Molecular docking studies of protein-nucleotide complexes using MOLSDOCK (mutually orthogonal Latin squares DOCK)

Understanding the principles of protein receptor recognition, interaction, and association with molecular substrates and inhibitors is of principal importance in the drug discovery process. MOLSDOCK is a molecular docking method that we have recently developed. It uses mutually orthogonal Latin square sampling (together with a variant of the mean field technique) to identify the optimal docking conformation and pose of a small molecule ligand in the appropriate receptor site. Here we report the application of this method to simultaneously identify both the low energy conformation and the one with the best pose in the case of 62 protein-bound nucleotide ligands. The experimental structures of all these complexes are known. We have compared our results with those obtained from two other well-known molecular docking software, viz. AutoDock 4.2.3 and GOLD 5.1. The results show that the MOLSDOCK method was able to sample a wide range of binding modes for these ligands and also scores them well.     

The structural determinants of macrolide-actin binding: in silico insights

By the use of x-ray structures and flexible docking, we have developed the first in silico ligand-based view of the structural determinants of the binding of small molecule mimics of gelsolin, natural products bound to actin. Our technique highlights those residues on the actin binding site forming important hydrophobic and hydrogen-bonding interactions with the ligands. Significantly, through the flexible docking of toxin fragments, we have also identified potential residues on the actin binding site that have yet to be exploited. Guided by these observations, we have demonstrated that kabiramide C can be modified to produce a structure with a predicted binding energy increased by 20% while the molecular mass is reduced by 20%, clearly indicating the potential for future elaboration of structures targeting this important component of the cytoskeleton.     

DFT study of the Lewis acid mediated synthesis of 3-acyltetramic acids

The recent emergence and re-emergence of alphaviruses, in particular the chikungunya virus (CHIKV), in numerous countries has invoked a worldwide threat to human health, while simultaneously generating an economic burden on affected countries. There are currently no vaccines or effective drugs available for the treatment of the CHIKV, and with few lead compounds reported, the vital medicinal chemistry is significantly more challenging. This study reports on the discovery of potential inhibitors for the nsP3 macro domain of CHIKV using molecular docking, virtual screening, and molecular dynamics simulations, as well as work done to evaluate and confirm the active site of nsP3. Virtual screening was carried out based on blind docking as well as focused docking, using the database of 1541 compounds from NCI Diversity Set II, to identify hit compounds for nsP3. The top hit compounds were further subjected to molecular dynamic simulations, yielding a greater understanding of the dynamic behavior of nsP3 and its complexes with various ligands, concurrently confirming the outcomes of docking, and establishing in silico lead compounds which target the CHIKV nsP3 enzyme.

Ensemble Docking in Drug Discovery

Ensemble docking corresponds to the generation of an “ensemble” of drug target conformations in computational structure-based drug discovery, often obtained by using molecular dynamics simulation, that is used in docking candidate ligands. This approach is now well established in the field of early-stage drug discovery. This review gives a historical account of the development of ensemble docking and discusses some pertinent methodological advances in conformational sampling.     

Molecular docking towards drug discovery

Fueled by advances in molecular structure determination, tools for structure-based drug design are proliferating rapidly. Lead discovery through searching of ligand databases with molecular docking techniques represents an attractive alternative to high-throughput random screening. The size of commercial databases imposes severe computational constraints on molecular docking, compromising the level of calculational detail permitted for each putative ligand. We describe alternative philosophies for docking which effectively address this challenge. With respect to the dynamic aspects of molecular recognition, these strategies lie along a spectrum of models bounded by the Lock-and-Key and Induced-Fit theories for ligand binding. We explore the potential of a rigid model in exploiting species specificity and of a tolerant model in predicting absolute ligand binding affinity. Current molecular docking methods are limited primarily by their ability to rank docked complexes; we therefore place particular emphasis on this aspect of the problem throughout our validation of docking strategies.     

Hit clustering can improve virtual fragment screening: CDK2 and PARP1 case studies

Virtual fragment screening could be a promising alternative to existing experimental screening techniques. However, reliable methods of in silico fragment screening are yet to be established and validated. In order to develop such an approach we first checked how successful the existing molecular docking methods can be in predicting fragment binding affinities and poses. Using our Lead Finder docking software the RMSD of the binding energy prediction was observed to be 1.35 kcal/mol(-1) on a set of 26 experimentally characterized fragment inhibitors, and the RMSD of the predicted binding pose from the experimental one was <1.5 ?. Then, we explored docking of 68 fragments obtained from 39 drug molecules for which co-crystal structures were available from the PDB. It appeared that fragments that participate in oriented non-covalent interactions, such as hydrogen bonds and metal coordination, could be correctly docked in 70-80% of cases suggesting the potential success of rediscovering of corresponding drugs by in silico fragment approach. Based on these findings we've developed a virtual fragment screening technique which involved structural filtration of protein-ligand complexes for specific interactions and subsequent clustering in order to minimize the number of preferable starting fragment candidates. Application of this method led to 2 millimolar-scale fragment PARP1 inhibitors with a new scaffold.     

