Small Molecule Binding Research Articles

A millisecond coarse-grained simulation approach to decipher allosteric cannabinoid binding at the glycine receptor α1

Glycine receptors (GlyR) are regulated by small-molecule binding at several allosteric sites. Cannabinoids like tetrahydrocannabinol (THC) and N-arachidonyl-ethanol-amide (AEA) potentiate the GlyR response but their mechanism of action is not fully established. By combining millisecond coarse-grained (CG) MD simulations powered by Martini 3 with backmapping to all-atom representations, we have characterized the cannabinoid-binding site(s) at the zebrafish GlyR-α1 active state with atomic resolution. Based on hundreds of thousand ligand-binding events, we find that cannabinoids bind to the transmembrane domain of the receptor at both intrasubunit and intersubunit sites. For THC, the intrasubunit binding mode predicted in simulation is in excellent agreement with recent cryo-EM structures, while intersubunit binding recapitulates in full previous mutagenesis experiments. Intriguingly, AEA is predicted to bind at the same intersubunit site despite the strikingly different chemistry. Statistical analyses of the ligand-receptor interactions highlight potentially relevant residues for GlyR potentiation, offering experimentally testable predictions. The predictions for AEA have been validated by electrophysiology recordings of rationally designed mutants. The results highlight the existence of multiple cannabinoid-binding sites for the allosteric regulation of GlyR and put forward an effective strategy for the identification and structural characterization of allosteric binding sites.

Small Molecule RNA Degraders

AbstractRNA is a central molecule in life, involved in a plethora of biological processes and playing a key role in many diseases. Targeting RNA emerges as a significant endeavor in drug discovery, diverging from conventional protein‐centric approaches to tackle various pathologies. Whilst identifying small molecules that bind to specific RNA regions is the first step, the abundance of non‐functional RNA segments renders many interactions biologically inert. Consequently, small molecule binding does not necessarily meet stringent criteria for clinical translation, calling for solutions to push the field forward. Converting RNA‐binders into RNA‐degraders presents a promising avenue to enhance RNA‐targeting. This mini‐review outlines strategies and exemplars wherein simple small molecule RNA binders are reprogrammed into active degraders through the linkage of functional groups. These approaches encompass mechanisms that induce degradation via endogenous enzymes, termed RIBOTACs, as well as those with functional moieties acting autonomously to degrade RNA. Through this exploration, we aim to offer insights into advancing RNA‐targeted therapeutic strategies.

Mechanistic Studies of Small Molecule Ligands Selective to RNA Single G Bulges.

Small-molecule RNA binders have emerged as an important pharmacological modality. A profound understanding of the ligand selectivity, binding mode, and influential factors governing ligand engagement with RNA targets is the foundation for rational ligand design. Here, we report a novel class of coumarin derivatives exhibiting selective binding affinity towards single G RNA bulges. Harnessing the computational power of all-atom Gaussian accelerated Molecular Dynamics (GaMD) simulations, we unveiled a rare minor groove binding mode of the ligand with a key interaction between the coumarin moiety and the G bulge. This predicted binding mode is consistent with results obtained from structure-activity-relationship (SAR) studies and transverse relaxation measurements by NMR spectroscopy. We further generated 444 molecular descriptors from 69 coumarin derivatives and identified key contributors to the binding events, such as charge state and planarity, by lasso (least absolute shrinkage and selection operator) regression. Strikingly, small structure perturbations on these key contributors, such as the addition of a methyl group that disrupts the planarity of the ligand resulted in > 100-fold reduction in the binding affinity. Our work deepened the understanding of RNA-small molecule interactions and integrated a new generalizable platform for the rational design of selective small-molecule RNA binders.

