A shape- and chemistry-based docking method and its use in the design of HIV-1 protease inhibitors

Natural Phytocompounds from Common Indian Spices for Identification of Three Potential Inhibitors of Breast Cancer: A Molecular Modelling Approach

Breast cancer is the second most common cancer-related cause of death for women throughout the globe. In spite of some effective measures, the main concerns with traditional anti-cancer chemotherapy are its low bioavailability, physical side effects, acquired resistance of cancer cells and non-specific targeting. Now researchers have taken the initiative to establish natural product-based therapy methods and to identify viable hits for future lead optimization in the development of breast cancer medication. Our study aims to identify the potent phytocompounds from five very popular Indian spices (Zingiber officinale Roscoe, Cuminum cyminum L., Piper nigrum L., Curcuma longa L., and Allium sativum L.). From these spices, a total of 200 phytocompounds were identified and screened against three target genes, namely, cyclin-dependent kinase 8 (CDK 8), progesterone receptor (PR) and epidermal growth factor receptor (EGFR), through structure-based virtual screening using iGEMDOCK 2.1 software. Based on the binding affinity score, the top three phytocompounds against each target protein (cynaroside (-149.66 Kcal/mol), apigetrin (-139.527 Kcal/mol) and curcumin (-138.149 Kcal/mol) against CDK8; apigetrin (-123.298 Kcal/mol), cynaroside (-118.635 Kcal/mol) and xyloglucan (-113.788 Kcal/mol) against PR; cynaroside (-119.18 Kcal/mol), apigetrin (-105.185 Kcal/mol) and xyloglucan (-105.106 Kcal/mol) against EGFR) were selected. Apigetrin, cynaroside, curcumin, and xyloglucan were finally identified for further docking analysis with the respective three target proteins. Autodock 4.2 was applied to screen the optimal binding position and to assess the relative intensity of binding interactions. In addition, the ADME/T property checks and bioactivity scores analysis of were performed to understand the suitability of these four phytocompounds to be potential candidates for developing effective and non-toxic anticancer agents. Based on this in silico analysis, we believe this study could contribute to current efforts to develop new drugs for treating breast cancer.

Discovery of Cobimetinib as a novel A-FABP inhibitor using machine learning and molecular docking-based virtual screening

Adipocyte fatty acid-binding protein (A-FABP, also called FABP4, aP2) is an adipokine identified as a critical regulator of metabolic function due to its dual functions of fatty acid transport and pro-inflammation. Because of the high therapeutic potential of A-FABP inhibition for the treatment of metabolic diseases and related vascular complications, numerous inhibitors have been developed against A-FABP. However, none of these inhibitors have been approved for use in patients due to severe side effects. Here, we used a virtual screening (VS) strategy to identify potential inhibitors of A-FABP in the latest FDA-approved drug library (∼2600 compounds), aiming to explore the existing drugs with proven safety profiles. We firstly combined ligand-based machine learning and structure-based molecular docking to develop a screening pipeline for identifying A-FABP inhibitors. The screening of FDA-approved drugs identified four compounds (Cobimetinib, Larotrectinib, Pantoprazole, and Vildagliptin) with the highest scores, whose inhibitory effects on A-FABP were further assessed in cellular assays. Among the selected compounds, Cobimetinib significantly inhibited the activation of the JNK/c-Jun signaling pathway by A-FABP in mouse macrophages without causing obvious cytotoxicity. In summary, we present an integrated VS pipeline for A-FABP inhibitor screening, and identified Cobimetinib as a novel A-FABP inhibitor that may be repurposed for the treatment of metabolic diseases and associated vascular complications.

Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.

Docking with AutoDock4

AutoDock is one of the most popular receptor-ligand docking simulation programs. It was first released in the early 1990s and is in continuous development and adapted to specific protein targets. AutoDock has been applied to a wide range of biological systems. It has been used not only for protein-ligand docking simulation but also for the prediction of binding affinity with good correlation with experimental binding affinity for several protein systems. The latest version makes use of a semi-empirical force field to evaluate protein-ligand binding affinity and for selecting the lowest energy pose in docking simulation. AutoDock4.2.6 has an arsenal of four search algorithms to carry out docking simulation including simulated annealing, genetic algorithm, and Lamarckian algorithm. In this chapter, we describe a tutorial about how to perform docking with AutoDock4. We focus our simulations on the protein target cyclin-dependent kinase 2.

