Docking Drug Discovery Research Articles

Molecular Docking in Drug Discovery: Techniques, Applications, and Advancements.

The primary objective of this study is to conduct a comprehensive review of the significance of molecular docking in the field of drug discovery. This includes an examination of the various approaches and methods used in molecular docking, as well as an exploration of the techniques used for interpreting and validating docking results. To gather relevant data, a systematic search was conducted using Web of Science, PubMed, and Google Scholar. The search focused on articles related to molecular docking methodologies and their applications in drug discovery. Additionally, alternative techniques that can be used for more precise simulations of ligand-protein interactions were also considered. Molecular docking has proven to be an incredibly rich and valuable process in the field of drug discovery. Its flexibility allows for the incorporation of advanced computational techniques, thereby enhancing the reliability and efficiency of drug discovery processes. The results of the study highlights the significant strides made in the field of molecular docking, demonstrating its potential to revolutionize drug discovery. Molecular docking continues to evolve, with new advancements being made regularly. Despite the challenges faced, these advancements have significantly contributed to the enhancement of molecular docking, solidifying its position as a crucial tool in the field of drug discovery.

Precise recognition of benzonitrile derivatives with supramolecular macrocycle of phosphorylated cavitand by co-crystallization method

The importance of molecular docking in drug discovery lies in the precise recognition between potential drug compounds and their target receptors, which is generally based on the computational method. However, it will become quite interesting if the rigid cavity structure of supramolecular macrocycles can precisely recognize a series of guests with specific fragments by mimicking molecular docking through co-crystallization experiments. Herein, we report a phenylphosphine oxide-bridged aromatic supramolecular macrocycle, F[3]A1-[P(O)Ph]3, which precisely recognizes benzonitrile derivatives through non-covalent interactions to form key-lock complexes by co-crystallization method. A total of 15 various benzonitrile derivatives as guest molecules are specifically bound by F[3]A1-[P(O)Ph]3 in co-crystal structures, respectively. Notably, among them, crisaborole (anti-dermatitis) and alectinib (anti-cancer) with the benzonitrile fragment, which are two commercial drug molecules approved by the U.S. Food and Drug Administration (FDA), could also form a key-lock complex with F[3]A1-[P(O)Ph]3 in the crystal state, respectively.

Covalent Flexible Peptide Docking in Drug Discovery: Current Challenges and Potential Interventions

Recently, there has been a significant increase in the search for new covalent inhibitors through drug discovery platforms, which has led to the development and implementation of new computational tools, including covalent docking methods to predict the binding mode and affinity of covalent ligands. Since the discovery of insulin nearly a century ago, more than 80 peptide medications have hit the market to treat a wide range of diseases, including diabetes, cancer, osteoporosis, multiple sclerosis, HIV infection, and chronic pain. Electrophilic peptides that form covalent bonds with target proteins have great potential for binding targets that have been previously considered undruggable. Despite the recent advancements in computational performance and docking algorithms, covalent docking still poses a number of challenges. Peptides covalent docking presents additional challenges, the main ones being the choice of the optimal peptide sequence, incorporation of electrophilic warheads, the inherent flexibility of peptide structures, and the fact that, unlike small molecules, peptides do fold. In this review, we present a brief overview of the current state of peptide therapeutics in drug discovery, covalent docking in general, covalent peptide binders, and-with particular emphasis-the difficulties that covalent peptide-protein docking is currently facing and some potential solutions.

VEGF and EGFR signaling pathways are involved in the baicalein attenuation of OVA-induced airway inflammation and airway remodeling in mice

