Browsing by Subject "Ligands"

Now showing 1 - 10 of 12
  • Thumbnail Image
    Item
    Addressing Intersite Coupling Unlocks Large Combinatorial Chemical Spaces for Alchemical Free Energy Methods
    (American Chemical Society, 2022) Hayes, Ryan L.; Vilseck, Jonah Z.; Brooks, Charles L., III.; Biochemistry and Molecular Biology, School of Medicine
    Alchemical free energy methods are playing a growing role in molecular design, both for computer-aided drug design of small molecules and for computational protein design. Multisite λ dynamics (MSλD) is a uniquely scalable alchemical free energy method that enables more efficient exploration of combinatorial alchemical spaces encountered in molecular design, but simulations have typically been limited to a few hundred ligands or sequences. Here, we focus on coupling between sites to enable scaling to larger alchemical spaces. We first discuss updates to the biasing potentials that facilitate MSλD sampling to include coupling terms and show that this can provide more thorough sampling of alchemical states. We then harness coupling between sites by developing a new free energy estimator based on the Potts models underlying direct coupling analysis, a method for predicting contacts from sequence coevolution, and find it yields more accurate free energies than previous estimators. The sampling requirements of the Potts model estimator scale with the square of the number of sites, a substantial improvement over the exponential scaling of the standard estimator. This opens up exploration of much larger alchemical spaces with MSλD for molecular design.
  • Thumbnail Image
    Item
    Atypical Chemokine Receptor 3 "Senses" CXC Chemokine Receptor 4 Activation Through GPCR Kinase Phosphorylation
    (Aspet, 2023) Schafer, Christopher T.; Chen, Qiuyan; Tesmer, John J. G.; Handel, Tracy M.; Biology, School of Science
    Atypical chemokine receptor 3 (ACKR3) is an arrestin-biased receptor that regulates extracellular chemokine levels through scavenging. The scavenging process restricts the availability of the chemokine agonist CXCL12 for the G protein-coupled receptor (GPCR) CXCR4 and requires phosphorylation of the ACKR3 C-terminus by GPCR kinases (GRKs). ACKR3 is phosphorylated by GRK2 and GRK5, but the mechanisms by which these kinases regulate the receptor are unresolved. Here we determined that GRK5 phosphorylation of ACKR3 results in more efficient chemokine scavenging and β-arrestin recruitment than phosphorylation by GRK2 in HEK293 cells. However, co-activation of CXCR4-enhanced ACKR3 phosphorylation by GRK2 through the liberation of Gβγ, an accessory protein required for efficient GRK2 activity. The results suggest that ACKR3 "senses" CXCR4 activation through a GRK2-dependent crosstalk mechanism, which enables CXCR4 to influence the efficiency of CXCL12 scavenging and β-arrestin recruitment to ACKR3. Surprisingly, we also found that despite the requirement for phosphorylation and the fact that most ligands promote β-arrestin recruitment, β-arrestins are dispensable for ACKR3 internalization and scavenging, suggesting a yet-to-be-determined function for these adapter proteins. Since ACKR3 is also a receptor for CXCL11 and opioid peptides, these data suggest that such crosstalk may also be operative in cells with CXCR3 and opioid receptor co-expression. Additionally, kinase-mediated receptor cross-regulation may be relevant to other atypical and G protein-coupled receptors that share common ligands. SIGNIFICANCE STATEMENT: The atypical receptor ACKR3 indirectly regulates CXCR4-mediated cell migration by scavenging their shared agonist CXCL12. Here, we show that scavenging and β-arrestin recruitment by ACKR3 are primarily dependent on phosphorylation by GRK5. However, we also show that CXCR4 co-activation enhances the contribution of GRK2 by liberating Gβγ. This phosphorylation crosstalk may represent a common feedback mechanism between atypical and G protein-coupled receptors with shared ligands for regulating the efficiency of scavenging or other atypical receptor functions.
