Markov state models and NMR uncover an overlooked allosteric loop in p53

Barros, Emília P.; Demir, Özlem; Soto, Jenaro; Cocco, Melanie J.; Amaro, Rommie E.

doi:10.1039/d0sc05053a

Cited by 26 publications

(27 citation statements)

References 73 publications

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“…When applying TICA to study functional conformational changes, we recommend using the subset of structural features chosen by Spectral-oASIS and other methods described in the previous section. 87 Furthermore, we suggest using crossvalidation tools, such as GMRQ 63 or VAMP-2 score, 28 to choose the optimal hyperparameters for the TICA analysis (e.g., number of TICs and TICA lag time). 68,69,88 Emerging deep Learning Algorithms for Feature Selection and Dimensionality Reduction…”

Section: Automatic Methods For Feature Selectionmentioning

confidence: 99%

“…TICA is one of the most popular methods to perform dimensionality reduction in the MSM construction, which performs the eigen decomposition of the time-lagged covariance matrix. , The leading eigenvectors (so-called time-lagged independent components, TICs) are linear approximations to the slowest dynamic modes of the system. When applying TICA to study functional conformational changes, we recommend using the subset of structural features chosen by Spectral-oASIS and other methods described in the previous section . Furthermore, we suggest using cross-validation tools, such as GMRQ or VAMP-2 score, to choose the optimal hyperparameters for the TICA analysis (e.g., number of TICs and TICA lag time). ,, …”

Section: Automatic Feature Selection and Dimensionality Reduction To ...mentioning

confidence: 99%

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Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning

Konovalov

Unarta

Cao

et al. 2021

JACS Au

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Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.

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Section: Automatic Methods For Feature Selectionmentioning

confidence: 99%

Section: Automatic Feature Selection and Dimensionality Reduction To ...mentioning

confidence: 99%

Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning

Konovalov

Unarta

Cao

et al. 2021

JACS Au

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“…In summary, the promising combination of dPaCS-MD/MSM can be used not only to investigate different pathways during dissociations of two large molecules but also to identify key residues for major dissociation pathways and to quantitatively calculate the binding free energy of the complex, which should also be useful in elucidating the effects of mutations. The presence of allosteric roles and inactivating effects of the p53-DBD mutations located distant from the DNA binding surface that were recently revealed [93][94][95] may also be investigated by dPaCS-MD/MSM to quantitatively analyze mutational effects on binding free energy and binding mechanisms in the future. We conclude that this combination sheds light on underlying mechanisms, which are highly necessitated for developing small molecules as anti-tumor drugs that can reactivate functions of p53 mutants.…”

Section: Discussionmentioning

confidence: 99%

Dissociation pathways of the p53 DNA binding domain from DNA and critical roles of key residues elucidated by dPaCS-MD/MSM

Sobeh

Kitao

2021

Preprint

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The dissociation process of the DNA binding domain of p53 (p53-DBD) from a DNA duplex that contains the consensus sequence, which is the specific target of p53-DBD, was investigated by a combination of dissociation parallel cascade selection molecular dynamics (dPaCS-MD) and the Markov state model (MSM). Based on an all-atom model including explicit solvent, we first simulated the p53-DBD dissociation processes by 75 trials of dPaCS-MD, which required an average simulation time of 11.2 ± 2.2 ns per trial. By setting the axis of the DNA duplex as the Z-axis and the binding side of p53-DBD on DNA as the + side of the X-axis, we found that dissociations took place along the +X and −Y directions (−Y directions) in 93% of the cases, while 7% of the cases moved along +X and +Y directions (+Y directions). Toward the −Y directions, p53-DBD dissociated first from the major groove and then detached from the minor groove, while unbinding from the minor groove occurred first in dissociations along the +Y directions. Analysis of the free energy landscape by MSM showed that loss of the minor groove interaction with p53-DBD toward the +Y directions incurred a relatively high energy cost (1.1 kcal/mol) upon a critical transition, whereas major groove detachment more frequently occurred with lower free energy costs. The standard binding free energy calculated from the free energy landscape was −10.9 ± 0.4 kcal/mol, which agrees with an experimental value of –11.1 kcal/mol. These results indicate that the dPaCS-MD/MSM combination can be a powerful tool to investigate dissociation mechanisms of two large molecules. Minor groove binding is mainly stabilized by R248, identified as the most important residue that tightly binds deep inside the minor groove. Analysis of the p53 key residues for DNA binding indicates high correlations with cancer-related mutations, confirming that impairment of the interactions between p53-DBD and DNA can be frequently related to cancer.

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“…Complementary to experiments, molecular dynamics (MD) simulations can provide atomic-level details of p53's conformational dynamics, 47–51 p53–MDM2 interactions, 50,52,53 p53–DNA interactions, 54–56 conformational ensemble of p53 TAD 57,58 and the aggregation of p53 segments. 59–62 For example, on the basis of 15 ns implicit-solvent MD simulations of the p53TD tetramer, an early study 47 reported that four residues R333, E349, R337 and D352 could form a fluid salt-bridging network, which may help stabilize the p53TD tetramer.…”

Section: Introductionmentioning

confidence: 99%

Mechanistic insight into the destabilization of p53TD tetramer by cancer-related R337H mutation: a molecular dynamics study

Dong

Tang

et al. 2022

Phys. Chem. Chem. Phys.

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Markov state models and NMR uncover an overlooked allosteric loop in p53

Abstract: Wildtype and Y220C L1 and L6 loops conformational landscape, with MSM-identified L6 states highlighted on the right.

Cited by 26 publications

References 73 publications

Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning

Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning

Dissociation pathways of the p53 DNA binding domain from DNA and critical roles of key residues elucidated by dPaCS-MD/MSM

Mechanistic insight into the destabilization of p53TD tetramer by cancer-related R337H mutation: a molecular dynamics study

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