Rational design of inhibitors against LpxA protein of Acinetobacter baumannii using a virtual screening method

Background: 3C-like protease also called the main protease is an essential enzyme for the completion of the life cycle of Middle East Respiratory Syndrome Coronavirus. In our study we predicted compounds which are capable of inhibiting 3C-like protease, and thus inhibit the lifecycle of Middle East Respiratory Syndrome Coronavirus using in silico methods. </P><P> Methods: Lead like compounds and drug molecules which are capable of inhibiting 3C-like protease was identified by structure-based virtual screening and ligand-based virtual screening method. Further, the compounds were validated through absorption, distribution, metabolism and excretion filtering. Results: Based on binding energy, ADME properties, and toxicology analysis, we finally selected 3 compounds from structure-based virtual screening (ZINC ID: 75121653, 41131653, and 67266079) having binding energy -7.12, -7.1 and -7.08 Kcal/mol, respectively and 5 compounds from ligandbased virtual screening (ZINC ID: 05576502, 47654332, 04829153, 86434515 and 25626324) having binding energy -49.8, -54.9, -65.6, -61.1 and -66.7 Kcal/mol respectively. All these compounds have good ADME profile and reduced toxicity. Among eight compounds, one is soluble in water and remaining 7 compounds are highly soluble in water. All compounds have bioavailability 0.55 on the scale of 0 to 1. Among the 5 compounds from structure-based virtual screening, 2 compounds showed leadlikeness. All the compounds showed no inhibition of cytochrome P450 enzymes, no blood-brain barrier permeability and no toxic structure in medicinal chemistry profile. All the compounds are not a substrate of P-glycoprotein. Our predicted compounds may be capable of inhibiting 3C-like protease but need some further validation in wet lab.

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Binding Ensembles of p53-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations

Molecules ◽

10.3390/molecules26010198 ◽

2021 ◽

Vol 26 (1) ◽

pp. 198

Author(s):

Lijun Lang ◽

Alberto Perez

Keyword(s):

Bayesian Inference ◽

Virtual Screening ◽

Statistical Mechanics ◽

Small Molecules ◽

Rational Design ◽

Driving Forces ◽

Peptide Inhibitors ◽

Atomistic Simulations ◽

Binding Mechanism ◽

Intrinsically Disordered

Designing peptide inhibitors of the p53-MDM2 interaction against cancer is of wide interest. Computational modeling and virtual screening are a well established step in the rational design of small molecules. But they face challenges for binding flexible peptide molecules that fold upon binding. We look at the ability of five different peptides, three of which are intrinsically disordered, to bind to MDM2 with a new Bayesian inference approach (MELD × MD). The method is able to capture the folding upon binding mechanism and differentiate binding preferences between the five peptides. Processing the ensembles with statistical mechanics tools depicts the most likely bound conformations and hints at differences in the binding mechanism. Finally, the study shows the importance of capturing two driving forces to binding in this system: the ability of peptides to adopt bound conformations (ΔGconformation) and the interaction between interface residues (ΔGinteraction).

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Rational Design of Macrolides by Virtual Screening of Combinatorial Libraries Generated through in Silico Manipulation of Polyketide Synthases

Journal of Medicinal Chemistry ◽

10.1021/jm051035i ◽

2006 ◽

Vol 49 (6) ◽

pp. 2077-2087 ◽

Cited By ~ 21

Author(s):

Sergey B. Zotchev ◽

Alla V. Stepanchikova ◽

Anastasia P. Sergeyko ◽

Boris N. Sobolev ◽

Dmitrii A. Filimonov ◽

...

Keyword(s):

Virtual Screening ◽

In Silico ◽

Rational Design ◽

Combinatorial Libraries ◽

Polyketide Synthases

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Generating Property-Matched Decoy Molecules Using Deep Learning

10.1101/2020.08.26.268193 ◽

2020 ◽

Author(s):

Fergus Imrie ◽

Anthony R. Bradley ◽

Charlotte M. Deane

Keyword(s):

