Direct Promoter Repression by BCL11A Controls the Fetal to Adult Hemoglobin Switch.

Fetal hemoglobin (HbF, α2γ2) level is genetically controlled and modifies severity of adult hemoglobin (HbA, α2ß2) disorders, sickle cell disease, and ß-thalassemia. Common genetic variation affects expression of BCL11A, a regulator of HbF silencing. To uncover how BCL11A supports the developmental switch from γ- to ß- globin, we use a functional assay and protein binding microarray to establish a requirement for a zinc-finger cluster in BCL11A in repression and identify a preferred DNA recognition sequence. This motif appears in embryonic and fetal-expressed globin promoters and is duplicated in γ-globin promoters. The more distal of the duplicated motifs is mutated in individuals with hereditary persistence of HbF. Using the CUT&RUN approach to map protein binding sites in erythroid cells, we demonstrate BCL11A occupancy preferentially at the distal motif, which can be disrupted by editing the promoter. Our findings reveal that direct γ-globin gene promoter repression by BCL11A underlies hemoglobin switching.

ISRIB Blunts the Integrated Stress Response by Allosterically Antagonising the Inhibitory Effect of Phosphorylated eIF2 on eIF2B.

The small molecule ISRIB antagonizes the activation of the integrated stress response (ISR) by phosphorylated translation initiation factor 2, eIF2(αP). ISRIB and eIF2(αP) bind distinct sites in their common target, eIF2B, a guanine nucleotide exchange factor for eIF2. We have found that ISRIB-mediated acceleration of eIF2B's nucleotide exchange activity in vitro is observed preferentially in the presence of eIF2(αP) and is attenuated by mutations that desensitize eIF2B to the inhibitory effect of eIF2(αP). ISRIB's efficacy as an ISR inhibitor in cells also depends on presence of eIF2(αP). Cryoelectron microscopy (cryo-EM) showed that engagement of both eIF2B regulatory sites by two eIF2(αP) molecules remodels both the ISRIB-binding pocket and the pockets that would engage eIF2α during active nucleotide exchange, thereby discouraging both binding events. In vitro, eIF2(αP) and ISRIB reciprocally opposed each other's binding to eIF2B. These findings point to antagonistic allostery in ISRIB action on eIF2B, culminating in inhibition of the ISR.

Context-Dependent Gene Regulation by Homeodomain Transcription Factor Complexes Revealed by Shape-Readout Deficient Proteins.

Eukaryotic transcription factors (TFs) form complexes with various partner proteins to recognize their genomic target sites. Yet, how the DNA sequence determines which TF complex forms at any given site is poorly understood. Here, we demonstrate that high-throughput in vitro DNA binding assays coupled with unbiased computational analysis provide unprecedented insight into how different DNA sequences select distinct compositions and configurations of homeodomain TF complexes. Using inferred knowledge about minor groove width readout, we design targeted protein mutations that destabilize homeodomain binding both in vitro and in vivo in a complex-specific manner. By performing parallel systematic evolution of ligands by exponential enrichment sequencing (SELEX-seq), chromatin immunoprecipitation sequencing (ChIP-seq), RNA sequencing (RNA-seq), and Hi-C assays, we not only classify the majority of in vivo binding events in terms of complex composition but also infer complex-specific functions by perturbing the gene regulatory network controlled by a single complex.

A TME-enlightened protein-binding photodynamic nanoinhibitor for highly effective oncology treatment.

