Protein-ligand Docking Research Articles

Inhibitory effect of gallic acid from Thunbergia mysorensis against α-glucosidase, α-amylase, aldose reductase and their interaction: Inhibition kinetics and molecular simulations

In this exploration, we assessed the antihyperglycaemic properties of methanol extract of flowers of Thunbergia mysorensis (MeT) against α-glucosidase, α-amylase and aldose reductase enzymes for the effective management of postprandial hyperglycemia. Hyperglycemia occurs when the body lacks enough insulin or is unable to correctly utilize it. MeT inhibited both the carbohydrate digestive enzymes (α-glucosidase and α-amylase) and aldose reductase, which are vital for the therapeutic control of postprandial hyperglycaemia. MeT was also found to have significant antioxidant activity. Using several spectroscopic approaches, the primary active component found in MeT was identified as gallic acid. With low Ki values, gallic acid significantly inhibited α-glucosidase (30.86 µg/mL) and α-amylase (6.50 µg/mL). Also, MeT and gallic acid both inhibited aldose reductase effectively, corresponding to an IC50 value of 3.31 and 3.05 µg/mL. Our findings imply that the presence of polyphenol compounds (identified via HPLC analysis) is more likely to be responsible for the antihyperglycaemic role exhibited by MeT via the inhibition of α-glucosidase and the polyol pathway. Further, gallic acid interacted with the key residues of the active sites of α-glucosidase (−6.4 kcal/mol), α-amylase (−5.8 kcal/mol) and aldose reductase (−5.8 kcal/mol) as observed in the protein-ligand docking. It was also predicted that gallic acid was stable inside the binding pockets of the target enzymes during molecular dynamics simulation. Overall, gallic acid derived from MeT via bioassay-guided isolation emerges as a natural antidiabetic drug and can be taken into in vivo and clinical studies shortly. Communicated by Ramaswamy H. Sarma

In silico identification and characterization of potential druggable targets among hypothetical proteins of Leptospira interrogans serovar Copenhageni: a comprehensive bioinformatics approach

Leptospirosis is one of the neglected zoonosis, affecting human and animal populations worldwide. Reliable effective therapeutics and concerns to look for more research into the molecular analysis of its genome is therefore needed. In the genomic pool of the Leptospira interrogans many hypothetical proteins are still uncharacterized. In the current research, we performed extensive in silico analysis to prioritize the potential hypothetical proteins of L. interrogans serovar Copenhageni via stepwise reducing the available hypothetical proteins (Total 3606) of the assembly to only 15, based on non-homologous to homosapien, essential, functional, virulent, cellular localization. Out of them, only two proteins WP_000898918.1 (Hypothetical Protein 1) & WP_001014594.1 (Hypothetical Protein 2) were found druggable and involved in protein-protein interaction network. The 3 D structures of these two target proteins were predicted via ab initio homology modeling followed by structures refinement and validation, as no structures were available till date. The analysis also revealed that the functional domains, families and protein-protein interacting partners identified in both proteins are crucial for the survival of the bacteria. The binding cavities were predicted for both the proteins through blind and specific protein-ligand docking with their respective ligands and inhibitors and were found to be in accordance with the druggable sites predicted by DoGSiteScorer. The docking interactions were found within the active functional domains for both the proteins while for Hypothetical Protein 2, the same residues were involved in interactions with Cytidine-5'-triphosphate in blind and specific docking. Furthermore, the simulations of molecular dynamics and free binding energy revealed the stable substrate binding and efficient binding energies, and were in accordance to our docking results. The work predicted two unique hypothetical proteins of L. interrogans as a potential druggable targets for designing of inhibitors for them. Communicated by Ramaswamy H. Sarma

Mode of action of vinca alkaloids against cancer using Insilco analysis technique