Characterization and Hemocompatibility of α, β, and γ Cyclodextrin-Modified Magnetic Nano-Adsorbents

Kidney dysfunction leads to the retention of metabolites within the blood that are not effectively cleared with conventional hemodialysis. Magnetic nanoparticle (MNP)-based absorbents have inherent properties that make them amenable to capturing toxins in the blood, notably a large surface area that can be chemically modified to enhance toxin capture and the ability to be easily collected from the blood using an external magnetic field. Cyclodextrins (CDs) present a chemical structure that facilitates the binding of small molecules. However, the hemocompatibility of MNPs modified with films composed of different native types of CDs (α, β, or γ) has not yet been investigated, which is information crucial to the potential clinical application of MNPs to supplement hemodialysis. To this end, films of α-, β-, or γ-CDs were formed on MNPs and characterized. The impact of these films on the adsorbed protein structure, composition of key adsorbed proteins, and clotting kinetics were evaluated. It was found that modified MNPs did not significantly affect the secondary structure of some proteins (albumin, lysozyme, α-lactalbumin). The adsorbed proteome from platelet-poor human plasma was evaluated as a function of film properties. Compared to non-modified nanoparticles, CD-modified MNPs exhibited a significant decrease in the adsorbed protein per surface area of MNPs. The immunoblot results showed variations in the adsorption levels of C3, fibrinogen, antithrombin, Factor XI, and plasminogen across CD-modified MNPs. The hemocompatibility experiments showed that CD-modified MNPs are compatible with human whole blood, with no significant impact on platelet activation, hemolysis, or hemostasis.

Potential allosteric pockets identification of glucagon receptor based on molecular dynamics simulations

Glucagon receptor (GCGR) is an important target for the treatment of type 2 diabetes mellitus. Although several small molecules with antagonistic activity have been discovered, so far, only one small molecule binding site has been resolved. To discover more novel allosteric pockets and allosteric molecules, we started with the unique full-length inactive conformation of GCGR and applied all-atom molecular dynamics (MD) simulations to obtain extensive dynamic conformations of the GCGR/glucagon complex. For the first time, MDpocket, FTMove and FTMap were used to detect allosteric pockets in simulation trajectories, selecting 4 stable pockets with a total of 14 structures as templates for virtual screening. From the results of virtual screening, 14 compounds were ultimately selected after a series of filtering steps. The cAMP accumulation assay indicated that compound gs6 has antagonistic activity, and MD simulations further revealed the allosteric mechanism of gs6. We are the first to identify new allosteric pockets and allosteric molecules in simulation trajectories of the GCGR/glucagon complex, providing a reference for research on other G-protein-coupled receptors (GPCR). However, there is still considerable room for improvement, such as using more simulation methods to obtain a richer set of dynamic conformations.

Dynamics of the Pre-Powerstroke Myosin Lever Arm and the Effects of Omecamtiv Mecarbil.

The binding of small molecules to sarcomeric myosin can elicit powerful effects on the chemomechanical cycle, making them effective therapeutics in the clinic and research tools at the benchtop. However, these myotropes can have complex effects that act on different phases of the crossbridge cycle and which depend on structural, dynamic, and environmental variables. While small molecule binding sites have been identified crystallographically and their effects on contraction studied extensively, small molecule-induced dynamic changes that link structure-function are less studied. Here, we use molecular dynamics simulations to explore how omecamtiv mecarbil (OM), a cardiac myosin-specific myotrope, alters the coordinated dynamics of the lever arm and the motor domain in the pre-powerstroke state. We show that the lever arm adopts a range of orientations and find that different lever arm orientations are accompanied by changes in the hydrogen bonding patterns near the converter. We find that the binding of OM to myosin reduces the conformational heterogeneity of the lever arm orientation and also adjusts the average lever arm orientation. Finally, we map out the distinct conformations and ligand-protein interactions adopted by OM. These results uncover some structural factors that govern the motor domain-tail orientations and the mechanisms by which OM primes the pre-powerstroke myosin heads.

Switchback RNA.