BackgroundAlthough Traditional Chinese Medicine (TCM) has been used for treating asthma for centuries, the understanding of its mechanism of action is still limited. Thus, the purpose of this study was to explore the possible therapeutic effects, and underlying mechanism of baicalein in the treatment of asthma.MethodsFreely availabled atabases (e.g. OMIM, TTD, Genecards, BATMAN-TCM, STITCH 5.0, SEA, SwissTargetPrediction) and software (e.g. Ligplot 2.2.5 and PyMoL) were used for disease drug target prediction and molecular docking by network pharmacology. The efficacy and mechanism of action of baicalein in the treatment of asthma were validated using an ovalbumin (OVA)-induced asthma mouse model and molecular biology techniques.ResultsA total of 1655 asthma-related genes and 161 baicalein-related targets were identified from public databases. Utilizing common databases and software for network pharmacology and molecular docking analysis, seven potential target proteins for the therapeutic effects of baicalein on asthma were selected, including v-akt murine thymoma viral oncogene homolog 1 (AKT1), vascular endothelial growth factor A (VEGFA), epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase Src (SRC), mitogen-activated protein kinase 3 (MAPK3), matrix metallopeptidase 9 (MMP9), and MAPK1. In vivo, baicalein treatment via intraperitoneal injection at a dose of 50 mg/kg significantly reduced airway inflammation, collagen deposition, smooth muscle thickness, lung interleukin (IL)-4 and IL-13 levels, peripheral blood immunoglobulin (Ig)E levels, as well as the count and ratio of eosinophils in bronchoalveolar lavage fluid (BALF) in an OVA-induced asthma mouse model. Further validation by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blotting analysis revealed that the VEGF and EGFR signaling pathways involving VEGFA, MAPK1, MAPK3, and EGFR were inhibited by baicalein in the asthma mouse model.ConclusionBaicalein attenuates airway inflammation and airway remodeling through inhibition of VEGF and EGFR signaling pathways in an OVA-induced asthma mouse model. This will provide a new basis for the development of baicalein as a treatment for asthma and highlights the potential of network pharmacology and molecular docking in drug discovery and development.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

Covalent Docking in Drug Discovery: Scope and Limitations.

Drug discovery efforts for new covalent inhibitors have drastically increased in the last few years. The binding mechanism of covalent compounds entails the formation of a chemical bond between their electrophilic warhead group and the protein of interest. The use of moderately reactive warheads targeting nonconserved nucleophilic residues can improve the affinity and selectivity profiles of covalent binders as compared to their non-covalent analogs. Recent advances have also enabled their use as chemical probes to disclose novel and also less tractable targets. Increasing interest in covalent drug discovery prompted the development of new computational tools, including covalent docking methods, that are available to predict the binding mode and affinity of covalent ligands. These tools integrate conventional non-covalent docking and scoring schemes by modeling the newly formed covalent bond and the interactions occurring at the reaction site. In this review, we provide a thorough analysis of state-of-the-art covalent docking programs by highlighting their main features and current limitations. Focusing on the implemented algorithms, we show the differences in handling the formation of the new covalent bond and their relative impact on the prediction. This analysis provides a comprehensive overview of the current technology and suggests future improvements in computer-aided covalent drug design. Finally, discussing successful retrospective and prospective covalent docking-based virtual screening applications, we intend to identify best practices for the drug discovery community.

Consensus Docking in Drug Discovery

Background: Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets. Methods: In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose. Results: The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus. Conclusion: In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.

Molecular Docking in Drug Discovery: A Review on Anti-snake Venom Development

Snakebite is a frequent accident faced by rural community's dwellers, and it has remained a neglected public health problem in many countries. Snake venom is a complex mixture of proteins, and they participate to envenomation through a diverse array of bioactivities, such as bleeding, inflammation, and pain, cytotoxic, cardiotoxic or neurotoxic effects. The only approved and accepted treatment for snakebite envenoming is the use of antivenoms produced by the purification of IgG immunoglobulins immunized against specific snake venom. However, various technological approaches are being pursued by different research groups, including the use of small-molecule inhibitors, antibody-based bio-therapeutics and peptide-based aptamer against enzymatic toxins and non-enzymatic toxins in snake venom. Modern bioinformatics tools have been recently developed to mine snake venoms, helping focus experimental research on the most potentially interesting toxins. Some computational techniques predict toxin molecular targets, and the binding mode to these targets. This review presents molecular docking studies of potential targeted key enzymes in snake venom.

Investigation of Crystal Structures in Structure-Based Virtual Screening for Protein Kinase Inhibitors.

Protein kinases are important drug targets in several therapeutic areas ,and structure-based virtual screening (SBVS) is an important strategy in discovering lead compounds for kinase targets. However, there are multiple crystal structures available for each target, and determining which one is the most favorable is a key step in molecular docking for SBVS due to the ligand induce-fit effect. This work aimed to find the most desirable crystal structures for molecular docking by a comprehensive analysis of the protein kinase database which covers 190 different kinases from all eight main kinase families. Through an integrated self-docking and cross-docking evaluation, 86 targets were eventually evaluated on a total of 2608 crystal structures. Results showed that molecular docking has great capability in reproducing conformation of crystallized ligands and for each target, the most favorable crystal structure was selected, and the AGC family outperformed the other family targets based on RMSD comparison. In addition, RMSD values, GlideScore, and corresponding bioactivity data were compared and demonstrated certain relationships. This work provides great convenience for researchers to directly select the optimal crystal structure in SBVS-based kinase drug design and further validates the effectiveness of molecular docking in drug discovery.

