MODE-TASK: Large-scale protein motion tools

Conventional analysis of molecular dynamics (MD) trajectories may not identify global motions of macromolecules. Normal Mode Analysis (NMA) and Principle Component Analysis (PCA) are two popular methods to quantify large-scale motions, and find the “essential motions”; and have been applied to problems such as drug resistant mutations (Nizami et al., 2016) and viral capsid expansion (Hsieh et al., 2016). MODE-TASK is an array of tools to analyse and compare protein dynamics obtained from MD simulations and/or coarse grained elastic network models. Users may perform standard PCA, kernel and incremental PCA (IPCA). Data reduction techniques (Multidimensional Scaling (MDS) and t-Distributed Stochastics Neighbor Embedding (t-SNE)) are implemented. Users may analyse normal modes by constructing elastic network models (ENMs) of a protein complex. A novel coarse graining approach extends its application to large biological assemblies

Resistance distance, information centrality, node vulnerability and vibrations in complex networks

We discuss three seemingly unrelated quantities that have been introduced in different fields of science for complex networks. The three quantities are the resistance distance, the information centrality and the node displacement. We first prove various relations among them. Then we focus on the node displacement, showing its usefulness as an index of node vulnerability.We argue that the node displacement has a better resolution as a measure of node vulnerability than the degree and the information centrality

Universal behavior of localization of residue fluctuations in globular proteins

Localization properties of residue fluctuations in globular proteins are studied theoretically by using the Gaussian network model. Participation ratio for each residue fluctuation mode is calculated. It is found that the relationship between participation ratio and frequency is similar for all globular proteins, indicating a universal behavior in spite of their different size, shape, and architecture.Comment: 4 pages, 3 figures. To appear in Phys. Rev.

Representation of protein secondary structure using bond-orientational order parameters

Structural studies of proteins for motif mining and other pattern recognition techniques require the abstraction of the structure into simpler elements for robust matching. In this study, we propose the use of bond-orientational order parameters, a well-established metric usually employed to compare atom packing in crystals and liquids. Creating a vector of orientational order parameters of residue centers in a sliding window fashion provides us with a descriptor of local structure and connectivity around each residue that is easy to calculate and compare. To test whether this representation is feasible and applicable to protein structures, we tried to predict the secondary structure of protein segments from those descriptors, resulting in 0.99 AUC (area under the ROC curve). Clustering those descriptors to 6 clusters also yield 0.93 AUC, showing that these descriptors can be used to capture and distinguish local structural information