Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics

Gong, Shiqi; He, Xinheng; Meng, Qi; Ma, Zhong‐Qi; Shao, Bin; Wang, Tong; Liu, Tie-Yan

doi:10.1021/acs.jpcb.2c03711

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…The projections on the PC1-PC2 coordinates were clustered into a number of microstates based on the k -centers clustering algorithm· For both Pt-PEG and Pt-PE, we took k = 300. Choice of lag time. Based on implied time scale analysis (Figures S3b and S4b), we chose 126 and 117.5 ps as the lag times to build MSMs for Pt-PEG and Pt-PE, respectively. The implied time scales for both systems reach plateaus after their lag times, indicating Markovian of the MSMs. Lumping and validation of MSMs.…”

Section: Methodsmentioning

confidence: 99%

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Markov State Models Reveal How Folding Kinetics Influence Absorption Spectra of Foldamers

Wang,

Li,

Zheng

2024

J. Chem. Theory Comput.

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Self-assembly of platinum(II) complex foldamers is an essential approach to fabricate advanced luminescent materials. However, a comprehensive understanding of folding kinetics and their absorption spectra remains elusive. By constructing Markov state models (MSMs) from large-scale molecular dynamics simulations, we reveal that two largely similar dinuclear alknylplatinum(II) terpyridine foldamers, Pt-PEG and Pt-PE with slightly different bridges, exhibit distinctive folding kinetics. Particularly, Pt-PEG bears bridge-dominant, plane-dominant, and cooperative pathways, while Pt-PE only prefers the plane-dominant pathway. Such preference originates from their difference in intrabridge electrostatic interactions, leading to contrastive distributions of metastable states. We also found that the bridge-dominant pathway for Pt-PEG becomes more favorable when lowering the temperature. Interestingly, based on the comprehensive conformation ensembles from our MSMs, we reveal the conformation-dependent absorption spectra of Pt-PEG and Pt-PE. Our theoretical spectra not only align with experimental results but also reveal the contributions of diverse conformations to the overall absorption bands explicitly, facilitating the rational design of stimuli-responsive smart luminescent molecules.

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Section: Methodsmentioning

confidence: 99%

“…(4) Choice of lag time. Based on implied time scale analysis 72 (Figures S3b and S4b), we chose 126 and 117.5 ps as the lag times to build MSMs for Pt-PEG and Pt-PE, respectively. The implied time scales for both systems reach plateaus after their lag times, indicating Markovian of the MSMs.…”

Section: Construction and Validation Of Markov State Modelsmentioning

confidence: 99%

Markov State Models Reveal How Folding Kinetics Influence Absorption Spectra of Foldamers

Wang,

Li,

Zheng

2024

J. Chem. Theory Comput.

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Ab initio characterization of protein molecular dynamics with AI2BMD

Wang,

He,

et al. 2024

Nature

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Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency 1 – 3 . Classical molecular dynamics simulation is fast but lacks chemical accuracy 4 , 5 . Quantum chemistry methods such as density functional theory can reach chemical accuracy but cannot scale to support large biomolecules 6 . Here we introduce an artificial intelligence-based ab initio biomolecular dynamics system (AI 2 BMD) that can efficiently simulate full-atom large biomolecules with ab initio accuracy. AI 2 BMD uses a protein fragmentation scheme and a machine learning force field 7 to achieve generalizable ab initio accuracy for energy and force calculations for various proteins comprising more than 10,000 atoms. Compared to density functional theory, it reduces the computational time by several orders of magnitude. With several hundred nanoseconds of dynamics simulations, AI 2 BMD demonstrated its ability to efficiently explore the conformational space of peptides and proteins, deriving accurate 3 J couplings that match nuclear magnetic resonance experiments, and showing protein folding and unfolding processes. Furthermore, AI 2 BMD enables precise free-energy calculations for protein folding, and the estimated thermodynamic properties are well aligned with experiments. AI 2 BMD could potentially complement wet-lab experiments, detect the dynamic processes of bioactivities and enable biomedical research that is impossible to conduct at present.

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Molecular basis of lipid and ligand regulation of prostaglandin receptor DP2

Xu,

Hou

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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Prostaglandin D2 receptor 2 (DP2) is an important anti-inflammatory and antiallergic drug target. While inactive DP2 structures are known, its activation mechanisms and biased signaling remain unclear. Here, we report cryo-EM structures of an apo DP2–Gi complex, a DP2–Gi complex bound to the endogenous ligand Prostaglandin D 2 (PGD 2 ), and a DP2–Gi complex bound to indomethacin, an arrestin-biased ligand, at resolutions of 2.5 Å, 2.8Å, and 2.3 Å, respectively. These structures reveal a distinct binding pose of PGD 2 and indomethacin and provide key insights into receptor activation and transducer coupling. Combining the structural data with functional studies, we uncover the molecular basis for biased signaling of indomethacin toward β-arrestin over G proteins. Notably, a phospholipid binding site was identified at the DP2–G protein interface that modulates DP2–G protein interactions. Together, our functional and structural findings provide insights into DP2 activation, biased signaling, drug interactions, and lipid regulation, enabling rational design of safer antiallergy therapeutics targeting this key immune receptor.

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Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics

Cited by 3 publications

References 41 publications

Markov State Models Reveal How Folding Kinetics Influence Absorption Spectra of Foldamers

Markov State Models Reveal How Folding Kinetics Influence Absorption Spectra of Foldamers

Ab initio characterization of protein molecular dynamics with AI2BMD

Molecular basis of lipid and ligand regulation of prostaglandin receptor DP2

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