Discovery of novel allosteric site and covalent inhibitors of FBPase with potent hypoglycemic effects

Huang, Yunyuan; Lin, Wenhan; Han, Xinya; Chen, Haifeng; Ren, Yanliang; Xu, Yu; Song, Rui; Rao, Li; Su, Chen; Peng, Chao; Feng, Lingling; Wan, Jian

doi:10.1016/j.ejmech.2019.111749

Cited by 17 publications

(25 citation statements)

References 95 publications

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“…Site-directed mutagenesis is one of the commonly used experimental methods to discover novel allosteric sites. Huang et al . mutated seven cysteine residues to serine and identified a functional cysteine site that affects the catalytic activity of fructose-1,6-bisphosphatase.…”

Section: Identification Of Allosteric Sites and Pathways In Proteinsmentioning

confidence: 99%

Engineering an Allosteric Control of Protein Function

2021

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Allosteric regulation in proteins is fundamental to many important biological processes. Allostery has been employed to control protein functions by regulating protein activity. Engineered allosteric regulation allows controlling protein activity in subsecond time scale and has a broad range of applications, from dissecting spatiotemporal dynamics in biochemical cascades to applications in biotechnology and medicine. Here, we review the concept of allostery in proteins and various approaches to identify allosteric sites and pathways. We then provide an overview of strategies and tools used in allosteric protein regulation and their utility in biological applications. We highlight various classes of proteins, where regulation is achieved through allostery. Finally, we analyze the current problems, critical challenges, and future prospective in achieving allosteric regulation in proteins.

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Section: Identification Of Allosteric Sites and Pathways In Proteinsmentioning

confidence: 99%

Engineering an Allosteric Control of Protein Function

2021

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“…Due primarily to their simplicity and speed, SAscore and SCScore have been used extensively across drug development pipelines including for compound screening (e.g., Omolabi et al, 2021;Basu et al, 2020;Lu and Li, 2021;Huang et al, 2019), dataset preparation (e.g., Imrie et al, 2021b;Humbeck et al, 2018) and molecule generation/optimization (e.g., Leguy et al, 2020;Zhou et al, 2019;Khemchandani et al, 2020a;Green et al, 2020). SAScore is one of the most popular metrics for biasing or discarding potentially infeasible compounds in methods for computational generation of de novo molecules (e.g., Yassine et al, 2021;Imrie et al, 2020;Prykhodko et al, 2019;Leguy et al, 2020;Khemchandani et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

CoPriNet: Deep learning compound price prediction for use in de novo molecule generation and prioritization.

Sánchez-García

Havasi

Takács

et al. 2022

Preprint

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Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite its multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available molecules has dramatically increased over the last decade, redefining the problem of compound accessibility as a matter of budget. In this paper we show that if compound prices are an alternative proxy for compound availability, then synthetic accessibility scores are not effective strategies for assessing availability. Instead, we learn how to predict prices directly from the catalogues. Our approached, CopriNet, is a retrosynthesis-free deep learning model trained on pairs of compound/prices extracted from the Mcule catalogue. CoPriNet is able to provide price predictions that exhibit far better correlation with actual compound prices than any synthetic accessibility measurement. Moreover, unlike standard retrosynthesis methods, CoPriNet is rapid, comparable in execution time to popular synthetic accessibility metrics and thus is suitable for high-throughput experiments including virtual screening and de novo compound generation.

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“…Another commonly used SA score, SCScore 11 employs an indirect estimation of complexity assuming that the complexity of the reactants is never larger than the complexity of the products. Due primarily to their simplicity and speed, SAscore and SCScore have been used extensively in drug development pipelines including for compound screening [20][21][22]26 , dataset preparation 23,24 and molecule generation/optimization [27][28][29][30] . SAScore is one of the most popular metrics for biasing or discarding potentially infeasible compounds in methods for computational generation of de novo molecules 27,[31][32][33][34] .…”

Section: Introductionmentioning

confidence: 99%

CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.

Sánchez-García

Havasi

Takács

et al. 2022

Preprint

View full text Add to dashboard Cite

Compound availability is a critical property for design prioritization across the drug discovery pipeline. Historically, and despite their multiple limitations, compound-oriented synthetic accessibility scores have been used as proxies for this problem. However, the size of the catalogues of commercially available molecules has dramatically increased over the last decade, redefining the problem of compound accessibility as a matter of budget. In this paper we show that if compound prices are the desired proxy for compound availability, then synthetic accessibility scores are not effective strategies for us in selection. Our approach, CopriNet, is a retrosynthesis-free deep learning model trained on 2D graph representations of compounds alongside their prices extracted from the Mcule catalogue. We show that CoPriNet provides price predictions that correlate far better with actual compound prices than any synthetic accessibility score. Moreover, unlike standard retrosynthesis methods, CoPriNet is rapid, with execution times comparable to popular synthetic accessibility metrics, and thus is suitable for high-throughput experiments including virtual screening and de novo compound generation. While the Mcule catalogue is a proprietary dataset, the CoPriNet source code and the model trained on the proprietary data as well as the fraction of the catalogue (100K compound/prices) used as test dataset have been made publicly available at https://github.com/oxpig/CoPriNet.

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Discovery of novel allosteric site and covalent inhibitors of FBPase with potent hypoglycemic effects

Cited by 17 publications

References 95 publications

Engineering an Allosteric Control of Protein Function

Engineering an Allosteric Control of Protein Function

CoPriNet: Deep learning compound price prediction for use in de novo molecule generation and prioritization.

CoPriNet: Graph Neural Networks provide accurate and rapid compound price prediction for molecule prioritisation.

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