding forces in the interaction interface. In addition, Y453F and F486L mutations reduced the binding affinities to some human monoclonal antibodies, and the SARS-CoV-2 pseudoviruses with Y453F, F486L, or N501T mutations were neutralized by human vaccinated sera. Therefore, our results provide valuable information for understanding the cross-species transmission mechanism of SARS-CoV-2.

Unraveling the Tomaralimab Epitope on the Toll-like Receptor 2 via Molecular Dynamics and Deep Learning.

Tomaralimab (OPN-305) is the first humanized immunoglobulin G4 monoclonal antibody against TLR2 and is designed to prevent inflammation that is driven by inappropriate or excessive activation of innate immune pathways. Here, we constructed a homology model of Tomaralimab and its complex with TLR2 at different mapped epitopes and unraveled their behavior at the atomistic level. Furthermore, we predicted a novel epitope (leucine-rich region 9–12) near the lipopeptide-binding site that can be targeted and studied for the utility of therapeutic antibodies. A geometric deep learning algorithm was used to envisage Tomaralimab binding affinity changes upon mutation. There was a significant difference in binding affinity for Tomaralimab following epitope-mutated alanine substitutions of Val266, Pro294, Arg295, Asn319, Pro326, and His372. Using deep learning-based ΔΔG prediction, we computationally contrasted human TLR2–TLR2, TLR2–TLR1, and TLR2–TLR6 dimerization. These results reveal the mechanism that underlies Tomaralimab binding to TLR2 and should help to design structure-based mimics or bispecific antibodies that can be used to inhibit both lipopeptide-binding and TLR2 dimerization.

5-HT3 Receptor MX Helix Contributes to Receptor Function.

5-HT3 receptors are members of the family of pentameric ligand-gated ion channels. Each subunit has an extracellular, transmembrane, and intracellular domain. Only part of the intracellular domain structure has been solved, revealing it contains two α-helical segments; one, the MA helix, is an extension of M4, while the other, the MX helix, is formed from residues located close to the end of M3. This MX helix is in distinct locations in open and closed receptor structures, suggesting it may play a role in function. Here, we explore this hypothesis using functional responses of Ala-substituted mutant receptors expressed in HEK293 cells. The data show altering many of the MX residues results in a small decrease in EC50 (up to 5-fold), although in one (H232A) this is increased. Radiolabeled ligand binding on selected mutants showed no change in binding affinity, indicating an effect on gating and not binding. In addition, five mutations (P316A, V317A, P318A, D319A, and H323A) initially resulted in nonfunctional receptors, but the function could be rescued by coexpression with a chaperone protein, suggesting a likely role in assembly or folding. Examination of previously obtained MD simulation data shows that the extent of MX encompassed by membrane lipids differs considerably in the open and closed structures, suggesting that lipid–protein interactions in this region could have a major effect on channel opening propensity. We conclude that the MX helix can modulate the function of the receptor and propose that its interactions with membrane lipids play a major role in this.

Structure and surface dynamics of genomic DNA as probed with surface-enhanced Raman spectroscopy: Trace level sensing of nucleic acids extracted from plants

In this work surface-enhanced Raman spectra of nucleic acids from in vitro grown Solanum tuberosum L. cultivars and populations (Buzau population, Lazarea population, Patraque d’Auvergne, RFA Roclas Clone 2.6 Ferma, Vitelotte Negresse, Roclas Clone C, Blue Congo) were measured with 532 nm laser line. Main surface-enhanced Raman modes of these DNAs have been analyzed.Also, DNA from two grapevine (Vitis vinifera L.) varieties were studied at acidic pHs by surface-enhanced Raman spectroscopy. Modified SERS intensities and wavenumber shifts of nucleic acids bands were observed upon lowering the pH, being a proof of binding affinity changes of DNA with silver nanoparticles (AgNPs) and of structural modifications induced at acidic pHs in DNA molecular groups.Furthermore, the (sub)picosecond surface dynamics of DNA extracted from leaf tissues of grapevine (Vitis vinifera L.) varieties was investigated. In this work, the bands full widths at half-maximum (FWHMs) have values in the wavenumber range from 8 to 34 cm−1. (Sub)picosecond molecular dynamics of DNA groups with global relaxation times between 0.31 ps − 1.33 ps has been found.

