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Membrane transporters are key determinants of therapeutic outcomes. They regulate systemic and cellular drug levels influencing efficacy as well as toxicities. Here we report a unique phosphorylation-dependent interaction between drug transporters and tyrosine kinase inhibitors (TKIs), which has uncovered widespread phosphotyrosine-mediated regulation of drug transporters. We initially found that organic cation transporters (OCTs), uptake carriers of metformin and oxaliplatin, were inhibited by several clinically used TKIs. Mechanistic studies showed that these TKIs inhibit the Src family kinase Yes1, which was found to be essential for OCT2 tyrosine phosphorylation and function. Yes1 inhibition in vivo diminished OCT2 activity, significantly mitigating oxaliplatin-induced acute sensory neuropathy. Along with OCT2, other SLC-family drug transporters are potentially part of an extensive ‘transporter-phosphoproteome’ with unique susceptibility to TKIs. On the basis of these findings we propose that TKIs, an important and rapidly expanding class of therapeutics, can functionally modulate pharmacologically important proteins by inhibiting protein kinases essential for their post-translational regulation. Organic cation transporters are important drug transporters that influence therapeutic outcomes. Here, the authors find that these transporters are regulated by tyrosine phosphorylation and propose that tyrosine kinase inhibitors can influence drug transporter function through post-translational mechanisms.

Variation in steroid hormone levels has wide implications for health and disease. The genes encoding the proteins involved in steroid disposition represent key determinants of interindividual variation in steroid levels and ultimately, their effects. Beginning with metabolomic data from genome-wide association studies (GWAS), we observed that genetic variants in the orphan transporter, SLC22A24 were significantly associated with levels of androsterone glucuronide and etiocholanolone glucuronide (sentinel SNPs p-value <1x10-30). In cells over-expressing human or various mammalian orthologs of SLC22A24, we showed that steroid conjugates and bile acids were substrates of the transporter. Phylogenetic, genomic, and transcriptomic analyses suggested that SLC22A24 has a specialized role in the kidney and appears to function in the reabsorption of organic anions, and in particular, anionic steroids. Phenome-wide analysis showed that functional variants of SLC22A24 are associated with human disease such as cardiovascular diseases and acne, which have been linked to dysregulated steroid metabolism. Collectively, these functional genomic studies reveal a previously uncharacterized protein involved in steroid homeostasis, opening up new possibilities for SLC22A24 as a pharmacological target for regulating steroid levels.

Acute myeloid leukemia (AML) is a clinically heterogeneous disease, with a 5-year disease-free survival (DFS) ranging from under 10% to over 70% for distinct groups of patients. At our institution, cytarabine, etoposide and busulfan are used in first or second remission patients treated with a two-step approach to autologous stem cell transplantation (ASCT). In this study, we tested the hypothesis that polymorphisms in the pharmacokinetic and pharmacodynamic pathway genes of these drugs are associated with DFS in AML patients. A total of 1659 variants in 42 genes were analyzed for their association with DFS using a Cox-proportional hazards model. One hundred and fifty-four genetically European patients were used for the primary analysis. An intronic single nucleotide polymorphism (SNP) in ABCC3 (rs4148405) was associated with a significantly shorter DFS (hazard ratios (HR)=3.2, P=5.6 × 10(-6)) in our primary cohort. In addition, a SNP in the GSTM1-GSTM5 locus, rs3754446, was significantly associated with a shorter DFS in all patients (HR=1.8, P=0.001 for 154 European ancestry; HR=1.7, P=0.028 for 125 non-European patients). Thus, for the first time, genetic variants in drug pathway genes are shown to be associated with DFS in AML patients treated with chemotherapy-based autologous ASCT.

