Drug-protein Binding Research Articles

Calibrated geometric deep learning improves kinase-drug binding predictions.

Protein kinases regulate various cellular functions and hold significant pharmacological promise in cancer and other diseases. Although kinase inhibitors are one of the largest groups of approved drugs, much of the human kinome remains unexplored but potentially druggable. Computational approaches, such as machine learning, offer efficient solutions for exploring kinase-compound interactions and uncovering novel binding activities. Despite the increasing availability of three-dimensional (3D) protein and compound structures, existing methods predominantly focus on exploiting local features from one-dimensional protein sequences and two-dimensional molecular graphs to predict binding affinities, overlooking the 3D nature of the binding process. Here we present KDBNet, a deep learning algorithm that incorporates 3D protein and molecule structure data to predict binding affinities. KDBNet uses graph neural networks to learn structure representations of protein binding pockets and drug molecules, capturing the geometric and spatial characteristics of binding activity. In addition, we introduce an algorithm to quantify and calibrate the uncertainties of KDBNet's predictions, enhancing its utility in model-guided discovery in chemical or protein space. Experiments demonstrated that KDBNet outperforms existing deep learning models in predicting kinase-drug binding affinities. The uncertainties estimated by KDBNet are informative and well-calibrated with respect to prediction errors. When integrated with a Bayesian optimization framework, KDBNet enables data-efficient active learning and accelerates the exploration and exploitation of diverse high-binding kinase-drug pairs.

Synthesis, characterization and anti-tubercular activities of copper(II) complexes of substituted 2,3-isatin bisthiosemicarbazones: An experimental and theoretical approach

Copper(II) acetate reacted with 2,3-isatin bisthiosemicarbazone (2,3 H2bitsc), 2,3-isatin bis-N1-methyl thiosemicarbazone (2,3 H2bitsc-N1-Me) and 2,3-isatin N1-phenyl thiosemicarbazone (2,3 H2bitsc-N1-Ph) in 1:1 (M: L) to form complexes of formula, [Cu(L)] (L = 2,3 bitsc 1, 2,3 bitsc-N1-Me 2, 2,3 bitsc-N1-Ph 3). The formation of bisthiosemicarbazone ligands and their complexes are supported by elemental analysis, FTIR, NMR (1H and 13C), ESR, and Mass. For complexes 1–3, two g values obtained are: g‖ = 2.24 (1), 2.17 (2), 2.26 (3) and g⊥ = 2.09 (1), 2.11 (2), 2.08 (3) which confirm axial symmetry. The square planar geometry is confirmed from higher g‖ values than g⊥ and presence g value of free electron in dx2-y2 ground term. Ligand to metal binding ratio of 1:1 is confirmed using UV–visible spectroscopy. The ligands (2,3 H2bitsc, 2,3 H2bitsc-N1-Me and 2,3 H2bitsc-N1-Ph) and complexes (1–3) were assessed for anti-tubercular (M. tuberculosis H37RV strain ATCC27294) and antibacterial (against E. coli and S. aureus) activities. The anti- tuberculosis activity of ligands (50 µg/mL-2,3 H2bitsc, 100 µg/mL-2,3 H2bitsc-N1-Me and H2bitsc-N1-Ph) gets enhanced on complexation with copper (25 µg /mL-1, 50 µg/mL 2 and 3). The minimal binding energies calculated through molecular modeling with mycobacterium tuberculosis enoyl reductase (PDB ID: 2H7M) is −7.6 (2,3 H2bitsc), −7.3 (2,3 H2bitsc-N1-Me), −9.6 (2,3 H2bitsc-N1-Ph), −8.7 (1), −8.9 (2), and −10.7 (3) Kcal/mol. Higher negative binding energy indicates more stabilized structure in the docked state for 1–3 which supports the experimental data. The drug-protein binding study with human serum albumin (HSA) was used to explore effective transport of molecules to their target sites in 2,3 H2bitsc and its complex 1. A high binding constant of complex 1 (6.09 × 105 M−1) with respect to the ligand 2,3 H2bitsc (4.90 × 105 M−1) indicates its strong binding affinities with HSA.