Research Techniques Made Simple: Molecular Docking in Dermatology - A Foray into In Silico Drug Discovery

Drug discovery is a complex process with many potential pitfalls. To go to market, a drug must undergo extensive preclinical optimization followed by clinical trials to establish its efficacy and minimize toxicity and adverse events. The process can take 10-15 years and command vast research and development resources costing over $1 billion. The success rates for new drug approvals in the United States are < 15%, and investment costs often cannot be recouped. With the increasing availability of large public datasets (big data) and computational capabilities, data science is quickly becoming a key component of the drug discovery pipeline. One such computational method, large-scale molecular modeling, is critical in the preclinical hit and lead identification process. Molecular modeling involves the study of the chemical structure of a drug and how it interacts with a potential disease-relevant target, as well as predicting its ADMET properties. The scope of molecular modeling is wide and complex. Here we specifically discuss docking, a tool commonly employed for studying drug-target interactions. Docking allows for the systematic exploration of how a drug interacts at a protein binding site and allows for the rank-ordering of drug libraries for prioritization in subsequent studies. This process can be efficiently used to virtually screen libraries containing over millions of compounds.

SAnDReS: A Computational Tool for Docking

Since the early 1980s, we have witnessed considerable progress in the development and application of docking programs to assess protein-ligand interactions. Most of these applications had as a goal the identification of potential new binders to protein targets. Another remarkable progress is taking place in the determination of the structures of protein-ligand complexes, mostly using X-ray diffraction crystallography. Considering these developments, we have a favorable scenario for the creation of a computational tool that integrates into one workflow all steps involved in molecular docking simulations. We had these goals in mind when we developed the program SAnDReS. This program allows the integration of all computational features related to modern docking studies into one workflow. SAnDReS not only carries out docking simulations but also evaluates several docking protocols allowing the selection of the best approach for a given protein system. SAnDReS is a free and open-source (GNU General Public License) computational environment for running docking simulations. Here, we describe the combination of SAnDReS and AutoDock4 for protein-ligand docking simulations. AutoDock4 is a free program that has been applied to over a thousand receptor-ligand docking simulations. The dataset described in this chapter is available for downloading at https://github.com/azevedolab/sandres.

Van der Waals Potential in Protein Complexes

Van der Waals forces are determinants of the formation of protein-ligand complexes. Physical models based on the Lennard-Jones potential can estimate van der Waals interactions with considerable accuracy and with a computational complexity that allows its application to molecular docking simulations and virtual screening of large databases of small organic molecules. Several empirical scoring functions used to evaluate protein-ligand interactions approximate van der Waals interactions with the Lennard-Jones potential. In this chapter, we present the main concepts necessary to understand van der Waals interactions relevant to molecular recognition of a ligand by the binding pocket of a protein target. We describe the Lennard-Jones potential and its application to calculate potential energy for an ensemble of structures to highlight the main features related to the importance of this interaction for binding affinity.

Virtual screening of specific insulin-like growth factor 1 receptor (IGF1R) inhibitors from the National Cancer Institute (NCI) molecular database

Insulin-like growth factor 1 receptor (IGF1R) is an attractive drug target for cancer therapy and research on IGF1R inhibitors has had success in clinical trials. A particular challenge in the development of specific IGF1R inhibitors is interference from insulin receptor (IR), which has a nearly identical sequence. A few potent inhibitors that are selective for IGF1R have been discovered experimentally with the aid of computational methods. However, studies on the rapid identification of IGF1R-selective inhibitors using virtual screening and confidence-level inspections of ligands that show different interactions with IGF1R and IR in docking analysis are rare. In this study, we established virtual screening and binding-mode prediction workflows based on benchmark results of IGF1R and several kinase receptors with IGF1R-like structures. We used comprehensive analysis of the known complexes of IGF1R and IR with their binding ligands to screen specific IGF1R inhibitors. Using these workflows, 17 of 139,735 compounds in the NCI (National Cancer Institute) database were identified as potential specific inhibitors of IGF1R. Calculations of the potential of mean force (PMF) with GROMACS were further conducted for three of the identified compounds to assess their binding affinity differences towards IGF1R and IR.

Comparison of three preprocessing filters efficiency in virtual screening: identification of new putative LXRbeta regulators as a test case

In silico screening methodologies are widely recognized as efficient approaches in early steps of drug discovery. However, in the virtual high-throughput screening (VHTS) context, where hit compounds are searched among millions of candidates, three-dimensional comparison techniques and knowledge discovery from databases should offer a better efficiency to finding novel drug leads than those of computationally expensive molecular dockings. Therefore, the present study aims at developing a filtering methodology to efficiently eliminate unsuitable compounds in VHTS process. Several filters are evaluated in this paper. The first two are structure-based and rely on either geometrical docking or pharmacophore depiction. The third filter is ligand-based and uses knowledge-based and fingerprint similarity techniques. These filtering methods were tested with the Liver X Receptor (LXR) as a target of therapeutic interest, as LXR is a key regulator in maintaining cholesterol homeostasis. The results show that the three considered filters are complementary so that their combination should generate consistent compound lists of potential hits.

