World Health Organization characterized novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) as world pandemic. This infection has been ...spreading alarmingly by causing huge social and economic disruption. In order to response quickly, the inhibitors already designed against different targets of previous human coronavirus infections will be a great starting point for anti-SARS-CoV-2 inhibitors. In this study, our approach integrates different ligand based drug design strategies of some in-house chemicals. The study design was composed of some major aspects: (a) classification QSAR based data mining of diverse SARS-CoV papain-like protease (PLpro) inhibitors, (b) QSAR based virtual screening (VS) to identify in-house molecules that could be effective against putative target SARS-CoV PLpro and (c) finally validation of hits through receptor-ligand interaction analysis. This approach could be used to aid in the process of COVID-19 drug discovery. It will introduce key concepts, set the stage for QSAR based screening of active molecules against putative SARS-CoV-2 PLpro enzyme. Moreover, the QSAR models reported here would be of further use to screen large database. This study will assume that the reader is approaching the field of QSAR and molecular docking based drug discovery against SARS-CoV-2 PLpro with little prior knowledge.
Communicated by Ramaswamy H. Sarma
(1) Background: Drug repositioning is an unconventional drug discovery approach to explore new therapeutic benefits of existing drugs. Currently, it emerges as a rapid avenue to alleviate the ...COVID-19 pandemic disease. (2) Methods: Herein, we tested the antiviral activity of anti-microbial and anti-inflammatory Food and Drug Administration (FDA)-approved drugs, commonly prescribed to relieve respiratory symptoms, against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the viral causative agent of the COVID-19 pandemic. (3) Results: Of these FDA-approved antimicrobial drugs, Azithromycin, Niclosamide, and Nitazoxanide showed a promising ability to hinder the replication of a SARS-CoV-2 isolate, with IC
of 0.32, 0.16, and 1.29 µM, respectively. We provided evidence that several antihistamine and anti-inflammatory drugs could partially reduce SARS-CoV-2 replication in vitro. Furthermore, this study showed that Azithromycin can selectively impair SARS-CoV-2 replication, but not the Middle East Respiratory Syndrome Coronavirus (MERS-CoV). A virtual screening study illustrated that Azithromycin, Niclosamide, and Nitazoxanide bind to the main protease of SARS-CoV-2 (Protein data bank (PDB) ID: 6lu7) in binding mode similar to the reported co-crystalized ligand. Also, Niclosamide displayed hydrogen bond (HB) interaction with the key peptide moiety GLN: 493A of the spike glycoprotein active site. (4) Conclusions: The results suggest that Piroxicam should be prescribed in combination with Azithromycin for COVID-19 patients.
Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at ...leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.
Abstract
Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand ...generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.
•The batch processing of molecular docking was used to screen umami peptides.•Nine umami peptides in chicken soup were identified by sensory evaluation.•The key binding sites of T1R1 and T1R3 with ...umami peptides were analyzed.
By combining python script invocation, the batch processing of molecular docking was achieved to screen 20 potential umami peptides out of the 208 peptides identified in chicken soup. Nine peptides were dominated by umami taste according to sensory analysis, among which PPQEAAQF (2.56) has the highest umami intensity, followed by AEEHVEAVN (2.43) and NEFGYSNR (2.19). The threshold of nine peptides ranged from 0.08 mM to 0.58 mM. In 0.35 % MSG, the effective threshold of umami-enhancing effect of LPLQD was 0.24 mM. In addition, the molecular docking results indicated that His71, Ser107, and Asp147 of taste receptor type 1 member 1, and Asn68, Asp216, His387, and Ala302 of taste receptor type 1 member 3 play critical roles in the binding with umami peptides by forming hydrogen bonds and hydrophobic force. Thus, the combination of molecular docking and python script invocation was effective and economical for umami peptides screening.
IDH1 mutations occur in about 20–30% of gliomas and are a promising target for the treatment of cancer. In the present study, the performance of aIDH1
R132H
was verified
via
glide-docking-based ...virtual screening. On the basis of the two crystal structures (5TQH and 6B0Z) with the best discriminating ability to identify IDH1
R132H
inhibitors from a decoy set, a docking-based virtual screening strategy was employed for identifying new IDH1
R132H
inhibitors. In the end, 57 structurally diverse compounds were reserved and evaluated through experimental tests, and 10 of them showed substantial activity in targeting IDH1
R132H
(IC
50
< 50 μM). Molecular docking technology showed that L806-0255, V015-1671, and AQ-714/41674992 could bind to the binding pocket composed of hydrophobic residues. These findings indicate that L806-0255, V015-1671, and AQ-714/41674992 have the potential as lead compounds for the treatment of IDH1-mutated gliomas through further optimization.