Intricately designed DNA and RNA motifs guide the assembly of robust and functional nucleic acid nanostructures. In this work, we present a globally left-handed RNA motif with two parallel strands called switchback RNA and report its assembly, biophysical, and biochemical characterization. Switchback RNA can be assembled in buffers without Mg2+, with improved thermal stability in buffers containing Mg2+, Na+, or K+. Differences in the binding of small molecules to switchback RNA and conventional RNA indicate design-based approaches for small molecule loading on RNA nanostructures. Further, the differential affinity of the two component strands in switchback or conventional duplex conformations allows for toehold-less strand displacement. Enzyme studies showed that the switchback and conventional RNA structures have similar levels of nuclease resistance. These results provide insights for employing switchback RNA as a structural motif in RNA nanotechnology. Our observation that RNA strands with switchback complementarity can form stable complexes at low magnesium concentrations encourages studies into the potential occurrence of switchback RNA in nature.

Turbocharging protein binding site prediction with geometric attention, inter-resolution transfer learning, and homology-based augmentation

BackgroundLocating small molecule binding sites in target proteins, in the resolution of either pocket or residue, is critical in many drug-discovery scenarios. Since it is not always easy to find such binding sites using conventional methods, different deep learning methods to predict binding sites out of protein structures have been developed in recent years. The existing deep learning based methods have several limitations, including (1) the inefficiency of the CNN-only architecture, (2) loss of information due to excessive post-processing, and (3) the under-utilization of available data sources.MethodsWe present a new model architecture and training method that resolves the aforementioned problems. First, by layering geometric self-attention units on top of residue-level 3D CNN outputs, our model overcomes the problems of CNN-only architectures. Second, by configuring the fundamental units of computation as residues and pockets instead of voxels, our method reduced the information loss from post-processing. Lastly, by employing inter-resolution transfer learning and homology-based augmentation, our method maximizes the utilization of available data sources to a significant extent.ResultsThe proposed method significantly outperformed all state-of-the-art baselines regarding both resolutions—pocket and residue. An ablation study demonstrated the indispensability of our proposed architecture, as well as transfer learning and homology-based augmentation, for achieving optimal performance. We further scrutinized our model’s performance through a case study involving human serum albumin, which demonstrated our model’s superior capability in identifying multiple binding sites of the protein, outperforming the existing methods.ConclusionsWe believe that our contribution to the literature is twofold. Firstly, we introduce a novel computational method for binding site prediction with practical applications, substantiated by its strong performance across diverse benchmarks and case studies. Secondly, the innovative aspects in our method— specifically, the design of the model architecture, inter-resolution transfer learning, and homology-based augmentation—would serve as useful components for future work.

Development of a high throughput cytochrome P450 ligand-binding assay

Human cytochrome P450 enzymes are membrane-embedded monooxygenases responsible for xenobiotic metabolism, steroidogenesis, fatty acid metabolism, and vitamin metabolism. Their active sites can accommodate diverse small molecules and understanding these interactions is key to decoding enzymatic functionality and designing drugs. The most common method for characterizing small molecule binding is quantifying absorbance changes that typically occur when ligands enter the active site near the heme iron. Traditionally, such titrations are monitored by a spectrophotometer, requiring significant manual time, protein, and increasing solvents. This assay was adapted for semi-automated high throughput screening, increasing throughput 50-fold while requiring less protein and keeping solvent concentrations constant. This 384-well assay was validated for both type I and II shifts typically observed for substrates and heme-coordinating inhibitors, respectively. This assay was used to screen a library of ∼100 diverse imidazole-containing compounds which can coordinate with the heme iron if compatible with the overall active site. Three human cytochrome P450 enzymes were screened: drug-metabolizing CYP2A6 and CYP2D6 and sterol-metabolizing CYP8B1. Each bound different sets of imidazole compounds with varying Kd values, providing a unique binding fingerprint. As a final validation, the Kd values were used to generate pharmacophores to compare to experimental X-ray structures. Applications for the high-throughput assay include the following: 1) facilitating generation of pharmacophores for enzymes where structures are not available, 2) screening to identify ligands for P450 orphans, 3) screening for inhibitors of P450s drug targets, 4) screening potential new drugs to avoid and/or control P450 metabolism, and 5) efficient validation of computational ligand binding predictions.

Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE.

Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

Predicting Small Molecule Binding Nucleotides in RNA Structures Using RNA Surface Topography.

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying RNA small molecule binding nucleotides is essential and requires methods that exhibit a high predictive ability to facilitate drug discovery and inhibitor design. Existing methods can predict the binding nucleotides of simple RNA structures, but it is hard to predict binding nucleotides in complex RNA structures with junctions. To address this limitation, we developed a new deep learning model based on spatial correlation, ZHmolReSTasite, which can accurately predict binding nucleotides of small and large RNA with junctions. We utilize RNA surface topography to consider the spatial correlation, characterizing nucleotides from sequence and tertiary structures to learn a high-level representation. Our method outperforms existing methods for benchmark test sets composed of simple RNA structures, achieving precision values of 72.9% on TE18 and 76.7% on RB9 test sets. For a challenging test set composed of RNA structures with junctions, our method outperforms the second best method by 11.6% in precision. Moreover, ZHmolReSTasite demonstrates robustness regarding the predicted RNA structures. In summary, ZHmolReSTasite successfully incorporates spatial correlation, outperforms previous methods on small and large RNA structures using RNA surface topography, and can provide valuable insights into RNA small molecule prediction and accelerate RNA inhibitor design.

Predicting the Binding of Small Molecules to Proteins through Invariant Representation of the Molecular Structure.

We present a computational scheme for predicting the ligands that bind to a pocket of a known structure. It is based on the generation of a general abstract representation of the molecules, which is invariant to rotations, translations, and permutations of atoms, and has some degree of isometry with the space of conformations. We use these representations to train a nondeep machine learning algorithm to classify the binding between pockets and molecule pairs and show that this approach has a better generalization capability than existing methods.

Disulfide Tethering to Map Small Molecule Binding Sites Transcriptome-wide.

We report the development of Tether-seq, a transcriptome-wide screen to probe RNA-small molecule interactions using disulfide tethering. This technique uses s4U metabolic labeling to provide sites for reversible and covalent attachment of small molecule disulfides to the transcriptome. By screening under reducing conditions, we identify interactions that are stabilized by binding over those driven by the reactivity of the RNA sites. When applied to cellular RNA, Tether-seq with a disulfide analogue of risdiplam, an FDA-approved drug that targets RNA to treat spinal muscular atrophy (SMA), revealed a number of potential binding sites, most prominently at a site within the cytochrome C oxidase 1 (COX1) transcript. Structure probing by SHAPE-MaP revealed a structured motif and confirmed binding to the lead molecule. This work demonstrates that these screens have the power to identify binding sites throughout the transcriptome and provide invaluable insight into the thermodynamic properties that define small molecule binding.

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research.

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

Quantitative Characterization of the Impact of Protein-Protein Interactions on Ligand-Protein Binding: A Multi-Chain Dynamics Perturbation Analysis Method.

Many protein-protein interactions (PPIs) affect the ways in which small molecules bind to their constituent proteins, which can impact drug efficacy and regulatory mechanisms. While recent advances have improved our ability to independently predict both PPIs and ligand-protein interactions (LPIs), a comprehensive understanding of how PPIs affect LPIs is still lacking. Here, we examined 63 pairs of ligand-protein complexes in a benchmark dataset for protein-protein docking studies and quantified six typical effects of PPIs on LPIs. A multi-chain dynamics perturbation analysis method, called mcDPA, was developed to model these effects and used to predict small-molecule binding regions in protein-protein complexes. Our results illustrated that the mcDPA can capture the impact of PPI on LPI to varying degrees, with six similar changes in its predicted ligand-binding region. The calculations showed that 52% of the examined complexes had prediction accuracy at or above 50%, and 55% of the predictions had a recall of not less than 50%. When applied to 33 FDA-approved protein-protein-complex-targeting drugs, these numbers improved to 60% and 57% for the same accuracy and recall rates, respectively. The method developed in this study may help to design drug-target interactions in complex environments, such as in the case of protein-protein interactions.