Triggered azobenzene-based prodrugs and drug delivery systems.

Azobenzene-based molecules show unique trans-cis isomerization upon ultraviolet light irradiation, which induce the change of polarity, crystallinity, stability, and binding affinity with pharmacological target. Moreover, azobenzene is the substrate of azoreductase that is often overexpressed in many pathological sites, e.g. hypoxic solid tumor. Therefore, azobenzene can be a multifunctional molecule in material science, pharmaceutical science and biomedicine because of its sensitivity to light, hypoxia and certain enzymes, hence showing potential application in site-specific smart therapy. Herein we focus on the employment of azobenzene and its derivatives for engineering triggered prodrugs and drug delivery systems, and provide an overview of photoswitchable azo-based prodrugs, the associated problems regarding the reversible isomerization and tissue penetration of ultraviolet (UV) light, as well as the potential solutions. We also present the advance of azo-bearing delivery vehicles wherein azobenzene acts as the linker, capping agent, and building block, and discuss the corresponding mechanisms for controlled cargo release, endocytosis enhancement and sensitization of free radical cancer therapy.

A particle-based computational model to analyse remodelling of the red blood cell cytoskeleton during malaria infections.

Red blood cells can withstand the harsh mechanical conditions in the vasculature only because the bending rigidity of their plasma membrane is complemented by the shear elasticity of the underlying spectrin-actin network. During an infection by the malaria parasite Plasmodium falciparum, the parasite mines host actin from the junctional complexes and establishes a system of adhesive knobs, whose main structural component is the knob-associated histidine rich protein (KAHRP) secreted by the parasite. Here we aim at a mechanistic understanding of this dramatic transformation process. We have developed a particle-based computational model for the cytoskeleton of red blood cells and simulated it with Brownian dynamics to predict the mechanical changes resulting from actin mining and KAHRP-clustering. Our simulations include the three-dimensional conformations of the semi-flexible spectrin chains, the capping of the actin protofilaments and several established binding sites for KAHRP. For the healthy red blood cell, we find that incorporation of actin protofilaments leads to two regimes in the shear response. Actin mining decreases the shear modulus, but knob formation increases it. We show that dynamical changes in KAHRP binding affinities can explain the experimentally observed relocalization of KAHRP from ankyrin to actin complexes and demonstrate good qualitative agreement with experiments by measuring pair cross-correlations both in the computer simulations and in super-resolution imaging experiments.

Photoswitching Affinity and Mechanism of Multivalent Lectin Ligands

Multivalent receptor–ligand binding is a key principle in a plethora of biological recognition processes. Immense binding affinities can be achieved with the correct spatial orientation of the ligands. Accordingly, the incorporation of photoswitches, which can be used to reversibly change the spatial orientation of molecules, into multivalent ligands is a means to alter the binding affinity and possibly also the binding mode of such ligands. We report a divalent ligand for the model lectin wheat germ agglutinin (WGA) containing an arylazopyrazole photoswitch. This switch, which has recently been introduced as an alternative to the more commonly used azobenzene moiety, is characterized by almost quantitative E/Z photoswitching in both directions, high quantum yields, and high thermal stability of the Z isomer. The ligand was designed in a way that only one of the isomers is able to bridge adjacent binding sites of WGA leading to a chelating binding mode. Photoswitching induces an unprecedentedly high change in lectin binding affinity as determined by isothermal titration calorimetry (ITC). Furthermore, additional dynamic light scattering (DLS) data suggest that the binding mode of the ligand changes from chelating binding of the E isomer to crosslinking binding of the Z isomer.

