Overview of the detection methods for equilibrium dissociation constant KD of drug-receptor interaction

Prediction of drug-target binding affinity based on deep learning models

The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development. In recent years, the continuous development of deep learning (DL) technology has brought new opportunities for drug discovery through the DTA prediction. This shift has moved the prediction of DTA from traditional machine learning methods to DL. The DL frameworks used for DTA prediction include convolutional neural networks (CNN), graph convolutional neural networks (GCN), and recurrent neural networks (RNN), and reinforcement learning (RL), among others. This review article summarizes the available literature on DTA prediction using DL models, including DTA quantification metrics and datasets, and DL algorithms used for DTA prediction (including input representation of models, neural network frameworks, valuation indicators, and model interpretability). In addition, the opportunities, challenges, and prospects of the application of DL frameworks for DTA prediction in the field of drug discovery are discussed.

A review on magnetic beads-based SELEX technologies: Applications from small to large target molecules

This review summarizes the stepwise strategy and key points for magnetic beads (MBs)-based aptamer selection which is suitable for isolating aptamers against small and large molecules via systematic evolution of ligands by exponential enrichment (SELEX). Particularities, if any, are discussed according to the target size. Examples targeting small molecules (<1000 Da) such as xenobiotics, toxins, pesticides, herbicides, illegal additives, hormones, and large targets such as proteins (biomarkers, pathogens) are discussed and presented in tabular formats. Of special interest are the latest advances in more efficient alternatives, which are based on novel instrumentation, materials or microelectronics, such as fluorescence MBs-SELEX or microfluidic chip system-assisted MBs-SELEX. Limitations and perspectives of MBs-SELEX are also reviewed. Taken together, this review aims to provide practical insights into MBs-SELEX technologies and their ability to screen multiple potential aptamers against targets from small to large molecules.

FOTF-CPI: A compound-protein interaction prediction transformer based on the fusion of optimal transport fragments

Compound-protein interaction (CPI) affinity prediction plays an important role in reducing the cost and time of drug discovery. However, the interpretability of how fragments function in CPI is impacted by the fact that current methods ignore the affinity relationships between fragments of compounds and fragments of proteins in CPI modeling. This article introduces an improved Transformer called FOTF-CPI (a Fusion of Optimal Transport Fragments compound-protein interaction prediction model). We use an optimal transport-based fragmentation approach to improve the model's understanding of compound and protein sequences. Additionally, a fused attention mechanism is employed, which combines the features of fragments to capture full affinity information. This fused attention redistributes higher attention scores to fragments with higher affinity. Experimental results show FOTF-CPI achieves an average 2% higher performance than other models on all three datasets. Furthermore, the visualization confirms the potential of FOTF-CPI for drug discovery applications.

Exploring the natural products chemical space to abrogate the F3L-dsRNA interface of monkeypox virus to enhance the immune responses using molecular screening and free energy calculations

Amid the ongoing monkeypox outbreak, there is an urgent need for the rapid development of effective therapeutic interventions capable of countering the immune evasion mechanisms employed by the monkeypox virus (MPXV). The evasion strategy involves the binding of the F3L protein to dsRNA, resulting in diminished interferon (IFN) production. Consequently, our current research focuses on utilizing virtual drug screening techniques to target the RNA binding domain of the F3L protein. Out of the 954 compounds within the South African natural compound database, only four demonstrated notable docking scores: -6.55, -6.47, -6.37, and -6.35 kcal/mol. The dissociation constant (KD) analysis revealed a stronger binding affinity of the top hits 1-4 (-5.34, -5.32, -5.29, and -5.36 kcal/mol) with the F3L in the MPXV. All-atom simulations of the top-ranked hits 1 to 4 consistently exhibited stable dynamics, suggesting their potential to interact effectively with interface residues. This was further substantiated through analyses of parameters such as radius of gyration (Rg), Root Mean Square Fluctuation, and hydrogen bonding. Cumulative assessments of binding free energy confirmed the top-performing candidates among all the compounds, with values of -35.90, -52.74, -28.17, and -32.11 kcal/mol for top hits 1-4, respectively. These results indicate that compounds top hit 1-4 could hold significant promise for advancing innovative drug therapies, suggesting their suitability for both in vivo and in vitro experiments.

Designed novel nuclear localizing anticancer peptide targets p53 negative regulator MDM2 protein

Intracellular protein-protein interactions provide a major therapeutic target for the development of peptide-based anticancer therapeutic agents. MDM2 is the 491-residue protein encoded by the MDM2 oncogene. Being a ubiquitin-protein ligase, MDM2 represses the transcription ability of the tumor suppressor p53 by proteasome-mediated degradation. Under typical cellular circumstances, a sustained p53 expression level is maintained by negative regulation of MDM2, whereas under stress conditions, this is alleviated to increase the p53 level. Modulation of MDM2-p53 interaction via fabrication of an MDM2-interacting peptide could be a useful strategy to inhibit subsequent proteasomal degradation of p53 and initiation of p53 signaling leading to the initiation of p53-mediated apoptosis of tumor cells. Here, in this research work, a novel anticancer peptide mPNC-NLS targeting the nucleus and the MDM2 protein (p53 negative regulator) was designed to promote the p53 protein activity for the prevention of cancer. It induces effective apoptosis in both A549 and U87 cells and remains non-cytotoxic to normal lung fibroblast cells (WI38). Further, immunocytochemistry and Western blot results confirm that the designed mPNC-NLS peptide induces the apoptotic death of lung cancer cells via activation of p53 and p21 proteins and remarkably stifled the in vitro growth of 3D multicellular spheroids composed of A549 cells.

GAABind: a geometry-aware attention-based network for accurate protein-ligand binding pose and binding affinity prediction

Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket-ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.

Predicting drug-target binding affinity with cross-scale graph contrastive learning

Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction information of drug-target bipartite networks. They may fail to combine the molecule-scale and network-scale features to obtain high-quality representations. In this study, we propose CSCo-DTA, a novel cross-scale graph contrastive learning approach for drug-target binding affinity prediction. The proposed model combines features learned from the molecular scale and the network scale to capture information from both local and global perspectives. We conducted experiments on two benchmark datasets, and the proposed model outperformed existing state-of-art methods. The ablation experiment demonstrated the significance and efficacy of multi-scale features and cross-scale contrastive learning modules in improving the prediction performance. Moreover, we applied the CSCo-DTA to predict the novel potential targets for Erlotinib and validated the predicted targets with the molecular docking analysis.

