Label-free drug interaction screening via Raman microscopy.

Development of a simple, label-free screening technique capable of precisely and directly sensing interaction-in-solution over a size range from small molecules to large proteins such as antibodies could offer an important tool for researchers and pharmaceutical companies in the field of drug development. In this work, we present a thermostable Raman interaction profiling (TRIP) technique that facilitates low-concentration and low-dose screening of binding between protein and ligand in physiologically relevant conditions. TRIP was applied to eight protein-ligand systems, and produced reproducible high-resolution Raman measurements, which were analyzed by principal component analysis. TRIP was able to resolve time-depending binding between 2,4-dinitrophenol and transthyretin, and analyze biologically relevant SARS-CoV-2 spike-antibody interactions. Mixtures of the spike receptor-binding domain with neutralizing, nonbinding, or binding but nonneutralizing antibodies revealed distinct and reproducible Raman signals. TRIP holds promise for the future developments of high-throughput drug screening and real-time binding measurements between protein and drug.

Dockey: a modern integrated tool for large-scale molecular docking and virtual screening.

Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.

SAnDReS 2.0: Development of machine-learning models to explore the scoring function space.

Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit-Learn to calculate binding affinity based on protein-ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine-learning models based on crystal, docked, and AlphaFold-generated structures. As a proof of concept, we examine the performance of SAnDReS-generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS-generated models showed predictive performance close to or better than other machine-learning models such as KDEEP, CSM-lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.

Detecting Protein-Ligand Interactions with Nitroxide Based Paramagnetic Cosolutes.

NMR spectroscopy techniques can provide important information about protein-ligand interactions. Here we tested an NMR approach which relies on the measurement of paramagnetic relaxation enhancements (PREs) arising from analogous cationic, anionic or neutral soluble nitroxide molecules, which distribute around the protein-ligand complex depending on near-surface electrostatic potentials. We applied this approach to two protein-ligand systems, interleukin-8 interacting with highly charged glycosaminoglycans and the SH2 domain of Grb2 interacting with less charged phospho-tyrosine tripeptides. The electrostatic potential around interleukin-8 and its changes upon binding of glycosaminoglycans could be derived from the PRE data and confirmed by theoretical predictions from Poisson-Boltzmann calculations. The ligand influence on the PREs and NMR-derived electrostatic potentials of Grb2 SH2 was localized to a narrow protein region which allowed the localization of the peptide binding pocket. Our analysis suggests that experiments with nitroxide cosolutes can be useful for investigating protein-ligand electrostatic interactions and mapping ligand binding sites.

Exploring the Impact of Protein Chain Selection in Binding Energy Calculations with DFT.

Calculation of binding free energies between a protein and a ligand are highly desired for computer-aided drug design. Here we approximate the binding energies of ABL1, an enzyme which is the target for drugs used in the treatment of chronic myeloid leukaemia, with minimal models and density functional theory (DFT). Starting from the crystal structures of protein-drug complexes, we estimated the binding free energies having used all available individual molecules (protein chains) within each structure, not only a single one as commonly used, in order to see if the choice of the protein chain is important in such calculations. Differences were observed between chains in the same file. Energy decomposition analysis (EDA) revealed that the most important factors for binding were exchange, repulsion and electrostatics. The desolvation term varied dramatically between the inhibitors (between 4.2 and 92.3 kcal/mol). All functionals showed similar patterns in the EDA and in discriminating between the ligands. Non-covalent interactions (NCI) analysis was used to further explain the differences between protein chains and functionals. Overall, it is shown that small minimal models of a drug binding site can be useful to infer on the suitability of an initial crystal structure for further analysis such as EDA.

The Molecular Mechanisms of Cuproptosis and Small-Molecule Drug Design in Diabetes Mellitus.

