&lt;p&gt;Current Challenges and Opportunities in Designing Protein–Protein Interaction Targeted Drugs&lt;/p&gt;

Shin, Woong‐Hee; Kumazawa, Keiko; Imai, Keisuke; Hirokawa, Takatsugu; Kihara, Daisuke

doi:10.2147/aabc.s235542

Cited by 60 publications

(63 citation statements)

References 73 publications

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“…The availability of reliable tools for the rapid detection of protein–protein interactions (PPI) has become necessary for identifying biologically active compounds, especially in the early phases of drug discovery [ 1 , 2 ]. Among the different methods that are available, split proteins have provided an elegant way to detect and image PPI in real time [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

A Genetically Encoded Bioluminescence Intracellular Nanosensor for Androgen Receptor Activation Monitoring in 3D Cell Models

Calabretta

Lopreside

Montali

et al. 2021

Sensors

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In recent years, there has been an increasing demand for predictive and sensitive in vitro tools for drug discovery. Split complementation assays have the potential to enlarge the arsenal of in vitro tools for compound screening, with most of them relying on well-established reporter gene assays. In particular, ligand-induced complementation of split luciferases is emerging as a suitable approach for monitoring protein–protein interactions. We hereby report an intracellular nanosensor for the screening of compounds with androgenic activity based on a split NanoLuc reporter. We also confirm the suitability of using 3D spheroids of Human Embryonic Kidney (HEK-293) cells for upgrading the 2D cell-based assay. A limit of detection of 4 pM and a half maximal effective concentration (EC50) of 1.7 ± 0.3 nM were obtained for testosterone with HEK293 spheroids. This genetically encoded nanosensor also represents a new tool for real time imaging of the activation state of the androgen receptor, thus being suitable for analysing molecules with androgenic activity, including new drugs or endocrine disrupting molecules.

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Section: Introductionmentioning

confidence: 99%

A Genetically Encoded Bioluminescence Intracellular Nanosensor for Androgen Receptor Activation Monitoring in 3D Cell Models

Calabretta

Lopreside

Montali

et al. 2021

Sensors

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“…According to a recent review by Shin et al. [ 15 ], if only compounds with known structures are selected for X-ray crystallography of proteins and ligands, the amount of data that can be handled will involve tens to hundreds of compounds, although more than 720,000 human PPIs are known BioGrid [ 22 ] Current Build Statistics (4.3.196)—April 2021). Considering the number of known PPIs, structural information on PPIs likely remains insufficient.…”

Section: Resultsmentioning

confidence: 99%

“…reported a review of PPI-targeting drug designs. We applied QEPPI to one dataset in this review [ 15 ]. The dataset is described as the non-PPI dataset in the review (Soga dataset) [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

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Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

Kosugi

Ohue

2021

IJMS

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Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the ‘‘Rule-of-Four’’ (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.

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“…In addition, data on various candidate compounds are necessary for the initial stage of the search. According to a recent review, Shin et al [15], if only compounds with known structures are selected for Xray crystallography of proteins and ligands, the amount of data that can be handled will involve tens to hundreds of compounds, although more than the 720,000 human PPIs are known BioGrid [18] Current Build Statistics (4. 3.196) -April 2021).…”

Section: A Model Building For Qeppimentioning

confidence: 99%

Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness

Kosugi¹,

Ohue

2021

Preprint

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The quantification of drug-likeness is very useful for screening drug candidates. The quantitative estimate of drug-likeness (QED) is the most commonly used quantitative drug efficacy assessment method proposed by Bickerton et al. However, QED is not considered suitable for screening compounds that target protein-protein interactions (PPI), which have garnered significant interest in recent years. Therefore, we developed a method called the quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs and developed using the QED concept, involving modeling physicochemical properties based on the information available on the drug. QEPPI models the physicochemical properties of compounds that have been reported in the literature to act on PPIs. Compounds in iPPI-DB, which comprises PPI inhibitors and stabilizers, and FDA-approved drugs were evaluated using QEPPI. The results showed that QEPPI is more suitable for the early screening of PPI-targeting compounds than QED. QEPPI was also considered an extended concept of "Rules of Four" (RO4), a PPI inhibitor index proposed by Morelli et al. To compare the discriminatory performance of QEPPI and RO4, we evaluated their discriminatory performance using the datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. Results of the F-score of RO4 and QEPPI were 0.446 and 0.499, respectively. QEPPI demonstrated better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it could be used as an initial filter for efficient screening of PPI-targeting compounds, which has been difficult in the past.

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<p>Current Challenges and Opportunities in Designing Protein–Protein Interaction Targeted Drugs</p>

Cited by 60 publications

References 73 publications

A Genetically Encoded Bioluminescence Intracellular Nanosensor for Androgen Receptor Activation Monitoring in 3D Cell Models

A Genetically Encoded Bioluminescence Intracellular Nanosensor for Androgen Receptor Activation Monitoring in 3D Cell Models

Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions

Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness

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