Molecular Docking: Shifting Paradigms in Drug Discovery.

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Ensemble Docking in Drug Discovery: How Many Protein Configurations from Molecular Dynamics Simulations are Needed To Reproduce Known Ligand Binding?

Ensemble docking in drug discovery or chemical biology uses dynamical simulations of target proteins to generate binding site conformations for docking campaigns. We show that 600 ns molecular dynamics simulations of four G-protein-coupled receptors in their membrane environments generate ensembles of protein configurations that, collectively, are selected by 70?99% of the known ligands of these proteins. Therefore, the process of ligand recognition by conformational selection can be reproduced by combining molecular dynamics and docking calculations. Clustering of the molecular dynamics trajectories, however, does not necessarily identify the protein conformations that are most often selected by the ligands.

Molecular Docking Studies: The Success Should Overrule the Doubts?

The capability of molecular docking in drug discovery can never be underestimated. The success of number of FDA approved drugs for several dreadful diseases have enhanced the speed of drug discovery. Although the inconsistent track record of computational screening may increase the doubts that how well the docking methods can rank the New Chemical Entity. If the method is studied correctly the docking method can have capability to screen and rank a true ligand from false ligand. Here, the performance of molecular docking studies has been evaluated by correlations of experimental binding affinities of 3D ligand-enzyme complexes of Bcr-Abl. Here we evaluate the effect of the protein-ligand complex with the different scoring function and explained how to screen the analogs in better way by using existing computational approach. This review and computational work will certainly boost the confidence of computational biologists.

Theory and applications of covalent docking in drug discovery: merits and pitfalls.

The present art of drug discovery and design of new drugs is based on suicidal irreversible inhibitors. Covalent inhibition is the strategy that is used to achieve irreversible inhibition. Irreversible inhibitors interact with their targets in a time-dependent fashion, and the reaction proceeds to completion rather than to equilibrium. Covalent inhibitors possessed some significant advantages over non-covalent inhibitors such as covalent warheads can target rare, non-conserved residue of a particular target protein and thus led to development of highly selective inhibitors, covalent inhibitors can be effective in targeting proteins with shallow binding cleavage which will led to development of novel inhibitors with increased potency than non-covalent inhibitors. Several computational approaches have been developed to simulate covalent interactions; however, this is still a challenging area to explore. Covalent molecular docking has been recently implemented in the computer-aided drug design workflows to describe covalent interactions between inhibitors and biological targets. In this review we highlight: (i) covalent interactions in biomolecular systems; (ii) the mathematical framework of covalent molecular docking; (iii) implementation of covalent docking protocol in drug design workflows; (iv) applications covalent docking: case studies and (v) shortcomings and future perspectives of covalent docking. To the best of our knowledge; this review is the first account that highlights different aspects of covalent docking with its merits and pitfalls. We believe that the method and applications highlighted in this study will help future efforts towards the design of irreversible inhibitors.

Abstract 4772: Novel drug discovery approach targeting STAT3 for breast cancer therapy using MLSD and drug repositioning

Abstract Constitutive activation of Signal Transducers and Activators of Transcription 3 (STAT3) signaling is frequently detected in human cancer, including breast cancer, and has emerged as a viable molecular target for breast cancer treatment. Novel therapeutic approaches are needed for breast cancer if significant improvements in survival are to occur. To date, few STAT3 inhibitors are available for the therapy of breast cancer. Conventional high throughput screening and virtual docking drug discovery approaches identified many hits, but only a few of them may be developed into drugs. To accelerate STAT3 drug development, we used a novel drug discovery approach with multiple ligand simultaneous docking (MLSD) which we have developed and drug repositioning to search against FDA approved drug and experimental drug database in DrugBank. Using this new method, we identified the FDA-approved drug celecoxib (a COX-2 inhibitor) with a novel function to target STAT3 [Predictive STAT3 binding dissociation constant (Kd)= 8.6 μM]. In addition, a novel celecoxib analog (8A) was developed exhibiting stronger STAT3 binding (Predicted Kd= 0.12 μM, 71-fold stronger than celecoxib). Although 8A has stronger STAT3 binding, it has weaker COX-2 binding (521-fold weaker than celecoxib), suggesting its selectivity on STAT3 versus COX-2. Minimal COX-2 inhibition by 8A may reduce cardiovascular and gastrointestinal risks associated with COX-2 inhibitors. The ability of 8A to inhibit STAT3 was tested through a combination of Western Blot, MTT cell viability assay, and Immunofluorescence Standing. 8A inhibited the P-STAT3 stimulated by IL-6 but not the induction of STAT1, STAT2, STAT4, STAT5, and STAT6 phosphorylation stimulated by IFN-γ, IFN-γ, and IL-4. 8A also inhibited the induction of STAT3 nuclear localization by IL-6, which could prevent STAT3 function as a transcription factor. 8A inhibited STAT3 phosphorylation and resulted in the induction apoptosis on breast cancer cells as evidenced by caspase-3 cleavage. Further, 8A has a lower IC50 than celecoxib and was more potent than celecoxib to inhibit STAT3 phosphorylation and induce apoptosis in breast cancer cells, which confirmed the stronger STAT3 binding energy than celecoxib. In summary, our study is the first report using MLSD and drug repositioning to develop drug-like STAT3 inhibitors. This novel method has a potential to significantly speed up the STAT3 drug development for the therapy of breast cancer and possibly other types of human cancer which express constitutive STAT3 signaling. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4772. doi:1538-7445.AM2012-4772

