Ligand Conformation Research Articles

Structural mass spectrometry of membrane proteins

The analysis of proteins and protein complexes by mass spectrometry (MS) has come a long way since the invention of electrospray ionization (ESI) in the mid 80s. Originally used to characterize small soluble polypeptide chains, MS has progressively evolved over the past 3 decades towards the analysis of samples of ever increasing heterogeneity and complexity, while the instruments have become more and more sensitive and resolutive. The proofs of concepts and first examples of most structural MS methods appeared in the early 90s. However, their application to membrane proteins, key targets in the biopharma industry, is more recent. Nowadays, a wealth of information can be gathered from such MS-based methods, on all aspects of membrane protein structure: sequencing (and more precisely proteoform characterization), but also stoichiometry, non-covalent ligand binding (metals, drug, lipids, carbohydrates), conformations, dynamics and distance restraints for modelling. In this review, we present the concept and some historical and more recent applications on membrane proteins, for the major structural MS methods.

Accelerating AutoDock Vina with GPUs.

AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.

In Silico ADMET and Docking Study of Selected Drug Used in Therapy of COVID-19

Docking is one of the most widely utilized technique used method in structure -based drug design because of its capability to predict the binding conformation of ligands to appropriate target. Ability of binding/ affinity towards the target i.e., bioactive peptides or specific receptor provides strong evidence of binding conformation pattern and affinity for further investigation. Aim- The present study was conducted for evaluation of current API’s potential used in COVID-19. Methods: In-silico molecular docking was performed using softwares such as SWISS ADME, MOLSOFT, MOLINSPIRATION, PYMOL, AUTO-DOCK VINA AND BIOVIA DS VISUALIZER. Results: The current research comprehend the drug likeliness character of selected API’s and their binding affinity with various targets selected by SWISS TARGET PREDICTION. Conclusion: The present investigation suggests that all the targets follow Lipinski rule of five except Remdesivir and Anakinra besides which it possesses enhanced binding affinity toward targets, the binding energy of the protein ligand interaction additionally confirms that the ligand fits into the dynamite pockets which proves to be evident for further in- vivo and in-vitro evaluations.

Allosteric Binding of MDMA to the Human Serotonin Transporter (hSERT) via Ensemble Binding Space Analysis with ΔG Calculations, Induced Fit Docking and Monte Carlo Simulations.

Despite the recent promising results of MDMA (3,4-methylenedioxy-methamphetamine) as a psychotherapeutic agent and its history of misuse, little is known about its molecular mode of action. MDMA enhances monoaminergic neurotransmission in the brain and its valuable psychoactive effects are associated to a dual action on the 5-HT transporter (SERT). This drug inhibits the reuptake of 5-HT (serotonin) and reverses its flow, acting as a substrate for the SERT, which possesses a central binding site (S1) for antidepressants as well as an allosteric (S2) one. Previously, we characterized the spatial binding requirements for MDMA at S1. Here, we propose a structure-based mechanistic model of MDMA occupation and translocation across both binding sites, applying ensemble binding space analyses, electrostatic complementarity, and Monte Carlo energy perturbation theory. Computed results were correlated with experimental data (r = 0.93 and 0.86 for S1 and S2, respectively). Simulations on all hSERT available structures with Gibbs free energy estimations (ΔG) revealed a favourable and pervasive dual binding mode for MDMA at S2, i.e., adopting either a 5-HT or an escitalopram-like orientation. Intermediate ligand conformations were identified within the allosteric site and between the two sites, outlining an internalization pathway for MDMA. Among the strongest and more frequent interactions were salt bridges with Glu494 and Asp328, a H-bond with Thr497, a π-π with Phe556, and a cation-π with Arg104. Similitudes and differences with the allosteric binding of 5-HT and antidepressants suggest that MDMA may have a distinctive chemotype. Thus, our models may provide a framework for future virtual screening studies and pharmaceutical design and to develop hSERT allosteric compounds with a unique psychoactive MDMA-like profile.

