Visualizing correlated motion with HDBSCAN clustering

Melvin, Ryan L.; Xiao, Jiajie; Godwin, Ryan C.; Berenhaut, Kenneth S.; Salsbury, Freddie R.

doi:10.1002/pro.3268

Cited by 21 publications

(15 citation statements)

References 101 publications

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“…Subsequent Markov analysis 42–45 (Figure 5b–e) employed in-house scripts that turn the clustering data into a Markov chain for analysis. 46 Images of structures from the most populated cluster of each configuration (Figure 6) were produced using a visual statistics package, where the shadows represent one standard deviation of RMSD for that cluster with the median structure colored by secondary structure. 47 …”

Section: Methodsmentioning

confidence: 99%

Binding Site Configurations Probe the Structure and Dynamics of the Zinc Finger of NEMO (NF-κB Essential Modulator)

et al. 2017

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Zinc-finger proteins are regulators of critical signaling pathways for various cellular functions, including apoptosis and oncogenesis. Here, we investigate how binding site protonation states and zinc coordination influence protein structure, dynamics, and ultimately function, as these pivotal regulatory proteins are increasingly important for protein engineering and therapeutic discovery. To better understand the thermodynamics and dynamics of the zinc finger of NEMO (NF-κB essential modulator), as well as the role of zinc, we present results of 20 μs molecular dynamics trajectories, 5 μs for each of four active site configurations. Consistent with experimental evidence, the zinc ion is essential for mechanical stabilization of the functional, folded conformation. Hydrogen bond motifs are unique for deprotonated configurations yet overlap in protonated cases. Correlated motions and principal component analysis corroborate the similarity of the protonated configurations and highlight unique relationships of the zinc-bound configuration. We hypothesize a potential mechanism for zinc binding from results of the thiol configurations. The deprotonated, zinc-bound configuration alone predominantly maintains its tertiary structure throughout all 5 μs and alludes rare conformations potentially important for (im)proper zinc-finger-related protein–protein or protein–DNA interactions.

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Section: Methodsmentioning

confidence: 99%

Binding Site Configurations Probe the Structure and Dynamics of the Zinc Finger of NEMO (NF-κB Essential Modulator)

et al. 2017

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“…To automate the parsing of MD trajectory data into an appropriate input format for HDBSCAN and the output of HDBSCAN into plots for quick comprehension, we used an in-house script that we have made available online for free via figshare. 142 This code has as a dependency the HDBSCAN code from the Github repository mentioned above. Our code sets the default minimum cluster size to 2 but allows the user to override this default.…”

Section: Methodsmentioning

confidence: 99%

“…142 Furthermore, for those wishing to reproduce the analysis described here on one of our MD data sets, we have made the trajectory and structure data for villin headpiece used to produce Figure 4 available online (figshare.com/articles/Villin_Headpiece_Simulations/3983526). …”

Section: Methodsmentioning

confidence: 99%

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Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge

Melvin

Godwin

Xiao

et al. 2016

J. Chem. Theory Comput.

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As the length of molecular dynamics (MD) trajectories grows with increasing computational power, so does the importance of clustering methods for partitioning trajectories into conformational bins. Of the methods available, the vast majority require users to either have some a priori knowledge about the system to be clustered or to tune clustering parameters through trial and error. Here we present non-parametric uses of two modern clustering techniques suitable for first-pass investigation of an MD trajectory. Being non-parametric, these methods require neither prior knowledge nor parameter tuning. The first method, HDBSCAN, is fast—relative to other popular clustering methods—and is able to group unstructured or intrinsically disordered systems (such as intrinsically disordered proteins, or IDPs) into bins that represent global conformational shifts. HDBSCAN is also useful for determining the overall stability of a system—as it tends to group stable systems into one or two bins—and identifying transition events between metastable states. The second method, iMWK-Means, with explicit rescaling followed by K-Means, while slower than HDBSCAN, performs well with stable, structured systems such as folded proteins and is able to identify higher resolution details such as changes in relative position of secondary structural elements. Used in conjunction, these clustering methods allow a user to discern quickly and without prior knowledge the stability of a simulated system and identify both local and global conformational changes.

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“…It searches the input data space for regions of high density separated by regions of low density, using a cluster stability metric and a mutual reachability distance [27]. Beyond the minimum cluster size (MinPts), which is much easier to choose than Eps, the method requires no further setting of arbitrary or biasing parameters by a user [28]. Though HDBSCAN is claimed to be robust [29], the algorithm to build the hierarchy runs in quadratic time, in both the worst and the best case [30].…”

Section: Research Backgroundmentioning

confidence: 99%

The adaptive spatio-temporal clustering method in classifying direct labor costs for the manufacturing industry

Weichbroth¹,

Kalinowski²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Employee productivity is critical to the profitability of not only the manufacturing industry. By capturing employee locations using recent advanced tracking devices, one can analyze and evaluate the time spent during a workday of each individual. However, over time, the quantity of the collected data becomes a burden, and decreases the capabilities of efficient classification of direct labor costs. However, the results obtained from performed experiments show that the existing clustering methods have failed to deliver satisfactory results by taking advantage of spatial data. In contrast to this, the adaptive spatio-temporal clustering (ASTC) method introduced in this paper utilizes both spatial and time data, as well as prior data concerning the position and working status of deployed machines inside a factory. The results show that our method outperforms the bucket of three well-known methods, namely DBSCAN, HDBSCAN and OPTICS. Moreover, in a series of experiments, we also validate the underlying assumptions and design of the ASTC method, as well as its efficiency and scalability. The application of the method can help manufacturing companies analyze and evaluate employees, including the productive times of day and most productive locations.

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Visualizing correlated motion with HDBSCAN clustering

Cited by 21 publications

References 101 publications

Binding Site Configurations Probe the Structure and Dynamics of the Zinc Finger of NEMO (NF-κB Essential Modulator)

Binding Site Configurations Probe the Structure and Dynamics of the Zinc Finger of NEMO (NF-κB Essential Modulator)

Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge

The adaptive spatio-temporal clustering method in classifying direct labor costs for the manufacturing industry

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