Improved Accuracy for Modeling PROTAC-Mediated Ternary Complex Formation and Targeted Protein Degradation via New<i>In Silico</i>Methodologies

Drummond, Michael L.; Henry, Alasdair L.; Li, Huifang; Williams, Christopher I.

doi:10.1101/2020.07.10.197186

Cited by 14 publications

(16 citation statements)

References 73 publications

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“…Ternary complex formation is thought to be the most important factor in determining the degradability of protein targets 53,[55][56][57][58][59] . However, our analysis found that protein degradability can also be heavily influenced by protein-intrinsic features, especially the protein's endogenous ubiquitination potential.…”

Section: Discussionmentioning

confidence: 99%

“…It is widely believed that stable ternary complexes are associated with effective and selective target degradation 15,16,53,55 . A series of computational methods have been introduced to model PROTAC-mediated ternary complex formation [56][57][58][59] , which have facilitated the rational and efficient optimization of PROTACs 16,60 . However, several studies have shown that although some level of binary target engagement and ternary complex formation are necessary for target recruitment and ubiquitin transfer, they are not always sufficient for targeted protein degradation [51][52][53]61 .…”

Section: Introductionmentioning

confidence: 99%

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Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

Zhang

Burman

Chen

et al. 2021

Preprint

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Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed "degradability", is largely unknown. Recent systematic studies to map the degradable kinome have shown differences in degradation between kinases with similar drug-target engagement, suggesting yet unknown factors influencing degradability. We therefore developed a machine learning model, MAPD (Model-based Analysis of Protein Degradability), to predict degradability from protein features that encompass post-translational modifications, protein stability, protein expression and protein-protein interactions. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds (auPRC=0.759) and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins, including 278 cancer genes, that may be tractable to TPD drug development.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

Zhang

Burman

Chen

et al. 2021

Preprint

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“…Recently, this work was extended by the addition of clustering to improve its performance. 24 Very recently, a new Rosetta-based protocol was also reported that successfully rationalized PROTAC structure–activity relationships. 25 Some system specific modeling efforts were also reported.…”

Section: Introductionmentioning

confidence: 99%

PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes

Zaidman

Prilusky

London

2020

J. Chem. Inf. Model.

138

166

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Proteolysis-targeting chimeras (PROTACs), which induce degradation by recruitment of an E3 ligase to a target protein, are gaining much interest as a new pharmacological modality. However, designing PROTACs is challenging. Formation of a ternary complex between the protein target, the PROTAC, and the recruited E3 ligase is considered paramount for successful degradation. A structural model of this ternary complex could in principle inform rational PROTAC design. Unfortunately, only a handful of structures are available for such complexes, necessitating tools for their modeling. We developed a combined protocol for the modeling of a ternary complex induced by a given PROTAC. Our protocol alternates between sampling of the protein–protein interaction space and the PROTAC molecule conformational space. Application of this protocol—PRosettaC—to a benchmark of known PROTAC ternary complexes results in near-native predictions, with often atomic accuracy prediction of the protein chains, as well as the PROTAC binding moieties. It allowed the modeling of a CRBN/BTK complex that recapitulated experimental results for a series of PROTACs. PRosettaC generated models may be used to design PROTACs for new targets, as well as improve PROTACs for existing targets, potentially cutting down time and synthesis efforts. To enable wide access to this protocol, we have made it available through a web server ( ).

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“…With the most promising method of combining docking of the two protein-ligand structures with separate PROTAC conformer generation, they showed that it is possible to recover near-native ternary complexes for a few available PDBs. In follow-up work [31], Drummond et al added two additional methods which improve the previous performance. This work introduces clustering of the generated structures, an essential step in obtaining good results.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization for Ternary Complex Prediction (BOTCP)

Rao¹,

Tunjic²,

Brunsteiner³

et al. 2022

Preprint

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Proximity-inducing compounds (PICs) are an emergent drug technology through which a protein of interest (POI), often a drug target, is brought into the vicinity of a second protein which modifies the POI's function, abundance or localisation, giving rise to a therapeutic effect. One of the best-known examples for such compounds are heterobifunctional molecules known as proteolysis targeting chimeras (PROTACs). PROTACs reduce the abundance of the target protein by establishing proximity to an E3 ligase which targets the protein towards degradation via the ubiquitin-proteasomal pathway. Design of PROTACs in silico requires the computational prediction of the ternary complex consisting of POI, PROTAC molecule, and the E3 ligase. Here, we present a novel machine learning-based method for predicting PROTAC-mediated ternary complex structures using Bayesian optimization. We show how a fitness score combining an estimation of protein-protein interactions with PROTAC binding energy calculations enables the sample-efficient exploration of candidate structures. Furthermore, our method presents two novel scores for filtering and reranking which take PROTAC stability (Autodock-Vina based PROTAC stability score) and protein interaction restraints (the TCP-AIR score) into account. We evaluate our method using DockQ scores and demonstrate, that even with a clustering that require members to have a high similarity, i.e. with smaller clusters, we can assign high ranks to those clusters that contain poses close to the experimentally determined native structure of the ternary complexes. We also demonstrate the resultant improved yeild of near-native poses in these clusters.

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Improved Accuracy for Modeling PROTAC-Mediated Ternary Complex Formation and Targeted Protein Degradation via NewIn SilicoMethodologies

Cited by 14 publications

References 73 publications

Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

Machine learning modeling of protein-intrinsic features predicts tractability of targeted protein degradation

PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes

Bayesian Optimization for Ternary Complex Prediction (BOTCP)

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