Molecular dynamic assisted investigation on impact of mutations in deazaflavin dependent nitroreductase against pretomanid: a computational study

In the past decade, TB drugs belonging to the nitroimidazole class, pretomanid and delamanid, have been authorised to treat MDR-TB and XDR-TB. With a novel inhibition mechanism and a reduction in the span of treatment, it is now being administered in various combinations. This approach is not the ultimate remedy since the target protein Deazaflavin dependent nitroreductase (Ddn) has a high mutation frequency, and already pretomanid resistant clinical isolates are reported in various studies. Ddn is essential for M.tuberculosis to emerge from hypoxia, and point mutations in critical residues confer resistance to Nitro-imidazoles. Among the pool of available mutants, we have selected seven mutants viz DdnL49P, DdnY65S, DdnS78Y, DdnK79Q, DdnW88R, DdnY133C, and DdnY136S, all of which exhibited resistance to pretomanid. To address this issue, through computational study primarily by MD simulation, we attempted to elucidate these point mutations’ impact and investigate the resistance mechanism. Hence, the DdnWT and mutant (MT) complexes were subjected to all-atom molecular dynamics (MD) simulations for 100 ns. Interestingly, we observed the escalation of the distance between cofactor and ligand in some mutants, along with a significant change in ligand conformation relative to the DdnWT. Moreover, we confirmed that mutations rendered ligand instability and were ejected from the binding pocket as a result. In conclusion, the results obtained provide a new structural insight and vital clues for designing novel inhibitors to combat nitroimidazole resistance Communicated by Ramaswamy H. Sarma

Using Big Data Analytics to "Back Engineer" Protein Conformational Selection Mechanisms.

In the living cells, proteins bind small molecules (or “ligands”) through a “conformational selection” mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable of such binding. The present work uses machine learning approaches to identify, in a very large amount of protein:ligand complexes, what protein properties are associated with their capacity to bind small molecules. In order to do so, we calculate 40 physicochemical properties on about 1.5 millions of protein conformations: ligand and protein conformations. This work describes a machine learning approach to identify the unique physico-chemical descriptors of a protein that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2) and OPRK1 (Opioid Receptor Kappa 1). We find adequate machine learning techniques can increase by an order of magnitude the identification of “binding protein conformations” in an otherwise very large ensemble of protein conformations, compared to random selection of protein conformations. This opens the door to the systematic identification of such “binding conformations” for proteins and provides a big data approach to the conformational selection mechanism.

Synthesis and characterization of a tetradentate bispidine-based ligand and its zinc(II) complex

The efficient synthesis of a tetradentate diol-bispidine-based ligand and its metal complex with Zn(II) is here reported. The single-crystal X-ray diffraction (SC-XRD) analysis of the Zn(II) complex allowed the characterization of the coordination sphere around the cation as a distorted square pyramid and confirmed that the bispidine core was in a chair-chair (cc) conformation. This finding agreed with the theoretical analysis. The structural data demonstrated the influence of the ligand conformation on the coordination mechanism, confirming that the cc geometry of the bispidine core is a key factor to form a stable complex. Hirshfeld surface and two-dimensional fingerprint analysis showed that hydrogen bonds (O⋯H) and van der Waals interactions constituted the major contribution to the intermolecular forces, with O⋯H/H⋯O and H⋯H contacts accounting for 39.8% and 38.8% of the surface, respectively. NMR titration experiments provided information on the formation mechanism of the complex and on the species formed throughout the reaction.

Molecular Docking – Useful Tool in Drug Discovery

Molecular docking has been widely employed as a fast and inexpensive technique in past decades, both in academic and industrial setting. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. That’s why this review is focused on docking. This review presents the overview of the method and attempt to highlight recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual Screening, molecular Docking, MD simulation and MMGBSA approaches

The aldose reductase (AR) enzyme is an important target enzyme in the development of therapeutics against hyperglycaemia induced health complications such as retinopathy, etc. In the present study, a quantitative structure activity relationship (QSAR) evaluation of a dataset of 226 reported AR inhibitor (ARi) molecules is performed using a genetic algorithm – multi linear regression (GA-MLR) technique. Multi-criteria decision making (MCDM) analysis furnished two five variables based QSAR models with acceptably high performance reflected in various statistical parameters such as, R2 = 0.79–0.80, Q2LOO = 0.78–0.79, Q2LMO = 0.78–0.79. The QSAR model analysis revealed some of the molecular features that play crucial role in deciding inhibitory potency of the molecule against AR such as; hydrophobic Nitrogen within 2 Å of the center of mass of the molecule, non-ring Carbon separated by three and four bonds from hydrogen bond donor atoms, number of sp2 hybridized Oxygen separated by four bonds from sp2 hybridized Carbon atoms, etc. 14 in silico generated hits, using a compound 18 (a most potent ARi from present dataset with pIC50 = 8.04 M) as a template, on QSAR based virtual screening (QSAR-VS) furnished a scaffold 5 with better ARi activity (pIC50 = 8.05 M) than template compound 18. Furthermore, molecular docking of compound 18 (Docking Score = –7.91 kcal/mol) and scaffold 5 (Docking Score = –8.08 kcal/mol) against AR, divulged that they both occupy the specific pocket(s) in AR receptor binding sites through hydrogen bonding and hydrophobic interactions. Molecular dynamic simulation (MDS) and MMGBSA studies right back the docking results by revealing the fact that binding site residues interact with scaffold 5 and compound 18 to produce a stable complex similar to co-crystallized ligand's conformation. The QSAR analysis, molecular docking, and MDS results are all in agreement and complementary. QSAR-VS successfully identified a more potent novel ARi and can be used in the development of therapeutic agents to treat diabetes.