Polarized and Evanescent Guided Wave Surface-Enhanced Raman Spectroscopy of Ligand Interactions on a Plasmonic Nanoparticle Optical Chemical Bench.

This study examined applications of polarized evanescent guided wave surface-enhanced Raman spectroscopy to determine the binding and orientation of small molecules and ligand-modified nanoparticles, and the relevance of this technique to lab-on-a-chip, surface plasmon polariton and other types of field enhancement techniques relevant to Raman biosensing. A simplified tutorial on guided-wave Raman spectroscopy is provided that introduces the notion of plasmonic nanoparticle field enhancements to magnify the otherwise weak TE- and TM-polarized evanescent fields for Raman scattering on a simple plasmonic nanoparticle slab waveguide substrate. The waveguide construct is called an optical chemical bench (OCB) to emphasize its adaptability to different kinds of surface chemistries that can be envisaged to prepare optical biosensors. The OCB forms a complete spectroscopy platform when integrated into a custom-built Raman spectrograph. Plasmonic enhancement of the evanescent field is achieved by attaching porous carpets of Au@Ag core shell nanoparticles to the surface of a multi-mode glass waveguide substrate. We calibrated the OCB by establishing the dependence of SER spectra of adsorbed 4-mercaptopyridine and 4-aminobenzoic acid on the TE/TM polarization state of the evanescent field. We contrasted the OCB construct with more elaborate photonic chip devices that also benefit from enhanced evanescent fields, but without the use of plasmonics. We assemble hierarchies of matter to show that the OCB can resolve the binding of Fe2+ ions from water at the nanoscale interface of the OCB by following the changes in the SER spectra of 4MPy as it coordinates the cation. A brief introduction to magnetoplasmonics sets the stage for a study that resolves the 4ABA ligand interface between guest magnetite nanoparticles adsorbed onto host plasmonic Au@Ag nanoparticles bound to the OCB. In some cases, the evanescent wave TM polarization was strongly attenuated, most likely due to damping by inertial charge carriers that favor optical loss for this polarization state in the presence of dense assemblies of plasmonic nanoparticles. The OCB offers an approach that provides vibrational and orientational information for (bio)sensing at interfaces that may supplement the information content of evanescent wave methods that rely on perturbations in the refractive index in the region of the evanescent wave.

A Novel Confocal Scanning Protein-Protein Interaction Assay (PPI-CONA) Reveals Exceptional Selectivity and Specificity of CC0651, a Small Molecule Binding Enhancer of the Weak Interaction between the E2 Ubiquitin-Conjugating Enzyme CDC34A and Ubiquitin.

Protein-protein interactions (PPIs) are some of the most challenging target classes in drug discovery. Highly sensitive detection techniques are required for the identification of chemical modulators of PPIs. Here, we introduce PPI confocal nanoscanning (PPI-CONA), a miniaturized, microbead based high-resolution fluorescence imaging assay. We demonstrate the capabilities of PPI-CONA by detecting low affinity ternary complex formation between the human CDC34A ubiquitin-conjugating (E2) enzyme, ubiquitin, and CC0651, a small molecule enhancer of the CDC34A-ubiquitin interaction. We further exemplify PPI-CONA with an E2 enzyme binding study on CC0651 and a CDC34A binding specificity study of a series of CC0651 analogues. Our results indicate that CC0651 is highly selective toward CDC34A. We further demonstrate how PPI-CONA can be applied to screening very low affinity interactions. PPI-CONA holds potential for high-throughput screening for modulators of PPI targets and characterization of their affinity, specificity, and selectivity.