Ligand shape emerges in solvent dipole ordering region at ligand binding site of protein

Solvent dipole ordering (SDO), introduced by Higo et al. (Proteins Struct Funct Genet 2000, 40, 193), is an entity that captures an aspect of hydration structure. We have studied SDO in the ligand binding site of two proteins (FK506 binding protein and dihydrofolate reductase) and found that the high SDO regions overlap significantly with the 3D structures of known inhibitors bound to the proteins. Thus, the SDO region might be used to predict the preferred molecular shape of ligands that bind to a protein. Based on this finding, we propose a novel docking procedure using model molecules that mimic the shape of the SDO region. To prove the validity of thisapproach, we performed a redocking experiment for p38 mitogen-activated protein kinase ligands using model molecules for search queries; we succeeded in identifying the binding conformations and binding modes of known inhibitors.

Fully automated molecular mechanics based induced fit protein-ligand docking method

We describe a method for docking a ligand into a protein receptor while allowing flexibility of the protein binding site. The method employs a multistep procedure that begins with the generation of protein and ligand conformations. An initial placement of the ligand is then performed by computing binding site hotspots. This initial placement is followed by a protein side-chain refinement stage that models protein flexibility. The final step of the process is an energy minimization of the ligand pose in the presence of the rigid receptor. Thus the algorithm models flexibility of the protein at two stages, before and after ligand placement. We validated this method by performing docking and cross docking studies of eight protein systems for which crystal structures were available for at least two bound ligands. The resulting rmsd values of the 21 docked protein-ligand complexes showed values of 2 A or less for all but one of the systems examined. The method has two critical benefits for high throughput virtual screening studies. First, no user intervention is required in the docking once the initial binding site selection has been made in the protein. Second, the initial protein conformation generation needs to be performed only once for a given binding region. Also, the method may be customized in various ways depending on the particular scenario in which dockings are being performed. Each of the individual steps of the method is fully independent making it straightforward to explore different variants of the high level workflow to further improve accuracy and performance.

Analogue inhibitors by modifying oseltamivir based on the crystal neuraminidase structure for treating drug-resistant H5N1 virus

The worldwide spread of H5N1 avian influenza and the increasing reports about its resistance to the existing drugs have made a priority for the development of the new anti-influenza molecules. The crystal structure of H5N1 avian influenza neuraminidase reported recently by Russell et al. [R.J. Russell, L.F. Haire, D.J. Stevens, P.J. Collins, Y. P. Lin, G.M. Blackburn, A.J. Hay, S.J. Gamblin, J.J. Skehel, The structure of H5N1 avian influenza neuraminidase suggests new opportunities for drug design, Nature 443 (2006) 45-49] have provided new opportunities for drug design in this regard. It is revealed through the structure that the active sites of the group-1 neuraminidases, which contain the N1 subtype, have a very different three-dimensional structure from those of group-2 neuraminidases. The key difference is in the 150-loop cavity adjacent to the conserved active site in neuraminidase. Based on these findings and by modifying oseltamivir, six analog inhibitors were proposed as candidates for developing inhibitors against H5N1 virus, particularly against the oseltamivir-resistant H5N1 virus strain.

AFMoC enhances predictivity of 3D QSAR: a case study with DOXP-reductoisomerase

We present structure-activity relationships for 43 inhibitors of 1-deoxyxylulose-5-phosphate (DOXP)-reductoisomerase, derived from protein-based docking, ligand-based 3D QSAR, and a combination of both approaches as realized by AFMoC (adaptation of fields for molecular comparison). DOXP-reductoisomerase (DXR) is a key enzyme of the non-mevalonate pathway for isoprenoid building blocks. This target has been characterized as having potential in the treatment of malaria with fosmidomycin, an established DXR inhibitor, presently in clinical trials. As part of an effort to optimize the properties of fosmidomycin, analogues have been synthesized and tested to gain further insights into the primary determinants of structural affinity. These data have been used to create a predictive model for DXR inhibition applying data taken from several DXR X-ray structures. These structures still leave the active fosmidomycin conformation and detailed reaction mechanism undetermined. This together with the small inhibitor data set provides a major challenge for presently available docking programs and 3D QSAR tools. To overcome these difficulties we have applied the AFMoC protocol. AFMoC makes more efficient use of available modeling data by tailoring DrugScore knowledge-based potentials specifically toward a given protein using inhibitor potency data. While 3D QSAR methods achieved valid models which lack predictivity, AFMoC was found to provide superior performance, based both on cross-validation runs as well as for inhibitors not considered in the training set. In particular, AFMoC's ability to gradually transform between generally applicable unadapted interaction fields to case specifically adapted ones proved to be of major importance. Using 50% tailored fields was found to permit the precise prediction of binding affinities for related ligands without losing the capability to estimate the affinities of structurally distinct inhibitors.