Extracellular superoxide dismutase (EC-SOD) R213G variant reduces mitochondrial ROS and preserves mitochondrial function in bleomycin-induced lung injury: EC-SOD R213G variant and intracellular redox regulation

Extracellular superoxide dismutase (EC-SOD) is highly expressed in the lung and vasculature. A common human single nucleotide polymorphism (SNP) in the matrix binding region of EC-SOD leads to a single amino acid substitution, R213G, and alters EC-SOD tissue binding affinity. The change in tissue binding affinity redistributes EC-SOD from tissue to extracellular fluids. Mice (R213G mice) expressing a knock-in of this EC-SOD SNP exhibit elevated plasma and reduced lung EC-SOD content and activity and are protected against bleomycin-induced lung injury and inflammation. It is unknown how the redistribution of EC-SOD alters site-specific redox-regulated molecules relevant for protection. In this study, we tested the hypothesis that the change in the local EC-SOD content would influence not only the extracellular redox microenvironment where EC-SOD is localized but also protect the intracellular redox status of the lung. Mice were treated with bleomycin and harvested 7 days post-treatment. Superoxide levels, measured by electron paramagnetic resonance (EPR), were lower in plasma and Bronchoalveolar lavage fluid (BALF) cells in R213G mice compared to wild-type (WT) mice, while lung cellular superoxide levels in R213G mice were not elevated post-bleomycin compared to WT mice despite low lung EC-SOD levels. Lung glutathione redox potential (EhGSSG), determined by HPLC and fluorescence, was more oxidized in WT compared to R213G mice. In R213G mice, lung mitochondrial oxidative stress was reduced shown by mitochondrial superoxide level measured by EPR in lung and the resistance to bleomycin-induced cardiolipin oxidation. Bleomycin treatment suppressed mitochondrial respiration in WT mice. Mitochondrial function was impaired at baseline in R213G mice but did not exhibit further suppression in respiration post-bleomycin. Collectively, the results indicate that R213G variant preserves intracellular redox state and protects mitochondrial function in the setting of bleomycin-induced inflammation.

SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance.

Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistance. Therefore, quantitative estimations of how mutations would affect the interaction between a drug and the target protein would be of vital significance for the drug development and the clinical practice. Computational methods that rely on molecular dynamics simulations, Rosetta protocols, as well as machine learning methods have been proven to be capable of predicting ligand affinity changes upon protein mutation. However, the severely limited sample size and heavy noise induced overfitting and generalization issues have impeded wide adoption of machine learning for studying drug resistance. In this paper, we propose a robust machine learning method, termed SPLDExtraTrees, which can accurately predict ligand binding affinity changes upon protein mutation and identify resistance-causing mutations. Especially, the proposed method ranks training data following a specific scheme that starts with easy-to-learn samples and gradually incorporates harder and diverse samples into the training, and then iterates between sample weight recalculations and model updates. In addition, we calculate additional physics-based structural features to provide the machine learning model with the valuable domain knowledge on proteins for these data-limited predictive tasks. The experiments substantiate the capability of the proposed method for predicting kinase inhibitor resistance under three scenarios and achieve predictive accuracy comparable with that of molecular dynamics and Rosetta methods with much less computational costs.

PSnpBind: a database of mutated binding site protein\u2013ligand complexes constructed using a multithreaded virtual screening workflow

A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein–ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein–ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of 0.6 million mutated binding site protein–ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein–ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is open source and freely available at https://psnpbind.org.

A Quantitative Approach to Unravel the Role of Host Genetics in IgG-FcγR Complex Formation After Vaccination.