Plants have been known for their medicinal properties across the world for a long time. In the field of medicine, medicinal plants have played a critical role. Catharanthus roseus is one such plant with enormous medical significance and potential. C.roesus, often known as Madagascar periwinkle, is a major drug-producing plant. More than 200 alkaloids are found in all parts of the plant, including the leaf, root, shoot, and stem, and are used to treat a variety of diseases. Vinca alkaloids are also effective cancer fighters. To see the mode of action of vinca alkaloids against cancer, 3D structure of Tubulin alpha-beta dimer was obtained from the database, The vinca alkaloids were obtained from Zinc database. In recent years, protein-ligand docking has become a powerful tool for drug development. The final PDB file of Tubulin alpha-beta dimer (PDB Id: 1TUB) and alkaloids were directly fed into an online docking server, SwissDock (http://www.swissdock.ch/docking). In this research we will find out that how Vinca alkaloids are commonly used in combination chemotherapy regimens for medicinal treatments. The vinca alkaloids have cytotoxic properties that can halt cell division and cause cell death.

Repurposing of drugs for combined treatment of COVID-19 cytokine storm using machine learning

Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) induced cytokine storm is the major cause of COVID-19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. We targeted five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor‑Kappa B (NF‑κB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARS‑CoV‑2 induced cytokine storm pathway. We developed machine-learning (ML) models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID‑19. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein–ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments.

CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein–ligand docking

Structure prediction of protein–ligand complexes, called protein–ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein–ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein–ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to ab initio docking methods in cross-docking benchmark tests. Both CSAlign and CSAlign-Dock are freely available as a web server at https://galaxy.seoklab.org/csalign.

Deffini: A family-specific deep neural network model for structure-based virtual screening

Deep learning-based virtual screening methods have been shown to significantly improve the accuracy of traditional docking-based virtual screening methods. In this paper, we developed Deffini, a structure-based virtual screening neural network model. During training, Deffini learns protein-ligand docking poses to distinguish actives and decoys and then to predict whether a new ligand will bind to the protein target. Deffini outperformed Smina with an average AUC ROC of 0.92 and AUC PRC of 0.44 in 3-fold cross-validation on the benchmark dataset DUD-E. However, when tested on the maximum unbiased validation (MUV) dataset, Deffini achieved poor results with an average AUC ROC of 0.517. We used the family-specific training approach to train the model to improve the model performance and concluded that family-specific models performed better than the pan-family models. To explore the limits of the predictive power of the family-specific models, we constructed Kernie, a new protein kinase dataset consisting of 358 kinases. Deffini trained with the Kernie dataset outperformed all recent benchmarks on the MUV kinases, with an average AUC ROC of 0.745, which highlights the importance of quality datasets in improving the performance of deep neural network models and the importance of using family-specific models.

The Role of Phorbol Diesters in Mediating Human Placental Aromatase Cytochrome P450 Activity

Due to the aromatase enzyme’s involvement in estrogen biosynthesis, aromatase inhibitors have emerged as the preferred treatment for postmenopausal women with ER+ breast cancer. Using computational chemistry tools, we investigate how the human placental aromatase cytochrome P450 interacts with various phorbols with distinct chains at C-12, C-13, and C-20, as well as the well-known aromatase inhibitors anastrozole, exemestane, and letrozole. To identify phorbol-aromatase interactions, we performed a protein–ligand docking using the structures of our ligands and proteins using the Flare software (version 2.0, Cresset Software, Litlington, UK). These preliminary findings show that the phorbols considered (P-12,13-diAcPh, P-12,13-diiBu, P-12AcPh-13iBu, P-12Ang-13iBu, P-20Ac-12AcPh-13iBu and P-20Ac-12Ang-13iBu) had the highest binding energies in comparison with the commercially available aromatase inhibitors (anastrozole, letrozole, exemestane) used in this study. A subset of the previously described binding residues of testosterone (TST), the endogenous ligand, were also found to be responsible for the phorbol diesters’ binding to the aromatase enzyme, as demonstrated by the findings. This further suggests that the phorbol diesters can bind efficiently to CYP19A1 and may be able to alter its activity because they had higher binding energies than the commercially available drugs.

