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Journal of Chemical Information and... Sep 2020Using the DUD-E benchmark, we explore the impact of using a single protein pocket or ligand for virtual screening compared with using ensembles of alternative pockets,...
Using the DUD-E benchmark, we explore the impact of using a single protein pocket or ligand for virtual screening compared with using ensembles of alternative pockets, ligands, and sets thereof. For both structure-based and ligand-based approaches, the precise characterization of the binding site in question had a significant impact on screening performance. Using the single original DUD-E protein, Surflex-Dock yielded mean ROC area of 0.81 ± 0.11. Using the cognate ligand instead, with the eSim method for screening, yielded 0.77 ± 0.14. Moving to ensembles of five protein pocket variants increased docking performance to 0.84 ± 0.09. Results for the analogous ligand-based approach (using the five crystallographically aligned cognate ligands) was 0.83 ± 0.11. Using the same ligands, but making use of an automatically generated mutual alignment, yielded mean AUC nearly as good as from single-structure docking: 0.80 ± 0.12. Detailed results and statistical analyses show that structure- and ligand-based methods are complementary and can be fruitfully combined to enhance screening efficiency. A hybrid approach combining ensemble docking with eSim-based screening produced the best and most consistent performance (mean ROC area of 0.89 ± 0.08 and 1% early enrichment of 46-fold). Based on results from both the docking and ligand-similarity approaches, it is clearly unwise to make use of a single arbitrarily chosen protein structure for docking or single ligand query for similarity-based screening.
Topics: Binding Sites; Ligands; Protein Binding; Proteins
PubMed: 32271577
DOI: 10.1021/acs.jcim.0c00115 -
Drug Discovery Today Dec 2016Computational functional group mapping (cFGM) is emerging as a high-impact complement to existing widely used experimental and computational structure-based drug... (Review)
Review
Computational functional group mapping (cFGM) is emerging as a high-impact complement to existing widely used experimental and computational structure-based drug discovery methods. cFGM provides comprehensive atomic-resolution 3D maps of the affinity of functional groups that can constitute drug-like molecules for a given target, typically a protein. These 3D maps can be intuitively and interactively visualized by medicinal chemists to rapidly design synthetically accessible ligands. Given that the maps can inform selection of functional groups for affinity, specificity, and pharmacokinetic properties, they are of utility for both the optimization of existing drug candidates and creating novel ones. Here, I review recent advances in cFGM with emphasis on the unique information content in the approach that offers the potential of broadly facilitating structure-based ligand design.
Topics: Computational Biology; Drug Discovery; Ligands; Structure-Activity Relationship
PubMed: 27393487
DOI: 10.1016/j.drudis.2016.06.030 -
Yakugaku Zasshi : Journal of the... 2022Technologies for the optical control of biomolecular functions have recently attracted considerable attention because they can be combined with advanced laser and... (Review)
Review
Technologies for the optical control of biomolecular functions have recently attracted considerable attention because they can be combined with advanced laser and microscopic techniques for diverse applications at the cellular and intravital levels. In this account, I review the summary of optical control technologies for biomolecular functions based on organic chemistry or protein science, and then introduce our recent studies on the development of small molecule-based photoregulation techniques. The first is the development of a photoactivatable protein labeling method based on a caged ligand. This method was applied to the photocontrol of intracellular protein dimerization and localization. The second is the development of a reversibly photoswitchable enzyme inhibitor, which was designed from the conformation of the inhibitor in the crystal structure of the enzyme-inhibitor complex. Based on our research strategies and results, I have also outlined the respective advantages and disadvantages of these two technologies: caged compounds and photoswitchable compounds.
Topics: Cell Physiological Phenomena; Ligands; Light; Proteins
PubMed: 35491156
DOI: 10.1248/yakushi.21-00203-4 -
Chimia Sep 2020Interests in learning how to engineer most effective covalent ligands, identify novel functional targets, and define precise mechanism-of-action are rapidly growing in...