PRID: Prediction Model Using RWR for Interactions between Drugs.

Drug-drug interactions (DDI) occur because of the unexpected pharmacological effects of drug pairs. Although drug efficacy can be improved by taking two or more drugs in the short term, this may cause inevitable side effects. Currently, multiple drugs are prescribed based on the experience or knowledge of the clinician, and there is no standard database that can be referred to as safe co-prescriptions. Thus, accurately identifying DDI is critical for patient safety and treatment modalities. Many computational methods have been developed to predict DDIs based on chemical structures or biological features, such as target genes or functional mechanisms. However, some features are only available for certain drugs, and their pathological mechanisms cannot be fully employed to predict DDIs by considering the direct overlap of target genes. In this study, we propose a novel deep learning model to predict DDIs by utilizing chemical structure similarity and protein-protein interaction (PPI) information among drug-binding proteins, such as carriers, transporters, enzymes, and targets (CTET) proteins. We applied the random walk with restart (RWR) algorithm to propagate drug CTET proteins across a PPI network derived from the STRING database, which will lead to the successful incorporation of the hidden biological mechanisms between CTET proteins and disease-associated genes. We confirmed that the RWR propagation of CTET proteins helps predict DDIs by utilizing indirectly co-regulated biological mechanisms. Our method identified the known DDIs between clinically proven epilepsy drugs. Our results demonstrated the effectiveness of PRID in predicting DDIs in known drug combinations as well as unknown drug pairs. PRID could be helpful in identifying novel DDIs and associated pharmacological mechanisms to cause the DDIs.

Free energy along drug-protein binding pathways interactively sampled in virtual reality

We describe a two-step approach for combining interactive molecular dynamics in virtual reality (iMD-VR) with free energy (FE) calculation to explore the dynamics of biological processes at the molecular level. We refer to this combined approach as iMD-VR-FE. Stage one involves using a state-of-the-art ‘human-in-the-loop’ iMD-VR framework to generate a diverse range of protein–ligand unbinding pathways, benefitting from the sophistication of human spatial and chemical intuition. Stage two involves using the iMD-VR-sampled pathways as initial guesses for defining a path-based reaction coordinate from which we can obtain a corresponding free energy profile using FE methods. To investigate the performance of the method, we apply iMD-VR-FE to investigate the unbinding of a benzamidine ligand from a trypsin protein. The binding free energy calculated using iMD-VR-FE is similar for each pathway, indicating internal consistency. Moreover, the resulting free energy profiles can distinguish energetic differences between pathways corresponding to various protein–ligand conformations (e.g., helping to identify pathways that are more favourable) and enable identification of metastable states along the pathways. The two-step iMD-VR-FE approach offers an intuitive way for researchers to test hypotheses for candidate pathways in biomolecular systems, quickly obtaining both qualitative and quantitative insight.

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP

Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.Graphical

Multiple Ligand Simultaneous Docking of Antiviral Drugs and Cyanine Dyes with Proteins

Protein nanoparticles are currently regarded as promising biocompatible and biodegradable systems for targeted delivery of different types of pharmacological agents. Prior to fabricating such kind of drug nanocarriers it is reasonable to evaluate the drug-protein binding affinity and possible interaction modes using the computational tools, particularly, the molecular docking technique. The present study was undertaken to evaluate the possibility of creating the protein nanoparticles carrying the antiviral drugs and cyanine dyes as visualizing agents. The components of the examined systems included endogenous functional proteins cytochrome c, serum albumin, lysozyme and insulin, antiviral drugs favipiravir, molnupiravir, nirmatrelvir and ritonavir, mono- and heptamethinecyanine dyes. Using the multiple ligand simultaneous docking technique, it was demonstrated that: i) the drugs and the dyes occupy different binding sites on the protein molecule and do not interfere with each other; ii) the heptamethines AK7-5 and AK7-6 possess the highest affinity for the proteins; iii) among the examined systems the strongest complexes are formed between the heptamethine dyes and serum albumin. Taken together, the results obtained indicate that albumin-based nanoparticles functionalized by the heptamethine cyanine dyes can be used for targeted delivery of the explored antiviral agents.