Target-biased scoring approaches and expert systems in structure-based virtual screening

Structure-based virtual screening followed by selection of a top fraction of the rank-ordered result list suffers from many false positives and false negatives because the general scoring functions are not accurate enough. Many approaches have emerged to address this problem by including knowledge about the specific target in the scoring and selection steps. This target bias can include requirements for critical interactions, use of pharmacophore patterns or interaction patterns found in known co-crystal structures, and similarity to known ligands. Such biases are implemented in methods that vary from filtering tools for pre- or post-processing, to expert systems, quantitative (re)scoring functions, and docking tools that generate target-biased poses.

Validation of automated docking programs for docking and database screening against RNA drug targets

The increasing awareness of the essential role of RNA in controlling viral replication and in bacterial protein synthesis emphasizes the potential of ribonucleoproteins as targets for developing new antibacterial and antiviral drugs. RNA forms well defined three-dimensional structures with clefts and binding pockets reminiscent of the active sites of proteins. Furthermore, it precedes proteins in the translation pathway; inhibiting the function of a single RNA molecule would result in inhibition of multiple proteins. Thus, small molecules that bind RNA specifically would combine the advantages of antisense and RNAi strategies with the much more favorable medicinal chemistry of small-molecule therapeutics. The discovery of small-molecule inhibitors of RNA with attractive pharmacological potential would be facilitated if we had available effective computational tools of structure-based drug design. Here, we systematically test automated docking tools developed for proteins using existing three-dimensional structures of RNA-small molecule complexes. The results show that the native structures can generally be reproduced to within 2.5 angstroms more than 50-60% of the time. For more than half of the test complexes, the native ligand ranked among the top 10% compounds in a database-scoring test. Through this work, we provide parameters for the validated application of automated docking tools to the discovery of new inhibitors of RNA function.

LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites

We present a new shape-based method, LigandFit, for accurately docking ligands into protein active sites. The method employs a cavity detection algorithm for detecting invaginations in the protein as candidate active site regions. A shape comparison filter is combined with a Monte Carlo conformational search for generating ligand poses consistent with the active site shape. Candidate poses are minimized in the context of the active site using a grid-based method for evaluating protein-ligand interaction energies. Errors arising from grid interpolation are dramatically reduced using a new non-linear interpolation scheme. Results are presented for 19 diverse protein-ligand complexes. The method appears quite promising, reproducing the X-ray structure ligand pose within an RMS of 2A in 14 out of the 19 complexes. A high-throughput screening study applied to the thymidine kinase receptor is also presented in which LigandFit, when combined with LigScore, an internally developed scoring function, yields very good hit rates for a ligand pool seeded with known actives.

DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases

In this paper we describe the search strategies developed for docking flexible molecules to macomolecular sites that are incorporated into the widely distributed DOCK software, version 4.0. The search strategies include incremental construction and random conformation search and utilize the existing Coulombic and Lennard-Jones grid-based scoring function. The incremental construction strategy is tested with a panel of 15 crystallographic testcases, created from 12 unique complexes whose ligands vary in size and flexibility. For all testcases, at least one docked position is generated within 2 A of the crystallographic position. For 7 of 15 testcases, the top scoring position is also within 2 A of the crystallographic position. The algorithm is fast enough to successfully dock a few testcases within seconds and most within 100 s. The incremental construction and the random search strategy are evaluated as database docking techniques with a database of 51 molecules docked to two of the crystallographic testcases. Incremental construction outperforms random search and is fast enough to reliably rank the database of compounds within 15 s per molecule on an SGI R10000 cpu.

High-throughput and virtual screening: core lead discovery technologies move towards integration

In addition to high-throughput screening (HTS), the main lead discovery technology employed by most pharmaceutical companies today is virtual screening (VS). Although the two techniques have somewhat different philosophical origins, they contain many synergies that can potentially enhance the lead discovery process. Here, we describe many of the latest developments in VS technology with particular emphasis on their potential impact on HTS in, for example, focussed screening and data mining. In addition, we highlight key issues that need to be addressed before the potential of such efforts can be fully realized.