Deep Learning ◽

Virtual Screening ◽

Method Development ◽

Screening Method ◽

Screening Methods ◽

Additional Risk ◽

Link Type ◽

Screening Performance ◽

And Training ◽

Virtual Screening Performance

An essential step in the development of virtual screening methods is the use of established sets of actives and decoys for benchmarking and training. However, the decoy molecules in commonly used sets are biased meaning that methods often exploit these biases to separate actives and decoys, rather than learning how to perform molecular recognition. This fundamental issue prevents generalisation and hinders virtual screening method development. We have developed a deep learning method (DeepCoy) that generates decoys to a user’s preferred specification in order to remove such biases or construct sets with a defined bias. We validated DeepCoy using two established benchmarks, DUD-E and DEKOIS 2.0. For all DUD-E targets and 80 of the 81 DEKOIS 2.0 targets, our generated decoy molecules more closely matched the active molecules’ physicochemical properties while introducing no discernible additional risk of false negatives. The DeepCoy decoys improved the Deviation from Optimal Embedding (DOE) score by an average of 81% and 66%, respectively, decreasing from 0.163 to 0.032 for DUD-E and from 0.109 to 0.038 for DEKOIS 2.0. Further, the generated decoys are harder to distinguish than the original decoy molecules via docking with Autodock Vina, with virtual screening performance falling from an AUC ROC of 0.71 to 0.63. The code is available at https://github.com/oxpig/DeepCoy. Generated molecules can be downloaded from http://opig.stats.ox.ac.uk/resources.

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Mulberrofuran G, a Potent Inhibitor of SPIKE Protein of SARS Corona Virus 2

Journal of Pharmaceutical Care ◽

10.18502/jpc.v9i2.6610 ◽

2021 ◽

Author(s):

Fatemeh Sadat Hosseini ◽

Mohammad Reza Motamedi

Keyword(s):

Molecular Docking ◽

Virtual Screening ◽

Active Site ◽

Potential Role ◽

Potent Inhibitor ◽

Screening Method ◽

Spike Protein ◽

Treatment Agent ◽

Approved Drugs ◽

Fda Approved Drugs

Background: At the onset of the 2020 year, Coronavirus disease (COVID-19) has become a pandemic and infected many people worldwide. Despite all efforts, no cure was found for this infection. Bioinformatics and medicinal chemistry have a potential role in the primary consideration of drugs to treat this infection. With virtual screening and molecular docking, some potent compounds and medications can be found and modified and then applied to treat disease in the next steps. Methods: By virtual screening method and PRYX software, some Food and Drug Administration (FDA) approved drugs and natural compounds have been docked with the SPIKE protein of SARS-CoV-2. Some more potent agents have been selected, and then new structures are designed with better affinity than them. After that, we searched for the molecules with a similar structure to designed compounds to find the most potent compound to our target. Results: Because of the study of structures and affinities, mulberrofuran G was the most potent compound in this study. The compound has interacted strongly with residues in the probably active site of SPIKE. Conclusion: Mulberrofuran G can be a treatment agent candidate for COVID-19 because of its good affinity to SPIKE of the virus and inhibition of virus-cell adhesion and entrance.

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Targeting FGL2, a molecular drug target for glioblastoma, with natural compounds through virtual screening method

Future Medicinal Chemistry ◽

10.4155/fmc-2020-0331 ◽

2021 ◽

Author(s):

Fahad Hassan Shah ◽

Song Ja Kim

Keyword(s):

Virtual Screening ◽

Active Site ◽

Drug Target ◽

Screening Method ◽

Tumor Development ◽

Pharmacokinetic Profile ◽

Natural Compounds ◽

Screening Platform ◽

Proliferation In Vitro

Background: Fibroleukin-2 protein (FGL2) causes redevelopment of brain tumors. Inhibition of these proteins has shown to improve glioblastoma prognosis and treatment efficacy. Aim: The current study gathered recently exploited natural compounds that suppress glioblastoma proliferation in vitro, tested against FGL2 protein. Method: Twenty-five compounds were explored through a virtual screening platform. Results: Three natural compounds (betanine, hesperetin and ovatodiolide) hit the active site of FGL2. Furthermore, the influence of these compounds was also assessed using in silico gene expression, and ADMET tools showed downregulation of some genes, which caused rapid tumor development while possessing a moderate acute toxicity and pharmacokinetic profile. Conclusion: Our study presents three compounds that are good candidates for evaluation in FGL2 mutated glioblastoma animal models.