Chapter 3: Management of kidney injury caused by cancer drug therapy, from clinical practice guidelines for the management of kidney injury during anticancer drug therapy 2022.

Cisplatin should be administered with diuretics and Magnesium supplementation under adequate hydration to avoid renal impairment. Patients should be evaluated for eGFR (estimated glomerular filtration rate) during the treatment with pemetrexed, as kidney injury has been reported. Pemetrexed should be administered with caution in patients with a CCr (creatinine clearance) < 45mL/min. Mesna is used to prevent hemorrhagic cystitis in patients receiving ifosfamide. Febuxostat is effective in avoiding hyperuricemia induced by TLS (tumor lysis syndrome). Preventative rasburicase is recommended in high-risk cases of TLS. Thrombotic microangiopathy could be triggered by anticancer drugs and there is no evidence of efficacy of plasma exchange therapy. When proteinuria occurs during treatment with anti-angiogenic agents or multi-kinase inhibitors, dose reductions or interruptions based on grading should be considered. Grade 3 proteinuria and renal dysfunction require urgent intervention, including drug interruption or withdrawal, and referral to a nephrologist should be considered. The first-line drugs used for blood pressure elevation due to anti-angiogenic agents are ACE (angiotensin-converting enzyme) inhibitors and ARBs (angiotensin receptor blockers). The protein binding of drugs and their pharmacokinetics are considerably altered in patients with hypoalbuminemia. The clearance of rituximab is increased in patients with proteinuria, and the correlation with urinary IgG suggests similar pharmacokinetic changes when using other antibody drugs. AIN (acute interstitial nephritis) is the most common cause of ICI (immune checkpoint inhibitor)-related kidney injury that is often treated with steroids. The need for renal biopsy in patients with kidney injury that occurs during treatment with ICI remains controversial.

Small volume rapid equilibrium dialysis (RED) measures effects of interstitial parameters on the protein-bound fraction of topical drugs

The importance of plasma protein binding in the early stages of drug development is well recognized. Free and bound drug fractions in plasma are routinely determined with well-established methods. However, for physiological fluids with a small accessible volume and low protein concentrations, such as dermal interstitial fluid (dISF) validated methods are currently missing. Due to the low protein concentration and highly dynamic processes in the dermis, protein binding data obtained from plasma samples may underestimate in-vivo efficacy. This study aimed to validate a small volume rapid equilibrium dialysis (RED) for low protein samples, as a tool to examine drug-protein binding directly in the biological fluid at the site of action. The sample volume required for RED was successfully downscaled to 50 µl and plasma protein binding values of the four model drugs were consistent with previous studies with an average recovery of 88 ± 8% which makes all tested drugs suitable for small volume RED. Inter- and intra-batch variability showed sufficient reproducibility across RED plates. Small volume RED was successfully applied to assess the effects of interstitial parameters, including the evaluation of the major binding protein and the effects of binding protein concentration, drug concentration, and pH on the protein-bound drug fraction using 2% HSA and/or diluted human plasma as a surrogate for dISF.

Preclinical pharmacokinetic exploration of a novel osteoporotic quinazolinone-benzopyran-indole hybrid (S019-0385) using LC-MS/MS

1. The current investigation was to develop and validate the LC-MS/MS method in order to analyse the various pharmacokinetic parameters of S019-0385. A sensitive, selective, and robust LC-MS/MS approach was established and validated for measuring S019-0385 in female mice plasma and tissue, using optimal multiple reaction monitoring (MRM) transition m/z 488.25/329.12 on positive mode. On a Waters Symmetry Shield C18 column, the analyte was separated using acetonitrile and deionised water with formic acid within 6 min at 0.7 mL/min. Linearity (R2 ≥ 0.99) was observed across 0.195–100 ng/mL concentration range using linear least-squares regression. 2. Blood-to-plasma ratio and plasma protein drug binding (%) in mice and human was assessed and found to be less than 1 and >83%, respectively. Absolute bioavailability (%F) of S019-0385 in female Swiss mice was exhibited to be 6.90%. Percent dose excreted S019-0385 in unchanged form through urine and faecal was found to be less than 2% and 0.5%, respectively. 3. Following oral administration at 5 mg/kg, the concentration of S019-0385 in tissue distribution was found to be in the order of C small intestine > C bone > C lung > C spleen > C kidney > C liver > C heart > C brain.