  • Thumbnail Image
    Item
    Distinct Chemical Determinants are Essential for Achieving Ligands for Superior Optical Detection of Specific Amyloid-β Deposits in Alzheimer's Disease
    (Wiley, 2024) Wu, Xiongyu; Shirani, Hamid; Vidal, Ruben; Ghetti, Bernardino; Ingelsson, Martin; Klingstedt, Therése; Nilsson, K. Peter R.; Pathology and Laboratory Medicine, School of Medicine
    Aggregated forms of different proteins are common hallmarks for several neurodegenerative diseases, including Alzheimer's disease, and ligands that selectively detect specific protein aggregates are vital. Herein, we investigate the molecular requirements of thiophene-vinyl-benzothiazole based ligands to detect a specific type of Aβ deposits found in individuals with dominantly inherited Alzheimer's disease caused by the Arctic APP E693G mutation. The staining of these Aβ deposits was alternated when switching the terminal heterocyclic moiety attached to the thiophene-vinyl-benzothiazole scaffold. The most prevalent staining was observed for ligands having a terminal 3-methyl-1H-indazole moiety or a terminal 1,2-dimethoxybenzene moiety, verifying that specific molecular interactions between these ligands and the aggregates were necessary. The synthesis of additional thiophene-vinyl-benzothiazole ligands aided in pinpointing additional crucial chemical determinants, such as positioning of nitrogen atoms and methyl substituents, for achieving optimal staining of Aβ aggregates. When combining the optimized thiophene-vinyl-benzothiazole based ligands with a conventional ligand, CN-PiB, distinct staining patterns were observed for sporadic Alzheimer's disease versus dominantly inherited Alzheimer's disease caused by the Arctic APP E693G mutation. Our findings provide chemical insights for developing novel ligands that allow for a more precise assignment of Aβ deposits, and might also aid in creating novel agents for clinical imaging of distinct Aβ aggregates in AD.
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
  • Thumbnail Image
    Item
    Molecular Recognition in a Diverse Set of Protein-Ligand Interactions Studied with Molecular Dynamics Simulations and End-Point Free Energy Calculations
    (ACS Publications, 2013-10-28) Wang, Bo; Li, Liwei; Hurley, Thomas D.; Meroueh, Samy O.; Department of Biochemistry & Molecular Biology, School of Medicine
    End-point free energy calculations using MM-GBSA and MM-PBSA provide a detailed understanding of molecular recognition in protein-ligand interactions. The binding free energy can be used to rank-order protein-ligand structures in virtual screening for compound or target identification. Here, we carry out free energy calculations for a diverse set of 11 proteins bound to 14 small molecules using extensive explicit-solvent MD simulations. The structure of these complexes was previously solved by crystallography and their binding studied with isothermal titration calorimetry (ITC) data enabling direct comparison to the MM-GBSA and MM-PBSA calculations. Four MM-GBSA and three MM-PBSA calculations reproduced the ITC free energy within 1 kcal•mol−1 highlighting the challenges in reproducing the absolute free energy from end-point free energy calculations. MM-GBSA exhibited better rank-ordering with a Spearman ρ of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (ε = 1). An increase in ε resulted in significantly better rank-ordering for MM-PBSA (ρ = 0.91 for ε = 10). But larger ε significantly reduced the contributions of electrostatics, suggesting that the improvement is due to the non-polar and entropy components, rather than a better representation of the electrostatics. SVRKB scoring function applied to MD snapshots resulted in excellent rank-ordering (ρ = 0.81). Calculations of the configurational entropy using normal mode analysis led to free energies that correlated significantly better to the ITC free energy than the MD-based quasi-harmonic approach, but the computed entropies showed no correlation with the ITC entropy. When the adaptation energy is taken into consideration by running separate simulations for complex, apo and ligand (MM-PBSAADAPT), there is less agreement with the ITC data for the individual free energies, but remarkably good rank-ordering is observed (ρ = 0.89). Interestingly, filtering MD snapshots by pre-scoring protein-ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (ε = 1) from ρ = 0.40 to ρ = 0.81. Finally, the non-polar components of MM-GBSA and MM-PBSA, but not the electrostatic components, showed strong correlation to the ITC free energy; the computed entropies did not correlate with the ITC entropy.