Interests in learning how to engineer most effective covalent ligands, identify novel functional targets, and define precise mechanism-of-action are rapidly growing in both academia and pharmaceutical industries. We here illuminate the establishment of a multifunctional platform that offers new capabilities to logically engineer covalent ligands and dissect 'on-target' bioactivity with precise biological context and precision hitherto inaccessible. Broadly , this opinion piece is aimed to stoke the interest of emerging chemists and biologists/bioengineers, but the underlying technological and conceptual topicality is anticipated to leading ligand-target mining, validation, and -discovery research programs.
Topics: Drug Discovery; Ligands
PubMed: 32958101
DOI: 10.2533/chimia.2020.659 -
BMC Bioinformatics Jun 2023Three-dimensional structures of protein-ligand complexes provide valuable insights into their interactions and are crucial for molecular biological studies and drug...
BACKGROUND
Three-dimensional structures of protein-ligand complexes provide valuable insights into their interactions and are crucial for molecular biological studies and drug design. However, their high-dimensional and multimodal nature hinders end-to-end modeling, and earlier approaches depend inherently on existing protein structures. To overcome these limitations and expand the range of complexes that can be accurately modeled, it is necessary to develop efficient end-to-end methods.
RESULTS
We introduce an equivariant diffusion-based generative model that learns the joint distribution of ligand and protein conformations conditioned on the molecular graph of a ligand and the sequence representation of a protein extracted from a pre-trained protein language model. Benchmark results show that this protein structure-free model is capable of generating diverse structures of protein-ligand complexes, including those with correct binding poses. Further analyses indicate that the proposed end-to-end approach is particularly effective when the ligand-bound protein structure is not available.
CONCLUSION
The present results demonstrate the effectiveness and generative capability of our end-to-end complex structure modeling framework with diffusion-based generative models. We suppose that this framework will lead to better modeling of protein-ligand complexes, and we expect further improvements and wide applications.
Topics: Ligands; Proteins; Protein Conformation; Drug Design
PubMed: 37277701
DOI: 10.1186/s12859-023-05354-5 -
Molecules (Basel, Switzerland) Jan 2023Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is... (Review)
Review
Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.
Topics: Computer-Aided Design; Ligands; Drug Design; Psychotropic Drugs; Pharmaceutical Preparations
PubMed: 36770990
DOI: 10.3390/molecules28031324 -
Proteomics Mar 20221/f current noise is ubiquitous in protein pores, porins, and channels. We have previously shown that a protein-selective biological nanopore with an external protein...
1/f current noise is ubiquitous in protein pores, porins, and channels. We have previously shown that a protein-selective biological nanopore with an external protein receptor can function as a 1/f noise generator when a high-affinity protein ligand is reversibly captured by the receptor. Here, we demonstrate that the binding affinity and concentration of the ligand are key determinants for the nature of current noise. For example, 1/f was absent when a protein ligand was reversibly captured at a much lower concentration than its equilibrium dissociation constant against the receptor. Furthermore, we also analyzed the composite current noise that resulted from mixtures of low-affinity and high-affinity ligands against the same receptor. This study highlights the significance of protein recognition events in the current noise fluctuations across biological membranes.
Topics: Cell Membrane; Ligands; Nanopores; Porins; Proteins
PubMed: 34275190
DOI: 10.1002/pmic.202100077 -
European Journal of Pharmaceutical... Jul 2021In the well-known model for basic Target-Mediated Drug Disposition (TMDD), drug binds to the target and the resulting drug-target complex is removed by a first order...
In the well-known model for basic Target-Mediated Drug Disposition (TMDD), drug binds to the target and the resulting drug-target complex is removed by a first order process, leading to loss of both drug and target. In the present note we study what happens when, instead, drug is returned to the free drug pool so that it can a new target molecule. What results is a mechanism in which the drug, here referred to as the ligand, facilitates the removal of the target,and then returns to the free ligand pool. Accordingly the process will be referred to as Ligand-Facilitated Target Removal (LFTR). It is shown through simulations and mathematical analysis how the two models differ and how their signature profiles typically appear. We also derive a useful parameter of both models, the in vivo potency EC (L) which contains both ligand-target binding properties (k,k), target turnover (k) and ligand-target complex kinetics (k). Thus, this parameter contains a conglomerate of properties and is therefore potentially more informative about relevant (clinical) exposure than the binding affinity (K) alone. The derived potency parameter EC may therefore be used as a more robust ranking parameter among small and large drug molecules in drug discovery. Subsequently the LFTR model is applied to experimentally obtained literature data and the relevant parameters are estimated.
