Allosteric pluripotency: challenges and opportunities

Akimoto, Madoka; Pomier, Karla Martinez; VanSchouwen, Bryan; Byun, Jung Ah; Khamina, Mariia; Melacini, Giuseppe

doi:10.1042/bcj20210528

Cited by 7 publications

(5 citation statements)

References 73 publications

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“…Autoinhibition is a common inactivating switch mechanism in nature [80] , [81] , [82] , [83] , [84] , [85] . The principle is straightforward and since the inactivating segment is covalently linked, it is highly efficient.…”

Section: Resultsmentioning

confidence: 99%

Allosteric regulation of autoinhibition and activation of c-Abl

Liu

Zhang²,

Tsai³

et al. 2022

Computational and Structural Biotechnology Journal

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Section: Resultsmentioning

confidence: 99%

Allosteric regulation of autoinhibition and activation of c-Abl

Liu

Zhang²,

Tsai³

et al. 2022

Computational and Structural Biotechnology Journal

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“…Finally, we note that AlphaFold, which predicts single ranked structures for a protein sequence, is unable to address directly allosteric mechanisms, which are based on the populations of conformational states in the ensembles . − Allostery, where the signal propagates dynamically with the shifts in the populations, underlies regulation and thus cell life. , Due to its higher specificity and consequently lower toxicity, which results from targeting nonconserved allosteric sites, allostery also increasingly features in allosteric drugs. − …”

Section: Introductionmentioning

confidence: 99%

AlphaFold, Artificial Intelligence (AI), and Allostery

et al. 2022

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AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota–human protein–protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

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“…NMR chemical shift covariance (CHESCA) and projection (CHESPA) analyses − can identify residue interaction networks that show correlated changes in chemical shifts due to allosteric perturbations caused by ligand binding or mutations designed to modulate allosteric conformational equilibria. Using statistical comparative analyses of the NMR chemical shift variations elicited by the selected perturbations, the CHESCA approach characterizes perturbation-specific chemical shift patterns serving as distinctive signatures of allosteric mechanisms.…”

Section: Expanding the Horizons Of Experiment-guided Molecular Simula...mentioning

confidence: 99%

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

Agajanian

Alshahrani

Bai

et al. 2023

J. Chem. Inf. Model.

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Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

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Allosteric pluripotency: challenges and opportunities

Cited by 7 publications

References 73 publications

Allosteric regulation of autoinhibition and activation of c-Abl

Allosteric regulation of autoinhibition and activation of c-Abl

AlphaFold, Artificial Intelligence (AI), and Allostery

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

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