Molecular mechanism of GIRK2 channel gating modulated by cholesteryl hemisuccinate.

Cholesterol, an essential lipid of cell membranes, regulates G protein-gated inwardly rectifying potassium (GIRK) channel activity. Previous studies have shown that cholesterol activates GIRK2 homotetrameric channels, which are expressed in dopaminergic neurons of the brain. Deletion of GIRK2 channels affects both GIRK2 homo- and heterotetrames and can lead to abnormal neuronal excitability, including conditions such as epilepsy and addiction. A 3.5 Å cryo-EM structure of GIRK2 in complex with CHS (cholesteryl hemisuccinate) and PIP2 (phosphatidylinositol 4,5-bisphosphate) has been solved. This structure provides the opportunity to study GIRK2 channel gating dynamics regulated by cholesterol using gating molecular dynamics (GMD) simulations. In the present study, we conducted microsecond-long GMD simulations on the GIRK2 channel in its APO, PIP2, and PIP2/CHS bound states, followed by systematic analysis to gain molecular insights into how CHS modulates GIRK2 channel gating. We found that CHS binding facilitates GIRK2 channel opening, with 43 K+ ion permeation events observed, compared to 0 and 2 K+ ion permeation events for GIRK2-APO and GIRK2/PIP2, respectively. Binding of CHS to the GIRK2 channel enhances PIP2 and channel interactions, which is consistent with previous experimental results. The negatively charged PIP2 alters the internal electrostatic potential field in the channel and lowers the negative free energy barrier for K+ ion permeation.

Exploring the Impact of Subtle Differences in the Chemical Structure of 1-Alkylsulfates and 1-Alkylsulfonates on Their Interactions with Human Serum Albumin.

The objective of this study was to examine the interactions between anionic surfactants, specifically 1-alkylsulfonates (KXS) and 1-alkylsulfates (SXS) ions, with human serum albumin (HSA). A combination of experimental techniques, including isothermal titration calorimetry (ITC), steady-state fluorescence spectroscopy (SF), and molecular dynamics-based approaches was employed to gain a comprehensive understanding of these processes. It has been demonstrated that the subtle variations in the charge distribution on the anionic surfactant headgroups have a significant impact on the number of binding sites, the stoichiometry of the resulting complexes, and the strength of the interactions between the surfactants and the protein. Additionally, we established that the affinity of the investigated ligands to specific regions on the protein surface is governed by both the charge of the surfactant headgroup and the length of the aliphatic hydrocarbon chain. In summary, the findings highlight the crucial role of charge distribution on surfactant functional groups in the binding mode and the thermodynamic stability of surfactant-protein complexes.

PharmRL: Pharmacophore elucidation with Deep Geometric Reinforcement Learning.

Molecular interactions between proteins and their ligands are important for drug design. A pharmacophore consists of favorable molecular interactions in a protein binding site and can be utilized for virtual screening. Pharmacophores are easiest to identify from co-crystal structures of a bound protein-ligand complex. In this work, however, we develop a deep learning method that can identify pharmacophores in the absence of a ligand. Specifically, we train a CNN model to identify potential favorable interactions in the the binding site, and develop a deep geometric Q-learning algorithm that attempts to select an optimal subset of these interaction points to form a pharmacophore. With this algorithm, we show better prospective virtual screening performance, in terms of F1 scores, on the DUD-E dataset than random selection of ligand identified features from co-crystal structures. We also conduct experiments on the LIT-PCBA dataset and show that it provides efficient solutions for identifying active molecules. Finally, we test our method by screening the COVID moonshot dataset and show that it would be effective in identifying prospective lead molecules even in the absence of fragment screening experiments. Alongside, we provide a Google Colab notebook for ease of use of the developed method.

Multiresolution molecular dynamics simulations reveal the interplay between conformational variability and functional interactions in membrane-bound cytochrome P450 2B4.

Cytochrome P450 2B4 (CYP 2B4) is one of the best-characterized CYPs and serves as a key model system for understanding the mechanisms of microsomal class II CYPs, which metabolize most known drugs. The highly flexible nature of CYP 2B4 is apparent from crystal structures that show the active site with either a wide open or a closed heme binding cavity. Here, we investigated the conformational ensemble of the full-length CYP 2B4 in a phospholipid bilayer, using multiresolution molecular dynamics (MD) simulations. Coarse-grained MD simulations revealed two predominant orientations of CYP 2B4's globular domain with respect to the bilayer. Their refinement by atomistic resolution MD showed adaptation of the enzyme's interaction with the lipid bilayer, leading to open configurations that facilitate ligand access to the heme binding cavity. CAVER analysis of enzyme tunnels, AquaDuct analysis of water routes, and Random Acceleration Molecular Dynamics simulations of ligand dissociation support the conformation-dependent passage of molecules between the active site and the protein surroundings. Furthermore, simulation of the re-entry of the inhibitor bifonazole into the open conformation of CYP 2B4 resulted in binding at a transient hydrophobic pocket within the active site cavity that may play a role in substrate binding or allosteric regulation. Together, these results show how the open conformation of CYP 2B4 facilitates the binding of substrates from and release of products to the membrane, whereas the closed conformation prolongs the residence time of substrates or inhibitors and selectively allows the passage of smaller reactants via the solvent and water channels.