Sensitivity enhancement of capillary electrophoresis-frontal analysis-based method for characterization of drug-protein interactions using on-line sample preconcentration.

Capillary electrophoresis-frontal analysis is one of the most frequently used approaches for the study of plasma protein-drug interactions as a substantial part of new drug development. However, the capillary electrophoresis-frontal analysis typically combined with ultraviolet-visible detection suffers from insufficient concentration sensitivity, particularly for substances with limited solubility and low molar absorption coefficient. The sensitivity problem has been solved in this work by its combination with an on-line sample preconcentration. According to the knowledge of the authors this combination has never been used to characterize plasma protein-drug binding. It resulted in a fully automated and versatile methodology for the characterization of binding interactions. Further, the validated method minimalizes the experimental errors due to a reduction in the manipulation of samples. Moreover, employing an on-line preconcentration strategy with capillary electrophoresis-frontal analysis using human serum albumin-salicylic acid as a model system improves the drug concentration sensitivity 17-fold compared to the conventional method. The value of binding constant (1.51±0.63) ·104 L/mol obtained by this new capillary electrophoresis-frontal analysis modification is in agreement with the value (1.13±0.28) ·104 L/mol estimated by a conventional variant of capillary electrophoresis-frontal analysis without the preconcentration step, as well as with literature data obtained using different techniques.

Synthesis of multi fluorine containing polyoxometalate sandwich type compound with drug delivery, DNA interaction and protein binding studies

The combination of polyoxometalates (POMs) and fluorinated anhydride in a variety of modes ofinteractionturned out to be the key for unlocking new directions in the study of biological activities. In this study, two new compounds such as a sandwich type covalently attached fluorinated anhydride [C27H24F6N2O10](FDA-Di-Tris) and a Dawson POM hybrid, [{N(C4H9)4}10 {C22H16F6N2O128P4V6W30·CH3CN}](FDA@Di-POM) were synthesized. These compounds were clearly characterized and their formation was confirmed by spectroscopic(UV–visible, FT-IR, NMR) techniques, elemental analyses and scanning electron microscopy techniques(SEM). FDA-Di-Tris and FDA@Di-POM were evaluated for drug delivery, DNA binding and protein binding. It is found that drug loading efficiency was 82% and 57% for FDA@Di-POM and FDA-Di-Tris,respectively. While drug release for FDA-Di-Tris was found to be lowest (14%) in basic media (pH = 11) and highest (59%) in acidic media (pH = 3) and FDA@Di-POM exhibited approximately similar behavior in both media such as (55%) in basic media (pH = 11) and (49%) in acidic media (pH = 3) respectively. UV–visible study of FDA-Di-Tris and FDA@Di-POM with salmon sperm DNA (SS-DNA) showed hypochromism trend which indicate intercalation mode of attachment and exhibited binding constant value 6.29 × 105M−1 and 8.33 × 105 M−1 for FDA-Di-Tris and FDA@Di-POM, respectively, referring that FDA@Di-POM has more binding affinity than FDA-Di-Tris. Protein binding study presented binding constant value (Kb) FDA@Di-POM and FDA-Di-Tris, which are 2.28 × 108 M−1 and 1.55 108 M−1, correspondingly.

Substituted 2,5 thiophene dicarboxaldehyde bisthiosemicarbazones and their copper(II) complexes: Synthesis, structure elucidation, HSA binding, biological activities and docking studies

