Computational strategy for intrinsically disordered protein ligand design leads to the discovery of p53 transactivation domain I binding compounds that activate the p53 pathway

Ruan, Hao; Chen, Yu; Niu, Xiaogang; Zhang, Weilin; Liu, Hanzhong; Chen, Limin; Xiong, Ruoyao; Sun, Qi; Jin, Changwen; Liu, Ying; Lai, Luhua

doi:10.1039/d0sc04670a

Cited by 25 publications

(26 citation statements)

References 79 publications

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“…Our work also suggests novel applications in protein engineering and drug designation. For example, (1) by deleting or adding degrons to proteins, researchers can control protein abundance to regulate specific functions; (2) researchers can mutate the degrons or block E3-degron interactions to upregulate the expression of tumor suppressors [ 12 ], which may be a feasible way to treat cancer; and (3) chemists can design PROTAC drugs to link a substrate with a predicted binding E3 to form double bonds between them and achieve higher specificity.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, researchers explored the targetability of degrons by designing small molecules for a degron on tumor suppressor p53. Two resulting small molecules upregulated p53 expression and restored p53 function, which provide an opportunity to inhibit cancer cell growth [ 12 ]. Thus, identifying degrons on the substrates should greatly assist in investigating the pathogenesis of related diseases and provide potential therapeutic targets.…”

Section: Introductionmentioning

confidence: 99%

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Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning

Hou

Wang

et al. 2022

BMC Biol

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Background Degrons are short linear motifs, bound by E3 ubiquitin ligase to target protein substrates to be degraded by the ubiquitin-proteasome system. Mutations leading to deregulation of degron functionality disrupt control of protein abundance due to mistargeting of proteins destined for degradation and often result in pathologies. Targeting degrons by small molecules also emerges as an exciting drug design strategy to upregulate the expression of specific proteins. Despite their essential function and disease targetability, reliable identification of degrons remains a conundrum. Here, we developed a deep learning-based model named Degpred that predicts general degrons directly from protein sequences. Results We showed that the BERT-based model performed well in predicting degrons singly from protein sequences. Then, we used the deep learning model Degpred to predict degrons proteome-widely. Degpred successfully captured typical degron-related sequence properties and predicted degrons beyond those from motif-based methods which use a handful of E3 motifs to match possible degrons. Furthermore, we calculated E3 motifs using predicted degrons on the substrates in our collected E3-substrate interaction dataset and constructed a regulatory network of protein degradation by assigning predicted degrons to specific E3s with calculated motifs. Critically, we experimentally verified that a predicted SPOP binding degron on CBX6 prompts CBX6 degradation and mediates the interaction with SPOP. We also showed that the protein degradation regulatory system is important in tumorigenesis by surveying degron-related mutations in TCGA. Conclusions Degpred provides an efficient tool to proteome-wide prediction of degrons and binding E3s singly from protein sequences. Degpred successfully captures typical degron-related sequence properties and predicts degrons beyond those from previously used motif-based methods, thus greatly expanding the degron landscape, which should advance the understanding of protein degradation, and allow exploration of uncharacterized alterations of proteins in diseases. To make it easier for readers to access collected and predicted datasets, we integrated these data into the website http://degron.phasep.pro/.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning

Hou

Wang

et al. 2022

BMC Biol

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“…The current virtual screening strategies based on identifying compounds that bind to definite deep pockets in ordered proteins seem inappropriate, and more adaptable computational drug screening should be developed for the drug discovery of IDPs. More recently, a hierarchic computational approach for IDP drug visual screening (IDPDVS) was developed and successfully utilized in the exploitation of compounds that bind to p53 transactivation domain I (TAD1) to inhibit the p53‐MDM2/X interaction 420 . Based on the finding that IDP modulators are ought to bind contemporaneously to different conformations of the IDP with similar affinity, IDPDVS proceeds first with conformational sampling firstly, and thereafter, both considerations of conformational clustering and druggability evaluations are considered to locate feasible ligand‐binding pockets, afterward the multiple docking screening and multiconformation‐based compounds selections.…”

Section: Emerging Solutionsmentioning

confidence: 99%

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

Pei

Guo

Peng

et al. 2022

Medicinal Research Reviews

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The key proteins involved in transcriptional regulation play convergent roles in cellular homeostasis, and their dysfunction mediates aberrant gene expressions that underline the hallmarks of tumorigenesis. As tumor progression is dependent on such abnormal regulation of transcription, it is important to discover novel chemical entities as antitumor drugs that target key tumor‐associated proteins involved in transcriptional regulation. Despite most key proteins (especially transcription factors) involved in transcriptional regulation are historically recognized as undruggable targets, multiple targeting approaches at diverse levels of transcriptional regulation, such as epigenetic intervention, inhibition of DNA‐binding of transcriptional factors, and inhibition of the protein–protein interactions (PPIs), have been established in preclinically or clinically studies. In addition, several new approaches have recently been described, such as targeting proteasomal degradation and eliciting synthetic lethality. This review will emphasize on accentuating these developing therapeutic approaches and provide a thorough conspectus of the drug development to target key proteins involved in transcriptional regulation and their impact on future oncotherapy.

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“…[230][231][232][233] Recently, MD simulation of the intrinsically disordered p53 transactivation domain I (p53 TAD1) with RSFF2 was successfully used to guide virtual screening, which led to the identification of two promising compounds. 234…”

Section: Decomposition Of Free Energy Surfacementioning

confidence: 99%

“…Further, RSFF2 was used by Lin et al to investigate sequence‐structure relationships of cyclic hexapeptides, 228 and to design well‐structured cyclic pentapeptides, 229 among other applications 230–233 . Recently, MD simulation of the intrinsically disordered p53 transactivation domain I (p53 TAD1) with RSFF2 was successfully used to guide virtual screening, which led to the identification of two promising compounds 234 …”

Section: Optimization Strategies For Local Conformation and Secondary...mentioning

confidence: 99%

How to strike a conformational balance in protein force fields for molecular dynamics simulations?

Kang

Jiang

2021

WIREs Comput Mol Sci

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Molecular dynamics (MD) simulation is a powerful tool for exploring the conformational energy landscape of proteins, and the reliability of MD results is crucially dependent on the underlying force field (FF). An accurate FF capable of producing balanced distributions of diverse conformations at multiple levels has been a long‐sought goal. Towards this, several decades of joint efforts have been made to address FF deficiencies, manifested by conformational biases at different levels (local conformations, secondary structures, and global extendedness of polypeptide chain). We first present the major FF biases, then review the strategies to address them separately. Specifically, both nonresidue‐specific and residue‐specific strategies for torsional parameter optimization have been applied to achieve local conformation and secondary structure balances. Significant improvements can be gained with residue‐specific torsional parameters especially when explicit dihedral couplings are considered. Further, the additional balance between protein–protein and protein–water interactions has been optimized via multiple ways to reproduce the global extendedness of polypeptide chains, especially for unfolded or disordered proteins. This review aims to summarize the most valuable experience and lessons gained from the past, which, we hope, can facilitate further improvements of both classical FFs and more sophisticated models such as polarizable FFs. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Molecular and Statistical Mechanics > Molecular Mechanics

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Computational strategy for intrinsically disordered protein ligand design leads to the discovery of p53 transactivation domain I binding compounds that activate the p53 pathway

Abstract: A hierarchical computational strategy for IDP drug virtual screening (IDPDVS) was proposed and successfully applied to identify compounds that bind p53 TAD1 and restore wild-type p53 function in cancer cells.

Cited by 25 publications

References 79 publications

Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning

Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

How to strike a conformational balance in protein force fields for molecular dynamics simulations?

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