Study on Saccharide-Glucose Receptor Interactions with the Use of Surface Plasmon Resonance

The aim of this study was to investigate the process of attachment of saccharide particles differing in degree of complexity to cell receptors responsible for transport of glucose across the cell membrane (GLUT proteins). This phenomenon is currently considered when designing modern medicines, e.g., peptide drugs to which glucose residues are attached, enabling drugs to cross the barrier of cell membranes and act inside cells. This study aims to help us understand the process of assimilation of polysaccharide nanoparticles by tumour cells. In this study, the interactions between simple saccharides (glucose and sucrose) and dextran nanoparticles with two species of GLUT proteins (GLUT1 and GLUT4) were measured using the surface plasmon resonance technique. We managed to observe the interactions of glucose and sucrose with both applied proteins. The lowest concentration that resulted in the detection of interaction was 4 mM of glucose on GLUT1. Nanoparticles were measured using the same proteins with a detection limit of 40 mM. These results indicate that polysaccharide nanoparticles interact with GLUT proteins. The measured strengths of interactions differ between proteins; thus, this study can suggest which protein is preferable when considering it as a mean of nanoparticle carrier transport.

In silico pharmacological study of AQP2 inhibition by steroids contextualized to Ménière's disease treatments

Ménière's disease (MD) is characterized by an abnormal dilatation of the endolymphatic compartment called endolymphatic hydrops and is associated with fluctuating hearing losses and vertigo. Corticosteroid treatment is typically administered for its anti-inflammatory effects to MD patients. However, we recently described for the first time a direct interaction of two corticosteroids (dexamethasone and cortisol) with human AQP2 which strongly inhibited water fluxes. From these initial studies, we proposed an AQPs Corticosteroids Binding Site (ACBS). In the present work, we tested the interaction of 10 molecules associated to the steroid family for this putative ACBS. We observed a wide diversity of affinity and inhibitory potential of these molecules toward AQP2 and discussed the implications for inner ear physiology. Among the tested compounds, cholecalciferol, calcitriol and oestradiol were the most efficient AQP2 water permeability inhibitors.

Harringtonine: A more effective antagonist for Omicron variant

Fusion with host cell membrane is the main mechanism of infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we propose that a new strategy to screen small-molecule antagonists blocking SARS-CoV-2 membrane fusion. Using cell membrane chromatography (CMC), we found that harringtonine (HT) simultaneously targeted SARS-CoV-2 S protein and host cell surface TMPRSS2 expressed by the host cell, and subsequently confirmed that HT can inhibit membrane fusion. HT effectively blocked SARS-CoV-2 original strain entry with the IC50 of 0.217 μM, while the IC50 in delta variant decreased to 0.101 μM, the IC50 in Omicron BA.1 variant was 0.042 μM. Due to high transmissibility and immune escape, Omicron subvariant BA.5 has become the dominant strain of the SARS-CoV-2 virus and led to escalating COVID-19 cases, however, against BA.5, HT showed a surprising effectiveness. The IC50 in Omicron BA.5 was even lower than 0.0019 μM. The above results revealed the effect of HT on Omicron is very significant. In summary, we characterize HT as a small-molecule antagonist by direct targeting on the Spike protein and TMPRSS2.

Development of a Redox-Polymer-Based Electrochemical Glucose Biosensor Suitable for Integration in Microfluidic 3D Cell Culture Systems

The inclusion of online, in situ biosensors in microfluidic cell cultures is important to monitor and characterize a physiologically mimicking environment. This work presents the performance of second-generation electrochemical enzymatic biosensors to detect glucose in cell culture media. Glutaraldehyde and ethylene glycol diglycidyl ether (EGDGE) were tested as cross-linkers to immobilize glucose oxidase and an osmium-modified redox polymer on the surface of carbon electrodes. Tests employing screen printed electrodes showed adequate performance in a Roswell Park Memorial Institute (RPMI-1640) media spiked with fetal bovine serum (FBS). Comparable first-generation sensors were shown to be heavily affected by complex biological media. This difference is explained in terms of the respective charge transfer mechanisms. Under the tested conditions, electron hopping between Os redox centers was less vulnerable than H₂O₂ diffusion to biofouling by the substances present in the cell culture matrix. By employing pencil leads as electrodes, the incorporation of these electrodes in a polydimethylsiloxane (PDMS) microfluidic channel was achieved simply and at a low cost. Under flow conditions, electrodes fabricated using EGDGE presented the best performance with a limit of detection of 0.5 mM, a linear range up to 10 mM, and a sensitivity of 4.69 μA mM^-1 cm^-2.

Insight into mitochondrial dysfunction mediated by clozapine-induced inhibition of PGRMC1 in PC12 cells

Clozapine is usually considered as the last resort for treatment-resistant schizophrenia (TRS). However, it shows limited efficacy in cognition improvement. Moreover, the metabolic side effects induced by clozapine can aggravate cognitive impairment, which is closely related to its neurotoxicity. Nevertheless, the mechanisms underlying clozapine's neurotoxicity remain largely elusive. In this study, PC12 cells were simultaneously treated with different concentrations (0μM, 10μM, 20μM, 40μM and 80μM) of clozapine and AG205 which functions as a blocking reagent of progesterone receptor membrane component 1 (PGRMC1). In addition, we examined the effect of PGRMC1 in clozapine-induced neurotoxicity through overexpressing or downregulating PGRMC1. Molecular docking and surface plasmon resonance (SPR) analysis indicated that clozapine and AG205 inhibited the binding of endogenous progesterone to PGRMC1. The results showed that high concentration of clozapine and AG205 induced a significant increase in cytotoxicity, reactive oxygen species (ROS) accumulation and mitochondrial membrane potential (MMP) collapse, all of which were worsened as concentration increases, while overexpression of PGRMC1 reverted the above toxic effect of clozapine on PC12 cells. Furthermore, clozapine and AG205 also downregulated the expression of PGRMC1, glucagon-like peptide-1 receptor (GLP-1R) and mitofusin2 (Mfn2). Interestingly, overexpression of PGRMC1 could revert these effects. Our data suggest that overexpression of PGRMC1 in PC12 cells prevents and restores clozapine-induced oxidative and mitochondrial damage. We propose PGRMC1 activation as a promising therapeutic strategy for clozapine-induced neurotoxicity to facilitate the relief of neuronal damage.