Omicron SARS-CoV-2 Spike-1 Protein’s Decreased Binding Affinity to α7nAChr: Implications for Autonomic Dysregulation of the Parasympathetic Nervous System and the Cholinergic Anti-Inflammatory Pathway—An In Silico Analysis

Omicron is the dominant strain of COVID-19 in the United States and worldwide. Although this variant is highly transmissible and may evade natural immunity, vaccines, and therapeutic antibodies, preclinical results in animal models and clinical data in humans suggest omicron causes a less severe form of infection. The molecular basis for the attenuation of virulence when compared to previous variants is currently not well understood. Using protein–ligand docking simulations to evaluate and compare the capacity of SARS-CoV-2 spike-1 proteins with the different COVID-19 variants to bind to the human α7nAChr (i.e., the core receptor under the control of the vagus nerve regulating the parasympathetic nervous system and the cholinergic anti-inflammatory pathway), we found that 10 out of the 14 mutated residues on the RBD of the B.1.1.529 (Omicron) spike, compared to between 0 and 2 in all previous variants, were present at the interaction interface of the α7nAChr. We also demonstrated, through protein–protein docking simulations, that these genetic alterations cause a dramatic decrease in the ability of the Omicron SARS-CoV-2 spike-1 protein to bind to the α7nAChr. These results suggest, for the first time, that the attenuated nature of Omicron infection in humans and animals compared to previous variants may be attributable to a particular set of genetic alterations, specifically affecting the binding site of the SARS-CoV-2 spike-1 protein to the α7nAChr.

Getting Deeper into the Molecular Events of Heme Binding Mechanisms: A Comparative Multi-level Computational Study of HasAsm and HasAyp Hemophores.

Many biological systems obtain their activity by the inclusion of metalloporphyrins into one or several binding pockets. However, decoding the molecular mechanism under which these compounds bind to their receptors is something that has not been widely explored and is a field with open questions. In the present work, we apply computational techniques to unravel and compare the mechanisms of two heme-binding systems, concretely the HasA hemophores from Gram negative bacteria Serratiamarcescens (HasAsm) and Yersinia pestis (HasAyp). Despite the high sequence identity between both systems, the comparison between the X-ray structures of their apo and holo forms suggests different heme-binding mechanisms. HasAyp has extremely similar structures for heme-free and heme-bound forms, while HasAsm presents a very large displacement of a loop that ultimately leads to an additional coordination to the metal with respect to HasAyp. We combined Gaussian accelerated molecular dynamics simulations (GaMDs) in explicit solvent and protein-ligand docking optimized for metalloligands. GaMDs were first carried out on heme-free forms of both hemophores. Then, protein-ligand dockings of the heme were performed on cluster representatives of these simulations and the best poses were then subjected to a new series of GaMDs. A series of analyses reveal the following: (1) HasAyp has a conformational landscape extremely similar between heme-bound and unbound states with no to limited impact on the binding of the cofactor, (2) HasAsm presents as a slightly broader conformational landscape in its apo state but can only visit conformations similar to the X-ray of the holo form when the heme has been bound. Such behavior results from a complex cascade of changes in interactions that spread from the heme-binding pocket to the flexible loop previously mentioned. This study sheds light on the diversity of molecular mechanisms of heme-binding and discusses the weight between the pre-organization of the receptor as well as the induced motions resulting in association.

From Genome Mining to Protein Engineering: A Structural Bioinformatics Route.

This chapter outlines applications in genome mining, along with computational methods to predict protein structure and protein-ligand docking. It offers a simple computational route to rapidly identify proteins of interest from genomic and proteomic data, to accurately predict their three-dimensional structures, and to dock small molecules to their binding pockets and strategies to improve their biophysical properties depending on the needs of the experimental researcher.

