Machine Learning for Prioritization of Thermostabilizing Mutations for G-Protein Coupled Receptors

Muk, Sanychen; Ghosh, Soumadwip; Achuthan, Srisairam; Chen, Xiaomin; Yao, Xiao-Jie; Sandhu, Manbir; Griffor, Matthew C.; Fennell, Kimberly F.; Che, Ye; Shanmugasundaram, Veerabahu; Qiu, Xiayang; Tate, Christopher G.; Vaidehi, Nagarajan

doi:10.1016/j.bpj.2019.10.023

Cited by 16 publications

(7 citation statements)

References 49 publications

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“…Other than clinically relevant variants, experimentally important variants were found, such as activating substitutions in downstream cascades 55 , or alanine scanning panels for functional 56 , or thermostabilizing assessment 57 in GPCRs. Far from negligible, such panels can be repurposed for model training, consequently reducing the need for experimental assays 58 .…”

Section: Discussionmentioning

confidence: 99%

Excuse me, there is a mutant in my bioactivity soup! A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modelling

Gorostiola González,

Béquignon,

Manners

et al. 2024

Preprint

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Bioactivity prediction is essential in computational drug discovery, particularly within virtual screening campaigns. Despite advancements in model architectures and features, the sparsity and quality of relevant training data remain a major bottleneck. Notably, genetic variance annotation, crucial for understanding variant-specific bioactivity, is often neglected. Key efforts to tackle these issues are conducted by public bioactivity databases such as ChEMBL, but these are not free of challenges. Here, a comprehensive analysis of the extent and distribution of bioactivity data tested on genetic variants across organisms, protein families, individual targets, and specific variants, for the first time characterises in detail the genetic variability landscape in the ChEMBL database and sheds light on the range and consequences of protein amino acid substitutions in bioactivity data distribution and modelling. Furthermore, an extensive set of analysis resources (Python package and notebooks) and a variant-annotated bioactivity dataset are made available to help replicate the analyses described here for any protein of interest and make informed decisions regarding the quality of data for modelling. Finally, the potential to extract variants and subsets of the chemical space with desirable inter-variant bioactivity profiles is demonstrated for data-rich proteins. This approach contributes to more reliable bioactivity modelling, aids noise reduction and informs decision-making in computational drug discovery.

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Section: Discussionmentioning

confidence: 99%

Excuse me, there is a mutant in my bioactivity soup! A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modelling

Gorostiola González,

Béquignon,

Manners

et al. 2024

Preprint

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“…More recently, machine-learning approaches have been applied, in which the existing GPCR dataset of stabilising variants was used as the training set to successfully identify stabilising variants in naive sequences 56 . One piece of software that has used this approach is the software CompoMug (Computational Predictions of Mutations in GPCRs) 57 , which showed a 25% success rate for the identification of stabilising 5-HT 2C variants for crystallographic study.…”

Section: Comparison Of Improver To Other Computational Methodsmentioning

confidence: 99%

“…Instead, we extracted a linear scoring matrix, allowing us to observe trends in membrane protein stability and to devise a computationally-efficient tool that helped us stabilise three membrane proteins with different folds and modes of action. To apply a neural network approach that would work broadly with membrane proteins as has been done for GPCRs [56][57][58] would require access to large systematic stabilisation datasets, with a record of both stabilising and destabilising variants of membrane proteins from diverse folds. We hope to increase the reporting, sharing and depositing of such data by releasing IMPROvER for use in the academic community http://impro ver.ddns.net/IMPRO vER/.…”

Section: Comparison Of Improver To Other Computational Methodsmentioning

confidence: 99%

IMPROvER: the Integral Membrane Protein Stability Selector

Harborne

Strauss

Boakes

et al. 2020

Sci Rep

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Identifying stabilising variants of membrane protein targets is often required for structure determination. Our new computational pipeline, the Integral Membrane Protein Stability Selector (IMPROvER) provides a rational approach to variant selection by employing three independent approaches: deep-sequence, model-based and data-driven. In silico tests using known stability data, and in vitro tests using three membrane protein targets with 7, 11 and 16 transmembrane helices provided measures of success. In vitro, individual approaches alone all identified stabilising variants at a rate better than expected by random selection. Low numbers of overlapping predictions between approaches meant a greater success rate was achieved (fourfold better than random) when approaches were combined and selections restricted to the highest ranked sites. The mix of information IMPROvER uses can be extracted for any helical membrane protein. We have developed the first general-purpose tool for selecting stabilising variants of $$\upalpha$$ α -helical membrane proteins, increasing efficiency and reducing workload. IMPROvER can be accessed at http://improver.ddns.net/IMPROvER/.

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“…Typically, identifying thermostabilising mutations is a laborious experimental process (Heydenreich et al, 2015). Machine learning methods, trained on known thermostabilising point mutations of GPCRs, identified stabilising mutations for the complement component C5a 1 receptor (Muk et al, 2019) and the 5‐hydroxytryptamine (serotonin) 5‐HT 2C receptor (Popov et al, 2018). This enabled structure determination of inactive and active 5‐HT 2C receptors.…”

Section: At What Stages Can Ai Be Employed To Accelerate the Gpcr Dru...mentioning

confidence: 99%

The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery

Nguyen

Koh

et al. 2023

British J Pharmacology

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The application of artificial intelligence (AI) approaches to drug discovery for G protein‐coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand‐GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process “faster, smarter and cheaper,” we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery.

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Machine Learning for Prioritization of Thermostabilizing Mutations for G-Protein Coupled Receptors

Cited by 16 publications

References 49 publications

Excuse me, there is a mutant in my bioactivity soup! A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modelling

Excuse me, there is a mutant in my bioactivity soup! A comprehensive analysis of the genetic variability landscape of bioactivity databases and its effect on activity modelling

IMPROvER: the Integral Membrane Protein Stability Selector

The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery

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