In the field of human health research, the homeostasis of copper (Cu) is receiving increased attention due to its connection to pathological conditions, including diabetes mellitus (DM). Recent studies have demonstrated that proteins associated with Cu homeostasis, such as ATOX1, FDX1, ATP7A, ATPB, SLC31A1, p53, and UPS, also contribute to DM. Cuproptosis, characterized by Cu homeostasis dysregulation and Cu overload, has been found to cause the oligomerization of lipoylated proteins in mitochondria, loss of iron-sulfur protein, depletion of glutathione, production of reactive oxygen species, and cell death. Further research into how cuproptosis affects DM is essential to uncover its mechanism of action and identify effective interventions. In this article, we review the molecular mechanism of Cu homeostasis and the role of cuproptosis in the pathogenesis of DM. The study of small-molecule drugs that affect these proteins offers the possibility of moving from symptomatic treatment to treating the underlying causes of DM.

Molecular simulation for food protein-ligand interactions: A comprehensive review on principles, current applications, and emerging trends.

In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein-ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein-ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein-ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme-substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein-ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein-ligand interaction simulation. Overall, the use of molecular simulation to study food protein-ligand interactions has a promising prospect.

New Binding Sites, New Opportunities for GPCR Drug Discovery.

Many central biological events rely on protein-ligand interactions. The identification and characterization of protein-binding sites for ligands are crucial for the understanding of functions of both endogenous ligands and synthetic drug molecules. G protein-coupled receptors (GPCRs) typically detect extracellular signal molecules on the cell surface and transfer these chemical signals across the membrane, inducing downstream cellular responses via G proteins or ß-arrestin. GPCRs mediate many central physiological processes, making them important targets for modern drug discovery. Here, we focus on the most recent breakthroughs in finding new binding sites and binding modes of GPCRs and their potentials for the development of new medicines.

Template-guided method for protein-ligand complex structure prediction: Application to CASP15 protein-ligand studies.

Critical Assessment of Structure Prediction 15 (CASP15) added a new category of ligand prediction to promote the development of protein/RNA-ligand modeling methods, which have become important tools in modern drug discovery. A total of 22 targets were released, including 18 protein-ligand targets and 4 RNA-ligand targets. We applied our recently developed template-guided method to the protein-ligand complex structure predictions. The method combined a physicochemical, molecular docking method, and a bioinformatics-based ligand similarity method. The Protein Data Bank was scanned for template structures containing the target protein, homologous proteins, or proteins sharing a similar fold with the target protein. The binding modes of the co-bound ligands in the template structures were used to guide the complex structure prediction for the target. The CASP assessment results show that the overall performance of our method was ranked second when the top predicted model was considered for each target. Here, we analyzed our predictions in detail, and discussed the challenges including protein conformational changes, large and flexible ligands, and multiple diverse ligands in a binding pocket.

Evolutionarily conserved heat shock protein, HtpX, as an adjunct target against antibiotic-resistant Neisseria gonorrhoeae.

The emergence of multiple drug resistance and extreme drug resistance pathogens necessitates the continuous evaluation of the pathogenic genome to identify conserved molecular targets and their respective inhibitors. In this study, we mapped the global mutational landscape of Neisseria gonorrhoeae (an intracellular pathogen notoriously known to cause the sexually transmitted disease gonorrhoea). We identified highly variable amino acid positions in the antibiotic target genes like the penA, ponA, 23s rRNA, rpoB, gyrA, parC, mtrR and porB. Some variations are directly reported to confer resistance to the currently used front-line drugs like ceftriaxone, cefixime, azithromycin and ciprofloxacin. Further, by whole genome comparison and Shannon entropy analysis, we identified a completely conserved protein HtpX in the drug-resistant as well as susceptible isolates of N. gonorrhoeae (NgHtpX). Comparison with the only available information of Escherichia coli HtpX suggested it to be a transmembrane metalloprotease having a role in stress response. The critical zinc-binding residue of NgHtpX was mapped to E141. By applying composite high throughput screening followed by MD simulations, we identified pemirolast and thalidomide as high-energy binding ligands of NgHtpX. Following cloning and expression of the purified metal-binding domain of NgHtpX (NgHtpXd), its Zn2+ -binding (Kd = 0.4 µM) and drug-binding (pemirolast, Kd = 3.47 µM; and thalidomide, Kd = 1.04 µM) potentials were determined using in-vitro fluorescence quenching experiment. When tested on N. gonorrhoeae cultures, both the ligands imposed a dose-dependent reduction in viability. Overall, our results provide high entropy positions in the targets of presently used antibiotics, which can be further explored to understand the AMR mechanism. Additionally, HtpX and its specific inhibitors identified can be utilised effectively in managing gonococcal infections.

Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery.

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein-ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning-based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true-positive rate to false-positive rate. This work indicates that advances in modeling protein-ligand interactions, particularly using machine learning-based approaches, are needed to better harness AlphaFold2 for drug discovery.

Using Machine Learning and Molecular Docking to Leverage Urease Inhibition Data for Virtual Screening.

Urease is a metalloenzyme that catalyzes the hydrolysis of urea, and its modulation has an important role in both the agricultural and medical industry. Even though numerous molecules have been tested against ureases of different species, their clinical translation has been limited due to chemical and metabolic stability as well as side effects. Therefore, screening new compounds against urease would be of interest in part due to rising concerns regarding antibiotic resistance. In this work, we collected and curated a diverse set of 2640 publicly available small-molecule inhibitors of jack bean urease and developed a classifier using a random forest machine learning method with high predictive performance. In addition, the physicochemical features of compounds were paired with molecular docking and protein-ligand fingerprint analysis to gather insight into the current activity landscape. We observed that the docking score could not differentiate active from inactive compounds within each chemical family, but scores were correlated with compound activity when all compounds were considered. Additionally, a decision tree model was built based on 2D and 3D Morgan fingerprints to mine patterns of the known active-class compounds. The final machine learning model showed good prediction performance against the test set (81% and 77% precision for active and inactive compounds, respectively). Finally, this model was employed, as a proof-of-concept, on an in-house library to predict new hits that were then tested against urease and found to be active. This is, to date, the largest, most diverse dataset of compounds used to develop predictive in silico models. Overall, the results highlight the usefulness of using machine learning classifiers and molecular docking to predict novel urease inhibitors.

MD-Ligand-Receptor: A High-Performance Computing Tool for Characterizing Ligand-Receptor Binding Interactions in Molecular Dynamics Trajectories.

Molecular dynamics simulation is a widely employed computational technique for studying the dynamic behavior of molecular systems over time. By simulating macromolecular biological systems consisting of a drug, a receptor and a solvated environment with thousands of water molecules, MD allows for realistic ligand-receptor binding interactions (lrbi) to be studied. In this study, we present MD-ligand-receptor (MDLR), a state-of-the-art software designed to explore the intricate interactions between ligands and receptors over time using molecular dynamics trajectories. Unlike traditional static analysis tools, MDLR goes beyond simply taking a snapshot of ligand-receptor binding interactions (lrbi), uncovering long-lasting molecular interactions and predicting the time-dependent inhibitory activity of specific drugs. With MDLR, researchers can gain insights into the dynamic behavior of complex ligand-receptor systems. Our pipeline is optimized for high-performance computing, capable of efficiently processing vast molecular dynamics trajectories on multicore Linux servers or even multinode HPC clusters. In the latter case, MDLR allows the user to analyze large trajectories in a very short time. To facilitate the exploration and visualization of lrbi, we provide an intuitive Python notebook (Jupyter), which allows users to examine and interpret the results through various graphical representations.

Three Decades of REDOR in Protein Science: A Solid-State NMR Technique for Distance Measurement and Spectral Editing.

Solid-state NMR (ss-NMR) is a powerful tool to investigate noncrystallizable, poorly soluble molecular systems, such as membrane proteins, amyloids, and cell walls, in environments that closely resemble their physical sites of action. Rotational-echo double resonance (REDOR) is an ss-NMR methodology, which by reintroducing heteronuclear dipolar coupling under magic angle spinning conditions provides intramolecular and intermolecular distance restraints at the atomic level. In addition, REDOR can be exploited as a selection tool to filter spectra based on dipolar couplings. Used extensively as a spectroscopic ruler between isolated spins in site-specifically labeled systems and more recently as a building block in multidimensional ss-NMR pulse sequences allowing the simultaneous measurement of multiple distances, REDOR yields atomic-scale information on the structure and interaction of proteins. By extending REDOR to the determination of 1H-X dipolar couplings in recent years, the limit of measurable distances has reached ~15-20 Å, making it an attractive method of choice for the study of complex biomolecular assemblies. Following a methodological introduction including the most recent implementations, examples are discussed to illustrate the versatility of REDOR in the study of biological systems.