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.

Inhibitors of SARS-CoV-2 Main Protease (Mpro) as Anti-Coronavirus Agents.

The main protease (Mpro) of SARS-CoV-2 is an essential enzyme that plays a critical part in the virus's life cycle, making it a significant target for developing antiviral drugs. The inhibition of SARS-CoV-2 Mpro has emerged as a promising approach for developing therapeutic agents to treat COVID-19. This review explores the structure of the Mpro protein and analyzes the progress made in understanding protein-ligand interactions of Mpro inhibitors. It focuses on binding kinetics, origin, and the chemical structure of these inhibitors. The review provides an in-depth analysis of recent clinical trials involving covalent and non-covalent inhibitors and emerging dual inhibitors targeting SARS-CoV-2 Mpro. By integrating findings from the literature and ongoing clinical trials, this review captures the current state of research into Mpro inhibitors, offering a comprehensive understanding of challenges and directions in their future development as anti-coronavirus agents. This information provides new insights and inspiration for medicinal chemists, paving the way for developing more effective Mpro inhibitors as novel COVID-19 therapies.

MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions.

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

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.

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.

Ligandability at the membrane interface of GPx4 revealed through a reverse micelle fragment screening platform.

While they account for a large portion of drug targets, membrane proteins (MPs) present a unique challenge for drug discovery. Peripheral membrane proteins (PMPs), a class of proteins that bind reversibly to membranes, are also difficult targets, particularly those that function only while bound to membranes. The protein-membrane interface in PMPs is often where functional interactions and catalysis occur, making it a logical target for inhibition. However, interfaces are underexplored spaces in inhibitor design and there is a need for enhanced methods for small-molecule ligand discovery. In an effort to better initiate drug discovery efforts for PMPs, this study presents a screening methodology using membrane-mimicking reverse micelles (mmRM) and NMR-based fragment screening to assess ligandability in the protein-membrane interface. The proof-of-principle target, glutathione peroxidase 4 (GPx4), is a lipid hydroperoxidase which is essential for the oxidative protection of membranes and thereby the prevention of ferroptosis. GPx4 inhibition is promising for therapy-resistant cancer therapy, but current inhibitors are generally covalent ligands with limited clinical utility. Presented here is the discovery of non-covalent small-molecule ligands for membrane-bound GPx4 revealed through the mmRM fragment screening methodology. The fragments were tested against GPx4 in bulk aqueous conditions and displayed little to no binding to the protein without embedment into the membrane. The 9 hits had varying affinities and partitioning coefficients and revealed properties of fragments that bind within the protein-membrane interface. Additionally, a secondary screen confirmed the potential to progress the fragments by enhancing the affinity from > 200 µM to ~15 µM with the addition of certain hydrophobic groups. This study presents an advancement of screening capabilities for membrane associated proteins, reveals ligandability within the GPx4 protein-membrane interface, and may serve as a starting point for developing non-covalent inhibitors of GPx4.

Discovery of GluN2A subtype-selective N-methyl-d-aspartate (NMDA) receptor ligands.

The N-methyl-d-aspartate (NMDA) receptors, which belong to the ionotropic Glutamate receptors, constitute a family of ligand-gated ion channels. Within the various subtypes of NMDA receptors, the GluN1/2A subtype plays a significant role in central nervous system (CNS) disorders. The present article aims to provide a comprehensive review of ligands targeting GluN2A-containing NMDA receptors, encompassing negative allosteric modulators (NAMs), positive allosteric modulators (PAMs) and competitive antagonists. Moreover, the ligands' structure-activity relationships (SARs) and the binding models of representative ligands are also discussed, providing valuable insights for the clinical rational design of effective drugs targeting CNS diseases.

Enhancing plant-based cheese formulation through molecular docking and dynamic simulation of tocopherol and retinol complexes with zein, soy and almond proteins via SVM-machine learning integration.

The current study addresses the growing demand for sustainable plant-based cheese alternatives by employing molecular docking and deep learning algorithms to optimize protein-ligand interactions. Focusing on key proteins (zein, soy, and almond protein) along with tocopherol and retinol, the goal was to improve texture, nutritional value, and flavor characteristics via dynamic simulations. The findings demonstrated that the docking analysis presented high accuracy in predicting conformational changes. Flexible docking algorithms provided insights into dynamic interactions, while analysis of energetics revealed variations in binding strengths. Tocopherol exhibited stronger affinity (-5.8Kcal/mol) to zein compared to retinol (-4.1Kcal/mol). Molecular dynamics simulations offered comprehensive insights into stability and behavior over time. The integration of machine learning algorithms improved the classification and the prediction accuracy, achieving a rate of 71.59%. This study underscores the significance of molecular understanding in driving innovation in the plant-based cheese industry, facilitating the development of sustainable alternatives to traditional dairy products.

