Analysis of physicochemical properties of protein–protein interaction modulators suggests stronger alignment with the “rule of five”

Truong, Jia Q.; George, Ashwin; Holien, Jessica K.

doi:10.1039/d1md00213a

Cited by 15 publications

(13 citation statements)

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“…This was aimed at probing chemical knowledge. Lectures had introduced the students to how physicochemical properties relate to drug likeness via the introduction of Lipinski’s Rule of 5 , and specific examples such as the Rule of 3 for fragment-based drug discovery and protein–protein interaction filters . There was a specific focus on the importance in reducing cLogP below 3.5 to limit unwanted off-target effects. , …”

Section: Assignment Overview and Implementationmentioning

confidence: 99%

“…Lectures had introduced the students to how physicochemical properties relate to drug likeness via the introduction of Lipinski's Rule of 5 14,15 and specific examples such as the Rule of 3 for fragmentbased drug discovery 16 and protein−protein interaction filters. 17 There was a specific focus on the importance in reducing cLogP below 3.5 to limit unwanted off-target effects. 18,19 A table of five hit compounds with activity was provided (Table 1), and students were asked which they would develop further.…”

Section: ■ Assignment Overview and Implementationmentioning

confidence: 99%

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Drug Discovery in Real Life: An Online Learning Activity for Bioinformatics Students

et al. 2023

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During COVID-19 lockdowns, online learning activities had to be developed for the Undergraduate and Masters by Coursework Bioinformatics students at RMIT University. Therefore, we designed an integrative, industry-based research assignment, which guided the students through a drug discovery project from target identification to lead optimization. The students were able to utilize this real-life scenario to apply multiple diverse but complementary bioinformatic principles to analyze biological and chemical data leading to meaningful predictions. This activity was utilized as a final assessment of the students' knowledge.

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Section: Assignment Overview and Implementationmentioning

confidence: 99%

Section: ■ Assignment Overview and Implementationmentioning

confidence: 99%

Drug Discovery in Real Life: An Online Learning Activity for Bioinformatics Students

et al. 2023

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“…The TPSA value showed the compounds' capability to penetrate the cell membrane, and the BBB permeant showed the compound's capability to penetrate the blood-brain barrier 53,55 . Lipinski's rule of five was used to predict the similarity of compounds and medicine, which had a specific biological activity designed for oral treatment 53 .…”

Section: In Silico Studymentioning

confidence: 99%

Metabolite Profiling of the Environmental-Controlled Growth of Marsilea crenata Presl. and Its In Vitro and In Silico Antineuroinflammatory Properties

et al. 2022

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This study was aimed to evaluate the metabolite contents and antineuroinflammatory potential of Marsilea crenata Presl. grown under a controlled environmental condition. The antineuroinflammatory test has been carried out in vitro using ethanolic extract of M. crenata leaves on HMC3 microglia cells. An in silico approach was applied to predict the active compounds of the extract. The HMC3 microglia cells were induced with IFNγ to create prolonged inflammatory conditions and then treated with 96% ethanolic extract of the M. crenata leaves of 62.5, 125, and 250 μg/mL. The expression of MHC II was analyzed using the ICC method with the CLSM instrument. Metabolites of the extract were profiled using UPLC-QToF-MS/MS instrument and MassLynx 4.1 software. In silico evaluation was conducted with molecular docking on 3OLS protein using PyRx 0.8 software, and physicochemical properties of the compounds were analyzed using SwissADME webtool. The ethanolic extract of M. crenata leaves could reduce the MHC II expression in HMC3 microglia cells in all concentrations with the values 97.458, 139.574, and 82.128 AU. The result of metabolite profiling found 79 compounds in the extract. In silico evaluation showed that 19 compounds gave agonist interaction toward 3OLS, and three met all parameters of physicochemical analysis. The ethanolic extract of the environmental-controlled growth of M. crenata leaves antineuroinflammatory activity on HMC3 microglia cells. The extract was predicted to contain some phytoestrogen compounds which act as 3OLS agonists.

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“…While significant effort has been invested into the prediction and optimisation of peptides and proteins, the assessment of toxicity typically relies on expensive and time-consuming in-vivo assays late in the development process. Comparatively, we have seen that the introduction of rules and predictive algorithms to assess small molecule toxicities has helped to significantly reduce clinical trial failures [ 2 , 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

CSM-Toxin: A Web-Server for Predicting Protein Toxicity

2023

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Biologics are one of the most rapidly expanding classes of therapeutics, but can be associated with a range of toxic properties. In small-molecule drug development, early identification of potential toxicity led to a significant reduction in clinical trial failures, however we currently lack robust qualitative rules or predictive tools for peptide- and protein-based biologics. To address this, we have manually curated the largest set of high-quality experimental data on peptide and protein toxicities, and developed CSM-Toxin, a novel in-silico protein toxicity classifier, which relies solely on the protein primary sequence. Our approach encodes the protein sequence information using a deep learning natural languages model to understand “biological” language, where residues are treated as words and protein sequences as sentences. The CSM-Toxin was able to accurately identify peptides and proteins with potential toxicity, achieving an MCC of up to 0.66 across both cross-validation and multiple non-redundant blind tests, outperforming other methods and highlighting the robust and generalisable performance of our model. We strongly believe the CSM-Toxin will serve as a valuable platform to minimise potential toxicity in the biologic development pipeline. Our method is freely available as an easy-to-use webserver.

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Analysis of physicochemical properties of protein–protein interaction modulators suggests stronger alignment with the “rule of five”

Abstract: Despite the important roles played by Protein-Protein Interactions (PPIs) in disease, they have been long considered as ‘undruggable’. However, recent advances have suggested that the PPIs are druggable but may...

Cited by 15 publications

References 64 publications

Drug Discovery in Real Life: An Online Learning Activity for Bioinformatics Students

Drug Discovery in Real Life: An Online Learning Activity for Bioinformatics Students

Metabolite Profiling of the Environmental-Controlled Growth of Marsilea crenata Presl. and Its In Vitro and In Silico Antineuroinflammatory Properties

CSM-Toxin: A Web-Server for Predicting Protein Toxicity

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