Identification of and Mechanistic Insights into SARS-CoV-2 Main Protease Non-Covalent Inhibitors: An In-Silico Study.

The indispensable role of the SARS-CoV-2 main protease (Mpro) in the viral replication cycle and its dissimilarity to human proteases make Mpro a promising drug target. In order to identify the non-covalent Mpro inhibitors, we performed a comprehensive study using a combined computational strategy. We first screened the ZINC purchasable compound database using the pharmacophore model generated from the reference crystal structure of Mpro complexed with the inhibitor ML188. The hit compounds were then filtered by molecular docking and predicted parameters of drug-likeness and pharmacokinetics. The final molecular dynamics (MD) simulations identified three effective candidate inhibitors (ECIs) capable of maintaining binding within the substrate-binding cavity of Mpro. We further performed comparative analyses of the reference and effective complexes in terms of dynamics, thermodynamics, binding free energy (BFE), and interaction energies and modes. The results reveal that, when compared to the inter-molecular electrostatic forces/interactions, the inter-molecular van der Waals (vdW) forces/interactions are far more important in maintaining the association and determining the high affinity. Given the un-favorable effects of the inter-molecular electrostatic interactions-association destabilization by the competitive hydrogen bond (HB) interactions and the reduced binding affinity arising from the un-compensable increase in the electrostatic desolvation penalty-we suggest that enhancing the inter-molecular vdW interactions while avoiding introducing the deeply buried HBs may be a promising strategy in future inhibitor optimization.

Molecular Design of Magnetic Resonance Imaging Agents Binding to Amyloid Deposits.

The ability to detect and monitor amyloid deposition in the brain using non-invasive imaging techniques provides valuable insights into the early diagnosis and progression of Alzheimer's disease and helps to evaluate the efficacy of potential treatments. Magnetic resonance imaging (MRI) is a widely available technique offering high-spatial-resolution imaging. It can be used to visualize amyloid deposits with the help of amyloid-binding diagnostic agents injected into the body. In recent years, a number of amyloid-targeted MRI probes have been developed, but none of them has entered clinical practice. We review the advances in the field and deduce the requirements for the molecular structure and properties of a diagnostic probe candidate. These requirements make up the base for the rational design of MRI-active small molecules targeting amyloid deposits. Particular attention is paid to the novel cryo-EM structures of the fibril aggregates and their complexes, with known binders offering the possibility to use computational structure-based design methods. With continued research and development, MRI probes may revolutionize the diagnosis and treatment of neurodegenerative diseases, ultimately improving the lives of millions of people worldwide.

The Pfizer Crystal Structure Database: An essential tool for structure-based design at Pfizer.

Pfizer's Crystal Structure Database (CSDB) is a key enabling technology that allows scientists on structure-based projects rapid access to Pfizer's vast library of in-house crystal structures, as well as a significant number of structures imported from the Protein Data Bank. In addition to capturing basic information such as the asymmetric unit coordinates, reflection data, and the like, CSDB employs a variety of automated methods to first ensure a standard level of annotations and error checking, and then to add significant value for design teams by processing the structures through a sequence of algorithms that prepares the structures for use in modeling. The structures are made available, both as the original asymmetric unit as submitted, as well as the final prepared structures, through REST-based web services that are consumed by several client desktop applications. The structures can be searched by keyword, sequence, submission date, ligand substructure and similarity search, and other common queries.

Rationalizing the binding and α subtype selectivity of synthesized imidazodiazepines and benzodiazepines at GABAA receptors by using molecular docking studies.