Topics: Drug Delivery Systems; Drug Discovery; Ligands; Models, Biological; Pharmaceutical Preparations
PubMed: 33848634
DOI: 10.1016/j.ejps.2021.105835 -
Frontiers in Bioscience (Landmark... Jan 2009NMR has a long history in drug discovery and hit-to-lead optimization. Compared to many other methods NMR has the advantage of combining structural and functional... (Review)
Review
NMR has a long history in drug discovery and hit-to-lead optimization. Compared to many other methods NMR has the advantage of combining structural and functional parameters to characterize protein inhibitor interactions. NMR methods used in this context can be split into two categories; protein based experiments using isotopically labelled protein samples and a broad range of ligand based methods. Recently, there has been a strong emphasis on so-called ligand-based methods which offer a broad range of options to determine binding epitopes. Ligand-based methods are attractive because they are broadly applicable, impose few constraints on the composition of the target protein and don't require isotopic labeling of the protein or ligands. Such experiments include diffusion experiments, saturation transfer difference (STD-NMR), NOE pumping, waterLOGSY, SALMON, transferred-NOE and INPHARMA. Ligand-based NMR methods have been employed in screening and in lead optimization. One key advantage arises from their capability to pick up specific interactions for compounds of relatively low affinity and their ability to provide limited structural information without any need of crystallization or isotopic labeling.
Topics: Diffusion; Drug Discovery; Ligands; Magnetic Resonance Spectroscopy
PubMed: 19273371
DOI: 10.2741/3549 -
Biophysical Journal Nov 2020Amide hydrogen-deuterium exchange mass spectrometry is powerful for describing combinatorial coupling effects of a cooperative ligand pair binding at noncontiguous...
Amide hydrogen-deuterium exchange mass spectrometry is powerful for describing combinatorial coupling effects of a cooperative ligand pair binding at noncontiguous sites: adenosine at the ATP-pocket and a docking peptide (PIFtide) at the PIF-pocket, on a model protein kinase PDK1. Binding of two ligands to PDK1 reveal multiple hotspots of synergistic allostery with cumulative effects greater than the sum of individual effects mediated by each ligand. We quantified this synergism and ranked these hotspots using a difference in deuteration-based approach, which showed that the strongest synergistic effects were observed at three of the critical catalytic loci of kinases: the αB-αC helices, and HRD-motif loop, and DFG-motif. Additionally, we observed weaker synergistic effects at a distal GHI-subdomain locus. Synergistic changes in deuterium exchange observed at a distal site but not at the intermediate sites of the large lobe of the kinase reveals allosteric propagation in proteins to operate through two modes. Direct electrostatic interactions between polar and charged amino acids that mediate targeted relay of allosteric signals, and diffused relay of allosteric signals through soft matter-like hydrophobic core amino acids. Furthermore, we provide evidence that the conserved β-3 strand lysine of protein kinases (Lys111 of PDK1) functions as an integrator node to coordinate allosteric coupling of the two ligand-binding sites. It maintains indirect interactions with the ATP-pocket and mediates a critical salt bridge with a glutamate (Glu130) of αC helix, which is conserved across all kinases. In summary, allosteric propagation in cooperative, dual-liganded enzyme targets is bidirectional and synergistic and offers a strategy for combinatorial drug development.
Topics: Allosteric Regulation; Allosteric Site; Binding Sites; Ligands; Peptides; Protein Kinases
PubMed: 33086047
DOI: 10.1016/j.bpj.2020.09.019