Targeting protein-protein interaction interfaces in COVID-19 drug discovery

Chang, Chung-ke; Lin, Shanmeng; Satange, Roshan; Lin, Shih‐Chao; Sun, Sin-Cih; Wu, Hung‐Yi; Kehn-Hall, Kylene; Hou, Ming-Hon

doi:10.1016/j.csbj.2021.04.003

Cited by 34 publications

(21 citation statements)

References 130 publications

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“…Ouzounis et al used the Orf-3a as a structural template to predict low-resolution three-dimensional models of M proteins [ 15 ]. Interactions between M and N proteins stabilize virion RNA genome [ 9 , 10 , 11 , 16 ], making this Protein–Protein Interaction (PPI) a potential drug target [ 17 ]. Interactions between M protein and E proteins induce membrane curvature, strong enough to assemble and release virus-like particles [ 9 , 10 , 11 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

SARS-CoV-2 Membrane Protein: From Genomic Data to Structural New Insights

Marques-Pereira

Pires

Gouveia

et al. 2022

IJMS

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Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) is composed of four structural proteins and several accessory non-structural proteins. SARS-CoV-2’s most abundant structural protein, Membrane (M) protein, has a pivotal role both during viral infection cycle and host interferon antagonism. This is a highly conserved viral protein, thus an interesting and suitable target for drug discovery. In this paper, we explain the structural nature of M protein homodimer. To do so, we developed and applied a detailed and robust in silico workflow to predict M protein dimeric structure, membrane orientation, and interface characterization. Single Nucleotide Polymorphisms (SNPs) in M protein were retrieved from over 1.2 M SARS-CoV-2 genomes and proteins from the Global Initiative on Sharing All Influenza Data (GISAID) database, 91 of which were located at the predicted dimer interface. Among those, we identified SNPs in Variants of Concern (VOC) and Variants of Interest (VOI). Binding free energy differences were evaluated for dimer interfacial SNPs to infer mutant protein stabilities. A few high-prevalent mutated residues were found to be especially relevant in VOC and VOI. This realization may be a game-changer to structure-driven formulation of new therapeutics for SARS-CoV-2.

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Section: Introductionmentioning

confidence: 99%

SARS-CoV-2 Membrane Protein: From Genomic Data to Structural New Insights

Marques-Pereira

Pires

Gouveia

et al. 2022

IJMS

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“…Some researchers are focusing on compounds that target PPIs. One of the most promising PPI targets is the interaction between the SARS-CoV-2 S protein and human angiotensin-converting enzyme 2 receptor [ 34 , 35 , 36 ]. Therefore, we also calculated QEPPI for small-molecules targeting PPIs against SARS-CoV/SARS-CoV-2 (see Supplementary Table S8 ).…”

Section: Discussionmentioning

confidence: 99%

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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“…Secondly, to identify a small list of important proteins from a large network, we assume sub-communities of nodes (that are highly connected with each other and well connected with experimentally validated nodes) to be more important than other nodes. In the literature, highly connected nodes such as hotspots or hub genes [ 10 , 91 , 92 ] have received substantial attention. Our analysis targets ‘high-activity sub-network’ and can be considered as an extension to hotspot (i.e.…”

Section: Discussionmentioning

confidence: 99%

Integrative COVID-19 biological network inference with probabilistic core decomposition

Guo

Esfahani

Shao

et al. 2021

Briefings in Bioinformatics

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein–protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

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Targeting protein-protein interaction interfaces in COVID-19 drug discovery

Cited by 34 publications

References 130 publications

SARS-CoV-2 Membrane Protein: From Genomic Data to Structural New Insights

SARS-CoV-2 Membrane Protein: From Genomic Data to Structural New Insights

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

Integrative COVID-19 biological network inference with probabilistic core decomposition

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