The efficiency of photodynamic therapy (PDT) is greatly dependent on intrinsic features of photosensitizers (PSs), but most PSs suffer from narrow diffusion distances and short life span of singlet oxygen (1O2). Here, to conquer this issue, we propose a strategy for in situ formation of complexes between PSs and proteins to deactivate proteins, leading to highly effective PDT. The tetrafluorophenyl bacteriochlorin (FBC), a strong near-infrared absorbing photosensitizer, can tightly bind to intracellular proteins to form stable complexes, which breaks through the space-time constraints of PSs and proteins. The generated singlet oxygen directly causes the protein dysfunction, leading to high efficiency of PSs. To enable efficient delivery of PSs, a charge-conversional and redox-responsive block copolymer POEGMA-b-(PAEMA/DMMA-co-BMA) (PB) was designed to construct a protein-binding photodynamic nanoinhibitor (FBC@PB), which not only prolongs blood circulation and enhances cellular uptake but also releases FBC on demand in tumor microenvironment (TME). Meanwhile, PDT-induced destruction of cancer cells could produce tumor-associated antigens which were capable to trigger robust antitumor immune responses, facilitating the eradication of residual cancer cells. A series of experiments in vitro and in vivo demonstrated that this multifunctional nanoinhibitor provides a promising strategy to extend photodynamic immunotherapy.

TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes.

The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer-based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.

Driving forces of the complex formation between highly charged disordered proteins.

Highly disordered complexes between oppositely charged intrinsically disordered proteins present a new paradigm of biomolecular interactions. Here, we investigate the driving forces of such interactions for the example of the highly positively charged linker histone H1 and its highly negatively charged chaperone, prothymosin α (ProTα). Temperature-dependent single-molecule Förster resonance energy transfer (FRET) experiments and isothermal titration calorimetry reveal ProTα-H1 binding to be enthalpically unfavorable, and salt-dependent affinity measurements suggest counterion release entropy to be an important thermodynamic driving force. Using single-molecule FRET, we also identify ternary complexes between ProTα and H1 in addition to the heterodimer at equilibrium and show how they contribute to the thermodynamics observed in ensemble experiments. Finally, we explain the observed thermodynamics quantitatively with a mean-field polyelectrolyte theory that treats counterion release explicitly. ProTα-H1 complex formation resembles the interactions between synthetic polyelectrolytes, and the underlying principles are likely to be of broad relevance for interactions between charged biomolecules in general.

The force-dependent filamin A-G3BP1 interaction regulates phase-separated stress granule formation.

Filamin A (FLNA) is an actin crosslinking protein that mediates mechanotransduction. External and internal mechanical forces, through the actin cytoskeleton, can induce conformational changes of the FLNA molecule to expose cryptic binding sites for its binding partners. Here, we identified Ras GTPase-activating protein SH3 domain-binding protein 1 (G3BP1) as a new FLNA mechanobinding partner. Unlike other FLNA binding partners to the mechanosensing domain repeat 21 (R21), G3BP1 requires an additional neighboring repeat R22 to interact. We demonstrated that their interaction occurs in the cytosol of living cells in an actin polymerization-dependent manner. We also mapped the FLNA-binding site on G3BP1 and found that a F360A point mutation in the RNA recognition motif disrupts the interaction. RNA interfered with the FLNA-G3BP1 interaction, and FLNA did not localize in RNA-rich stress granules (SGs). Disruption of the interaction was sufficient to promote phase-separated SG formation, and arsenite treatment further stimulated the formation of SGs. Taken together, these data identify G3BP1 as a new mechanobinding protein that interacts with the FLNA mechanosensing domain R21 and suggest that SG formation is partially regulated by mechanical force.

MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions.

The accurate prediction of the effect of amino acid mutations for protein-protein interactions (PPI $\Delta \Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI  $\Delta \Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein-protein complex structures annotated with PPI $\Delta \Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein-protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein-protein complexes for downstream $\Delta \Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein-protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\Delta \Delta G$ predictions. The data and source code are available at https://github.com/arantir123/MpbPPI.

Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.

Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.

Quantification of biases in predictions of protein-protein binding affinity changes upon mutations.

Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein-protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the severe acute respiratory syndrome coronavirus 2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches, while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases toward specific mutation properties, the most marked being toward destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness.

ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes.

Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein-protein and protein-peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI-Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open-source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN.

Transcription factor binding specificities of the oomycete Phytophthora infestans reflect conserved and divergent evolutionary patterns and predict function.