Expanding GPCR homology model binding sites via a balloon potential: A molecular dynamics refinement approach

Homology modeling of G protein-coupled receptors is becoming a widely used tool in drug discovery. However, unrefined models built using the bovine rhodopsin crystal structure as the template, often have binding sites that are too small to accommodate known ligands. Here, we present a novel systematic method to refine model active sites based on a pressure-guided molecular dynamics simulation. A distinct advantage of this approach is the ability to introduce systematic perturbations in model backbone atoms in addition to side chain adjustments. The method is validated on two test cases: (1) docking of retinal into an MD-relaxed structure of opsin and (2) docking of known ligands into a homology model of the CCR2 receptor. In both cases, we show that the MD expansion algorithm makes it possible to dock the ligands in poses that agree with the crystal structure or mutagenesis data.     

Discovery of potent apoptosis signal-regulating kinase 1 inhibitors via integrated computational strategy and biological evaluation

Abstract

Apoptosis signal-regulating Kinase 1 (ASK1) has been confirmed as a potential therapeutic target for the treatment of non-alcoholic steatohepatitis (NASH) disorder and the discovery of ASK1 inhibitors has attracted increasing attention. In this work, a series of in silico methods including pharmacophore screening, docking binding site analysis, protein-ligand interaction fingerprint (PLIF) similarity investigation and molecular docking were applied to find the potential hits from commercial compound databases. Five compounds with potential inhibitory activity were purchased and submitted to biological activity validation. Thus, one hit compound was discovered with micromolar IC₅₀ value (10.59?μM) against ASK1. Results demonstrated that the integration of computation methods and biological test was quite reliable for the discovery of potent ASK1 inhibitors and the strategy could be extended to other similar targets of interest.     

Exploring c-Met kinase flexibility by sampling and clustering its conformational space

It is now widely recognized that the flexibility of both partners has to be considered in molecular docking studies. However, the question how to handle the best the huge computational complexity of exploring the protein binding site landscape is still a matter of debate. Here we investigate the flexibility of c-Met kinase as a test case for comparing several simulation methods. The c-Met kinase catalytic site is an interesting target for anticancer drug design. In particular, it harbors an unusual plasticity compared with other kinases ATP binding sites. Exploiting this feature may eventually lead to the discovery of new anticancer agents with exquisite specificity. We present in this article an extensive investigation of c-Met kinase conformational space using large-scale computational simulations in order to extend the knowledge already gathered from available X-ray structures. In the process, we compare the relevance of different strategies for modeling and injecting receptor flexibility information into early stage in silico structure-based drug discovery pipeline. The results presented here are currently being exploited in on-going virtual screening investigations on c-Met.     

Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach

Recent work has gradually been clarifying the binding site of non-electrophilic agonists on the transient receptor potential A1 (TRPA1). This study searched for non-electrophilic TRPA1 agonists by means of in silico drug discovery techniques based on three-dimensional (3-D) protein structure. First, agonist-bound pocket structures were explored using an advanced molecular dynamics simulation starting from the cryo-electron microscopic structure of TRPA1, and several pocket structures suitable for virtual screening were extracted by structure evaluation using known non-electrophilic TRPA1 agonists. Next, 49 compounds were selected as new non-electrophilic agonist candidates from a library of natural products comprising 10,555 compounds by molecular docking toward these pocket structures. Measurement of the TRPA1 agonist activity of these compounds showed notable TRPA1 activation with three compounds (decanol, 2-ethyl-1-hexanol, phenethyl butanoate). Decanol and 2-ethyl-1-hexanol, which are categorized as fatty alcohols, in particular have a novel chemical scaffold for TRPA1 activation. The results of this study are expected to be of considerable use in understanding the molecular mechanism of TRPA1 recognition by non-electrophilic agonists.     