Reaction of copper (II) acetate with 2,5 thiophenedicarboxaldehyde bisthiosemicarbazone (2,5 H2bttsc, 1H2L), 2,5 thiophenedicarboxaldehyde-N1-methyl bisthiosemicarbazone (2,5 H2bttsc-N1-Me, 2H2L) and 2,5 thiophenedicarboxaldehyde -N1-phenyl bisthiosemicarbazone (2,5 H2bttsc-N1-Ph, 3H2L) in 1:1 (M:L) molar ratio yielded complexes of stoichiometry, [Cu(L)] 1-3 (1L, 1; 2L 2; 3L 3). All the complexes are characterized using elemental analysis FTIR, Mass and ESR spectroscopy. The two distinct g values, g‖ = 2.40 (1), 2.15 (2), 2.20 (3) and g⊥ = 2.08 (1), 2.07 (2), 2.12 (3) indicate axial symmetry for the complexes. The greater g‖ value than g⊥, confirms the presence of free electrons in dx2-y2 ground term in square planar geometry. The ligands (1H2L - 3H2L) and their complexes (1–3) were tested for DPPH radical scavenging, ABTS radical scavenging, FRAP ferric reducing, and CUPRAC cupric reducing antioxidant capacity assays. In general, all the ligands and their metal complexes exhibited moderate antioxidant potential, however 3H2L has shown best Cu2+ reducing activity (7.34 μg TE/ml) and ABTS radicals scavenging activity (IC50, 16.95) amongst all. Ligands (1H2L - 3H2L) and their complexes (1–3) were also evaluated for anti-tuberculosis activity against M. tuberculosis H37RV strain. It has been observed that with the increase of hydrophobicity of ligand due to substituent present at N1 atom, anti-TB activity increased (H, MIC = 6.25 µg/ml < Me, MIC = 3.12 µg/ml < Ph, MIC = 1.6 µg/ml). Molecular modeling studies have shown considerable intermolecular interaction of these compounds with minimum binding energy −5.8 (1H2L), −5.8 (2H2L), −9.6 (3H2L), −6.9 (1), −6.6 (2) and −9.6 (3) Kcal/ mol, which also supports the experimental data. Pharmacokinetics (effective transport of molecule to their target sites) was studied using drug-protein binding study of molecules with human serum albumin (HSA) was performed using UV–vis and fluorescence spectroscopy. The ligand 2H2L and complex 2 showed strong binding interactions with HSA having binding constant values of 4.24 × 105 M−1 and 4.92 × 105 M−1 respectively.

A Quantitative Structure-Activity Relationship for Human Plasma Protein Binding: Prediction, Validation and Applicability Domain.

The purpose of this study was to develop a robust and externally predictive in silico QSAR-neural network model for predicting plasma protein binding of drugs. This model aims to enhance drug discovery processes by reducing the need for chemical synthesis and extensive laboratory testing. A dataset of 277 drugs was used to develop the QSAR-neural network model. The model was constructed using a Filter method to select 55 molecular descriptors. The validation set's external accuracy was assessed through the predictive squared correlation coefficient Q2 and the root mean squared error (RMSE). The developed QSAR-neural network model demonstrated robustness and good applicability domain. The external accuracy of the validation set was high, with a predictive squared correlation coefficient Q2 of 0.966 and a root mean squared error (RMSE) of 0.063. Comparatively, this model outperformed previously published models in the literature. The study successfully developed an advanced QSAR-neural network model capable of predicting plasma protein binding in human plasma for a diverse set of 277 drugs. This model's accuracy and robustness make it a valuable tool in drug discovery, potentially reducing the need for resource-intensive chemical synthesis and laboratory testing.

Covalent synthesis, structural characterization and biological behavioral study of tin captured porphyrin-polyoxometalate based polymeric hybrid

