Comparison of current docking tools for the simulation of inhibitor binding by the transmembrane domain of the sarco/endoplasmic reticulum calcium ATPase

In Silico Structure-Based Vaccine Design

Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

Fragmented blind docking: a novel protein-ligand binding prediction protocol

In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.

A review on phytochemical and pharmacological facets of tropical ethnomedicinal plants as reformed DPP-IV inhibitors to regulate incretin activity

Type 2 diabetes mellitus is a metabolic disorder resulting from impaired insulin secretion and resistance. Dipeptidyl peptidase (DPP)-IV is an enzyme known to trigger the catalysis of insulinotropic hormones, further abating the endogenous insulin levels and elevating the glucose levels in blood plasma. In the field of drug development, DPP-IV inhibitors have opened up numerous opportunities for leveraging this target to generate compounds as hypoglycemic agents by regulating incretin activity and subsequently decreasing blood glucose levels. However, the practice of synthetic drugs is an apparent choice but poses a great pharmacovigilance issue due to their incessant undesirable effects. The ideology was set to inventively look upon different ethnomedicinal plants for their anti-diabetic properties to address these issues. To date, myriads of phytochemicals are characterized, eliciting an anti-diabetic response by targeting various enzymes and augmenting glucose homeostasis. Antioxidants have played a crucial role in alleviating the symptoms of diabetes by scavenging free radicals or treating the underlying causes of metabolic disorders and reducing free radical formation. Plant-based DPP-IV inhibitors, including alkaloids, phenolic acid, flavonoids, quercetin, and coumarin, also possess antioxidant capabilities, providing anti-diabetic and antioxidative protection. This review article provides a new gateway for exploring the ability of plant-based DPP-IV inhibitors to withstand oxidative stress under pathological conditions related to diabetes and for reforming the strategic role of ethnomedicinal plants as potent DPP-IV inhibitors through the development of polyherbal formulations and nanophytomedicines to regulate incretin activity.

Structure-based design of 5'-substituted 1,2,3-triazolylated oseltamivir derivatives as potent influenza neuraminidase inhibitors

Resistant viruses containing mutant neuraminidases (NAs) with diminished drug affinity continue to emerge, and new anti-influenza agents are urgently required. Several potent inhibitors targeting the hydrophobic 150-cavity of viral NAs have been developed by modifying the antiviral drugs, oseltamivir carboxylate (OSC) and zanamivir, with hydrophobic groups. Here, we describe a different strategy for exploring novel and efficient NA inhibitors by targeting the charged amino acid residues around the entrance to the 150-cavity. We synthesized a C5-substituted OSC derivative (1e) with a 4'-phenyl-1,2,3-triazolyl group capable of entering the 150-cavity, and solved the crystal structure of 1e in complex with influenza A virus N5 NA. Using the resulting structural information, we next designed and synthesized two series of OSC derivatives carrying various polar substituents at the triazolyl group of 1e and 2e, with 2e being a 5'-phenyl-1,2,3-triazole regioisomer of 1e. The NA inhibition assays demonstrated that the 2 series (2e-n) generally had superior activity compared with the 1 series (1e-n). Compound 2j, bearing a 3-phenylamino group on the triazole ring, was the most potent inhibitor of all tested NAs including an N2 NA containing the E119V OSC-resistant mutation. Moreover, 2j potently inhibited viral replication in vitro, and molecular docking studies revealed that its phenylamino group can form an additional strong hydrogen bond with residue D151 near the entrance of the 150-cavity. The design method described in this study provides useful insights into the development of novel NA inhibitors. Compound 2j warrants further structural optimization to obtain a candidate for clinical use.

Targeting oncogenic Notch signaling with SERCA inhibitors

P-type ATPase inhibitors are among the most successful and widely prescribed therapeutics in modern pharmacology. Clinical transition has been safely achieved for H+/K+ ATPase inhibitors such as omeprazole and Na+/K+-ATPase inhibitors like digoxin. However, this is more challenging for Ca2+-ATPase modulators due to the physiological role of Ca2+ in cardiac dynamics. Over the past two decades, sarco-endoplasmic reticulum Ca2+-ATPase (SERCA) modulators have been studied as potential chemotherapy agents because of their Ca2+-mediated pan-cancer lethal effects. Instead, recent evidence suggests that SERCA inhibition suppresses oncogenic Notch1 signaling emerging as an alternative to γ-secretase modulators that showed limited clinical activity due to severe side effects. In this review, we focus on how SERCA inhibitors alter Notch1 signaling and show that Notch on-target-mediated antileukemia properties of these molecules can be achieved without causing overt Ca2+ cellular overload.

LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates

Structure-based virtual screening (VS) uses computer docking to prioritize candidate small-molecule ligands for subsequent experimental testing. Docking programs evaluate molecular binding in part by predicting the geometry with which a given compound might bind a target receptor (e.g., the docked "pose" relative to a protein target). Candidate ligands predicted to participate in the same intermolecular interactions typical of known ligands (or ligands that bind related proteins) are arguably more likely to be true binders. Some docking programs allow users to apply constraints during the docking process with the goal of prioritizing these critical interactions. But these programs often have restrictive and/or expensive licenses, and many popular open-source docking programs (e.g., AutoDock Vina) lack this important functionality. We present LigGrep, a free, open-source program that addresses this limitation. As input, LigGrep accepts a protein receptor file, a directory containing many docked-compound files, and a list of user-specified filters describing critical receptor/ligand interactions. LigGrep evaluates each docked pose and outputs the names of the compounds with poses that pass all filters. To demonstrate utility, we show that LigGrep can improve the hit rates of test VS targeting H. sapiens poly(ADPribose) polymerase 1 (HsPARP1), H. sapiens peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (HsPin1p), and S. cerevisiae hexokinase-2 (ScHxk2p). We hope that LigGrep will be a useful tool for the computational biology community. A copy is available free of charge at http://durrantlab.com/liggrep/ .

Targeting the C-Terminal Domain Small Phosphatase 1

The human C-terminal domain small phosphatase 1 (CTDSP1/SCP1) is a protein phosphatase with a conserved catalytic site of DXDXT/V. CTDSP1’s major activity has been identified as dephosphorylation of the 5th Ser residue of the tandem heptad repeat of the RNA polymerase II C-terminal domain (RNAP II CTD). It is also implicated in various pivotal biological activities, such as acting as a driving factor in repressor element 1 (RE-1)-silencing transcription factor (REST) complex, which silences the neuronal genes in non-neuronal cells, G1/S phase transition, and osteoblast differentiation. Recent findings have denoted that negative regulation of CTDSP1 results in suppression of cancer invasion in neuroglioma cells. Several researchers have focused on the development of regulating materials of CTDSP1, due to the significant roles it has in various biological activities. In this review, we focused on this emerging target and explored the biological significance, challenges, and opportunities in targeting CTDSP1 from a drug designing perspective.

Fragment Pose Prediction Using Non-equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC)-based method (NCMC + MD) perform in predicting binding modes for a set of 12 fragment-like molecules, which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures. We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 μs each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions between binding modes in our study. Following this, we build Markov State Models to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD, we have better success in sampling the crystal structure while obtaining the correct populations.

KRDS: a web server for evaluating drug resistance mutations in kinases by molecular docking

Kinases are major targets of anti-cancer therapies owing to their importance in signaling processes that regulate cell growth and proliferation. However, drug resistance has emerged as a major obstacle to cancer therapy. Resistance to drugs has various underlying mechanisms, including the acquisition of mutations at drug binding sites and the resulting reduction in drug binding affinity. Therefore, the identification of mutations that are relevant to drug resistance may be useful to overcome this issue. We hypothesized that these mutations can be identified by combining recent advances in computational methods for protein structure modeling and ligand docking simulation. Hence, we developed a web-based tool named the Kinase Resistance Docking System (KRDS) that enables the assessment of the effects of mutations on kinase-ligand interactions. KRDS receives a list of mutations in kinases, generates structural models of the mutants, performs docking simulations, and reports the results to users. The changes in docking scores and docking conformations can be analyzed to infer the effects of mutations on drug binding and drug resistance. We expect our tool to improve our understanding of drug binding mechanisms and facilitate the development of effective new drugs to overcome resistance related to kinases; it may be particularly useful for biomedical researchers who are not familiar with computational environments. Our tool is available at http://bcbl.kaist.ac.kr/KRDS/.

Computational tools for enzyme improvement: why everyone can - and should - use them

This review presents computational methods that experimentalists can readily use to create smart libraries for enzyme engineering and to obtain insights into protein-substrate complexes. Computational tools have the reputation of being hard to use and inaccurate compared to experimental methods in enzyme engineering, yet they are essential to probe datasets of ever-increasing size and complexity. In recent years, bioinformatics groups have made a huge leap forward in providing user-friendly interfaces and accurate algorithms for experimentalists. These methods guide efficient experimental planning and allow the enzyme engineer to rationalize time and resources. Computational tools nevertheless face challenges in the realm of transient modern technology.