Abstract A223: Novel drug development approach targeting STAT3 for sarcoma therapy using MLSD and drug reposition.

Abstract Constitutive activation of Signal Transducers and Activators of Transcription 3 (STAT3) signaling is frequently detected in human cancer including rhabdomyosarcoma, osteosarcoma, and Ewing's sarcoma and has emerged as a viable molecular target for sarcoma treatment. Rhabdomyosarcoma is the most common soft tissue sarcoma in pediatric patients{Niu, 1999 #1867}. Osteosarcoma is the most commonly diagnosed primary malignant tumor of bone, which mainly affects adolescents and young adults. Ewing's sarcoma is the second most common primary bone malignancy in pediatric patients. Novel therapeutic approaches are needed for these childhood sarcomas if significant improvements in survival are to occur. To date, however, no known STAT3 inhibitors are available for the therapy of childhood sarcomas yet. Conventional high throughput screening and virtual docking drug discovery approaches identified many hits, but only a few of them may be developed into drugs. To accelerate STAT3 drug development, we used a novel drug discovery approach with multiple ligand simultaneous docking (MLSD), which we have developed and drug repositioning to search against FDA approved drug and experimental drug database in DrugBank. Using this new method, we identified the FDA-approved drug celecoxib (a COX-2 inhibitor) with a novel function to target STAT3 [Predictive STAT3 binding dissociation constant (Kd)=8.6μM]. In addition, a novel celecoxib analog (8A) was developed exhibiting stronger STAT3 binding (Predicted Kd=0.12 M; 71-fold stronger than celecoxib). Although 8A has stronger STAT3 binding, it has weaker COX-2 binding (521-fold weaker than celecoxib), suggesting its selectivity on STAT3 versus COX-2. Minimal COX-2 inhibition of 8A may reduce cardiovascular and gastrointestinal risks associated with COX-2 inhibitors. The ability of 8A to inhibit STAT3 was tested. 8A inhibited the P-STAT3 stimulated by IL-6 but not the induction of STAT1 phosphorylation by IFN-γ. 8A also inhibited the induction of STAT3 nuclear localization by IL-6, which could prevent STAT3 functions as a transcription factor. 8A inhibited STAT3 phosphorylation and resulted in the induction apoptosis on sarcoma cells as evidenced by caspase-3 cleavage. Further, 8A was more potent than celecoxib to inhibit STAT3 phosphorylation and induce apoptosis in sarcoma cells, which confirmed the stronger STAT3 binding energy than celecoxib. In summary, our study is the first report using MLSD and drug repositioning to develop drug-like STAT3 inhibitors. This novel method has a potential to significantly speed up the STAT3 drug development for the therapy of childhood sarcoma and possibly other types of human cancer express constitutive STAT3 signaling. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A223.

A novel class of Hsp90 inhibitors isolated by structure-based virtual screening

A novel class of 3-phenyl-2-styryl-3H-quinazolin-4-one Hsp90 inhibitors with in vitro anti-tumor activity are identified by structure-based virtual screening of a chemical database with docking simulations in the N-terminal ATP-binding site, in vitro ATPase assay using yeast Hsp90, and cell-based Her2 degradation assay in a consecutive fashion. These results exemplify the usefulness of the structure-based virtual screening with molecular docking in drug discovery. The structural features responsible for a tight binding of the inhibitors in the active site of Hsp90 are discussed in detail.