The combination of polyoxometalates(POMs) and tin-based porphyrin in a variety of modes ofinteractionturned out to be the key for unlocking new directions in the study of biological activities. In this study, a new sandwich type covalently attached porphyrin C52H48N6O6Sn (SnTPP-Di-Tris) and Anderson POM-porphyrin polymeric hybrid, with molecular formula as [{N(C4H9)4}8{C62H60Mn2Mo12N6O48Sn·CH3CN}](PolySnP@POM). These compounds were clearly characterized and their formation was confirmed by spectroscopic (UV–visible, FT-IR, NMR) techniques, powder XRD, elemental analyses, cyclic voltammetry and scanning electron microscopy techniques(SEM). SnTPP-Di-Tris and PolySnP@POM were evaluated for drug delivery, DNA binding, protein binding and antibacterial studies. It is found that drug loading efficiency was 89% and 93% for PolySnP@POM and SnTPP-Di-Tris, respectively. While drug release was more efficient in acidic media (pH = 3) as 100 % for both compounds in 1.5 h as compared to basic media (pH = 11) 65.93% and 72.6% for SnTPP-Di-Tris and PolySnP@POM, respectively. Uv–visible study of SnTPP-Di-Tris and PolySnP@POM with salmon sperm DNA (SS-DNA) showed hypochromism trend which indicate intercalation mode of attachment and exhibited binding constant value 4.9 × 103 M−1 and 2.7 × 104 M−1 for SnTPP-Di-Tris and PolySnP@POM, respectively, referring that PolySnP@POM has more binding affinity than SnTPP-Di-Tris. Protein binding Study presented binding constant value (Kb) 3.09 × 104 M−1 and 1.30 × 104 M−1 for PolySnP@POM, and SnTPP-Di-Tris, correspondingly. Antibacterial activity results showed minor to significant activity. Furthermore, enhanced antibacterial activity of SnTPP-Di-Tris over its corresponding hybrid PolySnP@POM was observed because of lipophilic and more proliferating nature of SnTPP-Di-Tris.

Immobilization of M3 Muscarinic Receptor to Rapidly Analyze Drug-Protein Interactions and Bioactive Components in a Natural Plant.

Despite its increasing application in pursing potential ligands, the capacity of receptor affinity chromatography is greatly challenged as most current research studies lack a comprehensive characterization of the ligand-receptor interaction, particularly when simultaneously determining their binding thermodynamics and kinetics. This work developed an immobilized M3 muscarinic receptor (M3R) affinity column by fixing M3R on amino polystyrene microspheres via the interaction of a 6-chlorohexanoic acid linker with haloalkane dehalogenase. The efficiency of the immobilized M3R was tested by characterizing the binding thermodynamics and kinetics of three known drugs to immobilized M3R using a frontal analysis and the peak profiling method, as well as by analyzing the bioactive compounds in Daturae Flos (DF) extract. The data showed that the immobilized M3R demonstrated good specificity, stability, and competence for analyzing drug-protein interactions. The association constants of (-)-scopolamine hydrochloride, atropine sulfate, and pilocarpine to M3R were determined to be (2.39 ± 0.03) × 104, (3.71 ± 0.03) × 104, and (2.73 ± 0.04) × 104 M-1, respectively, with dissociation rate constants of 27.47 ± 0.65, 14.28 ± 0.17, and 10.70 ± 0.35 min-1, respectively. Hyoscyamine and scopolamine were verified as the bioactive compounds that bind to M3R in the DF extract. Our results suggest that the immobilized M3R method was capable of determining drug-protein binding parameters and probing specific ligands in a natural plant, thus enhancing the effectiveness of receptor affinity chromatography in diverse stages of drug discovery.

Abstract 2024: High resolution limited proteolysis (HR-LiP), a novel structural proteomics approach for the prediction of small molecule-protein binding events