Discovery of Tricyclic Clerodane Diterpenes as Sarco/Endoplasmic Reticulum Ca(2+)-ATPase Inhibitors and Structure-Activity Relationships

Tricyclic clerodane diterpenes (TCDs) are natural compounds that often show potent cytotoxicity for cancer cells, but their mode of action remains elusive. A computationally based similarity search (CDRUG), combined with principal component analysis (ChemGPS-NP) and docking calculations (GOLD 5.2), suggested TCDs to be inhibitors of the sarco/endoplasmic reticulum Ca(2+)-ATPase (SERCA) pump, which is also the target of the sesquiterpene lactone thapsigargin. Biochemical studies were performed with 11 TCDs on purified rabbit skeletal muscle sarcoplasmic reticulum membranes, which are highly enriched with the SERCA1a isoform. Casearborin D (2) exhibited the highest affinity, with a KD value of 2 μM and giving rise to complete inhibition of SERCA1a activity. Structure-activity relationships revealed that functionalization of two acyl side chains (R1 and R4) and the hydrophobicity imparted by the aliphatic chain at C-9, as well as a C-3,C-4 double bond, play crucial roles for inhibitory activity. Docking studies also suggested that hydrophobic interactions in the binding site, especially with Phe256 and Phe834, may be important for a strong inhibitory activity of the TCDs. In conclusion, a novel class of SERCA inhibitory compounds is presented.

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets

Background

Computational approaches have emerged as an instrumental methodology in modern research. For example, virtual screening by molecular docking is routinely used in computer-aided drug discovery. One of the critical parameters for ligand docking is the size of a search space used to identify low-energy binding poses of drug candidates. Currently available docking packages often come with a default protocol for calculating the box size, however, many of these procedures have not been systematically evaluated.

Methods

In this study, we investigate how the docking accuracy of AutoDock Vina is affected by the selection of a search space. We propose a new procedure for calculating the optimal docking box size that maximizes the accuracy of binding pose prediction against a non-redundant and representative dataset of 3,659 protein-ligand complexes selected from the Protein Data Bank. Subsequently, we use the Directory of Useful Decoys, Enhanced to demonstrate that the optimized docking box size also yields an improved ranking in virtual screening. Binding pockets in both datasets are derived from the experimental complex structures and, additionally, predicted by eFindSite.

Results

A systematic analysis of ligand binding poses generated by AutoDock Vina shows that the highest accuracy is achieved when the dimensions of the search space are 2.9 times larger than the radius of gyration of a docking compound. Subsequent virtual screening benchmarks demonstrate that this optimized docking box size also improves compound ranking. For instance, using predicted ligand binding sites, the average enrichment factor calculated for the top 1 % (10 %) of the screening library is 8.20 (3.28) for the optimized protocol, compared to 7.67 (3.19) for the default procedure. Depending on the evaluation metric, the optimal docking box size gives better ranking in virtual screening for about two-thirds of target proteins.

Conclusions

This fully automated procedure can be used to optimize docking protocols in order to improve the ranking accuracy in production virtual screening simulations. Importantly, the optimized search space systematically yields better results than the default method not only for experimental pockets, but also for those predicted from protein structures. A script for calculating the optimal docking box size is freely available at www.brylinski.org/content/docking-box-size.

Graphical Abstract

Docking assay of small molecule antivirals to p7 of HCV

Protein p7 of HCV is a 63 amino acid channel forming membrane protein essential for the progression of viral infection. With this momentousness, p7 emerges as an important target for antiviral therapy. A series of small molecule drugs, such as amantadine, rimantadine, amiloride, hexamethylene amiloride, NN-DNJ and BIT225 have been found to affect the channel activity. These compounds are docked against monomeric and hexameric structures of p7 taken at various time steps from a molecular dynamics simulation of the protein embedded in a hydrated lipid bilayer. The energetics of binding identifies the guanidine based ligands as the most potent ligands. The adamantanes and NN-DNJ show weaker binding energies. The lowest energy poses are those at the site of the loop region for the monomer and hexamer. For the latter, the poses show a tendency of the ligand to face the lumen of the pore. The mode of binding is that of a balance between hydrophobic interactions and hydrogen bond formation with backbone atoms of the protein.