ELIXIR-A: An Interactive Visualization Tool for Multi-Target Pharmacophore Refinement

Pharmacophore modeling is an important step in computer-aided drug design for identifying interaction points between the receptor and ligand complex. Pharmacophore-based models can be used for de novo drug design, lead identification, and optimization in virtual screening as well as for multi-target drug design. There is a need to develop a user-friendly interface to filter the pharmacophore points resulting from multiple ligand conformations. Here, we present ELIXIR-A, a Python-based pharmacophore refinement tool, to help refine the pharmacophores between multiple ligands from multiple receptors. Furthermore, the output can be easily used in virtual pharmacophore-based screening platforms, thereby contributing to the development of drug discovery.

Supercomputer Search for the New Inhibitors of the Coagulation Factor XIIa

A virtual structure-oriented screening of candidates for inhibitors of blood coagulation factor XIIa was carried out. This coagulation factor is one of the most promising therapeutic targets for the development of anticoagulants without disturbing normal hemostasis. The screening was carried out in the database of organic compounds of the Voronezh State University, consisting of more than 19 thousand molecules. At the first stage of virtual screening, ligands were positioned in the active center of factor XIIa using the SOL docking program. At the second stage, for the best ligands, the protein-ligand binding enthalpy was calculated using the MOPAC program and the PM7 quantum-chemical method, taking into account the solvent in the COSMO continuum model. All calculations were carried out using the supercomputing resources at Lomonosov Moscow State University. In total, more than 30 thousand ligand conformers are docked, and for more than 400 of the best of them, the protein-ligand binding enthalpy was calculated. 16 compounds are selected as the most promising candidates for subsequent in vitro testing of their ability to inhibit factor XIIa.

Highly emissive coordination polymer derived from tetraphenylethylene-tetrazole chromophore: Synthesis, characterization and piezochromic luminescent behavior

Solid-state materials that exhibit pressure stimulus-response characteristics in a manner of emission signal, known as piezochromic luminescence (PCL), demonstrate great potential in photoelectric devices. The weakened luminescence and insignificant color change in the aggregation state, however, hampers their practical applications. Herein, a highly emissive coordination polymer, [Zn2(H4TTPE)(H2O)4]·H2O (CUST-805), is successfully constructed by employing an AIE-active chromophore as the building block. The structural characterization and photophysical properties are systematically studied. Owing to intrinsic twisted conformation and AIE feature of tetraphenylethylene-tetrazole ligand, CUST-805 achieves the visible and reversible PCL from blue to green switched by different external stimuli. The transformation between crystalline and amorphous states is proved to be the origin of present PCL behavior. Moreover, on basis of electron and energy transfer quenching mechanism, the highly selective and sensitive sensor based on CUST-805 is realized, showing the low detection limit of 0.29 ppm towards 2,4,6-trinitrophenol.

Discovery of MAP855, an Efficacious and Selective MEK1/2 Inhibitor with an ATP-Competitive Mode of Action.

Mutations in MEK1/2 have been described as a resistance mechanism to BRAF/MEK inhibitor treatment. We report the discovery of a novel ATP-competitive MEK1/2 inhibitor with efficacy in wildtype (WT) and mutant MEK12 models. Starting from a HTS hit, we obtained selective, cellularly active compounds that showed equipotent inhibition of WT MEK1/2 and a panel of MEK1/2 mutant cell lines. Using a structure-based approach, the optimization addressed the liabilities by systematic analysis of molecular matched pairs (MMPs) and ligand conformation. Addition of only three heavy atoms to early tool compound 6 removed Cyp3A4 liabilities and increased the cellular potency by 100-fold, while reducing log P by 5 units. Profiling of MAP855, compound 30, in pharmacokinetic-pharmacodynamic and efficacy studies in BRAF-mutant models showed comparable efficacy to clinical MEK1/2 inhibitors. Compound 30 is a novel highly potent and selective MEK1/2 kinase inhibitor with equipotent inhibition of WT and mutant MEK1/2, whose drug-like properties allow further investigation in the mutant MEK setting upon BRAF/MEK therapy.

