In silico carborane docking to proteins and potential drug targets

The presence of boron atoms has made carboranes, C(2)B(10)H(12), attractive candidates for boron neutron capture therapy. Because of their chemistry and possible conjugation with proteins, they can also be used to enhance interactions between pharmaceuticals and their targets and to increase the in vivo stability and bioavailability of compounds that are normally metabolized rapidly. Carboranes are isosteric to a rotating phenyl group, which they can substitute successfully in biologically active systems. A reverse ligand-protein docking approach was used in this work to identify binding proteins for carboranes. The screening was carried out on the drug target database PDTD that contains 1207 entries covering 841 known potential drug targets with structures taken from the Protein Data Bank. First, for validation, the protocol was applied to three crystal structures of proteins in which carborane derivatives are present. Then, the model was applied to systems for which the protein structure is available, but the binding site of carborane has not been reported. These systems were used for further validation of the protocol, while simultaneously providing new insight into the interactions between cage and protein. Finally, the screening was carried out on the database to reveal potential carborane binding targets of interest for biological and pharmacological activity. Carboranes are predicted to bind well to protease and metalloprotease enzymes. Other carborane pharmaceutical targets are also discussed, together with possible protein carriers.     

Computer-aided drug design of falcipain inhibitors: virtual screening, structure-activity relationships, hydration site thermodynamics, and reactivity analysis

Falcipains (FPs) are hemoglobinases of Plasmodium falciparum that are validated targets for the development of antimalarial chemotherapy. A combined ligand- and structure-based virtual screening of commercial databases was performed to identify structural analogs of virtual screening hits previously discovered in our laboratory. A total of 28 low micromolar inhibitors of FP-2 and FP-3 were identified and the structure-activity relationship (SAR) in each series was elaborated. The SAR of the compounds was unusually steep in some cases and could not be explained by a traditional analysis of the ligand-protein interactions (van der Waals, electrostatics, and hydrogen bonds). To gain further insights, a statistical thermodynamic analysis of explicit solvent in the ligand binding domains of FP-2 and FP-3 was carried out to understand the roles played by water molecules in binding of these inhibitors. Indeed, the energetics associated with the displacement of water molecules upon ligand binding explained some of the complex trends in the SAR. Furthermore, low potency of a subset of FP-2 inhibitors that could not be understood by the water energetics was explained in the context of poor chemical reactivity of the reactive centers of these compounds. The present study highlights the importance of considering energetic contributors to binding beyond traditional ligand-protein interactions.     

Novel strategy for three-dimensional fragment-based lead discovery

Fragment-based drug design (FBDD) is considered a promising approach in lead discovery. However, for a practical application of this approach, problems remain to be solved. Hence, a novel practical strategy for three-dimensional lead discovery is presented in this work. Diverse fragments with spatial positions and orientations retained in separately adjacent regions were generated by deconstructing well-aligned known inhibitors in the same target active site. These three-dimensional fragments retained their original binding modes in the process of new molecule construction by fragment linking and merging. Root-mean-square deviation (rmsd) values were used to evaluate the conformational changes of the component fragments in the final compounds and to identify the potential leads as the main criteria. Furthermore, the successful validation of our strategy is presented on the basis of two relevant tumor targets (CDK2 and c-Met), demonstrating the potential of our strategy to facilitate lead discovery against some drug targets.     

Spectroscopic elucidation (FT-IR,FT-Raman and UV-visible) with NBO,NLO, ELF,LOL, drug likeness and molecular docking analysis on 1-(2-ethylsulfonylethyl)-2-methyl-5-nitro-imidazole: An antiprotozoal agent

1-(2-ethylsulfonylethyl)-2-methyl-5-nitro-imidazole (1EMI) C₈H₁₃N₃O₄S also known as Tinidazole, selected for its antiprotozoal property is extensively used for spectroscopic elucidations and computational aspects using density functional methods. Along with spectral conclusions, further investigations on fundamental reactive properties such as electrical, optical, nonlinear combined with DFT simulations were performed. Molecular docking procedure supports the results of chosen appropriate antiprotozoal agent based on ligand-protein interactions. Experimental and simulated (B3LYP/6-311++G (d,p)) IR and Raman spectra showed concurrence. NLO analysis through first order hyperpolarizability parameter helps in finding the potential of 1EMI as a good NLO candidate. Charge delocalization and the stability of the compound were discussed using natural bond orbital (NBO) analysis. Furthermore, Electron localization function (ELF), local orbital locator (LOL), and Frontier molecular orbitals (FMO) were studied. Besides, Mulliken population analysis on atomic charges, Energy gap, chemical potential, global hardness, softness, ionization potential, electronegativity, electrophilicity index along thermodynamic parameters (enthalpy, entropy and heat capacity) have been calculated. Drug likeness parameters and molecular docking approach enabled to check pharmaceutical potential and biological activity of 1EMI. The biological activity of 1EMI through ligand and protein interactions have been confirmed theoretically for the treatment of Malaria, Invasive aspergillosis and Mycobacterium tuberculosis with respect to chosen proteins. Three different activity targets and protein interactions are quite successful revealing the bond distances, intermolecular energy, binding energy and inhibition constant. 2D interaction profile image of the two maximum interacted proteins and also Ramachandran plot used to show stereochemistry of selected protein. The activities of 1EMI were studied in accordance with literature survey and the results were presented.     