Do conformational changes contribute to the surface plasmon resonance signal?

Surface plasmon resonance (SPR)-based biosensors are widely used instruments for characterizing molecular interactions. In theory the SPR signal depends only on mass changes for interacting molecules of same chemical nature. Whether conformational changes of interacting molecules also contribute to the SPR signal is still a subject of lively debates. Works have been published claiming that conformational changes were detected but all factors contributing to the SPR signal were not carefully considered, in addition to often using no or improper controls. In the present work we used a very well-characterized oligonucleotide, the thrombin-binding DNA aptamer (TBA), which upon binding of potassium ions folds into a two G-tetrad antiparallel G-quadruplex structure. All terms contributing to the maximal expected SPR response, R_max, in particular the refractive index increment, RII, of both partners and the fraction of immobilized TBA target available, c_a, were experimentally assessed. The resulting R_max was then compared to the maximal experimental SPR response for potassium ions binding to TBA using appropriate controls. Regardless how the RIIs were measured, by SPR or refractometry, and how much TBA available for interacting with potassium ions was considered, the theoretical and the experimental SPR responses never matched, the former being always lower than the latter. Using a straightforward experimental model system and by thoroughly taking into account all contributing factors we therefore conclude that conformational changes can indeed contribute to the measured SPR signal.

Advances in receptor chromatography for drug discovery and drug-receptor interaction studies

Receptor chromatography involves high-throughput separation and accurate drug screening based on specific drug-receptor recognition and affinity, which has been widely used to screen active compounds in complex samples. This review summarizes the immobilization methods for receptors from three aspects: random covalent immobilization methods, site-specific covalent immobilization methods and dual-target receptor chromatography. Meanwhile, it focuses on its applications from three angles: screening active compounds in natural products, in natural-product-derived DNA-encoded compound libraries and drug-receptor interactions. This review provides new insights for the design and application of receptor chromatography, high-throughput and accurate drug screening, drug-receptor interactions and more. Teaser: This review summarizes the immobilization methods of receptors and the application of receptor chromatography, which will provide new insights for the design and application of receptor chromatography, rapid drug screening, drug-receptor interactions and more.

Protein Binding: A Fuzzy Concept

Our understanding of protein binding interactions has matured significantly over the last few years, largely as a result of trying to make sense of the binding interactions of intrinsically disordered proteins. Here, we bring together some disparate ideas that have largely developed independently, and show that they can be linked into a coherent picture that provides insight into quantitative aspects of protein interactions, in particular that transient protein interactions are often optimised for speed, rather than tight binding.

Functional characterization of SGLT1 using SSM-based electrophysiology: Kinetics of sugar binding and translocation

Beside the ongoing efforts to determine structural information, detailed functional studies on transporters are essential to entirely understand the underlying transport mechanisms. We recently found that solid supported membrane-based electrophysiology (SSME) enables the measurement of both sugar binding and transport in the Na⁺/sugar cotransporter SGLT1 (Bazzone et al, 2022a). Here, we continued with a detailed kinetic characterization of SGLT1 using SSME, determining K_M and K_D ^app for different sugars, k_obs values for sugar-induced conformational transitions and the effects of Na⁺, Li⁺, H⁺ and Cl^- on sugar binding and transport. We found that the sugar-induced pre-steady-state (PSS) charge translocation varies with the bound ion (Na⁺, Li⁺, H⁺ or Cl^-), but not with the sugar species, indicating that the conformational state upon sugar binding depends on the ion. Rate constants for the sugar-induced conformational transitions upon binding to the Na⁺-bound carrier range from 208 s^-1 for D-glucose to 95 s^-1 for 3-OMG. In the absence of Na⁺, rate constants are decreased, but all sugars bind to the empty carrier. From the steady-state transport current, we found a sequence for sugar specificity (V_max/K_M): D-glucose > MDG > D-galactose > 3-OMG > D-xylose. While K_M differs 160-fold across tested substrates and plays a major role in substrate specificity, V_max only varies by a factor of 1.9. Interestingly, D-glucose has the lowest V_max across all tested substrates, indicating a rate limiting step in the sugar translocation pathway following the fast sugar-induced electrogenic conformational transition. SGLT1 specificity for D-glucose is achieved by optimizing two ratios: the sugar affinity of the empty carrier for D-glucose is similarly low as for all tested sugars (K_D,K ^app = 210 mM). Affinity for D-glucose increases 14-fold (K_D,Na ^app = 15 mM) in the presence of sodium as a result of cooperativity. Apparent affinity for D-glucose during transport increases 8-fold (K_M = 1.9 mM) compared to K_D,Na ^app due to optimized kinetics. In contrast, K_M and K_D ^app values for 3-OMG and D-xylose are of similar magnitude. Based on our findings we propose an 11-state kinetic model, introducing a random binding order and intermediate states corresponding to the electrogenic transitions detected via SSME upon substrate binding.

Identification of AXL as a co-receptor for human parvovirus B19 infection of human erythroid progenitors

Parvovirus B19 (B19V) infects human erythroid progenitor cells (EPCs) and causes several hematological disorders and fetal hydrops. Amino acid (aa) 5-68 of minor capsid protein VP1 (VP1u5-68aa) is the minimal receptor binding domain for B19V to enter EPCs. Here, we carried out a genome-wide CRISPR-Cas9 guide RNA screen and identified tyrosine protein kinase receptor UFO (AXL) as a proteinaceous receptor for B19V infection of EPCs. AXL gene silencing in ex vivo expanded EPCs remarkably decreased B19V internalization and replication. Additions of the recombinant AXL extracellular domain or a polyclonal antibody against it upon infection efficiently inhibited B19V infection of ex vivo expanded EPCs. Moreover, B19V VP1u interacted with the recombinant AXL extracellular domain in vitro at a relatively high affinity (KD = 103 nM). Collectively, we provide evidence that AXL is a co-receptor for B19V infection of EPCs.