Abstract High resolution profiling of drug-protein interactions and binding mechanisms remains a major hurdle during lead selection and optimization. A key milestone in structure-based drug design is compound binding site identification and characterization. Structure-activity relationship (SAR) studies traditionally utilize time and cost intensive techniques such as nuclear magnetic resonance (NMR), x-ray crystallography (X-ray) and cryo-electron microscopy (cryo-EM). To date, hydrogen-deuterium exchange (HDX) is the only mass spectrometry-based approach that has been extensively utilized. Further, SAR studies are often complicated by protein size (large proteins and/or oligomers) and location (membrane proteins), which can lead to protocol adaptations that can introduce artifacts. High resolution limited proteolysis (HR-LiP) is a high-throughput, high-resolution approach based on LiP-Quant that utilizes peptide-level resolution to characterize drug-protein interactions. Two well-characterized drug target proteins, bromodomain-containing protein 4 (BRD4) and transitional endoplasmic reticulum ATPase (VCP), were selected for analysis as they represent both difficult targets for structural biology (size and oligomerization respectively). BRD4 was targeted with a well characterized inhibitor (JQ1), while VCP was investigated for binding with a known autophagy activator. Using HR-LiP we identify the binding site of the BRD4 inhibitor JQ1 in the full-length protein, which is typically too large to be used directly in with conventional methods. Further, we confirm binding of the autophagy activator to VCP at a site known to increase VCP activity. For both compounds tested, our data are in good accordance with orthogonal data obtained by other structural protein approaches including HDX-MS and “click” chemistry-based chemoproteomics. We demonstrate that HR-LiP can be used to dissect small molecule-protein binding events, including compound binding site prediction for protein targets classically considered to be difficult and time intensive. Further, we show that this data can be used to shed light on the biological mechanisms driving a phenotypic response. Given its biological power, broad applicability and ease of implementation, we envision the use of HR-LiP as a routine approach for target validation and lead optimization in small molecule drug discovery pipelines. Citation Format: Nigel Beaton, Roland Bruderer, Yuehan Feng, Jagat Adhikari, Ivan Cornella-Taracido, Lukas Reiter. High resolution limited proteolysis (HR-LiP), a novel structural proteomics approach for the prediction of small molecule-protein binding events [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2024.

Inner pore hydration free energy controls the activation of big potassium channels

Hydrophobic gating is an emerging mechanism in regulation of protein ion channels where the pore remains physically open but becomes dewetted to block ion permeation. Atomistic molecular dynamics simulations have played a crucial role in understanding hydrophobic gating by providing the molecular details to complement mutagenesis and structural studies. However, existing studies rely on direct simulations and do not quantitatively describe how the sequence and structural changes may control the delicate liquid-vapor equilibrium of confined water in the pore of the channel protein. To address this limitation, we explore two enhanced sampling methods, namely metadynamics and umbrella sampling, to derive free-energy profiles of pore hydration in both the closed and open states of big potassium (BK) channels, which are important in cardiovascular and neural systems. It was found that metadynamics required substantially longer sampling times and struggled to generate stably converged free-energy profiles due to the slow dynamics of cooperative pore water diffusion even in the barrierless limit. Using umbrella sampling, well-converged free-energy profiles can be readily generated for the wild-type BK channels as well as three mutants with pore-lining mutations experimentally known to dramatically perturb the channel gating voltage. The results show that the free energy of pore hydration faithfully reports the gating voltage of the channel, providing further support for hydrophobic gating in BK channels. Free-energy analysis of pore hydration should provide a powerful approach for quantitative studies of how protein sequence, structure, solution conditions, and/or drug binding may modulate hydrophobic gating in ion channels.

Efavirenz-Based Antiretroviral Therapy but Not Pregnancy Increased Unbound Piperaquine Exposure in Women during Malaria Chemoprevention.

Dihydroartemisinin-piperaquine (DP) is highly effective for malaria chemoprevention during pregnancy, but the standard dosing of DP that is used for nonpregnant adults may not be optimal for pregnant women. We previously reported that the pharmacokinetic exposure of total piperaquine (PQ; both bound and unbound to plasma proteins) is reduced significantly in the context of pregnancy or efavirenz (EFV)-based antiretroviral therapy (ART). However, as PQ is >99% protein-bound, reduced protein binding during pregnancy may lead to an increase in the pharmacologically active unbound drug fraction (fu), relative to the total PQ. We investigated the impact of pregnancy and EFV use on the fu of PQ to inform the interpretation of pharmacokinetics. Plasma samples from 0 to 24 h after the third (final) DP dose were collected from pregnant women at 28 weeks gestation who were receiving or not receiving EFV-based ART as well as from women 34 to 54 weeks postpartum who were not receiving EFV-based ART, who served as controls. Unbound PQ was quantified via ultrafiltration and liquid chromatography-tandem mass spectrometry, with fu being calculated as PQunbound/PQtotal. The geometric mean fu did not differ between pregnant and postpartum women (P = 0.66), but it was 23% (P < 0.01) greater in pregnant women receiving EFV-based ART, compared to that in postpartum women who were not receiving EFV-based ART. The altered drug-protein binding, potentially due to the displacement of PQ from plasma proteins by EFV, resulted in only a 14% lower unbound PQ exposure (P = 0.13) in the presence of a 31% lower total PQ exposure (P < 0.01), as estimated by the area under the concentration time curve from 0 to 24 h post-last dose in pregnant women who were receiving EFV-based ART. The results suggest that the impact of pregnancy and EFV-based ART on the exposure and, in turn, the efficacy of PQ for malaria prevention may not be as significant as was suggested by the changes in the total PQ exposure. Further study during the terminal elimination phase (e.g., on day 28 post-dose) would help better characterize the unbound PQ exposure during the full dosing interval and, thus, the overall efficacy of PQ for malaria chemoprevention in this special population.