Lewis Pair-Functionalized Pt(II) Complexes with Tunable Emission Color and Triplet-State Properties.

Two emissive Pt(II) complexes containing dynamic "flexible" Lewis pair (FlexLP) ligands are reported. The FlexLP ligand encompasses a diphenylphosphine oxide Lewis base and a dimesitylborane Lewis acid attached to a bithiophene scaffold, which can switch between an open unbound Lewis pair and a bound P-O-B Lewis adduct depending on the hydrogen bond-donating (HBD) strength of the solvent. [Pt(FlexLP)2] contains two FlexLP ligands, and [Pt(FlexLP)(Py)] contains one FlexLP ligand and one pyrene ligand. UV-vis absorption and fluorescence studies demonstrate that the FlexLP ligands switch between the open Lewis pair and the closed Lewis adduct in MeOH, a strong HBD solvent, and acetone, a weak HBD solvent, respectively, and exhibit tunable emission color depending on the acetone/MeOH solvent ratio. Transient absorption spectroscopy reveals a large difference in the triplet-state lifetime depending on the conformation of the FlexLP ligands for both complexes. In the closed form, the triplet-state lifetimes of the two complexes are over an order of magnitude longer compared to that of the complexes in the open conformation. Calculations of optimized geometries suggest that this difference in triplet-state lifetime is due to a difference in the thiophene-thiophene torsion angle between the two conformations.

Zn2+-Schiff’s Base Complex as an “On–Off-On” Molecular Switch and a Fluorescence Probe for Cu2+ and Ag+ Ions

The present study presents a thorough theoretical analysis of the electronic structure and conformational preference of Schiff's base ligand N,N-bis(2-hydroxybenzilidene)-2,4,6-trimethyl benzene-1,3-diamine (H2L) and its metal complexes with Zn2+, Cu2+ and Ag+ ions. This study aims to investigate the behavior of H2L and the binuclear Zn2+ complex (1) as fluorescent probes for the detection of metal ions (Zn2+, Cu2+ and Ag+) using density functional theory (DFT) and time-dependent density functional theory (TDDFT). The six conformers of the H2L ligand were optimized using the B3LYP/6-311 + + G** level of theory, while the L-2-metal complexes were optimized by applying the B3LYP functional with the LANL2DZ/6-311 + + G** mixed basis set. The gas-phase and solvated Enol-cis isomer (E-cis) was found to be the most stable species. The absorption spectra of the E-cis isomer and its metal complexes were simulated using B3LYP, CAM-B3LYP, M06-2X and ωB97X functionals with a 6-311 + + G** basis set for C, O, N and H atoms and a LANL2DZ basis set for the metal ions (Zn2+, Cu2+ and Ag+). The computational results of the B3LYP functional were in excellent agreement with the experimental results. Hence, it was adopted for performing the emission calculations. The results indicated that metal complex (1) can act as a fluorescent chemosensor for the detection of Ag+ and Cu2+ ions through the mechanism of intermolecular charge transfer (ICT) and as a molecular switch "On-Off-On" via the replacement of Cu2+ by Ag+ ions, as proved experimentally.

Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides.

In silico tools, such as molecular docking, are widely applied to study interactions and binding affinity of biological activity of proteins and peptides. However, restricted sampling of both ligand and receptor conformations and use of approximated scoring functions can produce results that do not correlate with actual experimental binding affinities. Molecular dynamics simulations (MDS) can provide valuable information in deciphering functional mechanisms of proteins/peptides and other biomolecules, overcoming the rigid sampling limitations in docking analysis. This review will discuss the information related to the traditional use of in silico models, such as molecular docking, and its application for studying food proteins and bioactive peptides, followed by an in-depth introduction to the theory of MDS and description of why these molecular simulation techniques are important in the theoretical prediction of structural and functional dynamics of food proteins and bioactive peptides. Applications, limitations, and future prospects of MDS will also be discussed.

Multinuclear Ni(II) and Cu(II) complexes of a meso 6 + 6 macrocyclic amine derived from trans-1,2-diaminocyclopentane and 2,6-diformylpyridine.