In silico assessment of potential druggable pockets on the surface of α1-antitrypsin conformers

The search for druggable pockets on the surface of a protein is often performed on a single conformer, treated as a rigid body. Transient druggable pockets may be missed in this approach. Here, we describe a methodology for systematic in silico analysis of surface clefts across multiple conformers of the metastable protein α(1)-antitrypsin (A1AT). Pathological mutations disturb the conformational landscape of A1AT, triggering polymerisation that leads to emphysema and hepatic cirrhosis. Computational screens for small molecule inhibitors of polymerisation have generally focused on one major druggable site visible in all crystal structures of native A1AT. In an alternative approach, we scan all surface clefts observed in crystal structures of A1AT and in 100 computationally produced conformers, mimicking the native solution ensemble. We assess the persistence, variability and druggability of these pockets. Finally, we employ molecular docking using publicly available libraries of small molecules to explore scaffold preferences for each site. Our approach identifies a number of novel target sites for drug design. In particular one transient site shows favourable characteristics for druggability due to high enclosure and hydrophobicity. Hits against this and other druggable sites achieve docking scores corresponding to a K(d) in the μM-nM range, comparing favourably with a recently identified promising lead. Preliminary ThermoFluor studies support the docking predictions. In conclusion, our strategy shows considerable promise compared with the conventional single pocket/single conformer approach to in silico screening. Our best-scoring ligands warrant further experimental investigation.     

Docking, virtual high throughput screening and in silico fragment-based drug design

The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomolecular target of therapeutical interest, the selection or the design of new lead compounds and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on molecular docking, virtual high-throughput screening and fragment-based ligand design.     

High-Performance Drug Discovery: Computational Screening by Combining Docking and Molecular Dynamics Simulations

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estimate the binding free energies. For the top-ranking 1,000 compounds obtained by docking to a target protein, approximately 6,000 molecular dynamics simulations were performed using multiple docking poses in about a week. As a result, the enrichment performance of the top 100 compounds by our approach was improved by 1.6–4.0 times that of the enrichment performance of molecular dockings. This result indicates that the application of molecular dynamics simulations to virtual screening for lead discovery is both effective and practical. However, further optimization of the computational protocols is required for screening various target proteins.     

Towards in silico lead optimization: scores from ensembles of protein/ligand conformations reliably correlate with biological activity

Accurately ranking protein/ligand interactions and distinguishing subtle differences between homologous compounds in a virtual focused library in silico is essential in a structure-based drug discovery program. In order to establish a predictive model to design novel inhibitors of dihydrofolate reductase (DHFR) from the parasitic protozoa, Cryptosporidium hominis, we docked a series of 30 DHFR inhibitors with measured inhibition constants against the crystal structure of the protein. By including protein flexibility and averaging the energies of the 25 lowest protein/ligand conformers we obtained more accurate total nonbonded energies from which we calculated a predicted biological activity. The calculated and measured biological activities showed reliable correlations of 72.9%. Additionally, visual analysis of the ensemble of protein/ligand conformations revealed alternative ligand binding pockets in the active site. Using the same principles we then created a homology model of DHFR from Toxoplasma gondii and docked 11 inhibitors. A correlation of 50.2% between docking score and activity validates both the method and the model. The correlations presented here are particularly compelling considering the high structural similarity of the ligands and the fact that we have used structures derived from crystallographic data and homology modeling. These docking principles may be useful in any lead optimization study where accurate ranking of similar compounds is desired.     

Ligand entry pathways in the ligand binding domain of PPARγ receptor

To address the question of ligand entry process, we report targeted molecular dynamics simulations of the entry of the flexible ionic ligand GW0072 in the ligand binding domain of the nuclear receptor PPARγ. Starting with the ligand outside the receptor the simulations led to a ligand docked inside the binding pocket resulting in a structure very close to the holo-form of the complex. The results showed that entry process is guided by hydrophobic interactions and that entry pathways are very similar to exit pathways. We suggest that TMD method may help in discriminating between ligands generated by in silico docking.     

Diazepam-bound GABAA receptor models identify new benzodiazepine binding-site ligands

Benzodiazepines exert their anxiolytic, anticonvulsant, muscle-relaxant and sedative-hypnotic properties by allosterically enhancing the action of GABA at GABA(A) receptors via their benzodiazepine-binding site. Although these drugs have been used clinically since 1960, the molecular basis of this interaction is still not known. By using multiple homology models and an unbiased docking protocol, we identified a binding hypothesis for the diazepam-bound structure of the benzodiazepine site, which was confirmed by experimental evidence. Moreover, two independent virtual screening approaches based on this structure identified known benzodiazepine-site ligands from different structural classes and predicted potential new ligands for this site. Receptor-binding assays and electrophysiological studies on recombinant receptors confirmed these predictions and thus identified new chemotypes for the benzodiazepine-binding site. Our results support the validity of the diazepam-bound structure of the benzodiazepine-binding pocket, demonstrate its suitability for drug discovery and pave the way for structure-based drug design.     