Cortisol Interaction with Aquaporin-2 Modulates Its Water Permeability: Perspectives for Non-Genomic Effects of Corticosteroids

Aquaporins (AQPs) are water channels widely distributed in living organisms and involved in many pathophysiologies as well as in cell volume regulations (CVR). In the present study, based on the structural homology existing between mineralocorticoid receptors (MRs), glucocorticoid receptors (GRs), cholesterol consensus motif (CCM) and the extra-cellular vestibules of AQPs, we investigated the binding of corticosteroids on the AQP family through in silico molecular dynamics simulations of AQP2 interactions with cortisol. We propose, for the first time, a putative AQPs corticosteroid binding site (ACBS) and discussed its conservation through structural alignment. Corticosteroids can mediate non-genomic effects; nonetheless, the transduction pathways involved are still misunderstood. Moreover, a growing body of evidence is pointing toward the existence of a novel membrane receptor mediating part of these rapid corticosteroids' effects. Our results suggest that the naturally produced glucocorticoid cortisol inhibits channel water permeability. Based on these results, we propose a detailed description of a putative underlying molecular mechanism. In this process, we also bring new insights on the regulatory function of AQPs extra-cellular loops and on the role of ions in tuning the water permeability. Altogether, this work brings new insights into the non-genomic effects of corticosteroids through the proposition of AQPs as the membrane receptor of this family of regulatory molecules. This original result is the starting point for future investigations to define more in-depth and in vivo the validity of this functional model.

Quantifying imaging agent binding and dissociation in 3D cancer spheroid tissue culture using paired-agent principles

Binding kinetics play an important role in cancer diagnosis and therapeutics. However, current methods of quantifying binding kinetics fail to consider the three-dimensional environment that drugs and imaging agents experience in biological tissue. In response, a methodology to assay agent binding and dissociation in 3D tissue culture was developed using paired-agent molecular imaging principles. To test the methodology, the uptakes of ABY-029 (an IRDye 800CW-labeled epidermal growth factor receptor (EGFR)-targeted antibody-mimetic) and IRDye 700DX-carboxylate in 3D spheroids were measured in four different human cancer cell lines throughout staining and rinsing. A compartment model (optimized for the application) was then fit to the kinetic curves of both imaging agents to estimate binding and dissociation rate constants of the EGFR targeted ABY-029 agent. A linear correlation was observed between apparent association rate constant (k 3 ) and the receptor concentration experimentally and in simulations (r = 0.99 , p < 0.05 ). Additionally, a similar binding affinity profile compared to a gold standard method was determined by this model. This low-cost methodology to quantify imaging agent or drug binding affinity in clinically relevant 3D tumor spheroid models, can be used to guide timing of imaging in molecular guided surgery and could have implications in drug development.

Molecule generation toward target protein (SARS-CoV-2) using reinforcement learning-based graph neural network via knowledge graph

AI-driven approaches are widely used in drug discovery, where candidate molecules are generated and tested on a target protein for binding affinity prediction. However, generating new compounds with desirable molecular properties such as Quantitative Estimate of Drug-likeness (QED) and Dopamine Receptor D2 activity (DRD2) while adhering to distinct chemical laws is challenging. To address these challenges, we proposed a graph-based deep learning framework to generate potential therapeutic drugs targeting the SARS-CoV-2 protein. Our proposed framework consists of two modules: a novel reinforcement learning (RL)-based graph generative module with knowledge graph (KG) and a graph early fusion approach (GEFA) for binding affinity prediction. The first module uses a gated graph neural network (GGNN) model under the RL environment for generating novel molecular compounds with desired properties and a custom-made KG for molecule screening. The second module uses GEFA to predict binding affinity scores between the generated compounds and target proteins. Experiments show how fine-tuning the GGNN model under the RL environment enhances the molecules with desired properties to generate 100 % valid and 100 % unique compounds using different scoring functions. Additionally, KG-based screening reduces the search space of generated candidate molecules by 96.64 % while retaining 95.38 % of promising binding molecules against SARS-CoV-2 protein, i.e., 3C-like protease (3CLpro). We achieved a binding affinity score of 8.185 from the top rank of generated compound. In addition, we compared top-ranked generated compounds to Indinavir on different parameters, including drug-likeness and medicinal chemistry, for qualitative analysis from a drug development perspective.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13721-023-00409-2.

Towards personalized antibody cancer therapy: development of a microfluidic cell culture device for antibody selection

Antibody therapy has been one of the most successful therapies for a wide range of diseases, including cancer. One way of expediting antibody therapy development is through phage display technology. Here, by screening thousands of randomly assembled peptide sequences, it is possible to identify potential therapeutic candidates. Conventional screening technologies do not accommodate perfusion through the system, as is the case of standard plate-based cultures. This leads to a poor translation of the experimental results obtained in vitro when moving to a more physiologically relevant setting, such as the case of preclinical animal models or clinical trials. Microfluidics is a technology that can improve screening efficacy by replicating more physiologically relevant conditions such as shear stress. In this work, a polydimethylsiloxane/polystyrene-based microfluidic system for a continuously perfused culture of cancer cells is reported. Human colorectal adenocarcinoma cells (HCT116) expressing CXCR4 were used as a cell target. Fluorescently labeled M13 phages anti-CXCR4 were used to study the efficiency of the microfluidic system as a tool to study the binding kinetics of the engineered bacteriophages. Using our microfluidic platform, we estimated a dissociation constant of 0.45 pM for the engineered phage. Additionally, a receptor internalization assay was developed using SDF-1α to verify phage specificity to the CXCR4 receptor. Upon receptor internalization there was a signal reduction, proving that the anti-CXCR4 fluorescently labelled M13 phages bound specifically to the CXCR4 receptor. The simplicity and ease of use of the microfluidic device design presented in this work can form the basis of a generic platform that facilitates the study and optimization of therapies based on interaction with biological entities such as mammalian cells.