The pharmacological actions exerted by benzodiazepines are dependent on the discrete α protein subunits of the γ-aminobutyric acid type A receptor (GABAA R). Recent developments via a cryo-EM structure of the α1ß3γ2L GABAA R ion channel provide crucial insights into ligand efficacy and binding affinity at this subtype. We investigated the molecular interactions of diazepam and alprazolam bound GABAA R structures (6HUP and 6HUO) to determine key binding interaction domains. A halogen bond between the chlorine atoms of diazepam and alprazolam with the group on the backbone of the α1 histidine amino acid 102 is important to the positive allosteric modulatory actions of diazepam and alprazolam in the α1ß3γ2L GABAA R ion channel. In order to gain insight into α subtype selectivity we designed and synthesized close structural analogs of diazepam and alprazolam. These compounds were then docked into the recently publish cryo-EM structures of GABAA Rs (6HUP and 6HUO). This modeling along with radio-ligand binding data resulted in the conclusion that the non-classical bioisosteric replacement of the chlorine atom at C7 with an ethinyl group (compound 5) resulted in an 11-fold gain in α5 binding selectivity over the α1 subtype. Moreover, the potency of compound 5 resulted in a ligand with less sedation than diazepam, while still maintaining the same anxiolytic potency. These modeling data extend our understanding of the structural requirements for α-subtype-selective compounds that can be utilized to achieve improved medical treatments. It is clear that the ethinyl group in place of a halogen atom decreases the affinity and efficacy of benzodiazepines and imidazodiazepines at α1 subtypes, which results in less sedation and ataxia.

A structure-based mechanism of cisplatin resistance mediated by glutathione transferase P1-1.

Cisplatin [cis-diamminedichloroplatinum(II) (cis-DDP)] is one of the most successful anticancer agents effective against a wide range of solid tumors. However, its use is restricted by side effects and/or by intrinsic or acquired drug resistance. Here, we probed the role of glutathione transferase (GST) P1-1, an antiapoptotic protein often overexpressed in drug-resistant tumors, as a cis-DDP-binding protein. Our results show that cis-DDP is not a substrate for the glutathione (GSH) transferase activity of GST P1-1. Instead, GST P1-1 sequesters and inactivates cisplatin with the aid of 2 solvent-accessible cysteines, resulting in protein subunits cross-linking, while maintaining its GSH-conjugation activity. Furthermore, it is well known that GST P1-1 binding to the c-Jun N-terminal kinase (JNK) inhibits JNK phosphorylation, which is required for downstream apoptosis signaling. Thus, in turn, GST P1-1 overexpression and Pt-induced subunit cross-linking could modulate JNK apoptotic signaling, further confirming the role of GST P1-1 as an antiapoptotic protein.

Identification of NRAS Diagnostic Biomarkers and Drug Targets for Endometrial Cancer-An Integrated in Silico Approach.

The diagnosis of endometrial cancer involves sequential, invasive tests to assess the thickness of the endometrium by a transvaginal ultrasound scan. In 6−33% of cases, endometrial biopsy results in inadequate tissue for a conclusive pathological diagnosis and 6% of postmenopausal women with non-diagnostic specimens are later discovered to have severe endometrial lesions. Thus, identifying diagnostic biomarkers could offer a non-invasive diagnosis for community or home-based triage of symptomatic or asymptomatic women. Herein, this study identified high-risk pathogenic nsSNPs in the NRAS gene. The nsSNPs of NRAS were retrieved from the NCBI database. PROVEAN, SIFT, PolyPhen-2, SNPs&GO, PhD-SNP and PANTHER were used to predict the pathogenicity of the nsSNPs. Eleven nsSNPs were identified as "damaging", and further stability analysis using I-Mutant 2.0 and MutPred 2 indicated eight nsSNPs to cause decreased stability (DDG scores < −0.5). Post-translational modification and protein−protein interactions (PPI) analysis showed putative phosphorylation sites. The PPI network indicated a GFR-MAPK signalling pathway with higher node degrees that were further evaluated for drug targets. The P34L, G12C and Y64D showed significantly lower binding affinity towards GTP than wild-type. Furthermore, the Kaplan−Meier bioinformatics analyses indicated that the NRAS gene deregulation affected the overall survival rate of patients with endometrial cancer, leading to prognostic significance. Findings from this could be considered novel diagnostic and therapeutic markers.