Rescoring docking hit lists for model cavity sites: predictions and experimental testing

Molecular docking computationally screens thousands to millions of organic molecules against protein structures, looking for those with complementary fits. Many approximations are made, often resulting in low “hit rates.” A strategy to overcome these approximations is to rescore top-ranked docked molecules using a better but slower method. One such is afforded by molecular mechanics-generalized Born surface area (MM-GBSA) techniques. These more physically realistic methods have improved models for solvation and electrostatic interactions and conformational change compared to most docking programs. To investigate MM-GBSA rescoring, we re-ranked docking hit lists in three small buried sites: a hydrophobic cavity that binds apolar ligands, a slightly polar cavity that binds aryl and hydrogen-bonding ligands, and an anionic cavity that binds cationic ligands. These sites are simple; consequently, incorrect predictions can be attributed to particular errors in the method, and many likely ligands may actually be tested. In retrospective calculations, MM-GBSA techniques with binding-site minimization better distinguished the known ligands for each cavity from the known decoys compared to the docking calculation alone. This encouraged us to test rescoring prospectively on molecules that ranked poorly by docking but that ranked well when rescored by MM-GBSA. A total of 33 molecules highly ranked by MM-GBSA for the three cavities were tested experimentally. Of these, 23 were observed to bind—these are docking false negatives rescued by rescoring. The 10 remaining molecules are true negatives by docking and false positives by MM-GBSA. X-ray crystal structures were determined for 21 of these 23 molecules. In many cases, the geometry prediction by MM-GBSA improved the initial docking pose and more closely resembled the crystallographic result; yet in several cases, the rescored geometry failed to capture large conformational changes in the protein. Intriguingly, rescoring not only rescued docking false positives, but also introduced several new false positives into the top-ranking molecules. We consider the origins of the successes and failures in MM-GBSA rescoring in these model cavity sites and the prospects for rescoring in biologically relevant targets.     

Efficient molecular docking of NMR structures: application to HIV-1 protease

Docking ligands into an ensemble of NMR conformers is essential to structure-based drug discovery if only NMR structures are available for the target. However, sequentially docking ligands into each NMR conformer through standard single-receptor-structure docking, referred to as sequential docking, is computationally expensive for large-scale database screening because of the large number of NMR conformers involved. Recently, we developed an efficient ensemble docking algorithm to consider protein structural variations in ligand binding. The algorithm simultaneously docks ligands into an ensemble of protein structures and achieves comparable performance to sequential docking without significant increase in computational time over single-structure docking. Here, we applied this algorithm to docking with NMR structures. The HIV-1 protease was used for validation in terms of docking accuracy and virtual screening. Ensemble docking of the NMR structures identified 91% of the known inhibitors under the criterion of RMSD < 2.0 A for the best-scored conformation, higher than the average success rate of single docking of individual crystal structures (66%). In the virtual screening test, on average, ensemble docking of the NMR structures obtained higher enrichments than single-structure docking of the crystal structures. In contrast, docking of either the NMR minimized average structure or a single NMR conformer performed less satisfactorily on both binding mode prediction and virtual screening, indicating that a single NMR structure may not be suitable for docking calculations. The success of ensemble docking of the NMR structures suggests an efficient alternative method for standard single docking of crystal structures and for considering protein flexibility.     

Describing inhibitor specificity for the amino acid transporter LAT1 from metainference simulations

The human L-type amino acid transporter 1 (LAT1; SLC7A5) is a membrane transporter of amino acids, thyroid hormones, and drugs such as the Parkinson’s disease drug levodopa (L-Dopa). LAT1 is found in the blood-brain barrier, testis, bone marrow, and placenta, and its dysregulation has been associated with various neurological diseases, such as autism and epilepsy, as well as cancer. In this study, we combine metainference molecular dynamics simulations, molecular docking, and experimental testing, to characterize LAT1-inhibitor interactions. We first conducted a series of molecular docking experiments to identify the most relevant interactions between LAT1’s substrate-binding site and ligands, including both inhibitors and substrates. We then performed metainference molecular dynamics simulations using cryoelectron microscopy structures in different conformations of LAT1 with the electron density map as a spatial restraint, to explore the inherent heterogeneity in the structures. We analyzed the LAT1 substrate-binding site to map important LAT1-ligand interactions as well as newly described druggable pockets. Finally, this analysis guided the discovery of previously unknown LAT1 ligands using virtual screening and cellular uptake experiments. Our results improve our understanding of LAT1-inhibitor recognition, providing a framework for rational design of future lead compounds targeting this key drug target.