Towards understanding the mechanisms of molecular recognition by computer simulations of ligand-protein interactions

The thermodynamic and kinetic aspects of molecular recognition for the methotrexate (MTX)-dihydrofolate reductase (DHFR) ligand-protein system are investigated by the binding energy landscape approach. The impact of 'hot' and 'cold' errors in ligand mutations on the thermodynamic stability of the native MTX-DHFR complex is analyzed, and relationships between the molecular recognition mechanism and the degree of ligand optimization are discussed. The nature and relative stability of intermediates and thermodynamic phases on the ligand-protein association pathway are studied, providing new insights into connections between protein folding and molecular recognition mechanisms, and cooperativity of ligand-protein binding. The results of kinetic docking simulations are rationalized based on the thermodynamic properties determined from equilibrium simulations and the shape of the underlying binding energy landscape. We show how evolutionary ligand selection for a receptor active site can produce well-optimized ligand-protein systems such as MTX-DHFR complex with the thermodynamically stable native structure and a direct transition mechanism of binding from unbound conformations to the unique native structure.

Predicting structural effects in HIV-1 protease mutant complexes with flexible ligand docking and protein side-chain optimization

We present a computational approach for predicting structures of ligand-protein complexes and analyzing binding energy landscapes that combines Monte Carlo simulated annealing technique to determine the ligand bound conformation with the dead-end elimination algorithm for side-chain optimization of the protein active site residues. Flexible ligand docking and optimization of mobile protein side-chains have been performed to predict structural effects in the V32I/I47V/V82I HIV-1 protease mutant bound with the SB203386 ligand and in the V82A HIV-1 protease mutant bound with the A77003 ligand. The computational structure predictions are consistent with the crystal structures of these ligand-protein complexes. The emerging relationships between ligand docking and side-chain optimization of the active site residues are rationalized based on the analysis of the ligand-protein binding energy landscape.

Alkylation of a catalytic aspartate group of the SIV protease by an epoxide inhibitor

Specific irreversible inhibition of the SIV protease by FMOC-protected piperidine epoxide 1 involves alkylation of the protein. Tryptic digestion of the alkylated protein and mass spectrometric analysis of the peptides identify an active site aspartic acid (Asp-25) as the single residue that is alkylated. Computer modeling of 1 bound in the crystal structure of the SIV protease using DOCK 3.5 indicates that 1 has appropriate access to the active site. It is able to align in an orientation that allows a proton to be transferred to the epoxide from one of the catalytic aspartic acid groups in conjunction with nucleophilic attack on the epoxide of the carboxylate moiety of the second catalytic aspartic acid residue. Hydrophobic interactions are not optimal for this process due, in part, to the rigidity of the inhibitor ring system and the planar conformation of the amide. The combination of modeling with protein alkylation can provide insights into structural modifications of the inhibitor that may lead to improved inhibitory activity.

Structure-based discovery of ligands targeted to the RNA double helix

Ligands capable of specific recognition of RNA structures are of interest in terms of the principles of molecular recognition as well as potential chemotherapeutic applications. We have approached the problem of identifying small molecules with binding specificity for the RNA double helix through application of the DOCK program [Kuntz, I. D., Meng, E. C., and Shoichet, B. K. (1994) Acc. Chem. Res. 27, 117-123], a structure-based method for drug discovery. A series of lead compounds was generated through a database search for ligands with shape complementarity to the RNA deep major groove. Compounds were then evaluated with regard to their fit into the minor groove of B DNA. Those compounds predicted to have an optimal fit to the RNA groove and strong discrimination against DNA were examined experimentally. Of the 11 compounds tested, 3, all aminoglycosides, exhibited pronounced stabilization of RNA duplexes against thermal denaturation with only marginal effects on DNA duplexes. One compound, lividomycin, was examined further, and shown to facilitate the ethanol-induced B to A transition in calf thymus DNA. Fluorine NMR solvent isotope shift measurements on RNA duplexes containing 5-fluorouracil provided evidence that lividomycin binds in the RNA major groove. Taken together, these results indicate that lividomycin recognizes the general features of the A conformation of nucleic acids through deep groove binding, confirming the predictions of our DOCK analysis. This approach may be of general utility for identifying ligands possessing specificity for additional RNA structures as well as other nucleic acid structural motifs.

Docking small-molecule ligands into active sites

Docking involves the development of computer algorithms that evaluate the binding modes of putative ligands in receptor sites. The principal advances of the past year have been the development of new algorithmic approaches, several of which incorporate conformational flexibility, and the increased use of docking to identify leads in drug-discovery programmes.