BACKGROUND: Identifying the DNA-binding specificities of transcription factors (TF) is central to understanding gene networks that regulate growth and development. Such knowledge is lacking in oomycetes, a microbial eukaryotic lineage within the stramenopile group. Oomycetes include many important plant and animal pathogens such as the potato and tomato blight agent Phytophthora infestans, which is a tractable model for studying life-stage differentiation within the group. RESULTS: Mining of the P. infestans genome identified 197 genes encoding proteins belonging to 22 TF families. Their chromosomal distribution was consistent with family expansions through unequal crossing-over, which were likely ancient since each family had similar sizes in most oomycetes. Most TFs exhibited dynamic changes in RNA levels through the P. infestans life cycle. The DNA-binding preferences of 123 proteins were assayed using protein-binding oligonucleotide microarrays, which succeeded with 73 proteins from 14 families. Binding sites predicted for representatives of the families were validated by electrophoretic mobility shift or chromatin immunoprecipitation assays. Consistent with the substantial evolutionary distance of oomycetes from traditional model organisms, only a subset of the DNA-binding preferences resembled those of human or plant orthologs. Phylogenetic analyses of the TF families within P. infestans often discriminated clades with canonical and novel DNA targets. Paralogs with similar binding preferences frequently had distinct patterns of expression suggestive of functional divergence. TFs were predicted to either drive life stage-specific expression or serve as general activators based on the representation of their binding sites within total or developmentally-regulated promoters. This projection was confirmed for one TF using synthetic and mutated promoters fused to reporter genes in vivo. CONCLUSIONS: We established a large dataset of binding specificities for P. infestans TFs, representing the first in the stramenopile group. This resource provides a basis for understanding transcriptional regulation by linking TFs with their targets, which should help delineate the molecular components of processes such as sporulation and host infection. Our work also yielded insight into TF evolution during the eukaryotic radiation, revealing both functional conservation as well as diversification across kingdoms.

Interspecies variability in protein binding of antibiotics basis for translational PK/PD studies-a case study using cefazolin.

For antimicrobial agents in particular, plasma protein binding (PPB) plays a pivotal role in deciphering key properties of drug candidates. Animal models are generally used in the preclinical development of new drugs to predict their effects in humans using translational pharmacokinetics/pharmacodynamics (PK/PD). Thus, we compared the protein binding (PB) of cefazolin as well as bacterial growth under various conditions in vitro. The PB extent of cefazolin was studied in human, bovine, and rat plasmas at different antibiotic concentrations in buffer and media containing 20-70% plasma or pure plasma using ultrafiltration (UF) and equilibrium dialysis (ED). Moreover, bacterial growth and time-kill assays were performed in Mueller Hinton Broth (MHB) containing various plasma percentages. The pattern for cefazolin binding to plasma proteins was found to be similar for both UF and ED. There was a significant decrease in cefazolin binding to bovine plasma compared to human plasma, whereas the pattern in rat plasma was more consistent with that in human plasma. Our growth curve analysis revealed considerable growth inhibition of Escherichia coli at 70% bovine or rat plasma compared with 70% human plasma or pure MHB. As expected, our experiments with cefazolin at low concentrations showed that E. coli grew slightly better in 20% human and rat plasma compared to MHB, most probably due to cefazolin binding to proteins in the plasma. Based on the example of cefazolin, our study highlights the interspecies differences of PB with potential impact on PK/PD. These findings should be considered before preclinical PK/PD data can be extrapolated to human patients.

Quantification of Cinpanemab (BIIB054) Binding to α- Synuclein in Cerebrospinal Fluid of Phase 1 Single Ascending Dose Samples.