Electrostatics of ligand binding: parametrization of the generalized Born model and comparison with the Poisson-Boltzmann approach

An accurate and fast evaluation of the electrostatics in ligand-protein interactions is crucial for computer-aided drug design. The pairwise generalized Born (GB) model, a fast analytical method originally developed for studying the solvation of organic molecules, has been widely applied to macromolecular systems, including ligand-protein complexes. However, this model involves several empirical scaling parameters, which have been optimized for the solvation of organic molecules, peptides, and nucleic acids but not for energetics of ligand binding. Studies have shown that a good solvation energy does not guarantee a correct model of solvent-mediated interactions. Thus, in this study, we have used the Poisson-Boltzmann (PB) approach as a reference to optimize the GB model for studies of ligand-protein interactions. Specifically, we have employed the pairwise descreening approximation proposed by Hawkins et al.(1) for GB calculations and DelPhi for PB calculations. The AMBER all-atom force field parameters have been used in this work. Seventeen protein-ligand complexes have been used as a training database, and a set of atomic descreening parameters has been selected with which the pairwise GB model and the PB model yield comparable results on atomic Born radii, the electrostatic component of free energies of ligand binding, and desolvation energies of the ligands and proteins. The energetics of the 15 test complexes calculated with the GB model using this set of parameters also agrees well with the energetics calculated with the PB method. This is the first time that the GB model has been parametrized and thoroughly compared with the PB model for the electrostatics of ligand binding.     

LigPlot+: multiple ligand-protein interaction diagrams for drug discovery

We describe a graphical system for automatically generating multiple 2D diagrams of ligand-protein interactions from 3D coordinates. The diagrams portray the hydrogen-bond interaction patterns and hydrophobic contacts between the ligand(s) and the main-chain or side-chain elements of the protein. The system is able to plot, in the same orientation, related sets of ligand-protein interactions. This facilitates popular research tasks, such as analyzing a series of small molecules binding to the same protein target, a single ligand binding to homologous proteins, or the completely general case where both protein and ligand change.     

Theoretical Exploitation of 1,2,3,4,5,6-Hexachloro- and 1,2,3,4,5,6-Hexafluorocyclohexane Isomers as Biologically Active Compounds

Hexachlorocyclohexanes (HCHs) have been widely explored as biological compounds during the last century. However, most of them were banned due to their potential toxicity in humans, animals, and the environment. Revisiting HCHs to explore their biological activity while improving key features is valuable and may lead to a new class of pesticides that utilizes the biological response of HCHs without their toxic characteristics. In this sense, the fluorine atom can be a possible alternative since a large number of therapeutics and agrochemicals have been developed with this halogen in their structure. We have evaluated herein the conformational behavior of HCHs and their bioisosteric fluorinated compounds, namely, hexafluorocyclohexanes (HFHs), through quantum-chemical calculations. We also explored the potential of the HCH and HFH isomers as biological compounds by docking them inside three possible targets. It was demonstrated that HCH and HFH have similar ligand-protein interactions with three pockets: the picrotoxin and barbiturate sites of the GABA_A receptor and the ryanodine receptor. The results support HFHs as possible alternatives for HCHs since the replacement of Cl with F does not forfeit the main ligand-protein interactions. Finally, we demonstrated that HFHs have a lower log P than HCHs by almost two logarithmic units. This result highlights the role of fluorine in distribution and bioaccumulation.     