Microfluidic Approaches for Affinity-Based Exosome Separation

As a subspecies of extracellular vesicles (EVs), exosomes have provided promising results in diagnostic and theranostic applications in recent years. The nanometer-sized exosomes can be extracted by liquid biopsy from almost all body fluids, making them especially suitable for mainly non-invasive point-of-care (POC) applications. To achieve this, exosomes must first be separated from the respective biofluid. Impurities with similar properties, heterogeneity of exosome characteristics, and time-related biofouling complicate the separation. This practical review presents the state-of-the-art methods available for the separation of exosomes. Furthermore, it is shown how new separation methods can be developed. A particular focus lies on the fabrication and design of microfluidic devices using highly selective affinity separation. Due to their compactness, quick analysis time and portable form factor, these microfluidic devices are particularly suitable to deliver fast and reliable results for POC applications. For these devices, new manufacturing methods (e.g., laminating, replica molding and 3D printing) that use low-cost materials and do not require clean rooms are presented. Additionally, special flow routes and patterns that increase contact surfaces, as well as residence time, and thus improve affinity purification are displayed. Finally, various analyses are shown that can be used to evaluate the separation results of a newly developed device. Overall, this review paper provides a toolbox for developing new microfluidic affinity devices for exosome separation.

The measurement of binding affinities by NMR chemical shift perturbation

We have carried out chemical shift perturbation titrations on three contrasting proteins. The resulting chemical shifts have been analysed to determine the best way to fit the data, and it is concluded that a simultaneous fitting of all raw shift data to a single dissociation constant is both the most accurate and the most precise method. It is shown that the optimal weighting of ¹⁵N chemical shifts to ¹H chemical shifts is protein dependent, but is around the consensus value of 0.14. We show that chemical shift changes of individual residues can be fit to give residue-specific affinities. Residues with affinities significantly stronger than average are found in close contact with the ligand and are suggested to form a rigid contact surface, but only when the binding involves little conformational change. This observation may be of value in analysing binding and conformational change.

A brief review of protein-ligand interaction prediction

The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in vitro experiments. There is a need to develop PLI computational prediction approaches to speed up the drug discovery process. In this review, we summarize a brief introduction to various computation-based PLIs. We discuss these approaches, in particular, machine learning-based methods, with illustrations of different emphases based on mainstream trends. Moreover, we analyzed three research dynamics that can be further explored in future studies.

Synthetic imperatorin derivatives alleviate allergic reactions via mast cells

Anaphylaxis is a severe systemic allergic reaction that exhibits multiple clinical symptoms. The Mas-related G protein-coupled receptor X2 (MRGPRX2) is recognized as a key cell receptor mediating allergic diseases and drug-induced anaphylactoid reactions. Thus, it has been a promising target for preventing and treating these reactions. Based on the potential activity of imperatorin and active structural feature of MRGPRX2, we first demonstrated that the synthetic imperatorin derivatives (IDs) could significantly inhibit MRGPRX2 agonist-induced degranulation and cytokine release in LAD2 cells, as well as alleviate local and systemic anaphylaxis in mice. The IC₅₀ value of the most promising compound is an order of magnitude lower than that of imperatorin. IDs were further identified to display anti-pseudo-allergic activity by binding MRGPRX2 with the tertiary nitrogen substructures, just liking the reported MRGPRX2-ligand. These results would propose evidence for discovery of agents for treating MCs-dependent allergic disorders.

UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning

Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.

A Perspective for Ménière’s Disease: In Silico Investigations of Dexamethasone as a Direct Modulator of AQP2

Ménière’s disease is a chronic illness characterized by intermittent episodes of vertigo associated with fluctuating sensorineural hearing loss, tinnitus and aural pressure. This pathology strongly correlates with a dilatation of the fluid compartment of the endolymph, so-called hydrops. Dexamethasone is one of the therapeutic approaches recommended when conventional antivertigo treatments have failed. Several mechanisms of actions have been hypothesized for the mode of action of dexamethasone, such as the anti-inflammatory effect or as a regulator of inner ear water homeostasis. However, none of them have been experimentally confirmed so far. Aquaporins (AQPs) are transmembrane water channels and are hence central in the regulation of transcellular water fluxes. In the present study, we investigated the hypothesis that dexamethasone could impact water fluxes in the inner ear by targeting AQP2. We addressed this question through molecular dynamics simulations approaches and managed to demonstrate a direct interaction between AQP2 and dexamethasone and its significant impact on the channel water permeability. Through compartmentalization of sodium and potassium ions, a significant effect of Na+ upon AQP2 water permeability was highlighted as well. The molecular mechanisms involved in dexamethasone binding and in its regulatory action upon AQP2 function are described.

Using ELP Repeats as a Scaffold for De Novo Construction of Gadolinium-Binding Domains within Multifunctional Recombinant Proteins for Targeted Delivery of Gadolinium to Tumour Cells

Three artificial proteins that bind the gadolinium ion (Gd3+) with tumour-specific ligands were de novo engineered and tested as candidate drugs for binary radiotherapy (BRT) and contrast agents for magnetic resonance imaging (MRI). Gd3+-binding modules were derived from calmodulin. They were joined with elastin-like polypeptide (ELP) repeats from human elastin to form the four-centre Gd3+-binding domain (4MBS-domain) that further was combined with F3 peptide (a ligand of nucleolin, a tumour marker) to form the F3-W4 block. The F3-W4 block was taken alone (E2-13W4 protein), as two repeats (E1-W8) and as three repeats (E1-W12). Each protein was supplemented with three copies of the RGD motif (a ligand of integrin αvβ3) and green fluorescent protein (GFP). In contrast to Magnevist (a Gd-containing contrast agent), the proteins exhibited three to four times higher accumulation in U87MG glioma and A375 melanoma cell lines than in normal fibroblasts. The proteins remained for >24 h in tumours induced by Ca755 adenocarcinoma in C57BL/6 mice. They exhibited stability towards blood proteases and only accumulated in the liver and kidney. The technological advantages of using the engineered proteins as a basis for developing efficient and non-toxic agents for early diagnosis of tumours by MRI as well as part of BRT were demonstrated.

GEFA: Early Fusion Approach in Drug-Target Affinity Prediction

Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA)problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues. This would lead to inaccurate learning of target representation which may change due to the drug binding effects. In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets. We propose GEFA (Graph Early Fusion Affinity), a novel graph-in-graph neural network with attention mechanism to address the changes in target representation because of the binding effects. Specifically, a drug is modeled as a graph of atoms, which then serves as a node in a larger graph of residues-drug complex. The resulting model is an expressive deep nested graph neural network. We also use pre-trained protein representation powered by the recent effort of learning contextualized protein representation. The experiments are conducted under different settings to evaluate scenarios such as novel drugs or targets. The results demonstrate the effectiveness of the pre-trained protein embedding and the advantages our GEFA in modeling the nested graph for drug-target interaction.