Activating mutations in FLT3 occur in ~30% of adult acute myeloid leukemia, primarily consisting of internal tandem duplication (ITD) mutations (~25%) and point mutations in the tyrosine kinase domain (~5%), commonly at the activation loop residue D835. Secondary kinase domain mutations in FLT3-ITD, particularly at the D835 residue are frequently associated with acquired clinical resistance to effective FLT3 tyrosine kinase inhibitors (TKIs). Molecular docking studies have suggested that D835 mutations primarily confer resistance by stabilizing an active Asp-Phe-Gly in (‘DFG-in’) kinase conformation unfavorable to the binding of type II FLT3 TKIs, which target a ‘DFG-out’ inactive conformation. We profiled the activity of active type II FLT3 TKIs against D835 kinase domain mutants that have been clinically detected to date. We found that type II inhibitors (quizartinib, sorafenib, ponatinib and PLX3397) retain activity against specific D835 substitutions. Modeling studies suggest that bulky hydrophobic substitutions (D835Y/V/I/F) at this residue are particularly resistant, whereas mutations that preserve interactions between D835 and S838 are relatively sensitive (D835E/N). All mutants retain sensitivity to the type I inhibitor crenolanib. These results suggest that patients with relatively sensitive D835 mutations should be included in clinical trials of type II FLT3 TKIs.

The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2 , 3 , 4 , 5 – 6 . Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein–ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein–nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody–antigen prediction accuracy compared with AlphaFold-Multimer v.2.3 7 , 8 . Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework. AlphaFold 3 has a substantially updated architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues with greatly improved accuracy over many previous specialized tools.

All functions of a protein involve its physical interactions, however transient, with specific other molecules (ligands), large or small. Protein structure and its dynamics, determined by sequence, in turn determine which molecules are able to bind to the protein. Druggable proteins are defined as those whose function can be modulated with a small molecule. The contents of this dissertation focus on leveraging the knowledge about protein structure and changes in protein structure to identify small molecule modulators of protein function, thus expanding the druggable proteome. The studied proteins include members of the kinase and SLC transporter superfamilies, both important drug targets. First, we separately examine the impact of mutations on the structures of two kinases, FLT3 and BCR-ABL, and the resistance of these mutants to drugs. We provide a structure-based rationale for the cause of resistance and offer treatment alternatives. Second, we address the modulation of function of human organic cation transporters (OCTs), either through phosphorylation or structure-guided screens to discover novel small molecule inhibitors of these transporters. We establish that by combining docking and in vitro high-throughput screens, competitive and non-competitive ligands of OCTs can be predicted accurately. Third, we examine the quaternary structure of the human concentrative nucleoside transporters (CNTs) to gain new insight into their functions and use structure-guided screens to discover novel ligands to modulate them. We show that human concentrative nucleoside transporter 3 forms homo-oligomers, thus encouraging efforts on finding allosteric inhibitors. Finally, we present a large-scale study of the impact of cancer mutations on protein structure, with the hope of expanding the druggable proteome through the discovery of mutant-specific binding pockets that would allow for selective, functional inhibition or activation.

Variation in sex hormone levels has wide implications for health and disease. The genes encoding the proteins involved in steroid disposition represent key determinants of interindividual variation in steroid levels and ultimately, their effects. Beginning with metabolomic data from genome-wide association studies (GWAS), we observed that genetic variants in the orphan transporter, SLC22A24 were significantly associated with levels of androsterone glucuronide and etiocholanolone glucuronide (sentinel SNPs p-value <1x10-30). In cells over-expressing human or various mammalian orthologs of SLC22A24, we showed that steroid conjugates and bile acids were substrates of the transporter. Phylogenetic, genomic, and transcriptomic analyses suggested that SLC22A24 has a specialized role in the kidney and appears to function in the reabsorption of organic anions, and in particular, anionic steroids. Phenome-wide analysis showed that functional variants of SLC22A24 are associated with human disease such as cardiovascular diseases and acne, which have been linked to dysregulated steroid metabolism. Collectively, these functional genomic studies reveal a previously uncharacterized protein involved in steroid homeostasis, opening up new possibilities for SLC22A24 as a pharmacological target for regulating steroid levels.

The goal of the research was to study the possibility of using the planned language Esperanto for text compression, and to compare the results of the text compression in Esperanto with the compression in natural languages, represented by Polish and English. The authors performed text compression in the created program in Python using four compression algorithms: zlib, lzma, bz2, and zl4 in four versions of the text: in Polish, English, Esperanto, and Esperanto in x notation (without characters outside ASCII encoding). After creating the compression program, and compressing the proper texts, authors conducted an analysis on the comparison of compression time and the volume of the text before and after compression. The results of the study confirmed the hypothesis, based on which the planned language, Esperanto, gives better text compression results than the natural languages represented by Polish and English. The confirmation by scientific methods that Esperanto is more optimal for text compression is the scientific added value of the paper.