AttentionDTA: Drug-Target Binding Affinity Prediction by Sequence-Based Deep Learning With Attention Mechanism.

The identification of drug-target relations (DTRs) is substantial in drug development. A large number of methods treat DTRs as drug-target interactions (DTIs), a binary classification problem. The main drawback of these methods are the lack of reliable negative samples and the absence of many important aspects of DTR, including their dose dependence and quantitative affinities. With increasing number of publications of drug-protein binding affinity data recently, DTRs prediction can be viewed as a regression problem of drug-target affinities (DTAs) which reflects how tightly the drug binds to the target and can present more detailed and specific information than DTIs. The growth of affinity data enables the use of deep learning architectures, which have been shown to be among the state-of-the-art methods in binding affinity prediction. Although relatively effective, due to the black-box nature of deep learning, these models are less biologically interpretable. In this study, we proposed a deep learning-based model, named AttentionDTA, which uses attention mechanism to predict DTAs. Different from the models using 3D structures of drug-target complexes or graph representation of drugs and proteins, the novelty of our work is to use attention mechanism to focus on key subsequences which are important in drug and protein sequences when predicting its affinity. We use two separate one-dimensional Convolution Neural Networks (1D-CNNs) to extract the semantic information of drug's SMILES string and protein's amino acid sequence. Furthermore, a two-side multi-head attention mechanism is developed and embedded to our model to explore the relationship between drug features and protein features. We evaluate our model on three established DTA benchmark datasets, Davis, Metz, and KIBA. AttentionDTA outperforms the state-of-the-art deep learning methods under different evaluation metrics. The results show that the attention-based model can effectively extract protein features related to drug information and drug features related to protein information to better predict drug target affinities. It is worth mentioning that we test our model on IC50 dataset, which provides the binding sites between drugs and proteins, to evaluate the ability of our model to locate binding sites. Finally, we visualize the attention weight to demonstrate the biological significance of the model. The source code of AttentionDTA can be downloaded from https://github.com/zhaoqichang/AttentionDTA_TCBB.

Elucidation of Drug Transport Mechanism by Serum Protein and Development for Pancreatic Cancer Treatment

Human serum albumin (HSA) and α1-acid glycoprotein (AGP) are the major drug-binding proteins in the blood and regulate the tissue transfer of bound drugs. We succeeded in clarifying the three-dimensional structure of AGP for the first time in the world from X-ray crystal structure analysis. Using a site-directed mutagenesis method by constructing yeast expression systems as well as the three-dimensional structure, we elucidated the properties of drug binding sites of AGP. We also found that structural change due to the interaction between AGP and cell membranes causes the release of bound drugs and reported an "AGP-mediated drug transport process." Pancreatic cancer has an extremely low response rate to anticancer drugs compared to other cancers and is resistant to starvation of nutrients including fatty acids. We clarified that glutamine metabolism is involved in this tolerance. Furthermore, aiming at efficient drug delivery and effective treatment for pancreatic cancer, we focused on nitric oxide (NO) which increases pancreatic blood flow and has a cell-killing effect on tumors and surrounding stromal tissues. We successfully synthesized nitrated phenylbutyrate (NPB), which binds to HSA and has an antitumor effect in vitro and vivo. The binding of NPB to HSA is considered to be useful for delivery to tumors through the enhanced permeability and retention (EPR) effect and HSA receptors.