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•Screening method of umami peptides based on peptidomics and virtual screening.•Peptide length and positive control were considered during screening.•His121, Ser146, Ser123, Tyr143 ...and Gly144 in T1R3 were the main binding sites.
The existing technology used for screening umami peptides is time-consuming and labor-intensive, making it difficult to meet the requirements of rapid screening of peptides. In this study, a high-throughput screening method for umami peptides was established based on peptidomics and virtual screening including the mass spectrometry, iUmami-SCM, PeptideRanker, and T1R1/T1R3 receptor. Subsequently, they were characterized and validated using sensory evaluation and electronic tongue. Results showed that 18 potential umami peptides were screened from two clams. Among them, 16 peptides had umami characteristics with thresholds range 0.123-1.481 mmol/L, and the accuracy of the screening method was about 88.9%. Additionally, active sites such as Tyr143, Gly144, Ser146, Ala145, His121, Ser123, and Glu277 may play a critical role in flavor presentation by molecular docking with T1R1/T1R3. The paper could provide a fast and reliable method for screening umami peptides as well as lay the foundation for novel strategies for evaluating umami taste.
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Novel PPARδ agonists, 2-(1-piperidinyl)-1,3-benzothiazole derivatives were discovered by our proprietary docking-based virtual screening technique. Compound 1 as the initial hit was ...effectively modified to acquire PPARδ agonist activity, resulting in the discovery of compound 12 with high agonistic potency for PPARδ and selectivity over PPARα and PPARγ. Compound 12 also had good ADME profiles and showed in vivo efficacy as a lead.
•Fingerprints are easy to use and fast.•Different kinds of fingerprints encode different aspects of the molecules.•Popular fingerprint algorithms and their uses.•Brief comparison with other virtual ...screening methods.
Molecular fingerprints have been used for a long time now in drug discovery and virtual screening. Their ease of use (requiring little to no configuration) and the speed at which substructure and similarity searches can be performed with them – paired with a virtual screening performance similar to other more complex methods – is the reason for their popularity. However, there are many types of fingerprints, each representing a different aspect of the molecule, which can greatly affect search performance. This review focuses on commonly used fingerprint algorithms, their usage in virtual screening, and the software packages and online tools that provide these algorithms.
•Compound 6 exhibits potent inhibitory activity against FGFR1 and FGFR1 V561M, with IC50 values of 0.24 nM and 1.24 nM.•In TNBC cell lines HS578T and SUM159, Compound 6 reduces migration and invasion ...by downregulating phosphorylated FGFR1.•Compound 6 influences EMT, reducing MMP11 levels and altering E-cadherin and N-cadherin expression, indicative of EMT inhibition.•Compound 6’s significant impact on inhibiting TNBC cell migration and invasion.•Results provide information for the design of new inhibitors of FGFR1 against TNBC.
The overexpression of FGFR1 is thought to significantly contribute to the progression of triple-negative breast cancer (TNBC), impacting aspects such as tumorigenesis, growth, metastasis, and drug resistance. Consequently, the pursuit of effective inhibitors for FGFR1 is a key area of research interest. In response to this need, our study developed a hybrid virtual screening method. Utilizing KarmaDock, an innovative algorithm that blends deep learning with molecular docking, alongside Schrödinger’s Residue Scanning. This strategy led us to identify compound 6, which demonstrated promising FGFR1 inhibitory activity, evidenced by an IC50 value of approximately 0.24 nM in the HTRF bioassay. Further evaluation revealed that this compound also inhibits the FGFR1 V561M variant with an IC50 value around 1.24 nM. Our subsequent investigations demonstrate that Compound 6 robustly suppresses the migration and invasion capacities of TNBC cell lines, through the downregulation of p-FGFR1 and modulation of EMT markers, highlighting its promise as a potent anti-metastatic therapeutic agent. Additionally, our use of molecular dynamics simulations provided a deeper understanding of the compound’s specific binding interactions with FGFR1.
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