Through its pathological and genetic association to Parkinson's Disease (PD), α-synuclein (α-syn) remains a favorable therapeutic target that is being investigated using various modalities, including many passive immunotherapy approaches clinically targeting different forms of α-syn and epitopes. Whereas published studies from some immunotherapy trials have demonstrated engagement in plasma, none have shown direct drug-antigen interactions in the disease-relevant compartment, the central nervous system (CNS). Cinpanemab (BIIB054) selectively targets pathological aggregated α-syn with low affinity binding to monomeric forms. The avidity-driven binding, low drug concentration, and the very low α-syn levels plus its heterogeneous nature in cerebrospinal fluid (CSF) made it not possible to measure drug-target interactions by conventional assays. Here we overcame these challenges by using zero-length crosslinking to stabilize the BIIB054-α-syn complexes and then quantified the crosslinked complexes using a Meso Scale Discovery (MSD) electrochemiluminescence assay. CSF samples from healthy volunteers (HV, n=46) and individuals with PD (PD, n=18) from study 228HV101 (Phase I clinical trial of BIIB054), demonstrated dose- and time- dependent binding of cinpanemab to α-syn with measurable complexes detected at doses {greater than or equal to}15 mg/kg. Complex formation displayed a direct positive correlation to drug concentration (Spearman rank correlation = 0.8295 (HV), 0.8032 (PD) p < 0.0001 (HV, PD)). The observed binding of cinpanemab to α-syn in CSF is consistent with its low intrinsic affinity for α-syn monomer and provides evidence that the drug is behaving with expected binding dynamics in the central nervous system compartment. Significance Statement A zero-length cross-linking method with MSD detection was developed to enable quantification of cinpanemab-α-syn complexes in Phase 1 clinical CSF samples by preventing signal loss caused by their rapid dissociation. Observed dose- and time-dependent binding were consistent with cinpanemab's affinity for α-syn and provided confidence that the drug had engaged its target at the desired site of action. This is the first demonstration of α-syn binding by an antibody in clinical samples from the CNS.

Structural landscapes of PPI interfaces.

Proteins are capable of highly specific interactions and are responsible for a wide range of functions, making them attractive in the pursuit of new therapeutic options. Previous studies focusing on overall geometry of protein-protein interfaces, however, concluded that PPI interfaces were generally flat. More recently, this idea has been challenged by their structural and thermodynamic characterisation, suggesting the existence of concave binding sites that are closer in character to traditional small-molecule binding sites, rather than exhibiting complete flatness. Here, we present a large-scale analysis of binding geometry and physicochemical properties of all protein-protein interfaces available in the Protein Data Bank. In this review, we provide a comprehensive overview of the protein-protein interface landscape, including evidence that even for overall larger, more flat interfaces that utilize discontinuous interacting regions, small and potentially druggable pockets are utilized at binding sites.

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics.

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

Entry of cannabidiol into the fetal, postnatal and adult rat brain.

Cannabidiol is a major component of cannabis but without known psychoactive properties. A wide range of properties have been attributed to it, such as anti-inflammatory, analgesic, anti-cancer, anti-seizure and anxiolytic. However, being a fairly new compound in its purified form, little is known about cannabidiol brain entry, especially during development. Sprague Dawley rats at four developmental ages: embryonic day E19, postnatal day P4 and P12 and non-pregnant adult females were administered intraperitoneal cannabidiol at 10 mg/kg with [3H] labelled cannabidiol. To investigate the extent of placental transfer, the drug was injected intravenously into E19 pregnant dams. Levels of [3H]-cannabidiol in blood plasma, cerebrospinal fluid and brain were estimated by liquid scintillation counting. Plasma protein binding of cannabidiol was identified by polyacrylamide gel electrophoresis and its bound and unbound fractions measured by ultrafiltration. Using available RNA-sequencing datasets of E19 rat brain, choroid plexus and placenta, as well as P5 and adult brain and choroid plexus, expression of 13 main cannabidiol receptors was analysed. Results showed that cannabidiol rapidly entered both the developing and adult brains. Entry into CSF was more limited. Its transfer across the placenta was substantially restricted as only about 50% of maternal blood plasma cannabidiol concentration was detected in fetal plasma. Albumin was the main, but not exclusive, cannabidiol binding protein at all ages. Several transcripts for cannabidiol receptors were expressed in age- and tissue-specific manner indicating that cannabidiol may have different functional effects in the fetal compared to adult brain.

Tetrel bond affects the self-assembly of acetylcholine and its analogues and is an ancillary interaction in protein binding.

