Visualization of molecular fingerprints

A visualization plot of a data set of molecular data is a useful tool for gaining insight into a set of molecules. In chemoinformatics, most visualization plots are of molecular descriptors, and the statistical model most often used to produce a visualization is principal component analysis (PCA). This paper takes PCA, together with four other statistical models (NeuroScale, GTM, LTM, and LTM-LIN), and evaluates their ability to produce clustering in visualizations not of molecular descriptors but of molecular fingerprints. Two different tasks are addressed: understanding structural information (particularly combinatorial libraries) and relating structure to activity. The quality of the visualizations is compared both subjectively (by visual inspection) and objectively (with global distance comparisons and local k-nearest-neighbor predictors). On the data sets used to evaluate clustering by structure, LTM is found to perform significantly better than the other models. In particular, the clusters in LTM visualization space are consistent with the relationships between the core scaffolds that define the combinatorial sublibraries. On the data sets used to evaluate clustering by activity, LTM again gives the best performance but by a smaller margin. The results of this paper demonstrate the value of using both a nonlinear projection map and a Bernoulli noise model for modeling binary data.

Sensitivity of peptide conformational dynamics on clustering of a classical molecular dynamics trajectory

We investigate the sensitivity of a Markov model with states and transition probabilities obtained from clustering a molecular dynamics trajectory. We have examined a 500 ns molecular dynamics trajectory of the peptide valine-proline-alanine-leucine in explicit water. The sensitivity is quantified by varying the boundaries of the clusters and investigating the resulting variation in transition probabilities and the average transition time between states. In this way, we represent the effect of clustering using different clustering algorithms. It is found that in terms of the investigated quantities, the peptide dynamics described by the Markov model is sensitive to the clustering; in particular, the average transition times are found to vary up to 46%. Moreover, inclusion of nonphysical sparsely populated clusters can lead to serious errors of up to 814%. In the investigation, the time step used in the transition matrix is determined by the minimum time scale on which the system behaves approximately Markovian. This time step is found to be about 100 ps. It is concluded that the description of peptide dynamics with transition matrices should be performed with care, and that using standard clustering algorithms to obtain states and transition probabilities may not always produce reliable results.

Different molecular pathologies result in similar spatial patterns of cellular inclusions in neurodegenerative disease:a comparative study of eight disorders

Recent research suggests cell-to-cell transfer of pathogenic proteins such as tau and α-synuclein may play a role in neurodegeneration. Pathogenic spread along neural pathways may give rise to specific spatial patterns of the neuronal cytoplasmic inclusions (NCI) characteristic of these disorders. Hence, the spatial patterns of NCI were compared in four tauopathies, viz., Alzheimer's disease, Pick's disease, corticobasal degeneration, and progressive supranuclear palsy, two synucleinopathies, viz., dementia with Lewy bodies and multiple system atrophy, the 'fused in sarcoma' (FUS)-immunoreactive inclusions in neuronal intermediate filament inclusion disease, and the transactive response DNA-binding protein (TDP-43)-immunoreactive inclusions in frontotemporal lobar degeneration, a TDP-43 proteinopathy (FTLD-TDP). Regardless of molecular group or morphology, NCI were most frequently aggregated into clusters, the clusters being regularly distributed parallel to the pia mater. In a significant proportion of regions, the regularly distributed clusters were in the size range 400-800 μm, approximating to the dimension of cell columns associated with the cortico-cortical pathways. The data suggest that cortical NCI in different disorders exhibit a similar spatial pattern in the cortex consistent with pathogenic spread along anatomical pathways. Hence, treatments designed to protect the cortex from neurodegeneration may be applicable across several different disorders. © 2012 Springer-Verlag.

Stress-induced platelet formation in silicon:a molecular dynamics study

The effect of stress on vacancy cluster configurations in silicon is examined using molecular dynamics. At zero pressure, the shape and stability of the vacancy clusters agrees with previous atomistic results. When stress is applied the orientation of small planar clusters changes to reduce the strain energy. The preferred orientation for the vacancy clusters under stress agrees with the experimentally observed orientations of hydrogen platelets in the high stress regions of hydrogen implanted silicon. These results suggest a theory for hydrogen platelet formation. © 2005 The American Physical Society.