Explaining and Predicting Allostery with Allosteric Database and Modern Analytical Techniques

Zha, Jinyin; Li, Mingyu; Kong, Ren; Lu, Shaoyong; Zhang, Jian

doi:10.1016/j.jmb.2022.167481

Cited by 12 publications

(7 citation statements)

References 156 publications

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“…The dualsteric approach 31 is an emerging pharmacological technique in the past twenty years. Compared to classical orthosteric or allosteric modulators, 15 dualsteric modulators simultaneously bind to orthosteric and allosteric sites. 37 Therefore, it could flourish with both superiorities from the two pharmacophores.…”

Section: Discussionmentioning

confidence: 99%

“…Among allosteric drugs, positive allosteric modulators (PAMs) and negative allosteric modulators (NAMs) affect the response of the protein to natural ligands or orthosteric agonists, while allosteric agonists and antagonists could directly activate and inhibit protein function, respectively. 1,15 A popular mechanism of these allosteric drugs is the ''population-shift'' model. It suggests that different conformations (such as active, half-active, and inactive) of a protein naturally exist, and the binding of allosteric ligands would stabilize certain conformations and ''shuffle'' the population of all conformations (Fig.…”

Section: Introductionmentioning

confidence: 99%

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Designing drugs and chemical probes with the dualsteric approach

Zha,

He,

et al. 2023

Chem. Soc. Rev.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Designing drugs and chemical probes with the dualsteric approach

Zha,

He,

et al. 2023

Chem. Soc. Rev.

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“…Its newest version contains a total of 1,949 entries of allosteric sites, each with different proteins and modulators Liu et al (2020) . However, data need to be filtered from ASD under certain criteria to ensure the data quality Zha et al (2022) . To ensure protein quality and diversity, Huang et al (2013 ) selected 90 proteins using the previous rules: protein structures with either resolution below 3 Å or missing residues in the allosteric sites were removed, and redundant proteins that have more than 30% sequence identity were filtered out.…”

Section: Methodsmentioning

confidence: 99%

PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning

Xiao

Tian

Tao

2022

Front. Mol. Biosci.

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Allostery is a fundamental process in regulating protein activities. The discovery, design, and development of allosteric drugs demand better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and protein dynamics. Here, we define a baseline model for allosteric site prediction and present a computational model using automated machine learning. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 82.7% of allosteric pockets appearing among the top three positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (PASSer) to facilitate allosteric drug discovery.

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“…Visible allosteric sites are well-defined in the protein crystal structures, which have been meticulously collected and organized into our ASD and ASBench [ 42 ] data platform. Such platforms have been extensively used for training and testing numerous computational site detection tools [ 43 ]. Most of these tools regard allosteric site prediction as a classification task.…”

Section: Allosteric Binding Site Identificationmentioning

confidence: 99%

A Structure-Based Allosteric Modulator Design Paradigm

Li,

Lan,

et al. 2023

Health Data Sci

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Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.

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Explaining and Predicting Allostery with Allosteric Database and Modern Analytical Techniques

Cited by 12 publications

References 156 publications

Designing drugs and chemical probes with the dualsteric approach

Designing drugs and chemical probes with the dualsteric approach

PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning

A Structure-Based Allosteric Modulator Design Paradigm

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