Interacción molecular de fructanos lineales y ramificados de distintos tamaños con levansacarasa e inulinsacarasa de Lactobacillus gasseri: modelado y análisis computacional

Human beings consume probiotics and prebiotics because of the health benefits they provide. Lactobacillus gasseri is a probiotic microorganism native to humans which produces glycolytic enzymes directed at the hydrolysis of soluble fibers as prebiotic fructans. In this work, levansucrase (LevG) and inulosucrase (InuGB) from L. gasseri were used to predict the ability of L. gasseri to interact with linear and branched fructans prebiotics (inulin and agavins). AlphaFold and SWISS-MODEL were the servers used for tertiary structure prediction of LevG and InuGB, and fructans with different degrees of polymerization (DP2, DP4 DP6, DP8, DP12 and DP20) were used to generate ligand-enzyme molecular dockings by AutodockVina, GlycoTorchVina, and Autodock FR software. The best affinity energies obtained by molecular docking were obtained with agavin and inulin with a DP of 6 and 8 units. Aspartic acid, glutamic acid, asparagine and arginin are the main amino acids involved in the interaction with these substrates. At the same time, the binding pocket shows hydrophilic characteristics; GlycoTorchVina was the best software for protein-ligand docking. These results demonstrate the ability of L. gasseri enzymes to interact with different molecular structures of fructans; levansucrase with better affinity to agavin and inulosucrase with better affinity to inulin. The above helps to understand the structure-functionality relationship of the prebiotic effect, both in symbiotic formulations and in the human digestive tract.

In Vitro and In Silico Evaluation of ACE2 and LOX Inhibitory Activity of Origanum Essential Oils and Carvacrol.

Origanum spp. are used both for culinary purposes and for their biological activities. In this study, commercial Origanum majorana, Origanum minutiflorum, Origanum vulgare, and Origanum onites essential oils and their prominent constituent carvacrol were evaluated for their in vitro and in silico angiotensin-converting enzyme 2 and lipoxygenase enzyme inhibitory potentials. The essential oils were analysed by gas chromatography-flame ionisation detection and gas chromatography-mass spectrometry, where carvacrol was identified as the major component (62 - 81%), confirming the quality. In vitro enzyme inhibition assays were conducted both with the essential oils (20 µg/mL) and with carvacrol (5 µg/mL). The comparative values of angiotensin-converting enzyme 2 percent inhibition for O. majorana, O. minutiflorum, O. vulgare, and O.onites essential oils were determined as 85.5, 79.1, 74.3, and 42.8%, respectively. As a result of the enzyme assays, carvacrol showed 90.7% in vitro angiotensin-converting enzyme 2 inhibitory activity. The in vitro lipoxygenase inhibition of the essential oils (in the same order) was 89.4, 78.9, 81.1, and 73.5%, respectively, where carvacrol showed 74.8% inhibition. In addition, protein-ligand docking and interaction profiling was used to gain structural and mechanistic insights into the angiotensin-converting enzyme 2 and lipoxygenase inhibitory potentials of major Origanum essential oil constituents. The in silico findings agreed with the significant enzyme inhibition activity observed in vitro. Further in vivo studies are suggested to confirm the safety and efficacy of the oils.

Design, synthesis, and properties of des-D-ring interphenylene derivatives of 1α,25-Dihydroxyvitamin D3

Novel des-D-ring interphenylene analogs of vitamin D3 were designed on the basis of their conformational analysis and affinity to vitamin D receptor (VDR) estimated using ligand–protein docking simulation. According to this design, a series of the des-D-ring interphenylene analogs were synthesized via Suzuki–Miyaura coupling reactions of des-D, C-ring and A-ring intermediates. A fluorescence polarization VDR competitor assay, a time-resolved fluorescence resonance energy transfer VDR coactivator binding assay, and a human VDR (NR1I1) reporter assay revealed that the des-D-ring interphenylene analogs exhibited a certain VDR binding affinity and ability to promote gene transcription. In particular, analogs having a side chain with an appropriate length at meta position showed high gene transcription promoting activities comparable to those of 1α,25-dihydroxyvitamin D3 and its 19-nor derivative.

