scholarly journals Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness

2021 ◽  
Author(s):  
Takatsugu Kosugi ◽  
Masahito Ohue
Keyword(s):  
Physicochemical Properties ◽  
Protein Interaction ◽  
Quantitative Estimate ◽  
Early Stage ◽  
Assessment Method ◽  
Early Screening ◽  
Protein Protein Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Discriminatory Performance

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 <i>et al</i>. 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 <i>et al</i>. 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.<br>

scholarly journals Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness

2021 ◽  
Author(s):  
Takatsugu Kosugi ◽  
Masahito Ohue
Keyword(s):  
Physicochemical Properties ◽  
Protein Interaction ◽  
Quantitative Estimate ◽  
Early Stage ◽  
Assessment Method ◽  
Early Screening ◽  
Protein Protein Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Discriminatory Performance

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 <i>et al</i>. 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 <i>et al</i>. 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.<br>

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

2021 ◽  
Vol 22 (20) ◽  
pp. 10925
Author(s):  
Takatsugu Kosugi ◽  
Masahito Ohue
Keyword(s):  
Protein Interactions ◽  
Quantitative Estimate ◽  
Early Stage ◽  
Drug Efficacy ◽  
Protein Protein Interactions ◽  
Early Screening ◽  
Drug Candidates ◽  
Quantitative Estimates ◽  
Approved Drugs ◽  
Fda Approved Drugs

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.

scholarly journals Selective killing of homologous recombination-deficient cancer cell lines by inhibitors of the RPA:RAD52 protein-protein interaction

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248941
Author(s):  
Mona Al-Mugotir ◽  
Jeffrey J. Lovelace ◽  
Joseph George ◽  
Mika Bessho ◽  
Dhananjaya Pal ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Homologous Recombination ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Protein Interaction ◽  
Ovarian Cancer Cells ◽  
Protein Protein Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs

Synthetic lethality is a successful strategy employed to develop selective chemotherapeutics against cancer cells. Inactivation of RAD52 is synthetically lethal to homologous recombination (HR) deficient cancer cell lines. Replication protein A (RPA) recruits RAD52 to repair sites, and the formation of this protein-protein complex is critical for RAD52 activity. To discover small molecules that inhibit the RPA:RAD52 protein-protein interaction (PPI), we screened chemical libraries with our newly developed Fluorescence-based protein-protein Interaction Assay (FluorIA). Eleven compounds were identified, including FDA-approved drugs (quinacrine, mitoxantrone, and doxorubicin). The FluorIA was used to rank the compounds by their ability to inhibit the RPA:RAD52 PPI and showed mitoxantrone and doxorubicin to be the most effective. Initial studies using the three FDA-approved drugs showed selective killing of BRCA1-mutated breast cancer cells (HCC1937), BRCA2-mutated ovarian cancer cells (PE01), and BRCA1-mutated ovarian cancer cells (UWB1.289). It was noteworthy that selective killing was seen in cells known to be resistant to PARP inhibitors (HCC1937 and UWB1 SYr13). A cell-based double-strand break (DSB) repair assay indicated that mitoxantrone significantly suppressed RAD52-dependent single-strand annealing (SSA) and mitoxantrone treatment disrupted the RPA:RAD52 PPI in cells. Furthermore, mitoxantrone reduced radiation-induced foci-formation of RAD52 with no significant activity against RAD51 foci formation. The results indicate that the RPA:RAD52 PPI could be a therapeutic target for HR-deficient cancers. These data also suggest that RAD52 is one of the targets of mitoxantrone and related compounds.

Analysis of physicochemical properties of protein-protein interaction modulators suggests stronger alignment with the “Rule-of-Five”

2021 ◽  
Author(s):  
Jia Truong ◽  
Ashwin George ◽  
Jessica Holien
Keyword(s):  
Physicochemical Properties ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Recent Advances

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...

scholarly journals Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

2020 ◽  
Author(s):  
Sugandh Kumar ◽  
Pratima Kumari ◽  
Geetanjali Agnihotri ◽  
Preethy VijayKumar ◽  
Shaheerah Khan ◽  
...  
Keyword(s):  
Interaction Network ◽  
Viral Proteins ◽  
Inhibitory Effect ◽  
Host Proteins ◽  
Disease Manifestation ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Approved Drugs ◽  
Viral Lifecycle ◽  
Fda Approved Drugs

<p>The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. </p> <p>In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect.</p> <p>Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.</p>

scholarly journals Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

2020 ◽  
Author(s):  
Sugandh Kumar ◽  
Pratima Kumari ◽  
Geetanjali Agnihotri ◽  
Preethy VijayKumar ◽  
Shaheerah Khan ◽  
...  
Keyword(s):  
Interaction Network ◽  
Viral Proteins ◽  
Inhibitory Effect ◽  
Host Proteins ◽  
Disease Manifestation ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Approved Drugs ◽  
Viral Lifecycle ◽  
Fda Approved Drugs

<p>The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. </p> <p>In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect.</p> <p>Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.</p>

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