Small Molecule RNA Degraders.

RNA is a central molecule in life, involved in a plethora of biological processes and playing a key role in many diseases. Targeting RNA emerges as a significant endeavor in drug discovery, diverging from conventional protein-centric approaches to tackle various pathologies. Whilst identifying small molecules that bind to specific RNA regions is the first step, the abundance of non-functional RNA segments renders many interactions biologically inert. Consequently, small molecule binding does not necessarily meet stringent criteria for clinical translation, calling for solutions to push the field forward. Converting RNA-binders into RNA-degraders presents a promising avenue to enhance RNA-targeting. This mini-review outlines strategies and exemplars wherein simple small molecule RNA binders are reprogrammed into active degraders through the linkage of functional groups. These approaches encompass mechanisms that induce degradation via endogenous enzymes, termed RIBOTACs, as well as those with functional moieties acting autonomously to degrade RNA. Through this exploration, we aim to offer insights into advancing RNA-targeted therapeutic strategies.

In Situ Biofilm Affinity-Based Protein Profiling Identifies the Streptococcal Hydrolase GbpB as the Target of a Carolacton-Inspired Chemical Probe.

Natural products are important precursors for antibiotic drug design. These chemical scaffolds serve as synthetic inspiration for chemists who leverage their structures to develop novel antibacterials and chemical probes. We have previously studied carolacton, a natural product macrolactone fromSorangium cellulosum, and discovered a simplified derivative, A2, that maintained apparent biofilm inhibitory activity, although the biological target was unknown. Herein, we utilize affinity-based protein profiling (AfBPP) in situ during biofilm formation to identify the protein target using a photoexcitable cross-linking derivative of A2. From these studies, we identified glucan binding protein B (GbpB), a peptidoglycan hydrolase, as the primary target of A2. Further characterization of the interaction between A2 and GbpB, as well as PcsB, a closely related homologue from the more pathogenic S. pneumoniae, revealed binding to the catalytic CHAP (cysteine, histidine, aminopeptidase) domain. To the best of our knowledge, this is the first report of a small-molecule binder of a conserved and essential bacterial CHAP hydrolase, revealing its potential as an antibiotic target. This work also highlights A2 as a useful tool compound for streptococci and as an initial scaffold for the design of more potent CHAP binders.

Adjusting the Energy Profile for CH-O Interactions Leads to Improved Stability of RNA Stem-Loop Structures in MD Simulations.

The role of ribonucleic acid (RNA) in biology continues to grow, but insight into important aspects of RNA behavior is lacking, such as dynamic structural ensembles in different environments, how flexibility is coupled to function, and how function might be modulated by small molecule binding. In the case of proteins, much progress in these areas has been made by complementing experiments with atomistic simulations, but RNA simulation methods and force fields are less mature. It remains challenging to generate stable RNA simulations, even for small systems where well-defined, thermostable structures have been established by experiments. Many different aspects of RNA energetics have been adjusted in force fields, seeking improvements that are transferable across a variety of RNA structural motifs. In this work, the role of weak CH···O interactions is explored, which are ubiquitous in RNA structure but have received less attention in RNA force field development. By comparing data extracted from high-resolution RNA crystal structures to energy profiles from quantum mechanics and force field calculations, it is shown that CH···O interactions are overly repulsive in the widely used Amber RNA force fields. A simple, targeted adjustment of CH···O repulsion that leaves the remainder of the force field unchanged was developed. Then, the standard and modified force fields were tested using molecular dynamics (MD) simulations with explicit water and salt, amassing over 300 μs of data for multiple RNA systems containing important features such as the presence of loops, base stacking interactions as well as canonical and noncanonical base pairing. In this work and others, standard force fields lead to reproducible unfolding of the NMR-based structures. Including a targeted CH···O adjustment in an otherwise identical protocol dramatically improves the outcome, leading to stable simulations for all RNA systems tested.