Dual role of azo compounds in inhibiting Plasmodium falciparum adenosine deaminase and hemozoin biocrystallization

Protein-ligand (GOLD) docking of the NCI compounds into the ligand-binding site of Plasmodium falciparum adenosine deaminase (PfADA) identified three most active azo compounds containing 4-[(4-hydroxy-2-oxo-1H-quinolin-3-yl) moiety. These compounds showed IC50 of 3.7–15.4 μM against PfADA, as well as inhibited the growth of P. falciparum strains 3D7 (chloroquine (CQ)-sensitive) and K1 (CQ-resistant) with IC50 of 1.8–3.1 and 1.7–3.6 μM, respectively. The identified compounds have structures similar to the backbone structure (4-N-(7-chloroquinolin-4-yl)) in CQ, and NSC45545 could mimic CQ by inhibiting the bioformation of hemozoin in parasitic food vacuole. The amount of in situ hemozoin in the ring-stage parasite was determined using a combination of synchrotron transmission Fourier transform infrared microspectroscopy and Principal Component Analysis. Stretching of the C–O bond of hemozoin propionate group measured at 1220–1210 cm−1 in untreated intraerythrocytic P. falciparum strains 3D7 and K1 was disappeared following treatment with 1.85 and 1.74 μM NSC45545, similar to those treated with 0.02 and 0.13 μM CQ, respectively. These findings indicate a novel dual function of 4-[(4-hydroxy-2-oxo-1H-quinolin-3-yl) azo compounds in inhibiting both PfADA and in situ hemozoin biocrystallization. These lead compounds hold promise for further development of new antimalarial therapeutics that could delay the onset of parasitic drug resistance.

Tribenuron-methyl metabolism and the rare Pro197Phe double mutation together with 2,4-D metabolism and reduced absorption can evolve in Papaver rhoeas with multiple and cross herbicide resistance to ALS inhibitors and auxin mimics

Multiple resistance mechanisms to ALS inhibitors and auxin mimics in two Papaver rhoeas populations were investigated in wheat fields from Portugal. Dose-response trials, also with malathion (a cytochrome P450 inhibitor), cross-resistance patterns for ALS inhibitors and auxin mimics, alternative herbicides tests, 2,4-D and tribenuron-methyl absorption, translocation and metabolism experiments, together with ALS activity, gene sequencing and enzyme modelling and ligand docking were carried out. Results revealed two different resistant profiles: one population (R1) multiple resistant to tribenuron-methyl and 2,4-D, the second (R2) only resistant to 2,4-D. In R1, several target-site mutations in Pro197 and enhanced metabolism (cytochrome P450-mediated) were responsible of tribenuron-methyl resistance. For 2,4-D, reduced transport was observed in both populations, while cytochrome P450-mediated metabolism was also present in R1 population. Moreover, this is the first P. rhoeas population with enhanced tribenuron-methyl metabolism. This study reports the first case for P. rhoeas of the amino acid substitution Pro197Phe due to a double nucleotide change. This double mutation could cause reduced enzyme sensitivity to most ALS inhibitors according to protein modelling and ligand docking. In addition, this study reports a P. rhoeas population resistant to 2,4-D, apparently, with reduced transport as the sole resistance mechanism.

A reinforcement learning approach for protein–ligand binding pose prediction

Protein ligand docking is an indispensable tool for computational prediction of protein functions and screening drug candidates. Despite significant progress over the past two decades, it is still a challenging problem, characterized by the still limited understanding of the energetics between proteins and ligands, and the vast conformational space that has to be searched to find a satisfactory solution. In this project, we developed a novel reinforcement learning (RL) approach, the asynchronous advantage actor-critic model (A3C), to address the protein ligand docking problem. The overall framework consists of two models. During the search process, the agent takes an action selected by the actor model based on the current location. The critic model then evaluates this action and predict the distance between the current location and true binding site. Experimental results showed that in both single- and multi-atom cases, our model improves binding site prediction substantially compared to a naïve model. For the single-atom ligand, copper ion (Cu2+), the model predicted binding sites have a median root-mean-square-deviation (RMSD) of 2.39 Å to the true binding sites when starting from random starting locations. For the multi-atom ligand, sulfate ion (SO42−), the predicted binding sites have a median RMSD of 3.82 Å to the true binding sites. The ligand-specific models built in this study can be used in solvent mapping studies and the RL framework can be readily scaled up to larger and more diverse sets of ligands.