PEARLS: program for energetic analysis of receptor-ligand system

Analysis of the energetics of small molecule ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions facilitates the quantitative understanding of molecular interactions that regulate the function and conformation of proteins. It has also been extensively used for ranking potential new ligands in virtual drug screening. We developed a Web-based software, PEARLS (Program for Energetic Analysis of Ligand-Receptor Systems), for computing interaction energies of ligand-protein, ligand-nucleic acid, protein-nucleic acid, and ligand-protein-nucleic acid complexes from their 3D structures. AMBER molecular force field, Morse potential, and empirical energy functions are used to compute the van der Waals, electrostatic, hydrogen bond, metal-ligand bonding, and water-mediated hydrogen bond energies between the binding molecules. The change in the solvation free energy of molecular binding is estimated by using an empirical solvation free energy model. Contribution from ligand conformational entropy change is also estimated by a simple model. The computed free energy for a number of PDB ligand-receptor complexes were studied and compared to experimental binding affinity. A substantial degree of correlation between the computed free energy and experimental binding affinity was found, which suggests that PEARLS may be useful in facilitating energetic analysis of ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions. PEARLS can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/rune.pl.     

Ligand-target interaction-based weighting of substructures for virtual screening

A methodology is introduced to assign energy-based scores to two-dimensional (2D) structural features based on three-dimensional (3D) ligand-target interaction information and utilize interaction-annotated features in virtual screening. Database molecules containing such fragments are assigned cumulative scores that serve as a measure of similarity to active reference compounds. The Interaction Annotated Structural Features (IASF) method is applied to mine five high-throughput screening (HTS) data sets and often identifies more hits than conventional fragment-based similarity searching or ligand-protein docking.     

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely β(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.     

Module assembly for protein-surface recognition: geranylgeranyltransferase I bivalent inhibitors for simultaneous targeting of interior and exterior protein surfaces

Synthetic chemical probes designed to simultaneously targeting multiple sites of protein surfaces are of interest owing to their potential application as site specific modulators of protein-protein interactions. A new approach toward bivalent inhibitors of mammalian type I geranylgeranyltransferase (GGTase I) based on module assembly for simultaneous recognition of both interior and exterior protein surfaces is reported. The inhibitors synthesized in this study consist of two modules linked by an alkyl spacer; one is the tetrapeptide CVIL module for binding to the interior protein surface (active pocket) and the other is a 3,4,5-alkoxy substituted benzoyl motif that contains three aminoalkyl groups designed to bind to the negatively charged protein exterior surface near the active site. The compounds were screened by two distinct enzyme inhibition assays based on fluorescence spectroscopy and incorporation of a [(3)H]-labeled prenyl group onto a protein substrate. The bivalent inhibitors block GGTase I enzymatic activity with K(i) values in the submicromolar range and are approximately one order of magnitude and more than 150 times more effective than the tetrapeptide CVIL and the methyl benzoate derivatives, respectively. The bivalent compounds 6 and 8 were shown to be competitive inhibitors, suggesting that the CVIL module anchors the whole molecule to the GGTase I active site and delivers the other module to the targeting protein surface. Thus, our module-assembly approach resulted in simultaneous multiple-site recognition, and as a consequence, synergetic inhibition of GGTase I activity, thereby providing a new approach in designing protein-surface-directed inhibitors for targeting protein-protein interactions.     

Are AM1 ligand‐protein binding enthalpies good enough for use in the rational design of new drugs?

We have examined the performance of semiempirical quantum mechanical methods in solving the problem of accurately predicting protein-ligand binding energies and geometries. Firstly, AM1 and PM3 geometries and binding enthalpies between small molecules that simulate typical ligand-protein interactions were compared with high level quantum mechanical techniques that include electronic correlation (e.g., MP2 or B3LYP). Species studied include alkanes, aromatic systems, molecules including groups with hypervalent sulfur or with donor or acceptor hydrogen bonding capability, as well as ammonium or carboxylate ions. B3LYP/6-311+G(2d,p) binding energies correlated very well with the BSSE corrected MP2/6-31G(d) values. AM1 binding enthalpies also showed good correlation with MP2 values, and their systematic deviation is acceptable when enthalpies are used for the comparison of interaction energies between ligands and a target. PM3 otherwise gave erratic energy differences in comparison to the B3LYP or MP2 approaches. As one would expect, the geometries of the binding complexes showed the known limitations of the semiempirical and DFT methods. AM1 calculations were subsequently applied to a test set consisting of "real" protein active site-ligand complexes. Preliminary results indicate that AM1 could be a valuable tool for the design of new drugs using proteins as templates. This approach also has a reasonable computational cost. The ligand-protein X-ray structures were reasonably reproduced by AM1 calculations and the corresponding AM1 binding enthalpies are in agreement with the results from the "small molecules" test set.     