Optimization of reverse NOE pumping experiments in the analysis of complex systems with densely crowded NMR spectra.

We present an optimization of Reverse NOE-pumping (RNP) in order to observe the 1H signals of ligands bound to proteins. Although various ligand-based NMR screening methods have been proposed, the most frequently used method has been Saturation-Transfer Difference (STD), owing to the relatively easy setup of experiments. Yet the critical point of STD is the selective irradiation of protein without irradiating ligand, and thus the STD technique is unable to observe 1H ligand signals, which resonate across the entire 1H spectral width. In the present study, the RNP experiment has been improved to develop an effective NMR-based screening technique. The optimized RNP spectra reveal less subtraction artifacts and phase distortion than the original RNP spectra, indicating its applicability to any type of ligand molecules.

Unraveling the ligand specificity and promiscuity of the Staphylococcus aureus NorA efflux pump: a computational study.

Staphylococcus aureus, a gram-positive bacterial pathogen, develops antibiotic resistance partly through enhanced activity of transmembrane multi-drug efflux pump proteins like NorA. Being a prominent member of the Major Facilitator Superfamily (MFS), NorA transports various small molecules including hydrophilic fluoroquinolone antibiotics across the cell membrane. Intriguingly, NorA is inhibited by a structurally diverse set of small molecule inhibitors as well, indicating a highly promiscuous ligand/inhibitor recognition. Our study aims to elucidate the structural facets of this promiscuity. Known NorA inhibitors were grouped into five clusters based on chemical class and docked into ligand binding pockets on NorA conformations generated via molecular dynamics simulations. We discovered that several key residues, such as I23, E222, and F303, are involved in inhibitor binding. Additionally, residues I244, T223, F303, and F140 were identified as prominent in interactions with specific ligand clusters. Our findings suggest that NorA's substrate binding site, encompassing residues aiding ligand recognition based on chemical nature, facilitates the recognition of chemically diverse ligands. This insight into NorA's structural promiscuity in ligand recognition not only enhances understanding of antibiotic resistance mechanisms in S. aureus but also sets the stage for the development of more effective efflux pump inhibitors, vital for combating multidrug resistance.Communicated by Ramaswamy H. Sarma.

Experimental and data analysis advances in thermal proteome profiling.

Method development for mass spectrometry (MS)-based thermal shift proteomic assays have advanced to probe small molecules with known and unknown protein-ligand interaction mechanisms and specificity, which is predominantly used in characterization of drug-protein interactions. In the discovery of target and off-target protein-ligand interactions, a thorough investigation of method development and their impact on the sensitivity and accuracy of protein-small molecule and protein-protein interactions is warranted. In this review, we discuss areas of improvement at each stage of thermal proteome profiling data analysis that includes processing of MS-based data, method development, and their effect on the overall quality of thermal proteome profiles. We also overview the optimization of experimental strategies and prioritization of an increased number of independent biological replicates over the number of evaluated temperatures.

Increasing the polarity of ß-lapachone does not affect its binding capacity with bovine plasma protein.

Despite ortho-quinones showing several biological and pharmacological activities, there is still a lack of biophysical characterization of their interaction with albumin - the main carrier of different endogenous and exogenous compounds in the bloodstream. Thus, the interactive profile between bovine serum albumin (BSA) with ß-lapachone (1) and its corresponding synthetic 3-sulfonic acid (2, under physiological pH in the sulphonate form) was performed. There is one main binding site of albumin for both ß-lapachones (n ≈ 1) and a static fluorescence quenching mechanism was proposed. The Stern-Volmer constant (KSV) values are 104 M-1, indicating a moderate binding affinity. The enthalpy (-3.41 ± 0.45 and - 8.47 ± 0.37 kJ mol-1, for BSA:1 and BSA:2, respectively) and the corresponding entropy (0.0707 ± 0.0015 and 0.0542 ± 0.0012 kJ mol-1 K-1) values indicate an enthalpically and entropically binding driven. Hydrophobic interactions and hydrogen bonding are the main binding forces. The differences in the polarity of 1 and 2 did not change significantly the affinity to albumin. In addition, the 1,2-naphthoquinones showed a similar binding trend compared with 1,4-naphthoquinones.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration.

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in ß2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

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.

Per-ARNT-Sim (PAS) Domains in Basic Helix-Loop-Helix (bHLH)-PAS Transcription Factors and Coactivators: Structures and Mechanisms.

PAS domains are ubiquitous in biology. They perform critically important roles in sensing and transducing a wide variety of environmental signals, and through their ability to bind small-molecule ligands, have emerged as targets for therapeutic intervention. Here, we discuss our current understanding of PAS domain structure and function in the context of basic helix-loop-helix (bHLH)-PAS transcription factors and coactivators. Unlike the bHLH-PAS domains of transcription factors, those of the steroid receptor coactivator (SRC) family are poorly characterized. Recent progress for this family and for the broader bHLH-PAS proteins suggest that these domains are ripe for deeper structural and functional studies.