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Study on Structure Activity Relationship of Natural Flavonoids against Thrombin by Molecular Docking Virtual Screening Combined with Activity Evaluation In Vitro

Molecules ◽

10.3390/molecules25020422 ◽

2020 ◽

Vol 25 (2) ◽

pp. 422

Author(s):

Xiaoyan Wang ◽

Zhen Yang ◽

Feifei Su ◽

Jin Li ◽

Evans Owusu Boadi ◽

...

Keyword(s):

Molecular Docking ◽

Virtual Screening ◽

Screening Method ◽

Thrombin Inhibitors ◽

Coagulation Cascade ◽

Oh Groups ◽

Structure Activity Relationships ◽

Thrombin Inhibition ◽

Structure Activity

Thrombin, a key enzyme of the serine protease superfamily, plays an integral role in the blood coagulation cascade and thrombotic diseases. In view of this, it is worthwhile to establish a method to screen thrombin inhibitors (such as natural flavonoid-type inhibitors) as well as investigate their structure activity relationships. Virtual screening using molecular docking technique was used to screen 103 flavonoids. Out of this number, 42 target compounds were selected, and their inhibitory effects on thrombin assayed by chromogenic substrate method. The results indicated that the carbon-carbon double bond group at the C2, C3 sites and the carbonyl group at the C4 sites of flavones were essential for thrombin inhibition, whereas the methoxy and O-glycosyl groups reduced thrombin inhibition. Noteworthy, introduction of OH groups at different positions on flavonoids either decreased or increased anti-thrombin potential. Myricetin exhibited the highest inhibitory potential against thrombin with an IC50 value of 56 μM. Purposively, the established molecular docking virtual screening method is not limited to exploring flavonoid structure activity relationships to anti-thrombin activity but also usefully discovering other natural active constituents.

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Structure–Activity Relationship of RGD-Containing Cyclic Octapeptide and αvβ3 Integrin Allows for Rapid Identification of a New Peptide Antagonist

International Journal of Molecular Sciences ◽

10.3390/ijms21093076 ◽

2020 ◽

Vol 21 (9) ◽

pp. 3076 ◽

Cited By ~ 1

Author(s):

Aaron Silva ◽

Wenwu Xiao ◽

Yan Wang ◽

Wei Wang ◽

Heng Wei Chang ◽

...

Keyword(s):

Rational Design ◽

Tumor Imaging ◽

Screening Method ◽

Structure Activity Relationship ◽

Activity Relationship ◽

Sequence Motif ◽

Αvβ3 Integrin ◽

Structure Activity ◽

Cyclic Octapeptide

The αvβ3 integrin, a receptor for many extracellular matrix proteins with RGD-sequence motif, is involved in multiple physiological processes and highly expressed in tumor cells, therefore making it a target for cancer therapy and tumor imaging. Several RGD-containing cyclic octapeptide (named LXW analogs) were screened as αvβ3 antagonists with dramatically different binding affinity, and their structure–activity relationship (SAR) remains elusive. We performed systematic SAR studies and optimized LXW analogs to improve antagonistic potency. The NMR structure of LXW64 was determined and docked to the integrin. Structural comparison and docking studies suggested that the hydrophobicity and aromaticity of the X7 amino acid are highly important for LXW analogs binding to the integrin, a potential hydrophobic pocket on the integrin surface was proposed to play a role in stabilizing the peptide binding. To develop a cost-efficient and fast screening method, computational docking was performed on LXW analogs and compared with in vitro screening. A consistency within the results of both methods was found, leading to the continuous optimization and testing of LXW mutants via in silico screening. Several new LXW analogs were predicted as the integrin antagonists, one of which—LXZ2—was validated by in vitro examination. Our study provides new insight into the RGD recognition specificity and valuable clues for rational design of novel αvβ3 antagonists.

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In silico rational design and virtual screening of antixoidant tripeptides based on 3D-QSAR modeling

Journal of Molecular Structure ◽

10.1016/j.molstruc.2019.05.002 ◽

2019 ◽

Vol 1193 ◽

pp. 223-230 ◽

Cited By ~ 4

Author(s):

Haiqiong Guo ◽

Yuxuan Wang ◽

Qingxiu He ◽

Yuping Zhang ◽

Yong Hu ◽

...

Keyword(s):

Virtual Screening ◽

In Silico ◽

Rational Design ◽

3D Qsar ◽

Qsar Modeling

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