The interaction between a protein and its ligands is one of the basic and most important processes in biological chemistry. Docking methods aim to predict the molecular 3D structure of protein-ligand ...complexes starting from coordinates of the protein and the ligand separately. They are widely used in both industry and academia, especially in the context of drug development projects. AutoDock4 is one of the most popular docking tools and, as for any docking method, its performance is highly system dependent. Knowledge about specific protein-ligand interactions on a particular target can be used to successfully overcome this limitation. Here, we describe how to apply the AutoDock Bias protocol, a simple and elegant strategy that allows users to incorporate target-specific information through a modified scoring function that biases the ligand structure towards those poses (or conformations) that establish selected interactions. We discuss two examples using different bias sources. In the first, we show how to steer dockings towards interactions derived from crystal structures of the receptor with different ligands; in the second example, we define and apply hydrophobic biases derived from Molecular Dynamics simulations in mixed solvents. Finally, we discuss general concepts of biased docking, its performance in pose prediction, and virtual screening campaigns as well as other potential applications.
Spike glycoprotein, a class I fusion protein harboring the surface of SARS-CoV-2 (SARS-CoV-2S), plays a seminal role in the viral infection starting from recognition of the host cell surface ...receptor, attachment to the fusion of the viral envelope with the host cells. Spike glycoprotein engages host Angiotensin-converting enzyme 2 (ACE2) receptors for entry into host cells, where the receptor recognition and attachment of spike glycoprotein to the ACE2 receptors is a prerequisite step and key determinant of the host cell and tissue tropism. Binding of spike glycoprotein to the ACE2 receptor triggers a cascade of structural transitions, including transition from a metastable pre-fusion to a post-fusion form, thereby allowing membrane fusion and internalization of the virus. From ancient times people have relied on naturally occurring substances like phytochemicals to fight against diseases and infection. Among these phytochemicals, flavonoids and non-flavonoids have been the active sources of different anti-microbial agents. We performed molecular docking studies using 10 potential naturally occurring compounds (flavonoids/non-flavonoids) against the SARS-CoV-2 spike protein and compared their affinity with an FDA approved repurposed drug hydroxychloroquine (HCQ). Further, our molecular dynamics (MD) simulation and energy landscape studies with fisetin, quercetin, and kamferol revealed that these molecules bind with the hACE2-S complex with low binding free energy. The study provided an indication that these molecules might have the potential to perturb the binding of hACE2-S complex. In addition, ADME analysis also suggested that these molecules consist of drug-likeness property, which may be further explored as anti-SARS-CoV-2 agents.
Communicated by Ramaswamy H. Sarma
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Neurodegenerative diseases (NDs) including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease are incurable and affect millions of people worldwide. The ...development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.
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Protein docking algorithms aim to predict the 3D structure of a protein complex from the structures of its separated components. In the past, most docking algorithms focused on docking pairs of ...proteins to form dimeric complexes. However, attention is now turning towards the more difficult problem of using docking methods to predict the structures of multicomponent complexes. In both cases, however, the constituent proteins often change conformation upon complex formation, and this can cause many algorithms to fail to detect near‐native binding orientations due to the high number of atomic steric clashes in the list of candidate solutions. An increasingly popular way to retain more near‐native orientations is to define one or more restraints that serve to modulate or override the effect of steric clashes. Here, we present an updated version of our “EROS‐DOCK” docking algorithm which has been extended to dock arbitrary dimeric and trimeric complexes, and to allow the user to define residue‐residue or atom‐atom interaction restraints. Our results show that using even just one residue‐residue restraint in each interaction interface is sufficient to increase the number of cases with acceptable solutions within the top 10 from 51 to 121 out of 173 pairwise docking cases, and to successfully dock 8 out of 11 trimeric complexes.
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A practical guide to large-scale docking Bender, Brian J; Gahbauer, Stefan; Luttens, Andreas ...
Nature protocols,
10/2021, Volume:
16, Issue:
10
Journal Article
Peer reviewed
Open access
Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to ...screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.
In structure-based drug design, scoring functions are widely used for fast evaluation of protein–ligand interactions. They are often applied in combination with molecular docking and de novo design ...methods. Since the early 1990s, a whole spectrum of protein–ligand interaction scoring functions have been developed. Regardless of their technical difference, scoring functions all need data sets combining protein–ligand complex structures and binding affinity data for parametrization and validation. However, data sets of this kind used to be rather limited in terms of size and quality. On the other hand, standard metrics for evaluating scoring function used to be ambiguous. Scoring functions are often tested in molecular docking or even virtual screening trials, which do not directly reflect the genuine quality of scoring functions. Collectively, these underlying obstacles have impeded the invention of more advanced scoring functions. In this Account, we describe our long-lasting efforts to overcome these obstacles, which involve two related projects. On the first project, we have created the PDBbind database. It is the first database that systematically annotates the protein–ligand complexes in the Protein Data Bank (PDB) with experimental binding data. This database has been updated annually since its first public release in 2004. The latest release (version 2016) provides binding data for 16 179 biomolecular complexes in PDB. Data sets provided by PDBbind have been applied to many computational and statistical studies on protein–ligand interaction and various subjects. In particular, it has become a major data resource for scoring function development. On the second project, we have established the Comparative Assessment of Scoring Functions (CASF) benchmark for scoring function evaluation. Our key idea is to decouple the “scoring” process from the “sampling” process, so scoring functions can be tested in a relatively pure context to reflect their quality. In our latest work on this track, i.e. CASF-2013, the performance of a scoring function was quantified in four aspects, including “scoring power”, “ranking power”, “docking power”, and “screening power”. All four performance tests were conducted on a test set containing 195 high-quality protein–ligand complexes selected from PDBbind. A panel of 20 standard scoring functions were tested as demonstration. Importantly, CASF is designed to be an open-access benchmark, with which scoring functions developed by different researchers can be compared on the same grounds. Indeed, it has become a popular choice for scoring function validation in recent years. Despite the considerable progress that has been made so far, the performance of today’s scoring functions still does not meet people’s expectations in many aspects. There is a constant demand for more advanced scoring functions. Our efforts have helped to overcome some obstacles underlying scoring function development so that the researchers in this field can move forward faster. We will continue to improve the PDBbind database and the CASF benchmark in the future to keep them as useful community resources.