Computational discovery of binding mode of anti-TRBC1 antibody and predicted key amino acids of TRBC1

Peripheral T-cell lymphoma (PTCL) is a type of non-Hodgkin lymphoma that progresses aggressively with poor survival rate. CAR T cell targeting T-cell receptor β-chain constant domains 1 (TRBC1) of malignant T cells has been developed recently by using JOVI.1 monoclonal antibody as a template. However, the mode of JOVI.1 binding is still unknown. This study aimed to investigate the molecular interaction between JOVI.1 antibody and TRBC1 by using computational methods and molecular docking. Therefore, the TRBC protein crystal structures (TRBC1 and TRBC2) as well as the sequences of JOVI.1 CDR were chosen as the starting materials. TRBC1 and TRBC2 epitopes were predicted, and molecular dynamic (MD) simulation was used to visualize the protein dynamic behavior. The structure of JOVI.1 antibody was also generated before the binding mode was predicted using molecular docking with an antibody mode. Epitope prediction suggested that the N3K4 region of TRBC1 may be a key to distinguish TRBC1 from TCBC2. MD simulation showed the major different surface conformation in this area between two TRBCs. The JOVI.1-TRBC1 structures with three binding modes demonstrated JOVI.1 interacted TRBC1 at N3K4 residues, with the predicted dissociation constant (K_d) ranging from 1.5 × 10⁸ to 1.1 × 10¹⁰ M. The analysis demonstrated JOVI.1 needed D1 residues of TRBC1 for the interaction formation to N3K4 in all binding modes. In conclusion, we proposed the three binding modes of the JOVI.1 antibody to TRBC1 with the new key residue (D1) necessary for N3K4 interaction. This data was useful for JOVI.1 redesign to improve the PTCL-targeting CAR T cell.

Mechanism on antigen delivery under mucosal vaccination using cell-penetrating peptides immobilized at multiple points on polymeric platforms

We have developed an aggregate of D-octaarginine immobilized at multiple points on a co-polymer of N-vinylacetamide and acrylic acid. Previous studies revealed that immunoglobulin G and A were induced when mice were inoculated with influenza virus antigens under coadministration with the D-octaarginine-immobilized polymers as a mucosal vaccine adjuvant. Infection experiments demonstrated that mice vaccinated with a mixture of inactivated influenza viruses and the polymers were protected from infection with mouse-adapted infectious viruses. In the present study, we investigated the mechanism on antigen delivery under mucosal vaccination using the polymers. Two-hour retention of fluorescein-labeled ovalbumin (F-OVA) on the nasal mucosa was observed when applied with the polymers; nevertheless F-OVA was eliminated less than 10 min under polymer-free conditions. F-OVA mixed with the polymers was vigorously taken up into murine dendritic cells. Electrophoresis and dynamic light scattering analysis indicated that OVA interacted with the polymers. The uptake of F-OVA was hardly ever inhibited by the addition of an excess amount of intact OVA. The results suggested that viral antigens were accumulated on the mucosa and delivered into dendritic cells under basolateral membranes via dendrites extending to the mucosal surface and/or subsequent to their permeation through epithelial cells, when they were coadministered with D-octaarginine-immobilized polymers.

Targeting and Covalently Immobilizing the EGFR through SNAP-Tag Technology for Screening Drug Leads

Membrane protein immobilization is particularly significant in in vitro drug screening and determining drug-receptor interactions. However, there are still some problems in the immobilization of membrane proteins with controllable direction and high conformational stability, activity, and specificity. Cell membrane chromatography (CMC) retains the complete biological structure of membrane proteins. However, conventional CMC has the limitation of poor stability, which results in its limited life span and low reproducibility. To overcome this limitation, we propose a method for the specific covalent immobilization of membrane proteins in cell membranes. We used the SNAP-tag as an immobilization tag fused to the epidermal growth factor receptor (EGFR), and Cys145 located at the active site of the SNAP-tag reacted with the benzyl group of O⁶-benzylguanine (BG). The SNAP-tagged EGFR was expressed in HEK293 cells. We captured the SNAP-tagged EGFR from the cell membrane suspension onto a BG-derivative-modified silica gel. Our immobilization strategy improved the life span and specificity of CMC and minimized loss of activity and nonspecific attachment of proteins. Next, a SNAP-tagged EGFR/CMC online HPLC-IT-TOF-MS system was established to screen EGFR antagonists from Epimedii folium. Icariin, magnoflorine, epimedin B, and epimedin C were retained in this model, and pharmacological assays revealed that magnoflorine could inhibit cancer cell growth by targeting the EGFR. This EGFR immobilization method may open up possibilities for the immobilization of other membrane proteins and has the potential to serve as a useful platform for screening receptor-binding leads from natural medicinal herbs.

β2-adrenergic receptor affinity chromatography with an interaction force analysis model: A method for analysis of active compounds targeting β2-adrenergic receptor

Asthma is one of the most prevalent diseases worldwide, and β₂-adrenergic receptor (β₂AR) agonists have been reported to be highly effective bronchodilators against this disease. In this study, we successfully constructed a novel CHO-β₂AR affinity chromatography (CHO-β₂AR/AC), which was evaluated by infrared spectroscopic and scanning electron microscope (SEM) analysis. In addition, CHO-β₂AR/AC model exhibited good selectivity and reliability with the relative standard deviation smaller than 5.6% after 30 days. Furthermore, an interaction force analysis model was developed based on CHO-β₂AR/AC. The results showed that the interaction force analysis model (Φ•E•pKa) exhibited a strong correlation with equilibrium dissociation constant (K_D) (r²=0.9284, p=0.002) and a good correlation with logarithm of half-maximum effective concentration (pEC₅₀) values (r²=0.7135, p=0.034). In addition, a pool of clinically approved drugs was screened by this CHO-β₂AR/AC model. Codeine wasfound to bind to and activate β₂AR with K_D value of 4.10 × 10^-7 M, leading to increased cyclic adenosine monophosphate (cAMP) production with EC₅₀ of 6.49 × 10^-7 M and reduction of intracellular Ca²⁺ concentration, which in turn relaxes bronchial contraction with EC₅₀ of 2.62 × 10^-6 M. Furthermore, the K_D value and pEC₅₀ of codeine were within the 95% prediction range of the interaction force analysis model. The results indicate that the CHO-β₂AR/AC with interaction force analysis model constructed in this study can be used to effectively and rapidly screen active compounds targeting β₂AR.