Four hexanuclear chloride and sulphate Ni(II) and Cu(II) complexes 1, 2, 4 and 5 and one tetranuclear nitrate Cu(II) complex 3 have been synthesised from appropriate metal salts and 6 + 6 octadecaaza macrocyclic ligands. All obtained coordination compounds have been characterised by elemental analysis, spectroscopic methods (ESI MS, NMR and EPR), magnetic susceptibility measurements and X-ray crystallography. Their X-ray crystal structures reveal different coordination modes of metal cations involved in the obtained centro-symmetrical coordination compounds. The conformational folding of the macrocyclic ligand adopted in the respective complexes depends on the number of metal cations bound within the macrocycle but not on their type. The cavities of these multinuclear complexes might be occupied by solvent molecules and counter anions bound by hydrogen bonds or might be empty in the case where the macrocyclic ring of the ligand is squeezed in the middle. All obtained Ni(II) and Cu(II) coordination compounds are paramagnetic. This has been proved by their 1H NMR and EPR spectra and magnetic measurements. Direct current (DC) variable-temperature magnetic susceptibility measurements on the polycrystalline samples of 1-5 were carried out in the temperature range of 1.8-300 K. The magnetic behaviour of 1 and 2 is dominated by the magnetic anisotropy of the nickel(II) ion masking the magnetic interactions between magnetic centres. The magnetic data of 3-5 reveal small antiferromagnetic interactions within the Cu4 and Cu6 units. EPR experiments for 3-5 show, at 9.6 and 34 GHz frequencies, that the predominant contribution to the orbitals occupied by the unpaired electrons in the ground state originates from dx2-y2.

Recent advances in structures and applications of coordination polymers based on cyclohexanepolycarboxylate ligands.

Coordination polymers (CPs) are emerging crystalline materials constructed from metal entities and organic ligands through coordination bonds, containing infinite coordination units in one, two, or three dimensions. Here an overview is given of recent structural research based on seven cyclohexanepolycarboxylates (CHPCs), namely cyclohexanecarboxylate, 1,x-cyclohexanedicarboxylate (x = 2, 3, or 4), 1,3,5-cyclohexanetricarboxylate, 1,2,4,5-cyclohexanetetracarboxylate and 1,2,3,4,5,6-cyclohexanehexacarboxylate, showing the effects of the conformation transformation and auxiliary ligands on the dimensionality and the geometric topology of the assemblies generated. The applications of these CPs as platforms for molecular adsorption, luminescent sensing, magnetism, and catalysis are also briefly discussed.

Performance evaluation of flexible macrocycle docking in AutoDock.

Macrocycles represent an important class of ligands, both in natural products and designed drugs. In drug design, macrocyclizations can impart specific ligand conformations and contribute to passive permeation by encouraging intramolecular H-bonds. AutoDock-GPU and Vina can model macrocyclic ligands flexibly, without requiring the enumeration of macrocyclic conformers before docking. Here, we characterize the performance of the method for handling macrocyclic compounds, which is implemented and the default behaviour for ligand preparation with our ligand preparation pipeline, Meeko. A pseudoatom is used to encode bond geometry and produce an anisotropic closure force for macrocyclic rings. This method is evaluated on a diverse set of small molecule and peptide macrocycles, ranging from 7- to 33-membered rings, showing little accuracy loss compared to rigid redocking of the X-ray macrocycle conformers. This suggests that for conformationally flexible macrocycles with unknown binding modes, this method can be effectively used to predict the macrocycle conformation.

DNA-Engineered iron-based metal-organic framework bio-interface for rapid visual determination of exosomes

In this study, a rapid, low-cost and facile method for detecting exosomes was developed by engineering DNA ligands on the surface of an iron-based metal–organic framework (Fe-MOF). Aptamers of exosomal transmembrane CD63 protein (CD63-aptamers) were utilized as both the optically active layer and the exosome-specific recognition element to engineer an Fe-MOF bio-interface for high-efficiency regulation of the catalytic behavior of Fe-MOF toward the chromogenic substrate. The effective enhancement of the intrinsic peroxidase-like catalytic activity was confirmed via the self-assembly of CD63-aptamers on the surface of Fe-MOF. The specific binding of exosomes with CD63-aptamers altered the conformation of DNA ligands on the surface of Fe-MOF, contributing to sensitive variation in Fe-MOF catalytic activity. This directly produced a distinct color change and enabled the visual detection of exosomes. Via one-step “mixing-and-detection”, the Fe-MOF bio-interface exhibited excellent performance in quantitative analysis of exosomes derived from human breast cancer cell lines ranging from 1.1 × 105 to 2.2 × 107 particles/μL with a detection limit of 5.2 × 104 particles/μL. The expression of exosomal CD63 proteins originated from three types of cancer cell lines, including breast cancer, gastric cancer and lung cancer cell lines, was differentiated within only 17 min. Furthermore, the method was successfully applied to the identification of exosomes in serum samples, suggesting its potential in clinical analysis as a valuable tool for the rapid, convenient and economical testing of exosomes.