Fc-mediated immune functions have been correlated with protection in the RV144 HIV vaccine trial and are important for immunity to a range of pathogens. IgG antibodies (Abs) that form complexes with Fc receptors (FcRs) on innate immune cells can activate Fc-mediated immune functions. Genetic variation in both IgGs and FcRs have the capacity to alter IgG-FcR complex formation via changes in binding affinity and concentration. A growing challenge lies in unraveling the importance of multiple variations, especially in the context of vaccine trials that are conducted in homogenous genetic populations. Here we use an ordinary differential equation model to quantitatively assess how IgG1 allotypes and FcγR polymorphisms influence IgG-FcγRIIIa complex formation in vaccine-relevant settings. Using data from the RV144 HIV vaccine trial, we map the landscape of IgG-FcγRIIIa complex formation predicted post-vaccination for three different IgG1 allotypes and two different FcγRIIIa polymorphisms. Overall, the model illustrates how specific vaccine interventions could be applied to maximize IgG-FcγRIIIa complex formation in different genetic backgrounds. Individuals with the G1m1,17 and G1m1,3 allotypes were predicted to be more responsive to vaccine adjuvant strategies that increase antibody FcγRIIIa affinity (e.g. glycosylation modifications), compared to the G1m-1,3 allotype which was predicted to be more responsive to vaccine boosting regimens that increase IgG1 antibody titers (concentration). Finally, simulations in mixed-allotype populations suggest that the benefit of boosting IgG1 concentration versus IgG1 affinity may be dependent upon the presence of the G1m-1,3 allotype. Overall this work provides a quantitative tool for rationally improving Fc-mediated functions after vaccination that may be important for assessing vaccine trial results in the context of under-represented genetic populations.

Analysis of the Neutralizing Activity of Antibodies Targeting Open or Closed SARS-CoV-2 Spike Protein Conformations.

SARS-CoV-2 infection elicits a polyclonal neutralizing antibody (nAb) response that primarily targets the spike protein, but it is still unclear which nAbs are immunodominant and what distinguishes them from subdominant nAbs. This information would however be crucial to predict the evolutionary trajectory of the virus and design future vaccines. To shed light on this issue, we gathered 83 structures of nAbs in complex with spike protein domains. We analyzed in silico the ability of these nAbs to bind the full spike protein trimer in open and closed conformations, and predicted the change in binding affinity of the most frequently observed spike protein variants in the circulating strains. This led us to define four nAb classes with distinct variant escape fractions. By comparing these fractions with those measured from plasma of infected patients, we showed that the class of nAbs that most contributes to the immune response is able to bind the spike protein in its closed conformation. Although this class of nAbs only partially inhibits the spike protein binding to the host’s angiotensin converting enzyme 2 (ACE2), it has been suggested to lock the closed pre-fusion spike protein conformation and therefore prevent its transition to an open state. Furthermore, comparison of our predictions with mRNA-1273 vaccinated patient plasma measurements suggests that spike proteins contained in vaccines elicit a different nAb class than the one elicited by natural SARS-CoV-2 infection and suggests the design of highly stable closed-form spike proteins as next-generation vaccine immunogens.

Reliable Prediction of the Protein-Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase.

Protein mutations that directly impair drug binding are related to therapeutic resistance, and accurate prediction of their impact on drug binding would benefit drug design and clinical practice. Here, we have developed a scoring strategy that predicts the effect of the mutations on the protein-ligand binding affinity. In view of the critical importance of electrostatics in protein-ligand interactions, the charge penetration corrected AMOEBA force field (AMOEBA_CP model) was employed to improve the accuracy of the calculated electrostatic energy. We calculated the electrostatic energy using an energy decomposition analysis scheme based on the generalized Kohn-Sham (GKS-EDA). The AMOEBA_CP model was validated by a protein-fragment-ligand complex data set (Abl236) constructed from the co-crystal structures of the cancer target Abl kinase with six inhibitors. To predict ligand binding affinity changes upon protein mutation of Abl kinase, we used sampling protocol with multistep simulated annealing to search conformations of mutant proteins. The scoring strategy based on AMOEBA_CP model has achieved considerable performance in predicting resistance for 8 kinase inhibitors across 144 clinically identified point mutations. Overall, this study illustrates that the AMOEBA_CP model, which accurately treats electrostatics through penetration correction, enables the accurate prediction of the mutation-induced variation of protein-ligand binding affinity.