Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification

Röblitz, Susanna; Weber, Marcus

doi:10.1007/s11634-013-0134-6

Cited by 343 publications

(409 citation statements)

References 32 publications

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“…Clustering in the reaction coordinates system followed to define microstates by using hierarchical clustering based on the Ward distance. A Markov‐state model was built with microstates, and it was further lumped by the Perron Cluster Cluster Analysis+ (PCCA+) algorithm to obtain a Markov‐state model with macrostates according to a desired transition time‐scale. We tested a range of lag times for building Markov‐state models and selected 5 ns because it showed a good balance between Markovian behavior and statistics (Figure S2).…”

Section: Methodsmentioning

confidence: 99%

Driven to near‐experimental accuracy by refinement via molecular dynamics simulations

2019

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Protein model refinement has been an essential part of successful protein structure prediction. Molecular dynamics simulation‐based refinement methods have shown consistent improvement of protein models. There had been progress in the extent of refinement for a few years since the idea of ensemble averaging of sampled conformations emerged. There was little progress in CASP12 because conformational sampling was not sufficiently diverse due to harmonic restraints. During CASP13, a new refinement method was tested that achieved significant improvements over CASP12. The new method intended to address previous bottlenecks in the refinement problem by introducing new features. Flat‐bottom harmonic restraints replaced harmonic restraints, sampling was performed iteratively, and a new scoring function and selection criteria were used. The new protocol expanded conformational sampling at reduced computational costs. In addition to overall improvements, some models were refined significantly to near‐experimental accuracy.

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Section: Methodsmentioning

confidence: 99%

Driven to near‐experimental accuracy by refinement via molecular dynamics simulations

2019

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“…The resulting Markov models were validated using a Chapman–Kolmogorov Test6772 (Supplementary Fig. 10, right panels), and coarse-grained into three metastable states using PCCA++73 (Figs 1d and 3g) to represent metastable conformational transitions on the microsecond scale. Molecular graphics and movies were generated with the program PyMol74.…”

Section: Methodsmentioning

confidence: 99%

MHC class II complexes sample intermediate states along the peptide exchange pathway

Wieczorek

Sticht

Stolzenberg³

et al. 2016

Nat Commun

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The presentation of peptide-MHCII complexes (pMHCIIs) for surveillance by T cells is a well-known immunological concept in vertebrates, yet the conformational dynamics of antigen exchange remain elusive. By combining NMR-detected H/D exchange with Markov modelling analysis of an aggregate of 275 microseconds molecular dynamics simulations, we reveal that a stable pMHCII spontaneously samples intermediate conformations relevant for peptide exchange. More specifically, we observe two major peptide exchange pathways: the kinetic stability of a pMHCII's ground state defines its propensity for intrinsic peptide exchange, while the population of a rare, intermediate conformation correlates with the propensity of the HLA-DM-catalysed pathway. Helix-destabilizing mutants designed based on our model shift the exchange behaviour towards the HLA-DM-catalysed pathway and further allow us to conceptualize how allelic variation can shape an individual's MHC restricted immune response.

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“…Once our MSM is built we can obtain coarse‐grained states (or macro‐states) by methods such as Robust Perron Cluster Cluster Analysis (PCCA+), that can be used as significant metastable states (Figure .G). Furthermore, possible pathways between states or metastable states, and their fluxes and reaction rates can also be obtained through transition path theory (TPT) algorithms (Figure .H) …”

Section: Methods For Exploring the Conformational Space Of Gpcrs Andmentioning

confidence: 99%

In silicoExploration of the Conformational Universe of GPCRs

2016

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The structural plasticity of G protein coupled receptors (GPCRs) leads to a conformational universe going from inactive to active receptor states with several intermediate states. Many of them have not been captured yet and their role for GPCR activation is not well understood. The study of this conformational space and the transition dynamics between different receptor populations is a major challenge in molecular biophysics. The rational design of effector molecules that target such receptor populations allows fine-tuning receptor signalling with higher specificity to produce drugs with safer therapeutic profiles. In this minireview, we outline highly conserved receptor regions which are considered determinant for the establishment of distinct receptor states. We then discuss in-silico approaches such as dimensionality reduction methods and Markov State Models to explore the GPCR conformational universe and exploit the obtained conformations through structure-based drug design.

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Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification

Cited by 343 publications

References 32 publications

Driven to near‐experimental accuracy by refinement via molecular dynamics simulations

Driven to near‐experimental accuracy by refinement via molecular dynamics simulations

MHC class II complexes sample intermediate states along the peptide exchange pathway

In silicoExploration of the Conformational Universe of GPCRs

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