Structural requirements for inhibitory effects of bisphenols on the activity of the sarco/endoplasmic reticulum calcium ATPase

Bisphenols (BPs) are a class of small organic compounds with widespread industrial applications. Previous studies have identified several BPs that interfere with the activity of the ion-translocating enzyme sarco/endoplasmic reticulum calcium ATPase (SERCA). In order to define the molecular determinants of BP-mediated SERCA inhibition, we conducted enzyme activity assays with rabbit SERCA to determine the inhibitory potencies of 27 commercially available BPs, which were the basis for structure-activity relationships. The most potent BPs inhibited SERCA at low micromolar concentrations and carried at their two phenyl rings multiple non-polar substituents, such as small alkyl groups or halides. Furthermore, the presence of methyl groups or a cyclohexyl group at the central carbon atom connecting the two phenyl moieties correlated with good potencies. For a characterization and visualization of enzyme/inhibitor interactions, molecular docking was performed, which suggested that hydrogen bonding with Asp254 and hydrophobic interactions were the major driving forces for BP binding to SERCA. Calcium imaging studies with a selection of BPs showed that these inhibitors were able to increase intracellular calcium levels in living human cells, a behavior consistent with that of a SERCA inhibitor.

Homology modeling of dopamine D2 and D3 receptors: molecular dynamics refinement and docking evaluation

Dopamine (DA) receptors, a class of G-protein coupled receptors (GPCRs), have been targeted for drug development for the treatment of neurological, psychiatric and ocular disorders. The lack of structural information about GPCRs and their ligand complexes has prompted the development of homology models of these proteins aimed at structure-based drug design. Crystal structure of human dopamine D₃ (hD₃) receptor has been recently solved. Based on the hD₃ receptor crystal structure we generated dopamine D₂ and D₃ receptor models and refined them with molecular dynamics (MD) protocol. Refined structures, obtained from the MD simulations in membrane environment, were subsequently used in molecular docking studies in order to investigate potential sites of interaction. The structure of hD₃ and hD_2L receptors was differentiated by means of MD simulations and D₃ selective ligands were discriminated, in terms of binding energy, by docking calculation. Robust correlation of computed and experimental K_i was obtained for hD₃ and hD_2L receptor ligands. In conclusion, the present computational approach seems suitable to build and refine structure models of homologous dopamine receptors that may be of value for structure-based drug discovery of selective dopaminergic ligands.

Identifying the sarco(endo)plasmic reticulum Ca2+ ATPase (SERCA) as a potential target for hypericin--a theoretical study

The exact cellular target for the potent anti-cancer agent hypericin has not yet been determined; this thus encourages the application of computational chemistry tools to be employed in order to provide insights that can be employed in further drug development studies. In the present study computational docking and molecular dynamics simulations are applied to investigate possible interactions between hypericin and the Ca(2+) pump SERCA as proposed in the literature. Hypericin was found to bind strongly both in pockets within the transmembrane region and in the cytosolic region of the protein, although the two studied isoforms of SERCA differ slightly in their preferred binding sites. The calculated binding energies for hypericin in the four investigated sites were of the same magnitude as for thapsigargin (TG), the most potent SERCA inhibitor, or in the range between TG and di-tert-butylhydroquinone (BHQ), which is also known to possess inhibitory activity. The hydrophobic character of hypericin indicates that the molecule initially binds in the ER membrane from which it diffuses into the transmembrane region of the protein and to binding pockets therein. The transmembrane TG and BHQ binding pockets provide suitable locations for hypericin as they allow for favourable interactions with the lipid tails that surround these. High binding energies were noted for hypericin in these pockets and are expected to constitute highly possible binding sites due to their accessibility from the ER membrane. Hypericin most likely binds to both isoforms of SERCA and acts as an inhibitor or, under light irradiation, as a singlet oxygen generator that in turn degrades the protein or induces lipid peroxidation.

Discovery of novel SERCA inhibitors by virtual screening of a large compound library

Two screening protocols based on recursive partitioning and computational ligand docking methodologies, respectively, were employed for virtual screens of a compound library with 345,000 entries for novel inhibitors of the enzyme sarco/endoplasmic reticulum calcium ATPase (SERCA), a potential target for cancer chemotherapy. A total of 72 compounds that were predicted to be potential inhibitors of SERCA were tested in bioassays and 17 displayed inhibitory potencies at concentrations below 100 μM. The majority of these inhibitors were composed of two phenyl rings tethered to each other by a short link of one to three atoms. Putative interactions between SERCA and the inhibitors were identified by inspection of docking-predicted poses and some of the structural features required for effective SERCA inhibition were determined by analysis of the classification pattern employed by the recursive partitioning models.