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•The article describes the synthesis, characterization, molecular docking, and quantum computational studies of the novel compound.•Hirshfeld surface analysis, molecular electrostatic potential, ...molecular docking are studied in detail.•DFT calculations are performed to optimize the molecular geometry.•HOMO, LUMO frontier molecular orbitals are analyzed.•The compound was investigated as an inhibitor for SARS CoV-2 protease using in-silico molecular docking analysis.
The novel series of hydrazones carrying both pyrazole and triazole moiety were synthesized and characterized using single crystal X-ray diffraction studies. It is found that the compound crystallizes in the triclinic system in the space group P1̅. Hirshfeld analysis was carried out to visualize the noncovalent intermolecular interactions which are responsible for molecular packing in the crystal. Further, density functional theory (DFT) calculations were used to calculate optimized molecular structure, stability, and a few of the quantum chemical parameters. Derived results are in good agreement with the experimentally obtained results. Analysis of frontier molecular orbitals (HOMO and LUMO) and their energy gap of the optimized structure revealed that the compound is stable in the gaseous state. Molecular electron densities, and electrostatic potential maps provide the reactive site and charge distribution of triazole group. Quantum theory of atoms in molecules (QTAIM) and reduced density gradient (RDG) analysis were carried out to explore the weak interactions present in the molecule. In addition, the synthesized compound was investigated as an inhibitor for SARS CoV-2 protease using in-silico molecular docking analysis to understand the mode of binding. The docking results showed the good binding score with Mpro protease 6LU7. Further, molecular dynamics (MD) were carried out for the best hit docked complexes.
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Structure-based virtual ligand screening is emerging as a key paradigm for early drug discovery owing to the availability of high-resolution target structures
and ultra-large libraries of virtual ...compounds
. However, to keep pace with the rapid growth of virtual libraries, such as readily available for synthesis (REAL) combinatorial libraries
, new approaches to compound screening are needed
. Here we introduce a modular synthon-based approach-V-SYNTHES-to perform hierarchical structure-based screening of a REAL Space library of more than 11 billion compounds. V-SYNTHES first identifies the best scaffold-synthon combinations as seeds suitable for further growth, and then iteratively elaborates these seeds to select complete molecules with the best docking scores. This hierarchical combinatorial approach enables the rapid detection of the best-scoring compounds in the gigascale chemical space while performing docking of only a small fraction (<0.1%) of the library compounds. Chemical synthesis and experimental testing of novel cannabinoid antagonists predicted by V-SYNTHES demonstrated a 33% hit rate, including 14 submicromolar ligands, substantially improving over a standard virtual screening of the Enamine REAL diversity subset, which required approximately 100 times more computational resources. Synthesis of selected analogues of the best hits further improved potencies and affinities (best inhibitory constant (K
) = 0.9 nM) and CB
/CB
selectivity (50-200-fold). V-SYNTHES was also tested on a kinase target, ROCK1, further supporting its use for lead discovery. The approach is easily scalable for the rapid growth of combinatorial libraries and potentially adaptable to any docking algorithm.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused about 2 million infections and is responsible for more than 100,000 deaths worldwide. To date, there is no specific drug ...registered to combat the disease it causes, named coronavirus disease 2019 (COVID-19). In the current study, we used an in silico approach to screen natural compounds to find potent inhibitors of the host enzyme transmembrane protease serine 2 (TMPRSS2). This enzyme facilitates viral particle entry into host cells, and its inhibition blocks virus fusion with angiotensin-converting enzyme 2 (ACE2). This, in turn, restricts SARS-CoV-2 pathogenesis. A three-dimensional structure of TMPRSS2 was built using SWISS-MODEL and validated by RAMPAGE. The natural compounds library Natural Product Activity and Species Source (NPASS), containing 30,927 compounds, was screened against the target protein. Two techniques were used in the Molecular Operating Environment (MOE) for this purpose, i.e., a ligand-based pharmacophore approach and a molecular docking-based screening. In total, 2140 compounds with pharmacophoric features were retained using the first approach. Using the second approach, 85 compounds with molecular docking comparable to or greater than that of the standard inhibitor (camostat mesylate) were identified. The top 12 compounds with the most favorable structural features were studied for physicochemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. The low-molecular-weight compound NPC306344 showed significant interaction with the active site residues of TMPRSS2, with a binding energy score of -14.69. Further in vitro and in vivo validation is needed to study and develop an anti-COVID-19 drug based on the structures of the most promising compounds identified in this study.
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