Natural diketones as potential covalent ligands for SARS-CoV-2 proteins: an <i>in silico</i> docking study

Our computer-aided protein-ligand docking test using Autodock Vina software allowed to reveal the potential of few α- and β-diketones from plants and alternative living organisms as covalent ligands for few proteins of coronavirus SARS-CoV-2 – a causative agent of COVID-19. It has been established that values for energy of binding (docking score, Ebind, kcal/mol) less than –7.5 and for distances of ligands’ carbonyl groups to side chain nitrogens of arginine residues of some coronaviral enzymes within 0.4 nm have been true for β-diketones 6-gingerdione (Pubchem code CID162952), 8-gingerdione (CID14440537), tetrahydrocurcumine (CID124072) as well as α-diketone wallitaxane E (CID132967478). The in silico revealed interactions are interesting to be verified in vitro and they point out a possibility of investigation of the compounds and related natural materials as tools for struggle against coronaviral infections.

Eckol, a potential inhibitor of aryl hydrocarbon receptor, inhibits proliferation and induces apoptosis in breast cancer cells

The present study was designed to explore phlorotannin eckol protein targets and evaluate the anti-cancer effects of eckol against human breast cancer cells. ProTox-II server and protein-ligand docking analysis deter-mined the aryl hydrocarbon receptor (AhR) as a putative target of eckol. The AhR has been determined as a potential target for breast cancer treatment. The effect of eckol treatment on proliferation, apoptosis, and cell cycle activities was detected in MDA-MB-231 and SK-BR-3 breast cancer cell lines which were selected dependent on AhR expression profiles. Consistent with the AhR expression profile, MDA-MB-231 cells were more sensitive to eckol treatment compared to SK-BR-3 cells in proliferation, apoptosis and cell cycle results. Eckol treatment shows a significant antiproliferative and apoptotic response in breast cancer cells. Overall, these results indicated that the action of eckol may be related to the AhR gene regulation in different breast cancer cell lines.

Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ1-Riboswitch.

Targeting RNA with small molecules is an emerging field. While several ligands for different RNA targets are reported, structure-based virtual screenings (VSs) against RNAs are still rare. Here, we elucidated the general capabilities of protein-based docking programs to reproduce native binding modes of small-molecule RNA ligands and to discriminate known binders from decoys by the scoring function. The programs were found to perform similar compared to the RNA-based docking tool rDOCK, and the challenges faced during docking, namely, protomer and tautomer selection, target dynamics, and explicit solvent, do not largely differ from challenges in conventional protein-ligand docking. A prospective VS with the Bacillus subtilis preQ1-riboswitch aptamer domain performed with FRED, HYBRID, and FlexX followed by microscale thermophoresis assays identified six active compounds out of 23 tested VS hits with potencies between 29.5 nM and 11.0 μM. The hits were selected not solely based on their docking score but for resembling key interactions of the native ligand. Therefore, this study demonstrates the general feasibility to perform structure-based VSs against RNA targets, while at the same time it highlights pitfalls and their potential solutions when executing RNA-ligand docking.

Effective Protein-Ligand Docking Strategy via Fragment Reuse and a Proof-of-Concept Implementation.

Virtual screening is a commonly used process to search for feasible drug candidates from a huge number of compounds during the early stages of drug design. As the compound database continues to expand to billions of entries or more, there remains an urgent need to accelerate the process of docking calculations. Reuse of calculation results is a possible way to accelerate the process. In this study, we first propose yet another virtual screening-oriented docking strategy by combining three factors, namely, compound decomposition, simplified fragment grid storing k-best scores, and flexibility consideration with pregenerated conformers. Candidate compounds contain many common fragments (chemical substructures). Thus, the calculation results of these common fragments can be reused among them. As a proof-of-concept of the aforementioned strategies, we also conducted the development of REstretto, a tool that implements the three factors to enable the reuse of calculation results. We demonstrated that the speed and accuracy of REstretto were comparable to those of AutoDock Vina, a well-known free docking tool. The implementation of REstretto has much room for further performance improvement, and therefore, the results show the feasibility of the strategy. The code is available under an MIT license at https://github.com/akiyamalab/restretto.