The -N+(CH3)3 residue is present in acetylcholine (ACh) and in many of its analogues which are used as selective ACh agonist or antagonists for human therapy.  The X-ray structures of four ACh derivatives show the presence of short and linear contacts between the C atoms of -N+(CH3)3 groups and lone pair possessing atoms.  These contacts can be rationalized as tetrel bonds (TtBs) thanks to their geometric features.  Interrogation of the Protein Data Bank suggests that similar -N+‒C···nucleophile contacts affect the details of the binding of ACh and its derivatives to proteins.  Quantum theory of atoms in molecules, noncovalent interaction plot, and natural bond orbital analyses consistently confirm that the -N+‒C···nucleophile contacts observed in small molecule crystals and in substrate/protein complexes are attractive in nature and can be rationalized as TtBs.  TtBs involving methyl groups of the -N+(CH3)3 moiety can be proposed as a new item in the palette of interactions allowing the compounds containing this pharmacophoric unit to bind to their target protein and/or to express their biological/pharmacological properties.

Simultaneous determination of plasma protein binding of five C-glycosylflavones from TFDS by rapid equilibrium dialysis.

The total flavonoids of Desmodium styracifolium (TFDS) are flavonoid-rich extracts obtained from Desmodii Styracifolii Herba, which is approved for the treatment of urolithiasis in China. C-glycosylflavones including schaftoside, vicenin-1, vicenin-2, vicenin-3, and isovitexin are the main active constituents. In this study, the plasma protein binding of these compounds was determined for the first time in rat and human plasma by rapid equilibrium dialysis combined with HPLC-MS/MS method. The developed method was validated in terms of specificity, linearity, accuracy, precision, extraction effect, matrix effect, and stability. Schaftoside, vicenin-1, vicenin-2, and vicenin-3 exhibited moderate plasma protein binding, ranging from 56.6% to 61.5% in rat plasma and 55.0%-62.9% in human plasma. In comparison, isovitexin demonstrated a higher plasma protein binding in the range of 92.3-93.1% and 95.1-96.2% in rat and human plasma, respectively. Furthermore, the potential interactions mediated via plasma protein binding between isovitexin and nonsteroidal anti-inflammatory drugs (NSAIDs) were investigated by rapid equilibrium dialysis. No significant changes were observed, indicating a lower likelihood of interaction between TFDS and NSAIDs due to plasma protein binding in the treatment of urinary system disorders.

Contribution of the α1-Acid Glycoprotein in Drug Pharmacokinetics: The Usefulness of α1-Acid Glycoprotein-Knockout Mice.

α1-Acid glycoprotein (AGP) is a primary binding protein for many basic drugs in plasma. The number of drugs that bind to AGP, such as molecular target anticancer drugs, has been continuously increasing. Since the plasma level of AGP fluctuates under various pathological conditions such as inflammation, it is important to evaluate the contribution of AGP to drug pharmacokinetics. Here, we generated conventional AGP-knockout (AGP-KO) mice and used them to evaluate the contribution of AGP. The pharmacokinetics of drugs that bind to two AGP variants (F1*S or A variants) or albumin were evaluated. Imatinib (a F1*S-binding drug) and disopyramide (an A-binding drug) or ibuprofen (an albumin-binding drug) were administered to wild-type (WT) and AGP-KO. The plasma level of imatinib and disopyramide decreased rapidly in AGP-KO as compared to WT. In AGP-KO, AUC and t1/2 were decreased, then CLtot was increased. Compared with disopyramide, imatinib pharmacokinetics showed more marked changes in AGP-KO as compared to WT. The results seemed to be due to the difference in plasma level of each AGP variant (F1*S:A = 2-3:1). No differences were observed in ibuprofen pharmacokinetics between the WT and AGP-KO mice. In vitro experiments using plasma from WT and AGP-KO showed that unbound fractions of imatinib and disopyramide were higher in AGP-KO. These results suggest that the rapid elimination of imatinib and disopyramide in AGP-KO could be due to decreased protein binding to AGP. Taken together, the AGP-KO mouse could be a potential animal model for evaluating the contribution of AGP to the pharmacokinetics of various drugs.