Binding characteristics of staphylococcal protein A and streptococcal protein G for fragment crystallizable portion of human immunoglobulin G

In the wide array of physiological processes, protein-protein interactions and their binding are the most basal activities for achieving adequate biological metabolism. Among the studies on binding proteins, the examination of interactions between immunoglobulin G (IgG) and natural immunoglobulin-binding ligands, such as staphylococcal protein A (spA) and streptococcal protein G (spG), is essential in the development of pharmaceutical science, biotechnology, and affinity chromatography. The widespread utilization of IgG-spA/spG binding characteristics has allowed researchers to investigate these molecular interactions. However, the detailed binding strength of each ligand and the corresponding binding mechanisms have yet to be fully investigated. In this study, the authors analyzed the binding strengths of IgG-spA and IgG-spG complexes and identified the mechanisms enabling these bindings using molecular dynamics simulation, steered molecular dynamics, and advanced Poisson-Boltzmann Solver simulations. Based on the presented data, the binding strength of the spA ligand was found to significantly exceed that of the spG ligand. To find out which non-covalent interactions or amino acid sites have a dominant role in the tight binding of these ligands, further detailed analyses of electrostatic interactions, hydrophobic bonding, and binding free energies have been performed. In investigating their binding affinity, a relatively independent and different unbinding mechanism was found in each ligand. These distinctly different mechanisms were observed to be highly correlated to the protein secondary and tertiary structures of spA and spG ligands, as explicated from the perspective of hydrogen bonding.

Dictamnine is an effective anti-anaphylactoid compound acting via the MrgX2 receptor located on mast cells

Anaphylactoid reactions are potentially fatal allergic diseases caused by mast cells (MCs), which release histamine and lipid mediators under certain stimuli. Therefore, there is an urgent need to develop new drug candidates to treat anaphylactoid reactions. The MrgX2 receptor mediates anaphylactoid reactions that cause inflammatory diseases. Cortex dictamni is a Chinese herb used for treating allergy-related diseases; however, its active compound is still unknown and its mechanism of action has not yet been reported. The aim of this study was to screen the anti-anaphylactoid compound from C. dictamni extracts. An MrgX2/CMC-HPLC method was established for screening MrgX2-specific compounds retained from the alcohol extract of C. dictamni. A mouse model of hindpaw extravasation was used to evaluate the anti-anaphylactoid effect of this ingredient. Intracellular Ca²⁺ mobilization was assessed using a calcium imaging assay. Enzyme immunoassays were performed to measure cytokine and chemokine release levels. The molecular signaling pathways were explored by western blotting. As a result, dictamnine was identified as an effective compound using the MrgX2/CMC method, which remarkably suppressed MC intracellular Ca²⁺ mobilization and the release of de novo degranulated substances, and inhibited PKC-PLCγ-IP3R-associated protein signaling molecules. Hence, dictamnine is a novel therapeutic candidate for anaphylactoid reactions via MrgX2.

Elucidating the Interactions Between Heparin/Heparan Sulfate and SARS-CoV-2-Related Proteins-An Important Strategy for Developing Novel Therapeutics for the COVID-19 Pandemic

Owing to the high mortality and the spread rate, the infectious disease caused by SARS-CoV-2 has become a major threat to public health and social economy, leading to over 70 million infections and 1. 6 million deaths to date. Since there are currently no effective therapeutic or widely available vaccines, it is of urgent need to look for new strategies for the treatment of SARS-CoV-2 infection diseases. Binding of a viral protein onto cell surface heparan sulfate (HS) is generally the first step in a cascade of interaction that is required for viral entry and the initiation of infection. Meanwhile, interactions of selectins and cytokines (e.g., IL-6 and TNF-α) with HS expressed on endothelial cells are crucial in controlling the recruitment of immune cells during inflammation. Thus, structurally defined heparin/HS and their mimetics might serve as potential drugs by competing with cell surface HS for the prevention of viral adhesion and modulation of inflammatory reaction. In this review, we will elaborate coronavirus invasion mechanisms and summarize the latest advances in HS-protein interactions, especially proteins relevant to the process of coronavirus infection and subsequent inflammation. Experimental and computational techniques involved will be emphasized.

Advances in cell membrane chromatography

Cell membrane chromatography (CMC) is a biomimetic chromatographic method based on the ability of membrane receptors to selectively interact with their ligands in vivo. Using membrane receptors as a stationary phase, the CMC method helps in determining the binding characteristics between ligands and membrane receptors and in efficiently identifying specific target components in a complex sample that produce the cellular biological effects of ligands (drugs, antibodies, enzymes, cytokines, etc.). CMC is an analytical tool for revealing characteristics of ligand-receptor interactions, screening and discovering target substances, and accurately controlling the quality of drugs. Since establishment of CMC in the early 1990s, with the rapid development of cell biology, significant progress has been made in the development of high-expression receptors, engineered cell cultures, and standardized preparations, which allowed in vitro immobilization of cell membrane receptors and miniaturization of binding assays. A variety of CMC models have been established using different membrane receptors as a stationary phase, and many new methods have been developed by combining CMC with high-performance liquid chromatography (HPLC)/mass spectrometry or HPLC-IT-TOF technologies. CMC methods have been widely used to study drug-receptor interactions and to screen complex samples for effective or harmful components.

Characterization of interactions between local anesthetics and histamine H1 receptor by cell membrane chromatography model

Local anesthetic has a wide application in clinical practice. However, angioedema, an adverse reaction caused by local anesthetics, has been reported to be related to histamine H₁ receptor (H₁R). Hence, an effective and practical method for investigating the interaction characteristics between local anesthetics and H₁R is needed. In this work, the competition binding assay and the relative standard method based on H₁R-HEK293/cell membrane chromatography (CMC) were developed to analyze the equilibrium dissociation constant (K_D) values of local anesthetics with H₁R. The activity of drugs toward H₁R was evaluated by intracellular Ca²⁺ imaging assay. Molecular docking was used to verify the interaction modes that occurred at the activate pocket of H₁R protein. Results showed that the local anesthetics can directly occupy histamine binding sites on H₁R, and the K_D values obtained from different CMC methods exhibited positive correlations with each other (p < 0.01). The K_D values of tetracaine, procaine, and lidocaine were much closer to that of histamine than bupivacaine and ropivacaine. This was not only in line with the Ca²⁺ responses in activating H₁R, but also consistent with the same amino acid residues shared with histamine in the H₁R active site. In conclusion, this study provided new insight into the interactions between local anesthetics and H₁R. The H₁R-HEK293/CMC methods developed in this study could be used to evaluate the interaction characteristics of those compounds acting on H₁R.