Inhibition and substrate recognition – a computational approach applied to HIV protease

We have developed a computational approach in which an inhibitor's strength is determined from its interaction energy with a limited set of amino acid residues of the inhibited protein. We applied this method to HIV protease. The method uses a consensus structure built from X-ray crystallographic data. All inhibitors are docked into the consensus structure. Given that not every ligand-protein interaction causes inhibition, we implemented a genetic algorithm to determine the relevant set of residues. The algorithm optimizes the q2 between the sum of interaction energies and the observed inhibition constants. The best possible predictive model resulting has a q2 of 0.63. External validation by examining the predictivity for compounds not used in derivation of the model leads to a prediction accuracy between 0.9 and 1.5 log10 unit. Out of 198 residues in the whole protein, the best internally predictive model defines a subset of 20 residues and the best externally predictive model one of 9 residues. These residues are distributed over the subsites of the enzyme. This approach provides insight in which interactions are important for inhibiting HIV protease and it allows for quantitative prediction of inhibitor strength.     

Investigating and characterizing the binding activity of the immobilized calmodulin to calmodulin-dependent protein kinase I binding domain with atomic force microscopy

Protein–protein interactions are responsible for many biological processes, and the study of how proteins undergo a conformational change induced by other proteins in the immobilized state can help us to understand a protein’s function and behavior, empower the current knowledge on molecular etiology of disease, as well as the discovery of putative protein targets of therapeutic interest. In this study, a bottom-up approach was utilized to fabricate micro/nanometer-scale protein patterns. One cysteine mutated calmodulin (CaM), as a model protein, was immobilized on thiol-terminated pattern surfaces. Atomic Force Microscopy (AFM) was then employed as a tool to investigate the interactions between CaM and CaM kinase I binding domain, and show that the immobilized CaM retains its activity to interact with its target protein. Our work demonstrate the potential of employing AFM to the research and assay works evolving surface-based protein–protein interactions biosensors, bioelectronics or drug screening.     

Exploring polypharmacology using a ROCS-based target fishing approach

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.     

Synthesis of a beta-estradiol-biotin chimera that potently heterodimerizes estrogen receptor and streptavidin proteins in a yeast three-hybrid system

Small molecules that dimerize proteins in living cells provide powerful probes of biological processes and have potential as tools for the identification of protein targets of natural products. We synthesized 7-alpha-substituted derivatives of beta-estradiol tethered to the natural product biotin to regulate heterodimerization of estrogen receptor (ER) and streptavidin (SA) proteins expressed as components of a yeast three-hybrid system. Addition of an estradiol-biotin chimera bearing a 19-atom linker to yeast expressing DNA-bound ER-alpha or ER-beta LexA fusion proteins and wild-type SA protein fused to the B42 activation domain activated reporter gene expression by as much as 450-fold in vivo (10 muM ligand). Comparative analysis of lower affinity Y43A (biotin Kd approximately 100 pM) and W120A (biotin Kd approximately 100 nM) mutants of SA indicated that moderate affinity interactions can be readily detected with this system. Comparison of a 7-alpha-substituted estradiol-biotin chimera with a structurally similar dexamethasone-biotin chimera revealed that yeast expressing ER proteins can detect cognate ligands with up to 5-fold greater potency and 70-fold higher activity than yeast expressing analogous glucocorticoid receptor (GR) proteins. This approach may facilitate the identification of protein targets of biologically active small molecules screened against genetically encoded libraries of proteins expressed in yeast three-hybrid systems.     

Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine,biomedical text mining and network analysis

In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein–protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies.     

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.     

Field-Template,QSAR, Ensemble Molecular Docking,and 3D-RISM Solvation Studies Expose Potential of FDA-Approved Marine Drugs as SARS-CoVID-2 Main Protease Inhibitors

Currently, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has infected people among all countries and is a pandemic as declared by the World Health Organization (WHO). SARS-CoVID-2 main protease is one of the therapeutic drug targets that has been shown to reduce virus replication, and its high-resolution 3D structures in complex with inhibitors have been solved. Previously, we had demonstrated the potential of natural compounds such as serine protease inhibitors eventually leading us to hypothesize that FDA-approved marine drugs have the potential to inhibit the biological activity of SARS-CoV-2 main protease. Initially, field-template and structure–activity atlas models were constructed to understand and explain the molecular features responsible for SARS-CoVID-2 main protease inhibitors, which revealed that Eribulin Mesylate, Plitidepsin, and Trabectedin possess similar characteristics related to SARS-CoVID-2 main protease inhibitors. Later, protein–ligand interactions are studied using ensemble molecular-docking simulations that revealed that marine drugs bind at the active site of the main protease. The three-dimensional reference interaction site model (3D-RISM) studies show that marine drugs displace water molecules at the active site, and interactions observed are favorable. These computational studies eventually paved an interest in further in vitro studies. Finally, these findings are new and indeed provide insights into the role of FDA-approved marine drugs, which are already in clinical use for cancer treatment as a potential alternative to prevent and treat infected people with SARS-CoV-2.