RESUMO
Comparative methods in molecular evolution and structural biology rely heavily upon the site-wise analysis of DNA sequence and protein structure, both static forms of information. However, it is widely accepted that protein function results from nanoscale nonrandom machine-like motions induced by evolutionarily conserved molecular interactions. Comparisons of molecular dynamics (MD) simulations conducted between homologous sites representative of different functional or mutational states can potentially identify local effects on binding interaction and protein evolution. In addition, comparisons of different (i.e., nonhomologous) sites within MD simulations could be employed to identify functional shifts in local time-coordinated dynamics indicative of logic gating within proteins. However, comparative MD analysis is challenged by the large fraction of protein motion caused by random thermal noise in the surrounding solvent. Therefore, properly denoised MD comparisons could reveal functional sites involving these machine-like dynamics with good accuracy. Here, we introduce ATOMDANCE, a user-interfaced suite of comparative machine learning-based denoising tools designed for identifying functional sites and the patterns of coordinated motion they can create within MD simulations. ATOMDANCE-maxDemon4.0 employs Gaussian kernel functions to compute site-wise maximum mean discrepancy between learned features of motion, thereby assessing denoised differences in the nonrandom motions between functional or evolutionary states (e.g., ligand bound versus unbound, wild-type versus mutant). ATOMDANCE-maxDemon4.0 also employs maximum mean discrepancy to analyze potential random amino acid replacements allowing for a site-wise test of neutral versus nonneutral evolution on the divergence of dynamic function in protein homologs. Finally, ATOMDANCE-Choreograph2.0 employs mixed-model analysis of variance and graph network to detect regions where time-synchronized shifts in dynamics occur. Here, we demonstrate ATOMDANCE's utility for identifying key sites involved in dynamic responses during functional binding interactions involving DNA, small-molecule drugs, and virus-host recognition, as well as understanding shifts in global and local site coordination occurring during allosteric activation of a pathogenic protease.
RESUMO
Hematopoietic cells play a crucial role in the adult retina in health and disease. Monocytes, macrophages, microglia and myeloid angiogenic cells (MACs) have all been implicated in retinal pathology. However, the role that hematopoietic cells play in retinal development is understudied. The temporal changes in recruitment of hematopoietic cells into the developing retina and the phenotype of the recruited cells are not well understood. In this study, we used the hematopoietic cell-specific protein Vav1 to track and investigate hematopoietic cells in the developing retina. By flow cytometry and immunohistochemistry, we show that hematopoietic cells are present in the retina as early as P0, and include microglia, monocytes and MACs. Even before the formation of retinal blood vessels, hematopoietic cells localize to the inner retina where they eventually form networks that intimately associate with the developing vasculature. Loss of Vav1 lead to a reduction in the density of medium-sized vessels and an increased inflammatory response in retinal astrocytes. When pups were subjected to oxygen-induced retinopathy, hematopoietic cells maintained a close association with the vasculature and occasionally formed 'frameworks' for the generation of new vessels. Our study provides further evidence for the underappreciated role of hematopoietic cells in retinal vasculogenesis and the formation of a healthy retina.