  • Thumbnail Image
    Item
    Optimizing Multisite λ-Dynamics Throughput with Charge Renormalization
    (American Chemical Society, 2022) Vilseck, Jonah Z.; Cervantes, Luis F.; Hayes, Ryan L.; Brooks, Charles L., III.; Biochemistry and Molecular Biology, School of Medicine
    With the ability to sample combinations of alchemical perturbations at multiple sites off a small molecule core, multisite λ-dynamics (MSλD) has become an attractive alternative to conventional alchemical free energy methods for exploring large combinatorial chemical spaces. However, current software implementations dictate that combinatorial sampling with MSλD must be performed with a multiple topology model (MTM), which is nontrivial to create by hand, especially for a series of ligand analogues which may have diverse functional groups attached. This work introduces an automated workflow, referred to as msld_py_prep, to assist in the creation of a MTM for use with MSλD. One approach for partitioning partial atomic charges between ligands to create a MTM, called charge renormalization, is also presented and rigorously evaluated. We find that msld_py_prep greatly accelerates the preparation of MSλD ready-to-use files and that charge renormalization can provide a successful approach for MTM generation, as long as bookending calculations are applied to correct small differences introduced by charge renormalization. Charge renormalization also facilitates the use of many different force field parameters with MSλD, broadening the applicability of MSλD for computer-aided drug design.
  • Thumbnail Image
    Item
    Photoinduced C(sp3)-H Chalcogenation of Amide Derivatives and Ethers via Ligand-to-Metal Charge-Transfer
    (American Chemical Society, 2022) Niu, Ben; Sachidanandan, Krishnakumar; Cooke, Maria Victoria; Casey, Taylor E.; Laulhé, Sébastien; Chemistry and Chemical Biology, School of Science
    A photoinduced, iron(III) chloride-catalyzed C-H activation of N-methyl amides and ethers leads to the formation of C-S and C-Se bonds via a ligand-to-metal charge transfer (LMCT) process. This methodology converts secondary and tertiary amides, sulfonamides, and carbamates into the corresponding amido-N,S-acetal derivatives in good yields. Mechanistic work revealed that this transformation proceeds through a hydrogen atom transfer (HAT) involving chlorine radical intermediates.
  • Thumbnail Image
    Item
    Structure-based computational studies of protein-ligand interactions
    (2014-12) Wang, Bo; Meroueh, Samy; Pu, Jingzhi; Boyd, Donald B.; Naumann, Christoph A.
    Molecular recognition plays an important role in biological systems. The purpose of this study was to get a better understanding of the process by incorporating computational tools.Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) method, the end-point free energy calculations provide the binding free energy the can be used to rank-order protein–ligand structures in virtual screening for compound or target identification. Free energy calculations were performed on a diverse set of 11 proteins bound to 14 small molecules was carried out for. A direct comparison was taken between the calculated free energy and the experimental isothermal titration calorimetry (ITC) data. Four and three systems in MM-GBSA and MM-PBSA calculations, respectively, reproduced the ITC free energy within 1 kcal•mol–1. MM-GBSA exhibited better rank-ordering with a Spearman ρ of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (ε = 1). The rank-ordering performance of MM-PBSA improved with increasing ε (ρ = 0.91 for ε = 10), but the contributions of electrostatics became significantly lower at larger ε level, suggesting that the only nonpolar and entropy components contribute to the improved results. Our previously developed scoring function, Support Vector Regression Knowledge-Based (SVRKB), resulted in excellent rank-ordering (ρ = 0.81) when applied into MD simulations. Filtering MD snapshots by prescoring protein–ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (ε = 1) from ρ = 0.40 to ρ = 0.81. Finally, the nonpolar components in the free energy calculations showed strong correlation to the ITC free energy while the electrostatic components did not; the computed entropies did not correlate with the ITC entropy. Explicit-solvent molecular dynamics (MD) simulations offer an opportunity to sample multiple conformational states of a protein-ligand system in molecular recognition. SVMSP is a target-specific rescoring method that combines machine learning with statistical potentials. We evaluate the performance of SVMSP in its ability to enrich chemical libraries docked to MD structures. Seven proteins from the Directory of Useful Decoys (DUD) were involved in the study. We followed an innovative approach by training SVMSP scoring models using MD structures (SVMSPMD). The resulting models remarkably improved enrichment in two cases. We also explored approaches for a prior identification of MD snapshots with high enrichment power from an MD simulation in the absence of active compounds. SVMSP rescoring of protein–compound MD structures was applied for the search of small-molecule inhibitors of the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2). Rank-ordering of a commercial library of 50,000 compounds docked to MD optimized structures of ALDH2 led to five small-molecule inhibitors. Four compounds had IC50s below 5 μM. These compounds serve as leads for the design and synthesis of more potent and selective ALDH2 inhibitors.