k-Means NANI: An Improved Clustering Algorithm for Molecular Dynamics Simulations.

One of the key challenges of k-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex data sets such as those obtained from molecular simulation, k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k-means++ will lead to a lack of reproducibility. K-means N-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse data sets and be used as a standalone tool or as part of our MDANCE clustering package.

k-Means NANI: an improved clustering algorithm for Molecular Dynamics simulations.

One of the key challenges of k-means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k-means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex datasets such as those obtained from molecular simulation, k-means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k-means++ will lead to a lack of reproducibility. K-means N-Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n-ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k-means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse datasets and be used as a standalone tool or as part of our MDANCE clustering package.

Atomic-Resolution 1.3 Å Crystal Structure, Inhibition by Sulfate, and Molecular Dynamics of the Bacterial Enzyme DapE.

We report the atomic-resolution (1.3 Å) X-ray crystal structure of an open conformation of the dapE-encoded N-succinyl-l,l-diaminopimelic acid desuccinylase (DapE, EC 3.5.1.18) from Neisseria meningitidis. This structure [Protein Data Bank (PDB) entry 5UEJ] contains two bound sulfate ions in the active site that mimic the binding of the terminal carboxylates of the N-succinyl-l,l-diaminopimelic acid (l,l-SDAP) substrate. We demonstrated inhibition of DapE by sulfate (IC50 = 13.8 ± 2.8 mM). Comparison with other DapE structures in the PDB demonstrates the flexibility of the interdomain connections of this protein. This high-resolution structure was then utilized as the starting point for targeted molecular dynamics experiments revealing the conformational change from the open form to the closed form that occurs when DapE binds l,l-SDAP and cleaves the amide bond. These simulations demonstrated closure from the open to the closed conformation, the change in RMS throughout the closure, and the independence in the movement of the two DapE subunits. This conformational change occurred in two phases with the catalytic domains moving toward the dimerization domains first, followed by a rotation of catalytic domains relative to the dimerization domains. Although there were no targeting forces, the substrate moved closer to the active site and bound more tightly during the closure event.