Recent progress in the molecular imaging of therapeutic monoclonal antibodies

Therapeutic monoclonal antibodies have become one of the central components of the healthcare system and continuous efforts are made to bring innovative antibody therapeutics to patients in need. It is equally critical to acquire sufficient knowledge of their molecular structure and biological functions to ensure the efficacy and safety by incorporating new detection approaches since new challenges like individual differences and resistance are presented. Conventional techniques for determining antibody disposition including plasma drug concentration measurements using LC-MS or ELISA, and tissue distribution using immunohistochemistry and immunofluorescence are now complemented with molecular imaging modalities like positron emission tomography and near-infrared fluorescence imaging to obtain more dynamic information, while methods for characterization of antibody's interaction with the target antigen as well as visualization of its cellular and intercellular behavior are still under development. Recent progress in detecting therapeutic antibodies, in particular, the development of methods suitable for illustrating the molecular dynamics, is described here.

Quantifying Protein-Protein Interactions by Molecular Counting with Mass Photometry

Interactions between biomolecules control the processes of life in health and their malfunction in disease, making their characterization and quantification essential. Immobilization- and label-free analytical techniques are desirable because of their simplicity and minimal invasiveness, but they struggle with quantifying tight interactions. Here, we show that mass photometry can accurately count, distinguish by molecular mass, and thereby reveal the relative abundances of different unlabelled biomolecules and their complexes in mixtures at the single-molecule level. These measurements determine binding affinities over four orders of magnitude at equilibrium for both simple and complex stoichiometries within minutes, as well as the associated kinetics. These results introduce mass photometry as a rapid, simple and label-free method for studying sub-micromolar binding affinities, with potential for extension towards a universal approach for characterizing complex biomolecular interactions.

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. These computational approaches aim to narrow down the search space for novel DTIs and shed light on drug functioning context. Most methods developed to date use binary classification to predict if the interaction between a drug and its target exists or not. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If that strength is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities (DTBA) are of great value. In this study, we provide a comprehensive overview of the existing methods that predict DTBA. We focus on the methods developed using artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches, as well as related benchmark datasets and databases. Furthermore, guidance and recommendations are provided that cover the gaps and directions of the upcoming work in this research area. To the best of our knowledge, this is the first comprehensive comparison analysis of tools focused on DTBA with reference to AI/ML/DL.

How Docking Programs Work

Protein-ligand docking simulations are of central interest for computer-aided drug design. Docking is also of pivotal importance to understand the structural basis for protein-ligand binding affinity. In the last decades, we have seen an explosion in the number of three-dimensional structures of protein-ligand complexes available at the Protein Data Bank. These structures gave further support for the development and validation of in silico approaches to address the binding of small molecules to proteins. As a result, we have now dozens of open source programs and web servers to carry out molecular docking simulations. The development of the docking programs and the success of such simulations called the attention of a broad spectrum of researchers not necessarily familiar with computer simulations. In this scenario, it is essential for those involved in experimental studies of protein-ligand interactions and biophysical techniques to have a glimpse of the basics of the protein-ligand docking simulations. Applications of protein-ligand docking simulations to drug development and discovery were able to identify hits, inhibitors, and even drugs. In the present chapter, we cover the fundamental ideas behind protein-ligand docking programs for non-specialists, which may benefit from such knowledge when studying molecular recognition mechanism.

Metal Chelation Therapy and Parkinson's Disease: A Critical Review on the Thermodynamics of Complex Formation between Relevant Metal Ions and Promising or Established Drugs

The present review reports a list of approximately 800 compounds which have been used, tested or proposed for Parkinson’s disease (PD) therapy in the year range 2014–2019 (April): name(s), chemical structure and references are given. Among these compounds, approximately 250 have possible or established metal-chelating properties towards Cu(II), Cu(I), Fe(III), Fe(II), Mn(II), and Zn(II), which are considered to be involved in metal dyshomeostasis during PD. Speciation information regarding the complexes formed by these ions and the 250 compounds has been collected or, if not experimentally available, has been estimated from similar molecules. Stoichiometries and stability constants of the complexes have been reported; values of the cologarithm of the concentration of free metal ion at equilibrium (pM), and of the dissociation constant K_d (both computed at pH = 7.4 and at total metal and ligand concentrations of 10⁻⁶ and 10⁻⁵ mol/L, respectively), charge and stoichiometry of the most abundant metal–ligand complexes existing at physiological conditions, have been obtained. A rigorous definition of the reported amounts is given, the possible usefulness of this data is described, and the need to characterize the metal–ligand speciation of PD drugs is underlined.

Advanced FRET normalization allows quantitative analysis of protein interactions including stoichiometries and relative affinities in living cells

FRET (Fluorescence Resonance Energy Transfer) measurements are commonly applied to proof protein-protein interactions. However, standard methods of live cell FRET microscopy and signal normalization only allow a principle assessment of mutual binding and are unable to deduce quantitative information of the interaction. We present an evaluation and normalization procedure for 3-filter FRET measurements, which reflects the process of complex formation by plotting FRET-saturation curves. The advantage of this approach relative to traditional signal normalizations is demonstrated by mathematical simulations. Thereby, we also identify the contribution of critical parameters such as the total amount of donor and acceptor molecules and their molar ratio. When combined with a fitting procedure, this normalization facilitates the extraction of key properties of protein complexes such as the interaction stoichiometry or the apparent affinity of the binding partners. Finally, the feasibility of our method is verified by investigating three exemplary protein complexes. Altogether, our approach offers a novel method for a quantitative analysis of protein interactions by 3-filter FRET microscopy, as well as flow cytometry. To facilitate the application of this method, we created macros and routines for the programs ImageJ, R and MS-Excel, which we make publicly available.